From 5b428f0655713ed3f7a0ef69d4e7aaac9aead2e7 Mon Sep 17 00:00:00 2001 From: Alexey Suhov Date: Wed, 13 May 2020 21:12:22 +0300 Subject: [PATCH] publish master branch snapshot, revision 49482ae3bea0cbaa07474f86f36db11943142687 --- .clang-format | 19 - .coveragerc | 39 - .gitignore | 36 +- CMakeLists.txt | 9 +- cmake/check_features.cmake | 6 +- cmake/developer_package.cmake | 9 +- cmake/fuzzing.cmake | 2 +- cmake/os_flags.cmake | 70 +- cmake/sanitizer.cmake | 7 +- cmake/sdl.cmake | 4 +- cmake/target_flags.cmake | 2 +- .../.editorconfig | 0 inference-engine/CMakeLists.txt | 2 +- inference-engine/cmake/coverage_ie.cmake | 9 +- inference-engine/cmake/cpplint.cmake | 6 +- .../cmake/developer_package_config.cmake.in | 4 +- inference-engine/cmake/features_ie.cmake | 15 +- inference-engine/cmake/models.cmake | 4 + .../share/InferenceEngineConfig.cmake.in | 2 +- .../object_detection_sample_ssd/main.c | 26 +- .../ie_bridges/c/tests/CMakeLists.txt | 2 + .../ie_bridges/c/tests/ie_c_api_test.cpp | 7 +- .../ie_bridges/python/CMakeLists.txt | 7 + .../ie_bridges/python/cmake/FindCython.cmake | 6 +- .../src/openvino/inference_engine/ie_api.pyx | 2 +- .../python/tests/test_InferRequest.py | 8 + inference-engine/include/cpp/ie_cnn_network.h | 22 +- .../include/cpp/ie_executable_network.hpp | 4 +- .../include/cpp/ie_infer_request.hpp | 9 +- .../include/cpp/ie_plugin_cpp.hpp | 2 + .../details/ie_cnn_network_iterator.hpp | 1 + .../include/details/ie_so_pointer.hpp | 6 +- inference-engine/include/ie_core.hpp | 26 +- inference-engine/samples/CMakeLists.txt | 14 +- .../samples/benchmark_app/README.md | 1 + .../samples/benchmark_app/benchmark_app.hpp | 7 + .../samples/benchmark_app/main.cpp | 45 +- .../samples/benchmark_app/utils.cpp | 72 ++ .../samples/benchmark_app/utils.hpp | 6 + .../scripts/create_vs2017_proj_x64.cmd | 31 + inference-engine/scripts/run_code_checks.sh | 2 + .../scripts/run_tests_myriad_multistick.sh | 87 ++ inference-engine/src/CMakeLists.txt | 2 + .../src/cldnn_engine/cldnn_engine.cpp | 12 +- .../src/cldnn_engine/cldnn_engine.h | 8 +- .../src/gna_plugin/backend/am_intel_dnn.cpp | 76 +- .../src/gna_plugin/backend/am_intel_dnn.hpp | 8 +- .../src/gna_plugin/gna_plugin.hpp | 2 +- .../src/gna_plugin/gna_plugin_internal.hpp | 2 +- .../src/gna_plugin/gna_plugin_query_api.cpp | 4 + .../hetero_async_infer_request.cpp | 1 - .../hetero_executable_network.cpp | 100 +- .../hetero_executable_network.hpp | 5 +- .../src/hetero_plugin/hetero_plugin.cpp | 232 ++--- .../src/hetero_plugin/hetero_plugin.hpp | 51 +- .../src/inference_engine/CMakeLists.txt | 6 +- .../cnn_network_ngraph_impl.cpp | 13 +- .../src/inference_engine/dll_main.hpp | 22 - .../src/inference_engine/ie_core.cpp | 338 ++++--- .../threading/ie_cpu_streams_executor.cpp | 344 ++++--- .../threading/ie_istreams_executor.cpp | 2 +- .../src/inference_engine/xml_parse_utils.cpp | 2 +- .../src/ir_readers/ie_ir_parser.cpp | 49 +- .../src/ir_readers/ie_ir_parser.hpp | 12 +- .../src/convert_function_to_cnn_network.cpp | 15 +- .../src/legacy_api/src/ie_deprecated.cpp | 3 +- .../legacy_api/src/ie_layer_validators.cpp | 8 +- .../ie_tensor_iterator_shape_infer.hpp | 1 + .../src/shape_infer/ie_reshaper.hpp | 2 +- .../low_precision_transformations/power.hpp | 24 + .../src/power.cpp | 75 ++ .../src/scaleshift_to_convolution.cpp | 5 +- .../src/transformer.cpp | 4 +- .../src/mkldnn_plugin/CMakeLists.txt | 14 +- .../src/mkldnn_plugin/bf16transformer.cpp | 6 + .../src/mkldnn_plugin/bf16transformer.h | 5 +- .../src/mkldnn_plugin/mkldnn_exec_network.cpp | 64 +- .../mkldnn_plugin/mkldnn_graph_optimizer.cpp | 10 +- .../src/mkldnn_plugin/mkldnn_plugin.cpp | 5 +- .../src/mkldnn_plugin/mkldnn_plugin.h | 7 +- .../src/mkldnn_plugin/nodes/list.hpp | 9 +- .../nodes/mkldnn_permute_node.cpp | 2 +- .../mkldnn_plugin/nodes/mkldnn_permute_node.h | 2 +- .../nodes/mkldnn_pooling_node.cpp | 41 +- .../mkldnn_plugin/nodes/mkldnn_pooling_node.h | 15 +- .../nodes/mkldnn_resample_node.cpp | 16 +- .../src/multi_device/CMakeLists.txt | 26 + .../src/multi_device/multi_device.cpp | 548 +++++++++++ .../src/multi_device/multi_device.hpp | 176 ++++ .../impl/ie_plugin_internal.hpp | 26 +- .../interface/ie_iplugin_internal.hpp | 2 +- inference-engine/src/plugin_api/ie_icore.hpp | 50 + .../src/plugin_api/ie_profiling.hpp | 6 +- .../threading/ie_cpu_streams_executor.hpp | 2 + .../threading/ie_istreams_executor.hpp | 6 + .../src/preprocessing/CMakeLists.txt | 6 +- .../common_optimizations_tbl.hpp | 12 +- .../convert_opset1_to_legacy_tbl.hpp | 2 - .../convert_broadcast3.hpp | 31 + .../convert_opset3_to_opset2/convert_nms3.hpp | 31 + .../convert_opset3_to_opset2.hpp | 26 + .../convert_opset3_to_opset2_tbl.hpp | 21 + .../convert_shapeof3.hpp | 30 + .../convert_topk3.hpp | 31 + .../convert_scatter_elements_to_scatter.hpp | 30 + .../optimize_strided_slice.hpp | 69 ++ .../remove_filtering_boxes_by_size.hpp | 30 + .../common_optimizations.cpp | 12 +- .../convert_opset1_to_legacy.cpp | 2 - .../convert_prior_to_ie_prior.cpp | 47 +- .../convert_broadcast3.cpp | 61 ++ .../convert_opset3_to_opset2/convert_nms3.cpp | 55 ++ .../convert_opset3_to_opset2.cpp | 32 + .../convert_shapeof3.cpp | 45 + .../convert_topk3.cpp | 56 ++ .../convert_scatter_elements_to_scatter.cpp | 211 +++++ .../optimize_strided_slice.cpp | 203 ++++ .../remove_filtering_boxes_by_size.cpp | 106 +++ .../operations/out_shape_of_reshape.hpp | 37 + .../operations/static_shape_broadcast.hpp | 47 + .../operations/static_shape_nonzero.hpp | 13 +- .../dynamic_to_static_shape.hpp | 2 +- .../dynamic_to_static_shape_broadcast.hpp | 15 + .../dynamic_to_static_shape_concat.hpp | 15 + .../dynamic_to_static_shape_gather.hpp | 15 + .../dynamic_to_static_shape_reshape.hpp | 15 + .../dynamic_to_static_shape_shapeof.hpp | 16 + ...dynamic_to_static_shape_variadic_split.hpp | 15 + .../common/include/vpu/utils/simple_math.hpp | 111 ++- .../operations/dynamic_shape_resolver.cpp | 5 +- .../operations/out_shape_of_reshape.cpp | 72 ++ .../operations/static_shape_broadcast.cpp | 147 +++ .../operations/static_shape_nonzero.cpp | 123 ++- .../dynamic_to_static_shape.cpp | 31 +- .../dynamic_to_static_shape_broadcast.cpp | 47 + .../dynamic_to_static_shape_concat.cpp | 116 +++ .../dynamic_to_static_shape_gather.cpp | 78 ++ .../dynamic_to_static_shape_nonzero.cpp | 10 +- .../dynamic_to_static_shape_reshape.cpp | 40 + .../dynamic_to_static_shape_shapeof.cpp | 41 + .../dynamic_to_static_shape_transpose.cpp | 4 +- ...dynamic_to_static_shape_variadic_split.cpp | 77 ++ .../src/vpu/common/src/utils/simple_math.cpp | 220 ++--- .../custom_kernels/binary_convolution1x1.cl | 4 + .../custom_kernels/binary_convolution3x3.cl | 3 + .../src/vpu/custom_kernels/binary_layers.cl | 12 +- .../src/vpu/custom_kernels/convolution1x1.cl | 4 + .../src/vpu/custom_kernels/convolution3x3.cl | 4 + .../src/vpu/custom_kernels/correlate.cl | 449 +++++++++ .../src/vpu/custom_kernels/ctc.cl | 234 +++-- .../custom_kernels/customLayerBindings.xml | 864 ++++++++++-------- .../src/vpu/custom_kernels/cvtf32f16.cl | 12 +- .../src/vpu/custom_kernels/cvtu8f16.cl | 103 ++- .../detectron_prior_grid_gen.cl | 14 +- .../src/vpu/custom_kernels/grn.cl | 101 +- .../src/vpu/custom_kernels/mvn.cl | 4 + .../src/vpu/custom_kernels/quantize.cl | 272 +++--- .../src/vpu/custom_kernels/region.cl | 474 ++++++++++ .../src/vpu/custom_kernels/region_chw.cl | 12 +- .../custom_kernels/region_chw_m7_branch0.cl | 12 +- .../custom_kernels/region_chw_m7_branch1.cl | 12 +- .../src/vpu/custom_kernels/reorg_chw.cl | 136 ++- .../src/vpu/custom_kernels/reorg_chw_local.cl | 12 +- .../src/vpu/custom_kernels/reorg_chw_stack.cl | 12 +- .../src/vpu/custom_kernels/reorg_hwc.cl | 171 +++- .../src/vpu/custom_kernels/resample_nn.cl | 177 +++- .../custom_kernels/resample_with_antialias.cl | 230 ++++- .../vpu/custom_kernels/shuffle_channels.cl | 12 +- inference-engine/src/vpu/custom_kernels/st.cl | 253 +++++ .../include/vpu/frontend/custom_kernel.hpp | 83 ++ .../include/vpu/frontend/custom_layer.hpp | 92 +- .../include/vpu/frontend/frontend.hpp | 10 +- .../include/vpu/graph_transformer.hpp | 1 + .../include/vpu/middleend/pass_manager.hpp | 1 + .../include/vpu/model/data.hpp | 2 +- .../include/vpu/model/model.hpp | 19 +- .../include/vpu/model/stage.hpp | 24 +- .../include/vpu/private_plugin_config.hpp | 6 + .../include/vpu/stage_builder.hpp | 3 +- .../src/backend/serialize.cpp | 36 +- .../src/frontend/custom_kernel.cpp | 426 +++++++++ .../src/frontend/custom_layer.cpp | 854 ++++------------- .../src/frontend/frontend.cpp | 190 +++- .../src/middleend/allocator/allocator.cpp | 57 +- .../src/middleend/pass_manager.cpp | 24 +- .../middleend/passes/adjust_data_location.cpp | 2 +- .../passes/eliminate_const_concat.cpp | 2 +- .../src/middleend/passes/final_check.cpp | 2 +- .../passes/process_special_stages.cpp | 2 +- .../middleend/passes/replace_with_screlu.cpp | 4 +- .../passes/split_large_kernel_conv.cpp | 163 ++++ .../passes/swap_concat_and_hw_ops.cpp | 2 +- .../vpu/graph_transformer/src/model/data.cpp | 54 +- .../vpu/graph_transformer/src/model/model.cpp | 50 +- .../graph_transformer/src/parsed_config.cpp | 5 + .../src/stages/broadcast.cpp | 159 ++++ .../graph_transformer/src/stages/concat.cpp | 247 +++-- .../src/stages/convolution.cpp | 13 +- .../graph_transformer/src/stages/custom.cpp | 426 ++++----- .../src/stages/dynamic_shape_resolver.cpp | 47 +- .../src/stages/out_shape_of_reshape.cpp | 103 +++ .../graph_transformer/src/stages/pooling.cpp | 4 + .../graph_transformer/src/stages/reduce.cpp | 84 +- .../vpu/graph_transformer/src/stages/topk.cpp | 6 +- .../src/vpu/myriad_plugin/myriad_plugin.cpp | 5 +- .../src/vpu/myriad_plugin/myriad_plugin.h | 1 - .../inference_engine/CMakeLists.txt | 7 +- .../inference_engine/caseless_tests.cpp} | 13 +- .../inference_engine/core_threading_tests.cpp | 149 +++ .../inference_engine}/data_test.cpp | 0 .../inference_engine}/debug_tests.cpp | 12 +- .../inference_engine/ie_extension_test.cpp | 30 +- .../ngraph_reader/abs_tests.cpp | 105 +++ .../ngraph_reader/linear_ops_tests.cpp | 2 +- .../inference_engine}/pre_allocator_test.cpp | 9 +- .../inference_engine}/preprocess_test.cpp | 12 +- .../response_buffer_test.cpp | 13 +- .../shared_object_loader_test.cpp | 20 +- .../inference_engine}/so_pointer_tests.cpp | 24 +- .../convert_broadcast3_test.cpp | 157 ++++ .../transformations/convert_nms3_test.cpp | 103 +++ ...nvert_scatter_elements_to_scatter_test.cpp | 131 +++ .../transformations/convert_shapeof3.cpp | 79 ++ .../transformations/convert_topk3_test.cpp | 161 ++++ .../transformations/ngraph_test_utils.cpp | 4 +- .../transformations/ngraph_test_utils.hpp | 3 + .../optimize_strided_slice_test.cpp | 274 ++++++ .../transpose_to_reshape_test.cpp | 137 +++ .../cpu/bfloat16/bf16_network_restoring.cpp | 4 +- .../plugin/cpu/bfloat16/bfloat16_helpers.hpp | 5 +- .../plugin/cpu/bfloat16/concat_in_place.cpp | 160 ++++ .../plugin/cpu/bfloat16/conv_add.cpp | 162 ++++ .../plugin/cpu/bfloat16/conv_conv.cpp | 35 +- .../plugin/cpu/bfloat16/conv_dwconv_relu.cpp | 21 +- .../cpu/bfloat16/conv_eltwise_depthwise.cpp | 256 ++++++ .../conv_relu_pool_conv_relu_pool.cpp | 29 +- .../plugin/cpu/bfloat16/elt_max.cpp | 151 +++ .../functional/plugin/cpu/bfloat16/elt_x3.cpp | 229 +++++ .../cpu/bfloat16/faster_100_5_1_1_conv.cpp | 14 +- .../plugin/cpu/bfloat16/gather_multiply.cpp | 130 +++ .../gather_x2_add_mul_relu_concat_matmul.cpp | 156 ++++ .../plugin/cpu/bfloat16/interpolation.cpp | 146 +++ .../bfloat16/mobilenet_ssd_with_branching.cpp | 20 +- .../plugin/cpu/bfloat16/resample.cpp | 146 +++ .../bfloat16/scaleshift_conv_eltwise_conv.cpp | 22 +- .../scaleshift_conv_eltwise_relu_conv.cpp | 22 +- .../scaleshift_conv_eltwise_scaleshift.cpp | 14 +- .../cpu/bfloat16/scaleshift_conv_elu_conv.cpp | 15 +- .../cpu/bfloat16/scaleshift_conv_relu.cpp | 14 +- .../scaleshift_conv_x2_concat_relu.cpp | 18 +- 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.../dynamic_to_static_shape_gather.cpp | 320 +++++++ ...ic_to_static_shape_non_max_suppression.cpp | 2 +- .../dynamic_to_static_shape_nonzero.cpp | 29 +- .../dynamic_to_static_shape_reshape.cpp | 113 +++ .../dynamic_to_static_shape_roialign.cpp | 6 +- .../dynamic_to_static_shape_scatter.cpp | 2 +- .../dynamic_to_static_shape_shapeof.cpp | 213 +++++ .../dynamic_to_static_shape_squeeze.cpp | 2 +- .../dynamic_to_static_shape_transpose.cpp | 2 +- ...amic_to_static_shape_unary_elementwise.cpp | 2 +- .../dynamic_to_static_shape_unsqueeze.cpp | 2 +- ...dynamic_to_static_shape_variadic_split.cpp | 151 +++ .../behavior/core_threading_tests.cpp | 22 + .../single_layer_tests/concat.cpp | 19 +- .../single_layer_tests/proposal.cpp | 50 + .../skip_tests_config.cpp | 3 + .../static_shape_nonzero.cpp | 119 +++ .../myriad/subgraph_tests/dsr_concat.cpp | 81 ++ .../myriad/subgraph_tests/dsr_gather.cpp | 143 +++ .../myriad/subgraph_tests/dsr_reshape.cpp | 71 ++ .../subgraph_tests/dsr_variadic_split.cpp | 81 ++ .../subgraph_tests/nonzero_broadcast.cpp | 62 ++ ...sr_transpose.cpp => nonzero_transpose.cpp} | 21 +- .../include/behavior/core_threading_tests.hpp | 198 ++++ .../include/single_layer_tests/concat.hpp | 1 + .../include/single_layer_tests/proposal.hpp | 66 ++ .../src/single_layer_tests/activation.cpp | 11 +- .../shared/src/single_layer_tests/concat.cpp | 4 +- .../src/single_layer_tests/proposal.cpp | 154 ++++ .../shared/src/single_layer_tests/select.cpp | 11 +- .../common_test_utils/data_utils.hpp | 40 +- .../common_test_utils}/test_assertions.hpp | 54 +- .../functional_test_utils/blob_utils.hpp | 254 ++++- .../layer_test_utils.cpp | 20 +- .../layer_test_utils.hpp | 22 +- .../impl/mock_inference_plugin_internal.hpp | 12 +- .../mocks/cpp_interfaces/mock_plugin_impl.hpp | 2 +- .../include/ngraph_functions/builders.hpp | 20 + .../ngraph_functions/src/params_vector.cpp | 15 + .../tests/ngraph_functions/src/proposal.cpp | 52 ++ .../src/utils/ngraph_helpers.cpp | 7 +- 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model-optimizer/extensions/ops/Log.py delete mode 100644 model-optimizer/extensions/ops/exp.py delete mode 100644 model-optimizer/extensions/ops/exp_test.py create mode 100644 model-optimizer/extensions/ops/non_max_suppression_test.py create mode 100644 model-optimizer/extensions/ops/slice_like.py create mode 100644 model-optimizer/extensions/ops/slice_like_test.py create mode 100644 model-optimizer/extensions/ops/topk_test.py create mode 100644 model-optimizer/install_prerequisites/protobuf-3.6.1-py3.5-win-amd64.egg create mode 100644 model-optimizer/install_prerequisites/protobuf-3.6.1-py3.6-win-amd64.egg create mode 100644 model-optimizer/install_prerequisites/protobuf-3.6.1-py3.7-win-amd64.egg create mode 100644 model-optimizer/mo/utils/broadcasting.py create mode 100644 model-optimizer/mo/utils/broadcasting_test.py create mode 100644 model-optimizer/mo/utils/get_ov_update_message.py create mode 100644 model-optimizer/mo/utils/ir_reader/extenders/non_max_suppression_extender.py create mode 100644 model-optimizer/mo/utils/ir_reader/extenders/shape_of_extender.py create mode 100644 tests/stress_tests/.automation/memcheck_pregen_irs_tests/nightly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_pregen_irs_tests/nightly_configs/desktop_references_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_pregen_irs_tests/nightly_configs/desktop_test_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_pregen_irs_tests/weekly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_pregen_irs_tests/weekly_configs/desktop_references_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_pregen_irs_tests/weekly_configs/desktop_test_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_tests/nightly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/memcheck_tests/weekly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/memleaks_tests/nightly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/memleaks_tests/weekly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/unittests/nightly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/.automation/unittests/weekly_configs/desktop_env_config.xml create mode 100644 tests/stress_tests/scripts/__init__.py create mode 100644 tests/stress_tests/scripts/memcheck_upload.py create mode 100644 tests/stress_tests/scripts/requirements.txt diff --git a/.clang-format b/.clang-format deleted file mode 100644 index b78e96de1d4..00000000000 --- a/.clang-format +++ /dev/null @@ -1,19 +0,0 @@ -BasedOnStyle: Google -IndentWidth: 4 -UseTab: Never ---- -Language: Cpp -Standard: Cpp11 - -AccessModifierOffset: -4 -AllowAllArgumentsOnNextLine: false -AllowShortFunctionsOnASingleLine: Empty -AllowShortLambdasOnASingleLine: Empty -AlwaysBreakBeforeMultilineStrings: false -ColumnLimit: 120 -DerivePointerAlignment: false -FixNamespaceComments: true -IndentCaseLabels: false -SpaceBeforeCpp11BracedList: true -SpaceBeforeCtorInitializerColon: false ---- diff --git a/.coveragerc b/.coveragerc deleted file mode 100644 index ce0be58aa66..00000000000 --- a/.coveragerc +++ /dev/null @@ -1,39 +0,0 @@ -# .coveragerc to control coverage.py -[run] -branch = True - -source = - mo/ - mo.py - -omit = - # omit anything in a .local directory anywhere - */.local/* - # omit everything in /usr - /usr/* - # omit tests - */test_*.py - # init scripts - */__init__.py - -[report] -# Regexes for lines to exclude from consideration -exclude_lines = - # Have to re-enable the standard pragma - pragma: no cover - - # Don't complain about missing debug-only code: - def __repr__ - - # Don't complain if tests don't hit defensive assertion code: - raise AssertionError - raise NotImplementedError - - # Don't complain if non-runnable code isn't run: - if 0: - if __name__ == .__main__.: - -ignore_errors = True - -[html] -directory = htmlcov \ No newline at end of file diff --git a/.gitignore b/.gitignore index 547ae62881b..867e60e941b 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,7 @@ _* # but ensure we don't skip __init__.py !__init__.py # developer tools -.idea +*.idea .vscode cmake-build-debug cmake-build-release @@ -18,7 +18,41 @@ build/ doc/ docs/build_documentation/work_dir/ inference-engine/plugins/ +inference-engine/temp +inference-engine/report .repo/ docs/template_plugin/html/ CMakeLists.txt.user docs/IE_PLUGIN_DG/html/ + +*.project +*.cproject +*.pydevproject +*.settings +*/gen/ +__pycache__ +*.swp +/config.xml + +# Python-specific +*.env3 +*.pyc + +# Tests-specific +*.coverage +*htmlcov +*pylint_report.txt +*pylint_report_comments.txt + +# Artifacts +/model-optimizer/*.bin +/model-optimizer/*.xml +/model-optimizer/*.json +/model-optimizer/*.so +/model-optimizer/*.txt +/model-optimizer/*.pb +/model-optimizer/*.pbtxt +/model-optimizer/!CMakeLists.txt +/model-optimizer/*.mapping +/model-optimizer/*.dat +/model-optimizer/*.svg \ No newline at end of file diff --git a/CMakeLists.txt b/CMakeLists.txt index 08cb5ab4574..a90c0100684 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -15,7 +15,6 @@ else() cmake_minimum_required(VERSION 3.7.2 FATAL_ERROR) endif() - project(OpenVINO) set(OpenVINO_MAIN_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}) @@ -26,7 +25,7 @@ include(CTest) include(features) # include developer package -include(developer_package NO_POLICY_SCOPE) +include(developer_package) # These options are shared with 3rdparty plugins # by means of developer package @@ -37,7 +36,7 @@ include(dependencies) message (STATUS "PROJECT ............................... " ${PROJECT_NAME}) message (STATUS "CMAKE_BINARY_DIR ...................... " ${CMAKE_BINARY_DIR}) message (STATUS "OpenVINO_MAIN_SOURCE_DIR .............. " ${OpenVINO_MAIN_SOURCE_DIR}) -message (STATUS "IE_MAIN_SOURCE_DIR .............. " ${IE_MAIN_SOURCE_DIR}) +message (STATUS "IE_MAIN_SOURCE_DIR .................... " ${IE_MAIN_SOURCE_DIR}) message (STATUS "CMAKE_GENERATOR ....................... " ${CMAKE_GENERATOR}) message (STATUS "CMAKE_C_COMPILER_ID ................... " ${CMAKE_C_COMPILER_ID}) message (STATUS "CMAKE_BUILD_TYPE ...................... " ${CMAKE_BUILD_TYPE}) @@ -76,7 +75,7 @@ function(build_ngraph) if (NOT ANDROID) ngraph_set(NGRAPH_UNIT_TEST_ENABLE TRUE) - ngraph_set(NGRAPH_UNIT_TEST_OPENVINO_ENABLE TRUE) + ngraph_set(NGRAPH_IE_ENABLE TRUE) ngraph_set(NGRAPH_ONNX_IMPORT_ENABLE TRUE) else() ngraph_set(NGRAPH_UNIT_TEST_ENABLE FALSE) @@ -85,7 +84,7 @@ function(build_ngraph) ngraph_set(NGRAPH_ONNX_IMPORT_ENABLE FALSE) endif() - if(CMAKE_CXX_COMPILER_ID MATCHES "Clang") + if(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$") ie_add_compiler_flags(-Wno-error=uninitialized -Wno-error=literal-conversion) elseif(UNIX) ie_add_compiler_flags(-Wno-error=maybe-uninitialized -Wno-error=return-type -fPIC) diff --git a/cmake/check_features.cmake b/cmake/check_features.cmake index eef993fa598..319bc8877f3 100644 --- a/cmake/check_features.cmake +++ b/cmake/check_features.cmake @@ -20,14 +20,14 @@ if (NOT ENABLE_MKL_DNN) endif() if(ENABLE_AVX512F) - if ((CMAKE_CXX_COMPILER_ID MATCHES MSVC) AND (MSVC_VERSION VERSION_LESS 1920)) + if ((CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") AND (MSVC_VERSION VERSION_LESS 1920)) # 1920 version of MSVC 2019. In MSVC 2017 AVX512F not work set(ENABLE_AVX512F OFF CACHE BOOL "" FORCE) endif() - if (CMAKE_CXX_COMPILER_ID MATCHES Clang) + if (CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$") set(ENABLE_AVX512F OFF CACHE BOOL "" FORCE) endif() - if ((CMAKE_CXX_COMPILER_ID STREQUAL GNU) AND (NOT (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9))) + if ((CMAKE_CXX_COMPILER_ID STREQUAL "GNU") AND (NOT (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9))) set(ENABLE_AVX512F OFF CACHE BOOL "" FORCE) endif() endif() diff --git a/cmake/developer_package.cmake b/cmake/developer_package.cmake index c7a43b40b0e..58f80add212 100644 --- a/cmake/developer_package.cmake +++ b/cmake/developer_package.cmake @@ -143,7 +143,10 @@ if("${CMAKE_BUILD_TYPE}" STREQUAL "") set(CMAKE_BUILD_TYPE "Release") endif() -set(OUTPUT_ROOT ${OpenVINO_MAIN_SOURCE_DIR}) +# allow to override default OUTPUT_ROOT root +if(NOT DEFINED OUTPUT_ROOT) + set(OUTPUT_ROOT ${OpenVINO_MAIN_SOURCE_DIR}) +endif() # Enable postfixes for Debug/Release builds set(IE_DEBUG_POSTFIX_WIN "d") @@ -206,8 +209,10 @@ endif() # Use solution folders set_property(GLOBAL PROPERTY USE_FOLDERS ON) +set(CMAKE_POLICY_DEFAULT_CMP0054 NEW) + include(sdl) -include(os_flags NO_POLICY_SCOPE) +include(os_flags) include(sanitizer) function(set_ci_build_number) diff --git a/cmake/fuzzing.cmake b/cmake/fuzzing.cmake index abc9114e2a7..4e62429f9a6 100644 --- a/cmake/fuzzing.cmake +++ b/cmake/fuzzing.cmake @@ -4,7 +4,7 @@ function(enable_fuzzing) # Enable (libFuzzer)[https://llvm.org/docs/LibFuzzer.html] if supported. - if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" AND NOT WIN32) + if(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$" AND NOT WIN32) # Communicate libfuzzer is enabled set(WITH_LIBFUZZER ON PARENT_SCOPE) add_compile_definitions(WITH_LIBFUZZER) diff --git a/cmake/os_flags.cmake b/cmake/os_flags.cmake index a91c4641ee9..72ca685a8a1 100644 --- a/cmake/os_flags.cmake +++ b/cmake/os_flags.cmake @@ -8,13 +8,13 @@ # macro(disable_deprecated_warnings) if(WIN32) - if(CMAKE_CXX_COMPILER_ID MATCHES Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(ie_c_cxx_deprecated "/Qdiag-disable:1478,1786") - elseif(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(ie_c_cxx_deprecated "/wd4996") endif() else() - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(ie_c_cxx_deprecated "-diag-disable=1478,1786") else() set(ie_c_cxx_deprecated "-Wno-deprecated-declarations") @@ -35,13 +35,13 @@ endmacro() # macro(ie_deprecated_no_errors) if(WIN32) - if(CMAKE_CXX_COMPILER_ID MATCHES Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(ie_c_cxx_deprecated "/Qdiag-warning:1478,1786") - elseif(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(ie_c_cxx_deprecated "/wd4996") endif() else() - if(CMAKE_CXX_COMPILER_ID MATCHES Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(ie_c_cxx_deprecated_no_errors "-diag-warning=1478,1786") else() set(ie_c_cxx_deprecated_no_errors "-Wno-error=deprecated-declarations") @@ -61,15 +61,15 @@ endmacro() # function(ie_sse42_optimization_flags flags) if(WIN32) - if(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") # No such option for MSVC 2019 - elseif(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "/arch:SSE4.2 /QxSSE4.2" PARENT_SCOPE) else() message(WARNING "Unsupported CXX compiler ${CMAKE_CXX_COMPILER_ID}") endif() else() - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "-msse4.2 -xSSE4.2" PARENT_SCOPE) else() set(${flags} "-msse4.2" PARENT_SCOPE) @@ -82,15 +82,15 @@ endfunction() # function(ie_avx2_optimization_flags flags) if(WIN32) - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "/QxCORE-AVX2" PARENT_SCOPE) - elseif(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(${flags} "/arch:AVX2" PARENT_SCOPE) else() message(WARNING "Unsupported CXX compiler ${CMAKE_CXX_COMPILER_ID}") endif() else() - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "-march=core-avx2 -xCORE-AVX2 -mtune=core-avx2" PARENT_SCOPE) else() set(${flags} "-mavx2 -mfma" PARENT_SCOPE) @@ -104,18 +104,18 @@ endfunction() # function(ie_avx512_optimization_flags flags) if(WIN32) - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "/QxCOMMON-AVX512" PARENT_SCOPE) - elseif(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(${flags} "/arch:AVX512" PARENT_SCOPE) else() message(WARNING "Unsupported CXX compiler ${CMAKE_CXX_COMPILER_ID}") endif() else() - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "-xCOMMON-AVX512" PARENT_SCOPE) endif() - if(CMAKE_CXX_COMPILER_ID STREQUAL GNU) + if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set(${flags} "-mavx512f -mfma" PARENT_SCOPE) endif() endif() @@ -138,12 +138,12 @@ macro(ie_enable_lto) set(CMAKE_RANLIB "gcc-ranlib") endif() elseif(WIN32) - if(CMAKE_BUILD_TYPE STREQUAL Release) - # set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /GL") - # set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /GL") - # set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /LTCG:STATUS") - # set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} /LTCG:STATUS") - # set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /LTCG:STATUS") + if(CMAKE_BUILD_TYPE STREQUAL "Release") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /GL") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /GL") + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /LTCG:STATUS") + set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} /LTCG:STATUS") + set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} /LTCG:STATUS") endif() endif() endmacro() @@ -167,7 +167,7 @@ set(THREADS_PREFER_PTHREAD_FLAG ON) # to allows to override CMAKE_CXX_STANDARD from command line if(NOT DEFINED CMAKE_CXX_STANDARD) - if(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(CMAKE_CXX_STANDARD 14) else() set(CMAKE_CXX_STANDARD 11) @@ -176,7 +176,7 @@ if(NOT DEFINED CMAKE_CXX_STANDARD) set(CMAKE_CXX_STANDARD_REQUIRED ON) endif() -if(COVERAGE) +if(ENABLE_COVERAGE) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} --coverage") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} --coverage") set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} --coverage") @@ -198,10 +198,10 @@ if(WIN32) set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /LARGEADDRESSAWARE") if (TREAT_WARNING_AS_ERROR) - if(CMAKE_CXX_COMPILER_ID MATCHES Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") ie_add_compiler_flags(/WX) ie_add_compiler_flags(/Qdiag-warning:47,1740,1786) - elseif (CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") # ie_add_compiler_flags(/WX) # Too many warnings endif() endif() @@ -212,14 +212,14 @@ if(WIN32) # Disable noisy warnings - if(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") # C4251 needs to have dll-interface to be used by clients of class ie_add_compiler_flags(/wd4251) # C4275 non dll-interface class used as base for dll-interface class ie_add_compiler_flags(/wd4275) endif() - if(CMAKE_CXX_COMPILER_ID MATCHES Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") # 161 unrecognized pragma # 177 variable was declared but never referenced # 556 not matched type of assigned function pointer @@ -236,20 +236,6 @@ if(WIN32) set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /Z7") set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} /Z7") - - if(ENABLE_DEBUG_SYMBOLS) - ie_add_compiler_flags(/Z7) - - set(DEBUG_SYMBOLS_LINKER_FLAGS "/DEBUG") - if (CMAKE_BUILD_TYPE STREQUAL "Release") - # Keep default /OPT values. See /DEBUG reference for details. - set(DEBUG_SYMBOLS_LINKER_FLAGS "${DEBUG_SYMBOLS_LINKER_FLAGS} /OPT:REF /OPT:ICF") - endif() - - set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${DEBUG_SYMBOLS_LINKER_FLAGS}") - set(CMAKE_SHARED_LINKER_FLAGS "${CMAKE_SHARED_LINKER_FLAGS} ${DEBUG_SYMBOLS_LINKER_FLAGS}") - set(CMAKE_MODULE_LINKER_FLAGS "${CMAKE_MODULE_LINKER_FLAGS} ${DEBUG_SYMBOLS_LINKER_FLAGS}") - endif() else() # TODO: enable for C sources as well # ie_add_compiler_flags(-Werror) diff --git a/cmake/sanitizer.cmake b/cmake/sanitizer.cmake index 047f370befe..a0103788a1c 100644 --- a/cmake/sanitizer.cmake +++ b/cmake/sanitizer.cmake @@ -14,7 +14,7 @@ if (ENABLE_SANITIZER) set(SANITIZER_LINKER_FLAGS "-fsanitize=address") if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=gold") - elseif(CMAKE_CXX_COMPILER_ID MATCHES "Clang" AND NOT WIN32) + elseif(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$" AND NOT WIN32) set(SANITIZER_LINKER_FLAGS "${SANITIZER_LINKER_FLAGS} -fuse-ld=lld") endif() @@ -26,9 +26,8 @@ if (ENABLE_SANITIZER) endif() if (ENABLE_THREAD_SANITIZER) - set(SANITIZER_COMPILER_FLAGS "-g -fsanitize=thread") - - set(SANITIZER_LINKER_FLAGS "-fsanitize=thread") + set(SANITIZER_COMPILER_FLAGS "-g -fsanitize=thread -fno-omit-frame-pointer") + set(SANITIZER_LINKER_FLAGS "-fsanitize=thread -static-libsan") set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SANITIZER_COMPILER_FLAGS}") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SANITIZER_COMPILER_FLAGS}") diff --git a/cmake/sdl.cmake b/cmake/sdl.cmake index 5c56528576b..27ed08a4d12 100644 --- a/cmake/sdl.cmake +++ b/cmake/sdl.cmake @@ -25,7 +25,7 @@ if (CMAKE_BUILD_TYPE STREQUAL "Release") if (NOT ENABLE_SANITIZER) set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -s") endif() - elseif(CMAKE_CXX_COMPILER_ID MATCHES "Clang") + elseif(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$") set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all") elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") if (NOT ENABLE_SANITIZER) @@ -36,7 +36,7 @@ if (CMAKE_BUILD_TYPE STREQUAL "Release") set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now") set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -z noexecstack -z relro -z now") endif() - elseif(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl") endif() diff --git a/cmake/target_flags.cmake b/cmake/target_flags.cmake index 1c89c096486..506f7d8728b 100644 --- a/cmake/target_flags.cmake +++ b/cmake/target_flags.cmake @@ -7,7 +7,7 @@ if(CMAKE_CL_64) set(MSVC64 ON) endif() -if(WIN32 AND CMAKE_CXX_COMPILER_ID MATCHES "GNU") +if(WIN32 AND CMAKE_CXX_COMPILER_ID STREQUAL "GNU") execute_process(COMMAND ${CMAKE_CXX_COMPILER} -dumpmachine OUTPUT_VARIABLE OPENVINO_GCC_TARGET_MACHINE OUTPUT_STRIP_TRAILING_WHITESPACE) diff --git a/.editorconfig b/inference-engine/.editorconfig similarity index 100% rename from .editorconfig rename to inference-engine/.editorconfig diff --git a/inference-engine/CMakeLists.txt b/inference-engine/CMakeLists.txt index efdb2635f79..52b94b11d42 100644 --- a/inference-engine/CMakeLists.txt +++ b/inference-engine/CMakeLists.txt @@ -188,7 +188,7 @@ configure_file( # Coverage # -if(COVERAGE) +if(ENABLE_COVERAGE) include(coverage_ie) endif() diff --git a/inference-engine/cmake/coverage_ie.cmake b/inference-engine/cmake/coverage_ie.cmake index 79eb496b16b..f288246bde2 100644 --- a/inference-engine/cmake/coverage_ie.cmake +++ b/inference-engine/cmake/coverage_ie.cmake @@ -13,14 +13,17 @@ ie_coverage_capture(INFO_FILE "dldt" # Generate reports -ie_coverage_extract(INPUT "dldt" OUTPUT "inference_engine_with_builders" +ie_coverage_extract(INPUT "dldt" OUTPUT "inference_engine" PATTERNS "${DLDT_COVERAGE_BASE_DIRECTORY}/inference_engine/*" "${DLDT_COVERAGE_BASE_DIRECTORY}/plugin_api/*") -ie_coverage_remove(INPUT "inference_engine_with_builders" OUTPUT "inference_engine" - PATTERNS "${DLDT_COVERAGE_BASE_DIRECTORY}/inference_engine/builders/*") ie_coverage_genhtml(INFO_FILE "inference_engine" PREFIX "${DLDT_COVERAGE_BASE_DIRECTORY}") +ie_coverage_extract(INPUT "dldt" OUTPUT "inference_engine_ir_reader" + PATTERNS "${DLDT_COVERAGE_BASE_DIRECTORY}/ir_readers/*") +ie_coverage_genhtml(INFO_FILE "inference_engine_ir_reader" + PREFIX "${DLDT_COVERAGE_BASE_DIRECTORY}") + ie_coverage_extract(INPUT "dldt" OUTPUT "inference_engine_legacy" PATTERNS "${DLDT_COVERAGE_BASE_DIRECTORY}/legacy_api/*") ie_coverage_genhtml(INFO_FILE "inference_engine_legacy" diff --git a/inference-engine/cmake/cpplint.cmake b/inference-engine/cmake/cpplint.cmake index 9e279ff239d..6c58d4aa532 100644 --- a/inference-engine/cmake/cpplint.cmake +++ b/inference-engine/cmake/cpplint.cmake @@ -3,10 +3,10 @@ # if(ENABLE_CPPLINT) - find_host_package(PythonInterp) + find_package(Python3 COMPONENTS Interpreter) - if(NOT PYTHONINTERP_FOUND) - message(WARNING "Python interpreter was not found (required for cpplint check)") + if(NOT Python3_Interpreter_FOUND) + message(WARNING "Python3 interpreter was not found (required for cpplint check)") set(ENABLE_CPPLINT OFF) endif() endif() diff --git a/inference-engine/cmake/developer_package_config.cmake.in b/inference-engine/cmake/developer_package_config.cmake.in index 93a27cb0e35..5600af6dfb8 100644 --- a/inference-engine/cmake/developer_package_config.cmake.in +++ b/inference-engine/cmake/developer_package_config.cmake.in @@ -25,6 +25,8 @@ endforeach() message("") set(gflags_DIR "@gflags_BINARY_DIR@") +# GNA lib dir +set(GNA "@GNA@") # Targets @@ -43,7 +45,7 @@ list(APPEND CMAKE_MODULE_PATH "${OpenVINO_MAIN_SOURCE_DIR}/cmake") list(APPEND CMAKE_MODULE_PATH "${IE_MAIN_SOURCE_DIR}/cmake") # generic stuff from developer package -include(developer_package NO_POLICY_SCOPE) +include(developer_package) include(developer_package_ie) # Don't threat deprecated API warnings as errors in 3rd party apps diff --git a/inference-engine/cmake/features_ie.cmake b/inference-engine/cmake/features_ie.cmake index 82f1a850ba7..e5b022c4a7b 100644 --- a/inference-engine/cmake/features_ie.cmake +++ b/inference-engine/cmake/features_ie.cmake @@ -62,8 +62,6 @@ if (ENABLE_GNA) endif() endif() -ie_option (ENABLE_IR_READER "Compile with IR readers / parsers" ON) - ie_option (ENABLE_VPU "vpu targeted plugins for inference engine" ON) ie_dependent_option (ENABLE_MYRIAD "myriad targeted plugin for inference engine" ON "ENABLE_VPU" OFF) @@ -72,7 +70,7 @@ ie_dependent_option (ENABLE_MYRIAD_NO_BOOT "myriad plugin will skip device boot" ie_option (ENABLE_TESTS "unit, behavior and functional tests" OFF) -ie_dependent_option (ENABLE_GAPI_TESTS "tests for GAPI kernels" OFF "ENABLE_TESTS" OFF) +ie_dependent_option (ENABLE_GAPI_TESTS "tests for GAPI kernels" ON "ENABLE_TESTS" OFF) ie_dependent_option (GAPI_TEST_PERF "if GAPI unit tests should examine performance" OFF "ENABLE_GAPI_TESTS" OFF) @@ -84,7 +82,7 @@ ie_dependent_option (ENABLE_SAME_BRANCH_FOR_MODELS "uses same branch for models ie_dependent_option (ENABLE_BEH_TESTS "tests oriented to check inference engine API corecteness" ON "ENABLE_TESTS" OFF) -ie_dependent_option (ENABLE_FUNCTIONAL_TESTS "functional tests" ON "ENABLE_TESTS;ENABLE_IR_READER" OFF) +ie_dependent_option (ENABLE_FUNCTIONAL_TESTS "functional tests" ON "ENABLE_TESTS" OFF) ie_dependent_option (ENABLE_SAMPLES "console samples are part of inference engine package" ON "NOT MINGW" OFF) @@ -98,18 +96,15 @@ ie_option (ENABLE_ALTERNATIVE_TEMP "in case of dependency conflict, to avoid mod ie_option (ENABLE_OPENCV "enables OpenCV" ON) -ie_option (ENABLE_DEBUG_SYMBOLS "generates symbols for debugging" OFF) - ie_option (ENABLE_PYTHON "enables ie python bridge build" OFF) -ie_option (ENABLE_CPP_CCT "enables C++ version of Cross Check Tool" OFF) - ie_option (ENABLE_C "enables ie c bridge build" ON) -ie_dependent_option(ENABLE_CPPLINT "Enable cpplint checks during the build" OFF "UNIX;NOT APPLE;NOT ANDROID" OFF) +ie_dependent_option(ENABLE_CPPLINT "Enable cpplint checks during the build" ON "UNIX;NOT APPLE;NOT ANDROID" OFF) + ie_dependent_option(ENABLE_CPPLINT_REPORT "Build cpplint report instead of failing the build" OFF "ENABLE_CPPLINT" OFF) -ie_option(ENABLE_CLANG_FORMAT "Enable clang-format checks during the build" OFF) +ie_option(ENABLE_CLANG_FORMAT "Enable clang-format checks during the build" ON) set(IE_EXTRA_PLUGINS "" CACHE STRING "Extra paths for plugins to include into DLDT build tree") diff --git a/inference-engine/cmake/models.cmake b/inference-engine/cmake/models.cmake index 3203068c86a..cb705dc1e1a 100644 --- a/inference-engine/cmake/models.cmake +++ b/inference-engine/cmake/models.cmake @@ -34,6 +34,10 @@ function (add_models_repo add_to_fetcher model_name) endfunction() function(add_lfs_repo name prefix url tag) + if(TARGET ${name}) + return() + endif() + ExternalProject_Add(${name} PREFIX ${prefix} GIT_REPOSITORY ${url} diff --git a/inference-engine/cmake/share/InferenceEngineConfig.cmake.in b/inference-engine/cmake/share/InferenceEngineConfig.cmake.in index ff273104bee..d3fc9f88304 100644 --- a/inference-engine/cmake/share/InferenceEngineConfig.cmake.in +++ b/inference-engine/cmake/share/InferenceEngineConfig.cmake.in @@ -150,7 +150,7 @@ else() set_target_properties(IE::inference_engine${ie_library_suffix} PROPERTIES IMPORTED_LOCATION "${IE${ie_library_usuffix}_RELEASE_LIBRARY}" INTERFACE_INCLUDE_DIRECTORIES "${IE_INCLUDE_DIR}") - if(CMAKE_CXX_COMPILER_ID MATCHES Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set_target_properties(IE::inference_engine${ie_library_suffix} PROPERTIES INTERFACE_COMPILE_OPTIONS "-diag-warning=1786") else() diff --git a/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c b/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c index f8699893080..3ac570fe401 100644 --- a/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c +++ b/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c @@ -105,9 +105,9 @@ void readInputFilesArgument(const char *arg) { const char *fileName = ep->d_name; if (strcmp(fileName, ".") == 0 || strcmp(fileName, "..") == 0) continue; char *file_path = (char *)calloc(strlen(arg) + strlen(ep->d_name) + 2, sizeof(char)); - strcpy(file_path, arg); - strcat(file_path, "/"); - strcat(file_path, ep->d_name); + memcpy(file_path, arg, strlen(arg)); + memcpy(file_path + strlen(arg), "/", strlen("/")); + memcpy(file_path + strlen(arg) + strlen("/"), ep->d_name, strlen(ep->d_name) + 1); if (file_num == 0) { file_paths = (char **)calloc(1, sizeof(char *)); @@ -131,7 +131,7 @@ void readInputFilesArgument(const char *arg) { dp = NULL; } else { char *file_path = (char *)calloc(strlen(arg) + 1, sizeof(char)); - strcpy(file_path, arg); + memcpy(file_path, arg, strlen(arg) + 1); if (file_num == 0) { file_paths = (char **)calloc(1, sizeof(char *)); } @@ -187,8 +187,8 @@ ie_config_t *parseConfig(const char *config_file, char comment) { if (fscanf(file, "%s", key)!= EOF && fscanf(file, "%s", value) != EOF) { char *cfg_name = (char *)calloc(strlen(key) + 1, sizeof(char)); char *cfg_value = (char *)calloc(strlen(value) + 1, sizeof(char)); - strcpy(cfg_name, key); - strcpy(cfg_value, value); + memcpy(cfg_name, key, strlen(key) + 1); + memcpy(cfg_value, value, strlen(value) + 1); ie_config_t *cfg_t = (ie_config_t *)calloc(1, sizeof(ie_config_t)); cfg_t->name = cfg_name; cfg_t->value = cfg_value; @@ -203,8 +203,8 @@ ie_config_t *parseConfig(const char *config_file, char comment) { } char *cfg_name = (char *)calloc(strlen(key) + 1, sizeof(char)); char *cfg_value = (char *)calloc(strlen(value) + 1, sizeof(char)); - strcpy(cfg_name, key); - strcpy(cfg_value, value); + memcpy(cfg_name, key, strlen(key) + 1); + memcpy(cfg_value, value, strlen(value) + 1); ie_config_t *cfg_t = (ie_config_t *)calloc(1, sizeof(ie_config_t)); cfg_t->name = cfg_name; cfg_t->value = cfg_value; @@ -345,8 +345,8 @@ int main(int argc, char **argv) { // --------------------------- 4. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------ input_weight = (char *)calloc(strlen(input_model) + 1, sizeof(char)); - strncpy(input_weight, input_model, strlen(input_model)-4); - strcat(input_weight, ".bin"); + memcpy(input_weight, input_model, strlen(input_model) - 4); + memcpy(input_weight + strlen(input_model) - 4, ".bin", strlen(".bin") + 1); printf("%sLoading network files:\n", info); printf("\t%s\n", input_model); printf("\t%s\n", input_weight); @@ -680,9 +680,9 @@ int main(int argc, char **argv) { char str_num[16] = {0}; int2str(str_num, batch_id); char *img_path = (char *)calloc(strlen(out) + strlen(str_num) + strlen(".bmp") + 1, sizeof(char)); - strcpy(img_path, out); - strcat(img_path, str_num); - strcat(img_path, ".bmp"); + memcpy(img_path, out, strlen(out)); + memcpy(img_path + strlen(out), str_num, strlen(str_num)); + memcpy(img_path + strlen(out) + strlen(str_num), ".bmp", strlen(".bmp") + 1); image_save(img_path, &originalImages[batch_id]); printf("%sImage %s created!\n", info, img_path); free(img_path); diff --git a/inference-engine/ie_bridges/c/tests/CMakeLists.txt b/inference-engine/ie_bridges/c/tests/CMakeLists.txt index 8bd1169efc7..4feae6bd93a 100644 --- a/inference-engine/ie_bridges/c/tests/CMakeLists.txt +++ b/inference-engine/ie_bridges/c/tests/CMakeLists.txt @@ -27,6 +27,8 @@ target_compile_definitions(${TARGET_NAME} DATA_PATH=\"${DATA_PATH}\" MODELS_PATH=\"${MODELS_PATH}\" ) +add_dependencies(${TARGET_NAME} MultiDevicePlugin) + if(ENABLE_MKL_DNN) add_dependencies(${TARGET_NAME} MKLDNNPlugin) endif() diff --git a/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp b/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp index 25c0972b7ad..752b24075b7 100644 --- a/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp +++ b/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp @@ -816,10 +816,8 @@ TEST(ie_exec_network_set_config, setConfig) { IE_ASSERT_OK(ie_core_create("", &core)); ASSERT_NE(nullptr, core); - ie_core_versions_t ie_core_versions_multi; ie_param_t param; - if (ie_core_get_versions(core, "MULTI", &ie_core_versions_multi) != IEStatusCode::OK || - ie_core_get_metric(core, "GPU", "AVAILABLE_DEVICES", ¶m) != IEStatusCode::OK) { + if (ie_core_get_metric(core, "GPU", "AVAILABLE_DEVICES", ¶m) != IEStatusCode::OK) { ie_core_free(&core); GTEST_SKIP(); } @@ -837,11 +835,10 @@ TEST(ie_exec_network_set_config, setConfig) { ie_config_t config_param = {"MULTI_DEVICE_PRIORITIES", "GPU,CPU", nullptr}; IE_EXPECT_OK(ie_exec_network_set_config(exe_network, &config_param)); - ie_core_versions_free(&ie_core_versions_multi); - ie_param_free(¶m); ie_exec_network_free(&exe_network); ie_network_free(&network); ie_core_free(&core); + ie_param_free(¶m); } TEST(ie_exec_network_get_metric, getMetric) { diff --git a/inference-engine/ie_bridges/python/CMakeLists.txt b/inference-engine/ie_bridges/python/CMakeLists.txt index 80db79f65ef..519a5c64338 100644 --- a/inference-engine/ie_bridges/python/CMakeLists.txt +++ b/inference-engine/ie_bridges/python/CMakeLists.txt @@ -55,6 +55,13 @@ endif() set (PYTHON_BRIDGE_SRC_ROOT ${CMAKE_CURRENT_SOURCE_DIR}) add_subdirectory (src/openvino/inference_engine) +# Check Cython version +if("${CYTHON_VERSION}" VERSION_LESS "0.29") + message(FATAL_ERROR "OpenVINO Python API needs at least Cython version 0.29, found verson ${CYTHON_VERSION}") +else() + message(STATUS "Found Cython version ${CYTHON_VERSION}") +endif() + # install ie_cpack_add_component(${PYTHON_VERSION} REQUIRED) diff --git a/inference-engine/ie_bridges/python/cmake/FindCython.cmake b/inference-engine/ie_bridges/python/cmake/FindCython.cmake index ae97e2417f5..f960fe20998 100644 --- a/inference-engine/ie_bridges/python/cmake/FindCython.cmake +++ b/inference-engine/ie_bridges/python/cmake/FindCython.cmake @@ -56,4 +56,8 @@ endif() include( FindPackageHandleStandardArgs ) FIND_PACKAGE_HANDLE_STANDARD_ARGS( Cython REQUIRED_VARS CYTHON_EXECUTABLE ) -mark_as_advanced( CYTHON_EXECUTABLE ) +# Find Cython version +execute_process(COMMAND ${CYTHON_EXECUTABLE} -V ERROR_VARIABLE CYTHON_OUTPUT OUTPUT_QUIET) +string(REGEX REPLACE "^Cython version ([0-9]+\\.[0-9]+\\.[0-9]+).*" "\\1" CYTHON_VERSION "${CYTHON_OUTPUT}") + +mark_as_advanced( CYTHON_EXECUTABLE CYTHON_VERSION ) diff --git a/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx b/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx index f60fd43ca0e..06bf8b106a0 100644 --- a/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx +++ b/inference-engine/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx @@ -1439,7 +1439,7 @@ cdef class IENetwork: # net = ie.read_network(model=path_to_xml_file, weights=path_to_bin_file) # input_layer = next(iter(net.inputs)) # n, c, h, w = net.inputs[input_layer] - # net.reshape({input_layer: (n, c, h*2, w*2)}] + # net.reshape({input_layer: (n, c, h*2, w*2)}) # ``` def reshape(self, input_shapes: dict): cdef map[string, vector[size_t]] c_input_shapes; diff --git a/inference-engine/ie_bridges/python/tests/test_InferRequest.py b/inference-engine/ie_bridges/python/tests/test_InferRequest.py index b68b8373e84..0dd7ef2a33c 100644 --- a/inference-engine/ie_bridges/python/tests/test_InferRequest.py +++ b/inference-engine/ie_bridges/python/tests/test_InferRequest.py @@ -2,6 +2,7 @@ import numpy as np import os import pytest import warnings +import threading from openvino.inference_engine import ie_api as ie from conftest import model_path, image_path @@ -324,15 +325,22 @@ def test_async_infer_callback_wait_in_callback(device): class InferReqWrap: def __init__(self, request): self.request = request + self.cv = threading.Condition() self.request.set_completion_callback(self.callback) self.status_code = self.request.wait(ie.WaitMode.STATUS_ONLY) assert self.status_code == ie.StatusCode.INFER_NOT_STARTED def callback(self, statusCode, userdata): self.status_code = self.request.wait(ie.WaitMode.STATUS_ONLY) + self.cv.acquire() + self.cv.notify() + self.cv.release() def execute(self, input_data): self.request.async_infer(input_data) + self.cv.acquire() + self.cv.wait() + self.cv.release() status = self.request.wait(ie.WaitMode.RESULT_READY) assert status == ie.StatusCode.OK assert self.status_code == ie.StatusCode.OK diff --git a/inference-engine/include/cpp/ie_cnn_network.h b/inference-engine/include/cpp/ie_cnn_network.h index 16fb7cd0b05..bcf850fa8b6 100644 --- a/inference-engine/include/cpp/ie_cnn_network.h +++ b/inference-engine/include/cpp/ie_cnn_network.h @@ -49,9 +49,7 @@ public: */ explicit CNNNetwork(std::shared_ptr network): network(network) { actual = network.get(); - if (actual == nullptr) { - THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; - } + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; } /** @@ -100,6 +98,7 @@ public: * @return outputs Reference to the OutputsDataMap object */ virtual OutputsDataMap getOutputsInfo() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; OutputsDataMap outputs; actual->getOutputsInfo(outputs); return outputs; @@ -113,6 +112,7 @@ public: * @return inputs Reference to InputsDataMap object */ virtual InputsDataMap getInputsInfo() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; InputsDataMap inputs; actual->getInputsInfo(inputs); return inputs; @@ -126,6 +126,7 @@ public: * @return The number of layers as an integer value */ size_t layerCount() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return actual->layerCount(); } @@ -136,7 +137,8 @@ public: * * @return Network name */ - const std::string& getName() const noexcept { + const std::string& getName() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return actual->getName(); } @@ -160,6 +162,7 @@ public: * @return The size of batch as a size_t value */ virtual size_t getBatchSize() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return actual->getBatchSize(); } @@ -168,7 +171,7 @@ public: * * @return A shared pointer of the current network */ - operator std::shared_ptr() { + operator ICNNNetwork::Ptr() { return network; } @@ -178,6 +181,7 @@ public: * @return An instance of the current network */ operator ICNNNetwork&() { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return *actual; } @@ -187,6 +191,7 @@ public: * @return A const reference of the current network */ operator const ICNNNetwork&() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return *actual; } @@ -195,7 +200,8 @@ public: * * @return constant nGraph function */ - std::shared_ptr getFunction() noexcept { + std::shared_ptr getFunction() { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return actual->getFunction(); } @@ -204,7 +210,8 @@ public: * * @return constant nGraph function */ - std::shared_ptr getFunction() const noexcept { + std::shared_ptr getFunction() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return actual->getFunction(); } @@ -278,6 +285,7 @@ public: * @return Map of pairs: input name and its dimension. */ virtual ICNNNetwork::InputShapes getInputShapes() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; ICNNNetwork::InputShapes shapes; InputsDataMap inputs; actual->getInputsInfo(inputs); diff --git a/inference-engine/include/cpp/ie_executable_network.hpp b/inference-engine/include/cpp/ie_executable_network.hpp index b6ec009ac0d..c88a645b2de 100644 --- a/inference-engine/include/cpp/ie_executable_network.hpp +++ b/inference-engine/include/cpp/ie_executable_network.hpp @@ -190,9 +190,7 @@ public: * @return A vector of Memory State objects */ std::vector QueryState() { - if (actual == nullptr) { - THROW_IE_EXCEPTION << "ExecutableNetwork wrapper was not initialized."; - } + if (actual == nullptr) THROW_IE_EXCEPTION << "ExecutableNetwork was not initialized."; IMemoryState::Ptr pState = nullptr; auto res = OK; std::vector controller; diff --git a/inference-engine/include/cpp/ie_infer_request.hpp b/inference-engine/include/cpp/ie_infer_request.hpp index 6c5ff95b080..f89d75e54d5 100644 --- a/inference-engine/include/cpp/ie_infer_request.hpp +++ b/inference-engine/include/cpp/ie_infer_request.hpp @@ -88,9 +88,7 @@ public: InferenceEngine::details::SharedObjectLoader::Ptr splg = {}): actual(request), plg(splg) { // plg can be null, but not the actual - if (actual == nullptr) { - THROW_IE_EXCEPTION << "InferRequest wrapper was not initialized."; - } + if (actual == nullptr) THROW_IE_EXCEPTION << "InferRequest was not initialized."; } /** @@ -230,9 +228,7 @@ public: */ StatusCode Wait(int64_t millis_timeout) { ResponseDesc resp; - if (actual == nullptr) { - THROW_IE_EXCEPTION << "InferRequest wrapper was not initialized."; - } + if (actual == nullptr) THROW_IE_EXCEPTION << "InferRequest was not initialized."; auto res = actual->Wait(millis_timeout, &resp); if (res != OK && res != RESULT_NOT_READY && res != INFER_NOT_STARTED) { InferenceEngine::details::extract_exception(res, resp.msg); @@ -259,6 +255,7 @@ public: * @return A shared pointer to underlying IInferRequest interface */ operator IInferRequest::Ptr&() { + if (actual == nullptr) THROW_IE_EXCEPTION << "InferRequest was not initialized."; return actual; } diff --git a/inference-engine/include/cpp/ie_plugin_cpp.hpp b/inference-engine/include/cpp/ie_plugin_cpp.hpp index e75fdff6110..db56c01d6bf 100644 --- a/inference-engine/include/cpp/ie_plugin_cpp.hpp +++ b/inference-engine/include/cpp/ie_plugin_cpp.hpp @@ -57,6 +57,7 @@ public: const Version* GetVersion() { const Version* versionInfo = nullptr; IE_SUPPRESS_DEPRECATED_START + if (actual == nullptr) THROW_IE_EXCEPTION << "InferencePlugin wrapper was not initialized"; actual->GetVersion(versionInfo); IE_SUPPRESS_DEPRECATED_END if (versionInfo == nullptr) { @@ -153,6 +154,7 @@ public: void QueryNetwork(const ICNNNetwork& network, const std::map& config, QueryNetworkResult& res) const { IE_SUPPRESS_DEPRECATED_START + if (actual == nullptr) THROW_IE_EXCEPTION << "InferencePlugin wrapper was not initialized"; actual->QueryNetwork(network, config, res); IE_SUPPRESS_DEPRECATED_END if (res.rc != OK) THROW_IE_EXCEPTION << res.resp.msg; diff --git a/inference-engine/include/details/ie_cnn_network_iterator.hpp b/inference-engine/include/details/ie_cnn_network_iterator.hpp index e5f1af39c62..4bbc93c67b2 100644 --- a/inference-engine/include/details/ie_cnn_network_iterator.hpp +++ b/inference-engine/include/details/ie_cnn_network_iterator.hpp @@ -53,6 +53,7 @@ public: * scope. */ explicit CNNNetworkIterator(const ICNNNetwork* network) { + if (network == nullptr) THROW_IE_EXCEPTION << "ICNNNetwork object is nullptr"; InputsDataMap inputs; network->getInputsInfo(inputs); if (!inputs.empty()) { diff --git a/inference-engine/include/details/ie_so_pointer.hpp b/inference-engine/include/details/ie_so_pointer.hpp index 643c8367930..4854aa0da09 100644 --- a/inference-engine/include/details/ie_so_pointer.hpp +++ b/inference-engine/include/details/ie_so_pointer.hpp @@ -38,7 +38,11 @@ public: * @brief The main constructor * @param loader Library to load from */ - explicit SymbolLoader(std::shared_ptr loader): _so_loader(loader) {} + explicit SymbolLoader(std::shared_ptr loader): _so_loader(loader) { + if (_so_loader == nullptr) { + THROW_IE_EXCEPTION << "SymbolLoader cannot be created with nullptr"; + } + } /** * @brief Calls a function from the library that creates an object and returns StatusCode diff --git a/inference-engine/include/ie_core.hpp b/inference-engine/include/ie_core.hpp index 50307e28ad2..eadfbca702f 100644 --- a/inference-engine/include/ie_core.hpp +++ b/inference-engine/include/ie_core.hpp @@ -38,7 +38,7 @@ struct QueryNetworkResult { StatusCode rc = OK; /** - * @brief Response mssage + * @brief Response message */ ResponseDesc resp; }; @@ -61,7 +61,7 @@ public: * @param xmlConfigFile A path to .xml file with plugins to load from. If XML configuration file is not specified, * then default Inference Engine plugins are loaded from the default plugin.xml file. */ - explicit Core(const std::string& xmlConfigFile = std::string()); + explicit Core(const std::string& xmlConfigFile = {}); /** * @brief Returns plugins version information @@ -92,7 +92,7 @@ public: * if bin file with the same name was not found, will load IR without weights. * @return CNNNetwork */ - CNNNetwork ReadNetwork(const std::wstring& modelPath, const std::wstring& binPath = std::wstring()) const { + CNNNetwork ReadNetwork(const std::wstring& modelPath, const std::wstring& binPath = {}) const { return ReadNetwork(details::wStringtoMBCSstringChar(modelPath), details::wStringtoMBCSstringChar(binPath)); } #endif @@ -104,7 +104,7 @@ public: * if bin file with the same name was not found, will load IR without weights. * @return CNNNetwork */ - CNNNetwork ReadNetwork(const std::string& modelPath, const std::string& binPath = "") const; + CNNNetwork ReadNetwork(const std::string& modelPath, const std::string& binPath = {}) const; /** * @brief Reads IR xml and bin (with the same name) files * @param model string with IR @@ -126,8 +126,8 @@ public: * @return An executable network reference */ ExecutableNetwork LoadNetwork( - const CNNNetwork network, const std::string& deviceName, - const std::map& config = std::map()); + const CNNNetwork& network, const std::string& deviceName, + const std::map& config = {}); /** * @brief Registers extension @@ -141,11 +141,11 @@ public: * @param context Pointer to RemoteContext object * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load * operation - * @return An executable network reference + * @return An executable network object */ ExecutableNetwork LoadNetwork( - const CNNNetwork network, RemoteContext::Ptr context, - const std::map& config = std::map()); + const CNNNetwork& network, RemoteContext::Ptr context, + const std::map& config = {}); /** * @brief Registers extension for the specified plugin @@ -166,7 +166,7 @@ public: */ ExecutableNetwork ImportNetwork( const std::string& modelFileName, const std::string& deviceName, - const std::map& config = std::map()); + const std::map& config = {}); /** * @brief Creates an executable network from a previously exported network @@ -199,11 +199,11 @@ public: * @param deviceName A name of a device to query * @param network Network object to query * @param config Optional map of pairs: (config parameter name, config parameter value) - * @return Pointer to the response message that holds a description of an error if any occurred + * @return An object containing a map of pairs a layer name -> a device name supporting this layer. */ QueryNetworkResult QueryNetwork( const ICNNNetwork& network, const std::string& deviceName, - const std::map& config = std::map()) const; + const std::map& config = {}) const; /** * @brief Sets configuration for device, acceptable keys can be found in ie_plugin_config.hpp @@ -213,7 +213,7 @@ public: * * @param config Map of pairs: (config parameter name, config parameter value) */ - void SetConfig(const std::map& config, const std::string& deviceName = std::string()); + void SetConfig(const std::map& config, const std::string& deviceName = {}); /** * @brief Gets configuration dedicated to device behaviour. diff --git a/inference-engine/samples/CMakeLists.txt b/inference-engine/samples/CMakeLists.txt index 3c00465c42c..9de36cf72ac 100644 --- a/inference-engine/samples/CMakeLists.txt +++ b/inference-engine/samples/CMakeLists.txt @@ -6,6 +6,10 @@ cmake_minimum_required (VERSION 2.8.12) project(Samples) +if(POLICY CMP0054) + cmake_policy(SET CMP0054 NEW) +endif() + if(POLICY CMP0063) cmake_policy(SET CMP0063 NEW) endif() @@ -66,7 +70,7 @@ if (WIN32) set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /WX") #treating warnings as errors endif () - if (CMAKE_CXX_COMPILER_ID MATCHES MSVC) + if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /wd4251 /wd4275 /wd4267") #disable some warnings endif() else() @@ -78,7 +82,7 @@ else() set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=unused-command-line-argument") elseif(UNIX) set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wuninitialized -Winit-self") - if(NOT CMAKE_CXX_COMPILER_ID STREQUAL Clang) + if(NOT CMAKE_CXX_COMPILER_ID STREQUAL "Clang") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wmaybe-uninitialized") endif() endif() @@ -100,7 +104,7 @@ if(NOT DEFINED CMAKE_CXX_STANDARD) set (CMAKE_CXX_STANDARD 11) set (CMAKE_CXX_EXTENSIONS OFF) set (CMAKE_CXX_STANDARD_REQUIRED ON) - if (CMAKE_CXX_COMPILER_ID STREQUAL GNU) + if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set (CMAKE_CXX_FLAGS "-std=c++11 ${CMAKE_CXX_FLAGS}") endif() endif() @@ -117,7 +121,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/gflags") set_target_properties(gflags_nothreads_static PROPERTIES FOLDER thirdparty) endif() -if (CMAKE_CXX_COMPILER_ID STREQUAL GNU) +if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall") endif() @@ -228,7 +232,7 @@ macro(ie_add_sample) if(COMMAND add_cpplint_target AND NOT IE_SAMPLE_EXCLUDE_CPPLINT) if(folder_name STREQUAL "c_samples") - set(custom_filters "-readability/casting,-runtime/printf") + set(custom_filters "-readability/casting") endif() add_cpplint_target(${IE_SAMPLE_NAME}_cpplint FOR_TARGETS ${IE_SAMPLE_NAME} CUSTOM_FILTERS ${custom_filters}) diff --git a/inference-engine/samples/benchmark_app/README.md b/inference-engine/samples/benchmark_app/README.md index 7f675a7198f..04cf851fc4c 100644 --- a/inference-engine/samples/benchmark_app/README.md +++ b/inference-engine/samples/benchmark_app/README.md @@ -84,6 +84,7 @@ Options: -stream_output Optional. Print progress as a plain text. When specified, an interactive progress bar is replaced with a multiline output. -t Optional. Time in seconds to execute topology. -progress Optional. Show progress bar (can affect performance measurement). Default values is "false". + -shape Optional. Set shape for input. For example, "input1[1,3,224,224],input2[1,4]" or "[1,3,224,224]" in case of one input size. CPU-specific performance options: -nstreams "" Optional. Number of streams to use for inference on the CPU or/and GPU in throughput mode diff --git a/inference-engine/samples/benchmark_app/benchmark_app.hpp b/inference-engine/samples/benchmark_app/benchmark_app.hpp index cefff685c83..9affebbd8bf 100644 --- a/inference-engine/samples/benchmark_app/benchmark_app.hpp +++ b/inference-engine/samples/benchmark_app/benchmark_app.hpp @@ -97,6 +97,9 @@ static const char load_config_message[] = "Optional. Path to XML/YAML/JSON file static const char dump_config_message[] = "Optional. Path to XML/YAML/JSON file to dump IE parameters, which were set by application."; #endif +static const char shape_message[] = "Optional. Set shape for input. For example, \"input1[1,3,224,224],input2[1,4]\" or \"[1,3,224,224]\"" + " in case of one input size."; + /// @brief Define flag for showing help message
DEFINE_bool(h, false, help_message); @@ -178,6 +181,9 @@ DEFINE_string(load_config, "", load_config_message); DEFINE_string(dump_config, "", dump_config_message); #endif +/// @brief Define flag for input shape
+DEFINE_string(shape, "", shape_message); + /** * @brief This function show a help message */ @@ -200,6 +206,7 @@ static void showUsage() { std::cout << " -stream_output " << stream_output_message << std::endl; std::cout << " -t " << execution_time_message << std::endl; std::cout << " -progress " << progress_message << std::endl; + std::cout << " -shape " << shape_message << std::endl; std::cout << std::endl << " device-specific performance options:" << std::endl; std::cout << " -nstreams \"\" " << infer_num_streams_message << std::endl; std::cout << " -nthreads \"\" " << infer_num_threads_message << std::endl; diff --git a/inference-engine/samples/benchmark_app/main.cpp b/inference-engine/samples/benchmark_app/main.cpp index 800e0f70a55..684753b8b55 100644 --- a/inference-engine/samples/benchmark_app/main.cpp +++ b/inference-engine/samples/benchmark_app/main.cpp @@ -316,33 +316,28 @@ int main(int argc, char *argv[]) { // ----------------- 5. Resizing network to match image sizes and given batch ---------------------------------- next_step(); - batchSize = cnnNetwork.getBatchSize(); - if ((FLAGS_b != 0) && (batchSize != FLAGS_b)) { - ICNNNetwork::InputShapes shapes = cnnNetwork.getInputShapes(); - bool reshape = false; - for (const InputsDataMap::value_type& item : inputInfo) { - auto layout = item.second->getTensorDesc().getLayout(); - - int batchIndex = -1; - if ((layout == Layout::NCHW) || (layout == Layout::NCDHW) || - (layout == Layout::NHWC) || (layout == Layout::NDHWC) || - (layout == Layout::NC)) { - batchIndex = 0; - } else if (layout == CN) { - batchIndex = 1; - } - if ((batchIndex != -1) && (shapes[item.first][batchIndex] != FLAGS_b)) { - shapes[item.first][batchIndex] = FLAGS_b; - reshape = true; - } - } - if (reshape) { - slog::info << "Resizing network to batch = " << FLAGS_b << slog::endl; - cnnNetwork.reshape(shapes); - } + // Parse input shapes if specified + InferenceEngine::ICNNNetwork::InputShapes shapes = cnnNetwork.getInputShapes(); + bool reshape = false; + if (!FLAGS_shape.empty()) { + reshape |= updateShapes(shapes, FLAGS_shape, inputInfo); + } + if ((FLAGS_b != 0) && (batchSize != FLAGS_b)) { + reshape |= adjustShapesBatch(shapes, FLAGS_b, inputInfo); + } + if (reshape) { + slog::info << "Reshaping network: " << getShapesString(shapes) << slog::endl; + startTime = Time::now(); + cnnNetwork.reshape(shapes); + auto duration_ms = double_to_string(get_total_ms_time(startTime)); + slog::info << "Reshape network took " << duration_ms << " ms" << slog::endl; + if (statistics) + statistics->addParameters(StatisticsReport::Category::EXECUTION_RESULTS, + { + {"reshape network time (ms)", duration_ms} + }); } - batchSize = cnnNetwork.getBatchSize(); topology_name = cnnNetwork.getName(); slog::info << (FLAGS_b != 0 ? "Network batch size was changed to: " : "Network batch size: ") << batchSize << slog::endl; diff --git a/inference-engine/samples/benchmark_app/utils.cpp b/inference-engine/samples/benchmark_app/utils.cpp index d0820bccbab..1c1186baa5e 100644 --- a/inference-engine/samples/benchmark_app/utils.cpp +++ b/inference-engine/samples/benchmark_app/utils.cpp @@ -7,6 +7,7 @@ #include #include #include +#include #include #include @@ -101,6 +102,77 @@ std::map parseNStreamsValuePerDevice(const std::vector return result; } +bool adjustShapesBatch(InferenceEngine::ICNNNetwork::InputShapes& shapes, + const size_t batch_size, const InferenceEngine::InputsDataMap& input_info) { + bool updated = false; + for (auto& item : input_info) { + auto layout = item.second->getTensorDesc().getLayout(); + + int batch_index = -1; + if ((layout == InferenceEngine::Layout::NCHW) || (layout == InferenceEngine::Layout::NCDHW) || + (layout == InferenceEngine::Layout::NHWC) || (layout == InferenceEngine::Layout::NDHWC) || + (layout == InferenceEngine::Layout::NC)) { + batch_index = 0; + } else if (layout == InferenceEngine::Layout::CN) { + batch_index = 1; + } + if ((batch_index != -1) && (shapes.at(item.first).at(batch_index) != batch_size)) { + shapes[item.first][batch_index] = batch_size; + updated = true; + } + } + return updated; +} + +bool updateShapes(InferenceEngine::ICNNNetwork::InputShapes& shapes, + const std::string shapes_string, const InferenceEngine::InputsDataMap& input_info) { + bool updated = false; + std::string search_string = shapes_string; + auto start_pos = search_string.find_first_of('['); + while (start_pos != std::string::npos) { + auto end_pos = search_string.find_first_of(']'); + if (end_pos == std::string::npos) + break; + auto input_name = search_string.substr(0, start_pos); + auto input_shape = search_string.substr(start_pos + 1, end_pos - start_pos - 1); + std::vector parsed_shape; + for (auto& dim : split(input_shape, ',')) { + parsed_shape.push_back(std::stoi(dim)); + } + if (!input_name.empty()) { + shapes[input_name] = parsed_shape; + updated = true; + } else { + for (auto& item : input_info) { + shapes[item.first] = parsed_shape; + } + updated = true; + } + search_string = search_string.substr(end_pos + 1); + if (search_string.empty() || search_string.front() != ',') + break; + search_string = search_string.substr(1); + start_pos = search_string.find_first_of('['); + } + if (!search_string.empty()) + throw std::logic_error("Can't parse `shape` parameter: " + shapes_string); + return updated; +} + +std::string getShapesString(const InferenceEngine::ICNNNetwork::InputShapes& shapes) { + std::stringstream ss; + for (auto& shape : shapes) { + if (!ss.str().empty()) ss << ", "; + ss << "\'" << shape.first << "': ["; + for (size_t i = 0; i < shape.second.size(); i++) { + if (i > 0) ss << ", "; + ss << shape.second.at(i); + } + ss << "]"; + } + return ss.str(); +} + #ifdef USE_OPENCV void dump_config(const std::string& filename, const std::map>& config) { diff --git a/inference-engine/samples/benchmark_app/utils.hpp b/inference-engine/samples/benchmark_app/utils.hpp index 70c2ce7747c..75b10257160 100644 --- a/inference-engine/samples/benchmark_app/utils.hpp +++ b/inference-engine/samples/benchmark_app/utils.hpp @@ -12,6 +12,12 @@ std::vector parseDevices(const std::string& device_string); uint32_t deviceDefaultDeviceDurationInSeconds(const std::string& device); std::map parseNStreamsValuePerDevice(const std::vector& devices, const std::string& values_string); +bool updateShapes(InferenceEngine::ICNNNetwork::InputShapes& shapes, + const std::string shapes_string, const InferenceEngine::InputsDataMap& input_info); +bool adjustShapesBatch(InferenceEngine::ICNNNetwork::InputShapes& shapes, + const size_t batch_size, const InferenceEngine::InputsDataMap& input_info); +std::string getShapesString(const InferenceEngine::ICNNNetwork::InputShapes& shapes); + #ifdef USE_OPENCV void dump_config(const std::string& filename, const std::map>& config); diff --git a/inference-engine/scripts/create_vs2017_proj_x64.cmd b/inference-engine/scripts/create_vs2017_proj_x64.cmd new file mode 100644 index 00000000000..ff91c5945a5 --- /dev/null +++ b/inference-engine/scripts/create_vs2017_proj_x64.cmd @@ -0,0 +1,31 @@ +@echo off +:: Copyright (C) 2018-2020 Intel Corporation +:: SPDX-License-Identifier: Apache-2.0 + +pushd ..\.. +if not exist "vs2017x64" ( + mkdir "vs2017x64" +) + +cmake -E chdir "vs2017x64" cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" -DOS_FOLDER=ON ^ + -DENABLE_MYRIAD=OFF -DENABLE_VPU=OFF -DENABLE_GNA=ON -DENABLE_CLDNN=OFF ^ + -DENABLE_OPENCV=ON -DENABLE_MKL_DNN=ON ^ + -DVERBOSE_BUILD=ON -DENABLE_TESTS=ON -DTHREADING=TBB .. + + +chdir +cd "vs2017x64\thirdparty\" +"C:\Program Files (x86)\Common Files\Intel\shared files\ia32\Bin\ICProjConvert180.exe" mkldnn.vcxproj /IC + +chdir +cd "..\src\mkldnn_plugin" +"C:\Program Files (x86)\Common Files\Intel\shared files\ia32\Bin\ICProjConvert180.exe" MKLDNNPlugin.vcxproj /IC +"C:\Program Files (x86)\Common Files\Intel\shared files\ia32\Bin\ICProjConvert180.exe" test_MKLDNNPlugin.vcxproj /IC + +chdir +cd "..\..\tests\unit" +"C:\Program Files (x86)\Common Files\Intel\shared files\ia32\Bin\ICProjConvert180.exe" InferenceEngineUnitTests.vcxproj /IC + + +popd +pause diff --git a/inference-engine/scripts/run_code_checks.sh b/inference-engine/scripts/run_code_checks.sh index 11689b7cc75..d3bdb355cce 100644 --- a/inference-engine/scripts/run_code_checks.sh +++ b/inference-engine/scripts/run_code_checks.sh @@ -1,4 +1,6 @@ #!/bin/bash +# Copyright (C) 2018-2020 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 CURRENT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" command -v realpath >/dev/null 2>&1 || { echo >&2 "cpplint require realpath executable but it's not installed. Aborting."; exit 1; } diff --git a/inference-engine/scripts/run_tests_myriad_multistick.sh b/inference-engine/scripts/run_tests_myriad_multistick.sh new file mode 100644 index 00000000000..c6082a9f336 --- /dev/null +++ b/inference-engine/scripts/run_tests_myriad_multistick.sh @@ -0,0 +1,87 @@ +#!/bin/bash +# Copyright (C) 2018-2020 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +APP_NAME="MyriadFunctionalTests" +APPS_TO_RUN=$1 +APPS_TO_RUN=${APPS_TO_RUN:=4} + +echo "Run in parallel ${APPS_TO_RUN} applications" + +TEST_DIR=../../bin/intel64 + +# Path to test dir is provided +if [[ -n "$2" ]]; then + TEST_DIR=$2 +# Search for test dir with binaries +else + # Windows default + if [[ -f "${TEST_DIR}/${APP_NAME}" ]]; then + TEST_DIR=${TEST_DIR} + # Search for Release or Debug config + elif [[ -f "${TEST_DIR}/Release/${APP_NAME}" ]]; then + TEST_DIR="$TEST_DIR/Release/" + elif [[ -f "${TEST_DIR}/Debug/${APP_NAME}" ]]; then + TEST_DIR="$TEST_DIR/Debug/" + else + echo "Directory with binaries not found!" + exit -1 + fi + +fi + +echo "Test directory: ${TEST_DIR}" +cd ${TEST_DIR} + +export IE_VPU_MYRIADX=1 + +pids="" + +if [[ "${APPS_TO_RUN}" -ge 1 ]] ; then + ./${APP_NAME} --gtest_filter=*VPURegTest*SSD*myriad* & + pids+=" $!" +fi + +if [[ "${APPS_TO_RUN}" -ge 2 ]] ; then + ./${APP_NAME} --gtest_filter=*VPURegTest*VGG*myriad* & + pids+=" $!" +fi + +if [[ "${APPS_TO_RUN}" -ge 3 ]] ; then + ./${APP_NAME} --gtest_filter=*VPURegTest*VGG*myriad* & + pids+=" $!" +fi + +if [[ "${APPS_TO_RUN}" -ge 4 ]] ; then + # For more then 4 multidevice testing + for (( VAR = 4; VAR <= ${APPS_TO_RUN}; ++VAR )); do + ./${APP_NAME} --gtest_filter=*VPURegTest*YOLO*myriad* & + pids+=" $!" + done +fi + + +# Wait for all processes to finish +sts="" +for p in ${pids}; do + if wait ${p}; then + sts+=" 1" + else + sts+=" 0" + fi + echo "--- Process $p finished" +done + +idx=0 +exit_code=0 +for s in ${sts}; do + if [[ ${s} -eq 1 ]]; then + echo "Task $idx PASSED" + else + echo "Task $idx FAILED" + exit_code=1 + fi + ((idx+=1)) +done + +exit ${exit_code} diff --git a/inference-engine/src/CMakeLists.txt b/inference-engine/src/CMakeLists.txt index 5774f02d239..a97e1c780ee 100644 --- a/inference-engine/src/CMakeLists.txt +++ b/inference-engine/src/CMakeLists.txt @@ -26,6 +26,8 @@ endif() add_subdirectory(hetero_plugin) +add_subdirectory(multi_device) + add_subdirectory(transformations) add_subdirectory(inference_engine) diff --git a/inference-engine/src/cldnn_engine/cldnn_engine.cpp b/inference-engine/src/cldnn_engine/cldnn_engine.cpp index e41e649431e..e728ea6be68 100644 --- a/inference-engine/src/cldnn_engine/cldnn_engine.cpp +++ b/inference-engine/src/cldnn_engine/cldnn_engine.cpp @@ -26,8 +26,10 @@ #include #include #include +#include #include #include +#include #include "convert_function_to_cnn_network.hpp" #undef min @@ -79,6 +81,8 @@ InferenceEngine::ICNNNetwork::Ptr clDNNEngine::CloneNetwork(const InferenceEngin ::ngraph::op::GenericIE::DisableReshape noReshape(nGraphFunc); // Note: instead of running all Conversion Transformations you can make up your own transformation pipeline + ngraph::pass::CommonOptimizations().run_on_function(nGraphFunc); + ngraph::pass::ConvertOpSet3ToOpSet2(transformations_callback).run_on_function(nGraphFunc); ngraph::pass::ConvertOpSet2ToOpSet1(transformations_callback).run_on_function(nGraphFunc); ngraph::pass::ConvertOpSet1ToLegacy(transformations_callback).run_on_function(nGraphFunc); clonedNetwork = InferenceEngine::details::convertFunctionToICNNNetwork(nGraphFunc, network); @@ -143,7 +147,7 @@ auto check_inputs = [](InferenceEngine::InputsDataMap _networkInputs) { } }; -ExecutableNetworkInternal::Ptr clDNNEngine::LoadExeNetworkImpl(const InferenceEngine::ICore * /*core*/, const InferenceEngine::ICNNNetwork &network, +ExecutableNetworkInternal::Ptr clDNNEngine::LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, const std::map &config) { // verification of supported input InferenceEngine::InputsDataMap _networkInputs; @@ -189,9 +193,9 @@ ExecutableNetworkInternal::Ptr clDNNEngine::LoadExeNetworkImpl(const InferenceEn return std::make_shared(*CloneNetwork(network), context, conf); } -ExecutableNetworkInternal::Ptr clDNNEngine::LoadExeNetworkImpl(const InferenceEngine::ICore * /*core*/, const InferenceEngine::ICNNNetwork &network, - RemoteContext::Ptr context, - const std::map &config) { +ExecutableNetworkInternal::Ptr clDNNEngine::LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, + RemoteContext::Ptr context, + const std::map &config) { InferenceEngine::InputsDataMap _networkInputs; network.getInputsInfo(_networkInputs); check_inputs(_networkInputs); diff --git a/inference-engine/src/cldnn_engine/cldnn_engine.h b/inference-engine/src/cldnn_engine/cldnn_engine.h index 50c06c81ea9..b1cf24b4a54 100644 --- a/inference-engine/src/cldnn_engine/cldnn_engine.h +++ b/inference-engine/src/cldnn_engine/cldnn_engine.h @@ -30,12 +30,12 @@ class clDNNEngine : public InferenceEngine::InferencePluginInternal, public: clDNNEngine(); - InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::ICore * core, const InferenceEngine::ICNNNetwork &network, + InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, const std::map &config) override; - InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::ICore * core, const InferenceEngine::ICNNNetwork &network, - InferenceEngine::RemoteContext::Ptr context, - const std::map &config) override; + InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, + InferenceEngine::RemoteContext::Ptr context, + const std::map &config) override; void SetConfig(const std::map &config) override; InferenceEngine::Parameter GetConfig(const std::string& name, const std::map& options) const override; diff --git a/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp b/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp index 6825717fc76..8c7d8f22255 100644 --- a/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp +++ b/inference-engine/src/gna_plugin/backend/am_intel_dnn.cpp @@ -42,11 +42,11 @@ void GNAPluginNS::backend::AMIntelDNN::BeginNewWrite(uint32_t index) { void GNAPluginNS::backend::AMIntelDNN::Init(void *ptr_memory, uint32_t num_memory_bytes, - intel_dnn_number_type_t number_type, + intel_dnn_number_type_t compute_precision, float scale_factor) { ptr_dnn_memory_ = ptr_memory; num_bytes_dnn_memory_ = num_memory_bytes; - number_type_ = number_type; + compute_precision_ = compute_precision; input_scale_factor_ = scale_factor; ptr_active_outputs_ = nullptr; @@ -342,7 +342,7 @@ void GNAPluginNS::backend::AMIntelDNN::Propagate() { reinterpret_cast(reinterpret_cast(comp->op.recurrent.ptr_feedbacks) + j * comp_pwl->num_columns_out); ApplyRecurrentTransform(comp, j, ptr_feedbacks); // PrintOutputs(i); - ApplyPiecewiseLinearTransform(comp_pwl, number_type_, num_active_outputs, j); + ApplyPiecewiseLinearTransform(comp_pwl, compute_precision_, num_active_outputs, j); } i++; // skip next component } else { @@ -352,9 +352,9 @@ void GNAPluginNS::backend::AMIntelDNN::Propagate() { break; case kDnnConvolutional1dOp:ApplyConvolutional1DTransform(comp); break; - case kDnnPiecewiselinearOp:ApplyPiecewiseLinearTransform(comp, number_type_, num_active_outputs); + case kDnnPiecewiselinearOp:ApplyPiecewiseLinearTransform(comp, compute_precision_, num_active_outputs); break; - case kDnnMaxPoolOp:ApplyMaxPoolTransform(comp, number_type_); + case kDnnMaxPoolOp:ApplyMaxPoolTransform(comp, compute_precision_); break; case kDnnInterleaveOp:ApplyTranspose(comp); break; @@ -633,8 +633,8 @@ void GNAPluginNS::backend::AMIntelDNN::WriteGraphWizModel(const char *filename) graph << "}"; } -void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_dnn_number_type_t number_type) { - if ((number_type_ == kDnnFloat) && (number_type == kDnnInt)) { +void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_dnn_number_type_t logging_precision) { + if ((compute_precision_ == kDnnFloat) && (logging_precision == kDnnInt)) { fprintf(stderr, "Error trying to write floating point DNN as integer in GNAPluginNS::backend::AMIntelDNN::WriteDnnText().\n"); fprintf(stderr, " Please convert to integer first.\n"); throw -1; @@ -653,7 +653,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ uint32_t layer = 0; out_file << "\n"; - out_file << " " << intel_dnn_number_type_name[number_type] << "\n"; + out_file << " " << intel_dnn_number_type_name[logging_precision] << "\n"; out_file << " " << intel_dnn_softmax_name[softmax_type] << "\n"; out_file << " " << std::dec << num_bytes_dnn_memory_ << "\n"; out_file << " " << std::dec << num_group << "\n"; @@ -701,7 +701,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ out_file << " " << std::dec << (component[i].orientation_out == kDnnInterleavedOrientation ? "interleaved" : "deinterleaved") << "\n"; - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << " " << std::dec << sizeof(float) << "\n"; out_file << " " << std::dec << sizeof(float) << "\n"; } else { @@ -721,14 +721,14 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ float output_scale_factor = component[i].output_scale_factor; uint32_t num_weight_rows = (component[i].operation == kDnnDiagonalOp) ? 1 : num_rows_out; uint32_t num_weight_columns = num_rows_in; - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << " " << std::dec << 4 << "\n"; out_file << " " << std::dec << 4 << "\n"; } else { out_file << " " << std::dec << num_bytes_per_weight << "\n"; out_file << " " << std::dec << num_bytes_per_bias << "\n"; } - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; } else { @@ -751,7 +751,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ #ifdef DUMP_WB for (uint32_t row = 0; row < num_weight_rows; row++) { for (uint32_t col = 0; col < num_weight_columns; col++) { - if (number_type == kDnnFloat) { + if (logging_precision == kDnnFloat) { float val = static_cast(ptr_weight[row * num_weight_columns + col]) * ptr_bias[row].multiplier / weight_scale_factor; @@ -768,7 +768,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ #ifdef DUMP_WB for (uint32_t row = 0; row < num_weight_rows; row++) { for (uint32_t col = 0; col < num_weight_columns; col++) { - if (number_type == kDnnFloat) { + if (logging_precision == kDnnFloat) { out_wfile << std::setprecision(12) << ptr_weight[row * num_weight_columns + col] / weight_scale_factor << " "; } else { @@ -778,7 +778,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ } } #endif - } else if (number_type_ == kDnnFloat) { + } else if (compute_precision_ == kDnnFloat) { float *ptr_weight = reinterpret_cast(component[i].op.affine.ptr_weights); #ifdef DUMP_WB for (uint32_t row = 0; row < num_weight_rows; row++) { @@ -793,21 +793,25 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ fprintf(stderr, "Unsupported weight type in WriteDnnText!\n"); throw -1; } - if (number_type_ == kDnnInt) { + if (compute_precision_ == kDnnInt) { if (num_bytes_per_weight == 1) { intel_compound_bias_t *ptr_biases = reinterpret_cast(component[i].op.affine.ptr_biases); #ifdef DUMP_WB for (uint32_t row = 0; row < num_rows_out; row++) { - out_bfile << std::setw(8) << ptr_biases[row].bias << ", "; - out_bfile << std::setw(8) << int(ptr_biases[row].multiplier) << "\n"; + if (logging_precision == kDnnInt) { + out_bfile << std::setw(8) << ptr_biases[row].bias << ", "; + out_bfile << std::setw(8) << int(ptr_biases[row].multiplier) << "\n"; + } else { + out_bfile << std::setw(8) << ptr_biases[row].bias / output_scale_factor << "\n"; + } } #endif } else { int32_t *ptr_biases = reinterpret_cast(component[i].op.affine.ptr_biases); #ifdef DUMP_WB for (uint32_t row = 0; row < num_rows_out; row++) { - if (number_type == kDnnInt) { + if (logging_precision == kDnnInt) { out_bfile << std::setw(8) << ptr_biases[row] << "\n"; } else { out_bfile << std::setw(8) << ptr_biases[row] / output_scale_factor << "\n"; @@ -844,14 +848,14 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ out_file << " " << std::dec << num_feature_maps << "\n"; out_file << " " << std::dec << num_feature_map_rows << "\n"; out_file << " " << std::dec << num_feature_map_columns << "\n"; - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << " " << std::dec << 4 << "\n"; out_file << " " << std::dec << 4 << "\n"; } else { out_file << " " << std::dec << num_bytes_per_weight << "\n"; out_file << " " << std::dec << num_bytes_per_bias << "\n"; } - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; } else { @@ -876,7 +880,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ #ifdef DUMP_WB for (uint32_t row = 0; row < num_filters; row++) { for (uint32_t col = 0; col < num_filter_coefficients; col++) { - if (number_type == kDnnFloat) { + if (logging_precision == kDnnFloat) { float val = static_cast(ptr_weight[row * num_filter_coefficients + col]) * ptr_bias[row].multiplier / weight_scale_factor; out_wfile << std::setprecision(12) <(component[i].op.conv1D.ptr_filters); #ifdef DUMP_WB for (uint32_t row = 0; row < num_filters; row++) { @@ -918,8 +922,8 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ throw -1; } - if (number_type_ == kDnnInt) { - if (number_type == kDnnInt) { + if (compute_precision_ == kDnnInt) { + if (logging_precision == kDnnInt) { if (num_bytes_per_weight == 1) { intel_compound_bias_t *ptr_biases = reinterpret_cast(component[i].op.conv1D.ptr_biases); @@ -969,14 +973,14 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ uint32_t num_weight_rows = num_columns_out; uint32_t num_weight_columns = num_columns_in + num_columns_out; out_file << " " << std::dec << num_vector_delay << "\n"; - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << " " << std::dec << 4 << "\n"; out_file << " " << std::dec << 4 << "\n"; } else { out_file << " " << std::dec << num_bytes_per_weight << "\n"; out_file << " " << std::dec << num_bytes_per_bias << "\n"; } - if ((number_type_ == kDnnInt) && (number_type == kDnnFloat)) { + if ((compute_precision_ == kDnnInt) && (logging_precision == kDnnFloat)) { out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; } else { @@ -999,7 +1003,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ for (uint32_t row = 0; row < num_weight_rows; row++) { out_file << " "; for (uint32_t col = 0; col < num_weight_columns; col++) { - if (number_type == kDnnFloat) { + if (logging_precision == kDnnFloat) { float val = static_cast(ptr_weight[row * num_weight_columns + col]) * ptr_bias[col].multiplier / weight_scale_factor; @@ -1018,7 +1022,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ for (uint32_t row = 0; row < num_weight_rows; row++) { out_file << " "; for (uint32_t col = 0; col < num_weight_columns; col++) { - if (number_type == kDnnFloat) { + if (logging_precision == kDnnFloat) { out_file << std::setprecision(12) << std::scientific << ptr_weight[row * num_weight_columns + col] / weight_scale_factor << " "; } else { @@ -1029,7 +1033,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ out_file << "\n"; } #endif - } else if (number_type_ == kDnnFloat) { + } else if (compute_precision_ == kDnnFloat) { float *ptr_weight = reinterpret_cast(component[i].op.recurrent.ptr_weights); #ifdef DUMP_WB for (uint32_t row = 0; row < num_weight_rows; row++) { @@ -1045,8 +1049,8 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ fprintf(stderr, "Unsupported weight type in WriteDnnText!\n"); throw -1; } - if (number_type_ == kDnnInt) { - if (number_type == kDnnInt) { + if (compute_precision_ == kDnnInt) { + if (logging_precision == kDnnInt) { if (num_bytes_per_weight == 1) { intel_compound_bias_t *ptr_biases = reinterpret_cast(component[i].op.recurrent.ptr_biases); @@ -1110,7 +1114,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ out_file << " " << std::dec << sizeof(int16_t) << "\n"; out_file << " " << std::dec << sizeof(int16_t) << "\n"; out_file << " " << std::dec << sizeof(int32_t) << "\n"; - if (number_type == kDnnFloat) { + if (logging_precision == kDnnFloat) { out_file << std::setprecision(12) << std::scientific << " " << 1.0 << "\n"; out_file << " " << std::dec << 0 << "\n"; out_file << " " << "0x" << std::setfill('0') << std::setw(8) << std::hex @@ -1121,7 +1125,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteDnnText(const char *filename, intel_ out_file << " " << std::dec << num_segments << "\n"; out_file << " " << "0x" << std::setfill('0') << std::setw(8) << std::hex << GNAPluginNS::memory::MemoryOffset(component[i].op.pwl.ptr_segments, ptr_dnn_memory_) << "\n"; - if (number_type_ == kDnnInt) { + if (compute_precision_ == kDnnInt) { out_file << " "; for (int segment = 0; segment < num_segments; segment++) { out_file << "0x" << std::setfill('0') << std::setw(4) << std::hex @@ -1862,7 +1866,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteInputAndOutputText() { for (int j = 0; j < component[i].num_columns_out; j++) { float floatValue = 0.f; if (component[i].num_bytes_per_output == 4) { - if (number_type_ == kDnnInt) { + if (compute_precision_ == kDnnInt) { auto value = reinterpret_cast(component[i].ptr_outputs)[k * component[i].num_columns_out+ j]; floatValue = static_cast(value); @@ -1903,7 +1907,7 @@ void GNAPluginNS::backend::AMIntelDNN::WriteInputAndOutputText() { for (int j = 0; j < component[i].num_columns_in; j++) { float floatValue = 0.f; if (component[i].num_bytes_per_input == 4) { - if (number_type_ == kDnnInt) { + if (compute_precision_ == kDnnInt) { auto value = reinterpret_cast(component[i].ptr_inputs)[k * component[i].num_columns_in + j]; floatValue = static_cast(value); } else { diff --git a/inference-engine/src/gna_plugin/backend/am_intel_dnn.hpp b/inference-engine/src/gna_plugin/backend/am_intel_dnn.hpp index fd8e347b20d..66536ff519e 100644 --- a/inference-engine/src/gna_plugin/backend/am_intel_dnn.hpp +++ b/inference-engine/src/gna_plugin/backend/am_intel_dnn.hpp @@ -36,14 +36,14 @@ public: ptr_priors(NULL), ptr_dnn_memory_(NULL), num_bytes_dnn_memory_(0), - number_type_(kDnnNumNumberType) { + compute_precision_(kDnnNumNumberType) { } ~AMIntelDNN(); void Init(void *ptr_memory, uint32_t num_memory_bytes, - intel_dnn_number_type_t number_type, + intel_dnn_number_type_t compute_precision, float scale_factor); void InitActiveList(uint32_t *ptr_active_list); @@ -235,7 +235,7 @@ public: void WriteGraphWizModel(const char *filename); - void WriteDnnText(const char *filename, intel_dnn_number_type_t number_type); + void WriteDnnText(const char *filename, intel_dnn_number_type_t logging_precision); #if GNA_LIB_VER == 2 @@ -291,7 +291,7 @@ private: uint32_t num_bytes_dnn_memory_; uint32_t *ptr_active_outputs_; uint32_t num_active_outputs_; - intel_dnn_number_type_t number_type_; + intel_dnn_number_type_t compute_precision_; float input_scale_factor_; uint32_t dump_write_index = 0; diff --git a/inference-engine/src/gna_plugin/gna_plugin.hpp b/inference-engine/src/gna_plugin/gna_plugin.hpp index f59d525a295..64a47467431 100644 --- a/inference-engine/src/gna_plugin/gna_plugin.hpp +++ b/inference-engine/src/gna_plugin/gna_plugin.hpp @@ -109,7 +109,7 @@ class GNAPlugin : public InferenceEngine::IInferencePluginInternal, public std:: InferenceEngine::RemoteContext::Ptr context) override { THROW_GNA_EXCEPTION << "Not implemented"; } void Infer(const InferenceEngine::Blob &input, InferenceEngine::Blob &result); void SetCore(InferenceEngine::ICore*) noexcept override {} - const InferenceEngine::ICore* GetCore() const noexcept override {return nullptr;} + InferenceEngine::ICore* GetCore() const noexcept override {return nullptr;} void Reset(); void QueryNetwork(const InferenceEngine::ICNNNetwork &network, const std::map& config, diff --git a/inference-engine/src/gna_plugin/gna_plugin_internal.hpp b/inference-engine/src/gna_plugin/gna_plugin_internal.hpp index dc5a5840035..0f9ec354f83 100644 --- a/inference-engine/src/gna_plugin/gna_plugin_internal.hpp +++ b/inference-engine/src/gna_plugin/gna_plugin_internal.hpp @@ -28,7 +28,7 @@ private: } public: - InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::ICore * core, + InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl( const InferenceEngine::ICNNNetwork &network, const std::map &config) override { Config updated_config(defaultConfig); diff --git a/inference-engine/src/gna_plugin/gna_plugin_query_api.cpp b/inference-engine/src/gna_plugin/gna_plugin_query_api.cpp index 9d1664217d1..ec7aad5b036 100644 --- a/inference-engine/src/gna_plugin/gna_plugin_query_api.cpp +++ b/inference-engine/src/gna_plugin/gna_plugin_query_api.cpp @@ -24,6 +24,10 @@ Parameter GNAPlugin::GetMetric(const std::string& name, const std::map> queryApiSupported = { {METRIC_KEY(AVAILABLE_DEVICES), [this]() {return GetAvailableDevices();}}, {METRIC_KEY(SUPPORTED_CONFIG_KEYS), [this]() {return config.GetSupportedKeys();}}, + {METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS), [this]() { + uint32_t nireq = 1; + return nireq; + }}, {METRIC_KEY(FULL_DEVICE_NAME), [&options, this]() { auto availableDevices = GetAvailableDevices().as>(); diff --git a/inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp b/inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp index 24c091ef856..0ad2023f48f 100644 --- a/inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp +++ b/inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp @@ -5,7 +5,6 @@ #include #include #include "hetero_async_infer_request.hpp" -#include #include using namespace HeteroPlugin; diff --git a/inference-engine/src/hetero_plugin/hetero_executable_network.cpp b/inference-engine/src/hetero_plugin/hetero_executable_network.cpp index e9660bbebb1..686f728cf05 100644 --- a/inference-engine/src/hetero_plugin/hetero_executable_network.cpp +++ b/inference-engine/src/hetero_plugin/hetero_executable_network.cpp @@ -7,7 +7,6 @@ #include "hetero_async_infer_request.hpp" #include "ie_util_internal.hpp" #include "hetero_graph_splitter.hpp" -#include "file_utils.h" #include "xml_parse_utils.h" #include @@ -22,12 +21,9 @@ #include #include -#include "details/caseless.hpp" #include "ie_plugin_config.hpp" #include "cpp_interfaces/interface/ie_internal_plugin_config.hpp" -#include "cpp_interfaces/base/ie_inference_plugin_api.hpp" #include "hetero/hetero_plugin_config.hpp" -#include "precision_utils.h" #include "hetero_plugin.hpp" #include "network_serializer.h" @@ -141,10 +137,10 @@ void dumpGraph(InferenceEngine::ICNNNetwork &network, HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::ICNNNetwork& network_, const Engine::Configs& config, - Engine* plugin): + Engine* heteroPlugin): InferenceEngine::ExecutableNetworkThreadSafeDefault( nullptr, std::make_shared()), - _plugin{plugin}, + _heteroPlugin(heteroPlugin), _name{network_.getName()}, _config{config} { auto networkPtr = cloneNet(network_); @@ -171,7 +167,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::ICNNNetw if (allEmpty) { auto it = _config.find("TARGET_FALLBACK"); if (it != _config.end()) { - plugin->SetAffinity(network, _config); + _heteroPlugin->SetAffinity(network, _config); } else { THROW_IE_EXCEPTION << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin"; } @@ -230,25 +226,8 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::ICNNNetw network.getOutputsInfo(externalOutputsData); auto subgraphs = splitGraph(network, getAffinities(network)); - sortSubgraphs(subgraphs); - - std::vector descs; - std::vector tempLayers; - - for (auto &&subgraph : subgraphs) { - assert(!subgraph.empty()); - auto affinity = (*subgraph.begin())->affinity; - assert(!affinity.empty()); - _affinities.push_back(affinity); - if (_plugin->_plugins.end() == _plugin->_plugins.find(affinity)) { - IE_SUPPRESS_DEPRECATED_START - _plugin->_plugins[affinity] = _plugin->GetDevicePlugin(affinity); - IE_SUPPRESS_DEPRECATED_END - } - } - if (dumpDotFile) { std::stringstream stream(std::stringstream::out); stream << "hetero_subgraphs_" << network.getName() << ".dot"; @@ -262,6 +241,8 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::ICNNNetw networkStats = nullptr; } + std::vector descs; + std::vector tempLayers; for (auto &&subgraph : subgraphs) { auto affinity = (*subgraph.begin())->affinity; tempLayers.assign(subgraph.begin(), subgraph.end()); @@ -289,11 +270,9 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::ICNNNetw inp->second->getPreProcess() = it.second->getPreProcess(); } } + // go over all inputs/outputs and right now // set precision for intermediate data (not for external) to FP32 - // later on we have to add Plugin::getPreferableInputPrecision(network) and - // Plugin::getPreferableOutputPrecision(network) and set precision based on this info - // TODO(amalyshe) add clever selectino of precision for intermediate blobs for (auto &&it : clonedInputs) { if (externalInputsData.find(it.first) == externalInputsData.end()) { it.second->setPrecision(Precision::FP32); @@ -327,36 +306,24 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::ICNNNetw })); auto cfg = _config; - cfg[PluginConfigInternalParams::KEY_SUBNETWORK_WITH_NETWORK_INPUTS] = isInputSubnetwork - ? CONFIG_VALUE(YES) - : CONFIG_VALUE(NO); - IE_SUPPRESS_DEPRECATED_START - auto plugin = _plugin->_plugins[d._device]; - d._network = plugin._ref.LoadNetwork(d._clonedNetwork, Engine::GetSupportedConfig(plugin._config, cfg, plugin._ref)); - IE_SUPPRESS_DEPRECATED_END + cfg[PluginConfigInternalParams::KEY_SUBNETWORK_WITH_NETWORK_INPUTS] = + isInputSubnetwork ? CONFIG_VALUE(YES) : CONFIG_VALUE(NO); + + auto deviceName = d._device; + auto metaDevices = _heteroPlugin->GetDevicePlugins(deviceName, cfg); + assert(metaDevices.size() == 1); + + auto loadConfig = metaDevices[deviceName]; + d._network = _heteroPlugin->GetCore()->LoadNetwork(d._clonedNetwork, deviceName, loadConfig); } networks = std::move(descs); } -namespace { - -IE_SUPPRESS_DEPRECATED_START -IInferencePluginAPI * getInferencePluginAPIInterface(IInferencePlugin * iplugin) { - return dynamic_cast(iplugin); -} - -IInferencePluginAPI * getInferencePluginAPIInterface(InferenceEnginePluginPtr iplugin) { - return getInferencePluginAPIInterface(static_cast(iplugin.operator->())); -} -IE_SUPPRESS_DEPRECATED_END - -} // namespace - HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& heteroModel, const std::map& configs, - Engine* plugin) : - _plugin(plugin) { + Engine* heteroPlugin) : + _heteroPlugin(heteroPlugin) { std::string heteroXmlStr; std::getline(heteroModel, heteroXmlStr); @@ -400,26 +367,17 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& pugi::xml_node subnetworksNode = heteroNode.child("subnetworks"); for (auto subnetworkNode = subnetworksNode.child("subnetwork"); !subnetworkNode.empty(); subnetworkNode = subnetworkNode.next_sibling("subnetwork")) { - auto device = GetStrAttr(subnetworkNode, "device"); - _affinities.push_back(device); + auto deviceName = GetStrAttr(subnetworkNode, "device"); - if (_plugin->_plugins.end() == _plugin->_plugins.find(device)) { - IE_SUPPRESS_DEPRECATED_START - _plugin->_plugins[device] = _plugin->GetDevicePlugin(device); - IE_SUPPRESS_DEPRECATED_END - } - - auto& plugin = _plugin->_plugins[device]; - auto supportedConfig = Engine::GetSupportedConfig(plugin._config, importedConfigs, plugin._ref); - IE_SUPPRESS_DEPRECATED_START - auto pluginAPI = getInferencePluginAPIInterface(plugin._ref); - IE_SUPPRESS_DEPRECATED_END + auto metaDevices = _heteroPlugin->GetDevicePlugins(deviceName, importedConfigs); + assert(metaDevices.size() == 1); + auto& loadConfig = metaDevices[deviceName]; InferenceEngine::ExecutableNetwork executableNetwork; CNNNetwork cnnnetwork; bool loaded = false; try { - executableNetwork = pluginAPI->ImportNetwork(heteroModel, supportedConfig); + executableNetwork = _heteroPlugin->GetCore()->ImportNetwork(heteroModel, deviceName, loadConfig); } catch(InferenceEngine::details::InferenceEngineException& ie_ex) { if (std::string::npos != std::string{ie_ex.what()}.find(NOT_IMPLEMENTED_str)) { // read XML content @@ -439,7 +397,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& heteroModel.read(dataBlob->buffer(), dataSize); } - cnnnetwork = _plugin->GetCore()->ReadNetwork(xmlString, std::move(dataBlob)); + cnnnetwork = _heteroPlugin->GetCore()->ReadNetwork(xmlString, std::move(dataBlob)); auto inputs = cnnnetwork.getInputsInfo(); auto inputsNode = subnetworkNode.child("inputs"); for (auto inputNode = inputsNode.child("input"); !inputNode.empty(); inputNode = inputNode.next_sibling("input")) { @@ -455,9 +413,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& for (auto outputNode = outputsNode.child("output"); !outputNode.empty(); outputNode = outputNode.next_sibling("output")) { outputs[GetStrAttr(outputNode, "name")]->setPrecision(Precision::FromStr(GetStrAttr(outputNode, "precision"))); } - IE_SUPPRESS_DEPRECATED_START - executableNetwork = plugin._ref.LoadNetwork(cnnnetwork, supportedConfig); - IE_SUPPRESS_DEPRECATED_END + executableNetwork = _heteroPlugin->GetCore()->LoadNetwork(cnnnetwork, deviceName, loadConfig); loaded = true; } else { throw; @@ -477,7 +433,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& } descs.emplace_back(NetworkDesc{ - device, + deviceName, loaded ? CNNNetwork{cloneNet(static_cast(cnnnetwork))} : CNNNetwork{}, executableNetwork, }); @@ -578,10 +534,10 @@ void HeteroExecutableNetwork::CreateInferRequest(IInferRequest::Ptr &asyncReques auto heteroInferRequest = std::dynamic_pointer_cast( CreateInferRequestImpl(_networkInputs, _networkOutputs)); heteroInferRequest->setPointerToExecutableNetworkInternal(shared_from_this()); - auto asyncTreadSafeImpl = std::make_shared(heteroInferRequest, _taskExecutor, _callbackExecutor); - asyncRequest.reset(new InferRequestBase(asyncTreadSafeImpl), + auto asyncThreadSafeImpl = std::make_shared(heteroInferRequest, _taskExecutor, _callbackExecutor); + asyncRequest.reset(new InferRequestBase(asyncThreadSafeImpl), [](IInferRequest *p) { p->Release(); }); - asyncTreadSafeImpl->SetPointerToPublicInterface(asyncRequest); + asyncThreadSafeImpl->SetPointerToPublicInterface(asyncRequest); } void HeteroExecutableNetwork::GetConfig(const std::string &name, InferenceEngine::Parameter &result, InferenceEngine::ResponseDesc *) const { diff --git a/inference-engine/src/hetero_plugin/hetero_executable_network.hpp b/inference-engine/src/hetero_plugin/hetero_executable_network.hpp index a27570a699c..736cdd162b2 100644 --- a/inference-engine/src/hetero_plugin/hetero_executable_network.hpp +++ b/inference-engine/src/hetero_plugin/hetero_executable_network.hpp @@ -39,7 +39,7 @@ public: /** * @brief constructor */ - HeteroExecutableNetwork(const InferenceEngine::ICNNNetwork& network, + HeteroExecutableNetwork(const InferenceEngine::ICNNNetwork& network, const std::map& config, Engine* plugin); @@ -71,9 +71,8 @@ private: }; std::vector networks; - Engine* _plugin; + Engine* _heteroPlugin; std::string _name; - std::vector _affinities; std::map _config; }; diff --git a/inference-engine/src/hetero_plugin/hetero_plugin.cpp b/inference-engine/src/hetero_plugin/hetero_plugin.cpp index 953fa9049ed..0e7d4b995ec 100644 --- a/inference-engine/src/hetero_plugin/hetero_plugin.cpp +++ b/inference-engine/src/hetero_plugin/hetero_plugin.cpp @@ -3,9 +3,7 @@ // #include "ie_metric_helpers.hpp" -#include "ie_plugin_dispatcher.hpp" #include "hetero_plugin.hpp" -#include "ie_util_internal.hpp" #include #include #include @@ -17,7 +15,6 @@ #include "hetero/hetero_plugin_config.hpp" #include #include "hetero_executable_network.hpp" -#include "cpp_interfaces/base/ie_inference_plugin_api.hpp" using namespace InferenceEngine; using namespace InferenceEngine::PluginConfigParams; @@ -31,163 +28,88 @@ static Version heteroPluginDescription = { "heteroPlugin" // plugin description message }; -void Engine::GetVersion(const Version *&versionInfo)noexcept { - versionInfo = &heteroPluginDescription; -} - Engine::Engine() { _pluginName = "HETERO"; - _config[InferenceEngine::PluginConfigParams::KEY_EXCLUSIVE_ASYNC_REQUESTS] = "YES"; + _config[KEY_EXCLUSIVE_ASYNC_REQUESTS] = YES; _config[HETERO_CONFIG_KEY(DUMP_GRAPH_DOT)] = NO; } -InferenceEngine::ExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl(const ICore* /*core*/, - const InferenceEngine::ICNNNetwork& network, - const Configs& config) { - // TODO(amalyshe) do we need here verification of input precisions? - Configs tconfig; - tconfig = config; +namespace { - // we must not override the parameter, but need to copy everything from plugin config - for (auto && c : _config) { - if (tconfig.find(c.first) == tconfig.end()) { - tconfig[c.first] = c.second; - } +Engine::Configs mergeConfigs(Engine::Configs config, const Engine::Configs & local) { + for (auto && kvp : local) { + config[kvp.first] = kvp.second; + } + return config; +} + +} // namespace + +InferenceEngine::ExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork& network, + const Configs& config) { + if (GetCore() == nullptr) { + THROW_IE_EXCEPTION << "Please, work with HETERO device via InferencEngine::Core object"; } - return std::make_shared(*cloneNet(network), tconfig, this); + return std::make_shared(*cloneNet(network), mergeConfigs(_config, config), this); } ExecutableNetwork Engine::ImportNetworkImpl(std::istream& heteroModel, const Configs& config) { - Configs tconfig; - tconfig = config; - - // we must not override the parameter, but need to copy everything from plugin config - for (auto && c : _config) { - if (tconfig.find(c.first) == tconfig.end()) { - tconfig[c.first] = c.second; - } + if (GetCore() == nullptr) { + THROW_IE_EXCEPTION << "Please, work with HETERO device via InferencEngine::Core object"; } IExecutableNetwork::Ptr executableNetwork; - // Use config provided by an user ignoring default config executableNetwork.reset(new ExecutableNetworkBase( - std::make_shared(heteroModel, tconfig, this)), + std::make_shared(heteroModel, mergeConfigs(_config, config), this)), [](InferenceEngine::details::IRelease *p) {p->Release();}); return ExecutableNetwork{executableNetwork}; } -namespace { - -IE_SUPPRESS_DEPRECATED_START - -IInferencePluginAPI * getInferencePluginAPIInterface(IInferencePlugin * iplugin) { - return dynamic_cast(iplugin); -} - -IInferencePluginAPI * getInferencePluginAPIInterface(InferenceEnginePluginPtr iplugin) { - return getInferencePluginAPIInterface(static_cast(iplugin.operator->())); -} - -IInferencePluginAPI * getInferencePluginAPIInterface(InferencePlugin plugin) { - return getInferencePluginAPIInterface(static_cast(plugin)); -} - -} // namespace - -Engine::Configs Engine::GetSupportedConfig(const Engine::Configs& globalConfig, - const Engine::Configs& localConfig, - const InferenceEngine::InferencePlugin& plugin) { - auto pluginApi = getInferencePluginAPIInterface(plugin); - std::vector supportedConfigKeys = pluginApi->GetMetric(METRIC_KEY(SUPPORTED_CONFIG_KEYS), {}); +Engine::Configs Engine::GetSupportedConfig(const Engine::Configs& config, const std::string & deviceName) const { + std::vector supportedConfigKeys = GetCore()->GetMetric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)); Engine::Configs supportedConfig; for (auto&& key : supportedConfigKeys) { - auto itKey = localConfig.find(key); - if (localConfig.end() != itKey) { + auto itKey = config.find(key); + if (config.end() != itKey) { supportedConfig[key] = itKey->second; - } else { - itKey = globalConfig.find(key); - if (globalConfig.end() != itKey) { - supportedConfig[key] = itKey->second; - } } } return supportedConfig; } -Engine::PluginEntry Engine::GetDevicePlugin(const std::string& deviceWithID) const { - InferenceEngine::InferencePlugin plugin; - DeviceIDParser deviceParser(deviceWithID); - std::string deviceName = deviceParser.getDeviceName(); +Engine::DeviceMetaInformationMap Engine::GetDevicePlugins(const std::string& targetFallback, + const Configs & localConfig) const { + auto getDeviceConfig = [&](const std::string & deviceWithID) { + DeviceIDParser deviceParser(deviceWithID); + std::string deviceName = deviceParser.getDeviceName(); + Configs tconfig = mergeConfigs(_config, localConfig); - if (nullptr == _core) { - IE_SUPPRESS_DEPRECATED_START - // try to create plugin - PluginDispatcher dispatcher({file_name_t()}); - plugin = dispatcher.getPluginByDevice(deviceName); - IE_SUPPRESS_DEPRECATED_END - } else { - plugin = InferencePlugin{_core->GetPluginByName(deviceName)}; - } - - try { - for (auto&& ext : _extensions) { - plugin.AddExtension(ext); + // set device ID if any + std::string deviceIDLocal = deviceParser.getDeviceID(); + if (!deviceIDLocal.empty()) { + tconfig[KEY_DEVICE_ID] = deviceIDLocal; } - } catch (InferenceEngine::details::InferenceEngineException &) {} - Configs pluginConfig = GetSupportedConfig(_config, {}, plugin); + return GetSupportedConfig(tconfig, deviceName); + }; - // set device ID if any - std::string deviceIDLocal = deviceParser.getDeviceID(); - if (!deviceIDLocal.empty()) { - pluginConfig = GetSupportedConfig(pluginConfig, { { KEY_DEVICE_ID, deviceIDLocal } }, plugin); - } - - return { plugin, pluginConfig }; -} - -IE_SUPPRESS_DEPRECATED_END - -Engine::Plugins Engine::GetDevicePlugins(const std::string& targetFallback) const { - auto devices = InferenceEngine::DeviceIDParser::getHeteroDevices(targetFallback); - Engine::Plugins plugins = _plugins; - for (auto&& device : devices) { - auto itPlugin = plugins.find(device); - if (plugins.end() == itPlugin) { - IE_SUPPRESS_DEPRECATED_START - plugins[device] = GetDevicePlugin(device); - IE_SUPPRESS_DEPRECATED_END + auto fallbackDevices = InferenceEngine::DeviceIDParser::getHeteroDevices(targetFallback); + Engine::DeviceMetaInformationMap metaDevices; + for (auto&& deviceName : fallbackDevices) { + auto itPlugin = metaDevices.find(deviceName); + if (metaDevices.end() == itPlugin) { + metaDevices[deviceName] = getDeviceConfig(deviceName); } } - return plugins; -} - -Engine::Plugins Engine::GetDevicePlugins(const std::string& targetFallback) { - _plugins = const_cast(this)->GetDevicePlugins(targetFallback); - return _plugins; + return metaDevices; } void Engine::SetConfig(const Configs &configs) { for (auto&& config : configs) { _config[config.first] = config.second; } - - for (auto&& plugin : _plugins) { - plugin.second._config = GetSupportedConfig(plugin.second._config, configs, plugin.second._ref); - } -} - -void Engine::AddExtension(InferenceEngine::IExtensionPtr extension) { - _extensions.emplace_back(extension); - try { - for (auto&& plugin : _plugins) { - IE_SUPPRESS_DEPRECATED_START - plugin.second._ref.AddExtension(extension); - IE_SUPPRESS_DEPRECATED_END - } - } catch (InferenceEngine::details::InferenceEngineException &) {} } HeteroLayerColorer::HeteroLayerColorer(const std::vector& devices) { @@ -206,19 +128,8 @@ void HeteroLayerColorer::operator()(const CNNLayerPtr layer, } void Engine::SetAffinity(InferenceEngine::ICNNNetwork &network, const Configs &config) { - Configs tconfig = _config; - for (auto && value : config) { - tconfig[value.first] = value.second; - } - - auto it = tconfig.find("TARGET_FALLBACK"); - if (it == tconfig.end()) { - THROW_IE_EXCEPTION << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin"; - } - - GetDevicePlugins(it->second); QueryNetworkResult qr; - QueryNetwork(network, tconfig, qr); + QueryNetwork(network, config, qr); details::CNNNetworkIterator i(&network); while (i != details::CNNNetworkIterator()) { @@ -230,7 +141,12 @@ void Engine::SetAffinity(InferenceEngine::ICNNNetwork &network, const Configs &c i++; } - if (YES == tconfig[HETERO_CONFIG_KEY(DUMP_GRAPH_DOT)]) { + auto dumpDot = [](const Configs & config) { + auto it = config.find(HETERO_CONFIG_KEY(DUMP_GRAPH_DOT)); + return it != config.end() ? it->second == YES : false; + }; + + if (dumpDot(config) || dumpDot(_config)) { std::unordered_set devicesSet; details::CNNNetworkIterator i(&network); while (i != details::CNNNetworkIterator()) { @@ -245,52 +161,49 @@ void Engine::SetAffinity(InferenceEngine::ICNNNetwork &network, const Configs &c stream << "hetero_affinity_" << network.getName() << ".dot"; std::ofstream file(stream.str()); - saveGraphToDot(network, file, HeteroLayerColorer{devices}); } } void Engine::QueryNetwork(const ICNNNetwork &network, const Configs& config, QueryNetworkResult &qr) const { - auto it = config.find("TARGET_FALLBACK"); - if (it == config.end()) { - it = _config.find("TARGET_FALLBACK"); - - if (it == _config.end()) { - THROW_IE_EXCEPTION << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin"; - } + if (GetCore() == nullptr) { + THROW_IE_EXCEPTION << "Please, work with HETERO device via InferencEngine::Core object"; } - Plugins plugins = GetDevicePlugins(it->second); + auto tconfig = mergeConfigs(_config, config); + auto it = tconfig.find("TARGET_FALLBACK"); + if (it == tconfig.end()) { + THROW_IE_EXCEPTION << "The 'TARGET_FALLBACK' option was not defined for heterogeneous plugin"; + } - qr.rc = StatusCode::OK; + std::string fallbackDevicesStr = it->second; + DeviceMetaInformationMap metaDevices = GetDevicePlugins(fallbackDevicesStr, tconfig); std::map queryResults; - // go over devices, create appropriate plugins and - for (auto&& value : plugins) { - auto& device = value.first; - auto& plugin = value.second; - QueryNetworkResult r; - IE_SUPPRESS_DEPRECATED_START - plugin._ref.QueryNetwork(network, GetSupportedConfig(plugin._config, config, plugin._ref), r); - IE_SUPPRESS_DEPRECATED_END - queryResults[device] = r; + // go over devices and call query network + for (auto&& metaDevice : metaDevices) { + auto& deviceName = metaDevice.first; + queryResults[deviceName] = GetCore()->QueryNetwork(network, deviceName, metaDevice.second); } // WARNING: Here is devices with user set priority - auto falbackDevices = InferenceEngine::DeviceIDParser::getHeteroDevices(it->second); + auto fallbackDevices = InferenceEngine::DeviceIDParser::getHeteroDevices(fallbackDevicesStr); details::CNNNetworkIterator i(&network); while (i != details::CNNNetworkIterator()) { CNNLayer::Ptr layer = *i; - for (auto&& device : falbackDevices) { - auto& deviceQueryResult = queryResults[device]; + for (auto&& deviceName : fallbackDevices) { + auto& deviceQueryResult = queryResults[deviceName]; if (deviceQueryResult.supportedLayersMap.find(layer->name) != deviceQueryResult.supportedLayersMap.end()) { - qr.supportedLayersMap[layer->name] = device; + qr.supportedLayersMap[layer->name] = deviceName; break; } } i++; } + + // set OK status + qr.rc = StatusCode::OK; } Parameter Engine::GetMetric(const std::string& name, const std::map & /*options*/) const { @@ -317,6 +230,13 @@ Parameter Engine::GetConfig(const std::string& name, const std::mapsecond == YES; return { dump }; + } else if (name == "TARGET_FALLBACK") { + auto it = _config.find("TARGET_FALLBACK"); + if (it == _config.end()) { + THROW_IE_EXCEPTION << "Value for TARGET_FALLBACK is not set"; + } else { + return { it->second }; + } } else { THROW_IE_EXCEPTION << "Unsupported config key: " << name; } diff --git a/inference-engine/src/hetero_plugin/hetero_plugin.hpp b/inference-engine/src/hetero_plugin/hetero_plugin.hpp index 030dc76abe5..9b6cbd6043e 100644 --- a/inference-engine/src/hetero_plugin/hetero_plugin.hpp +++ b/inference-engine/src/hetero_plugin/hetero_plugin.hpp @@ -21,55 +21,34 @@ namespace HeteroPlugin { class Engine : public InferenceEngine::InferencePluginInternal { public: using Configs = std::map; - - struct PluginEntry { - IE_SUPPRESS_DEPRECATED_START - InferenceEngine::InferencePlugin _ref; - IE_SUPPRESS_DEPRECATED_END - Configs _config; - }; - - using Plugins = std::unordered_map; - - using Devices = std::vector; + using DeviceMetaInformationMap = std::unordered_map; Engine(); - void GetVersion(const InferenceEngine::Version *&versionInfo) noexcept; - InferenceEngine::ExecutableNetworkInternal::Ptr - LoadExeNetworkImpl(const InferenceEngine::ICore * core, const InferenceEngine::ICNNNetwork &network, const Configs &config) override; + LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, const Configs &config) override; + void SetConfig(const Configs &config) override; - void SetAffinity(InferenceEngine::ICNNNetwork& network, const Configs &config); - - void AddExtension(InferenceEngine::IExtensionPtr extension)override; - void QueryNetwork(const InferenceEngine::ICNNNetwork &network, const Configs& config, InferenceEngine::QueryNetworkResult &res) const override; - InferenceEngine::Parameter GetMetric(const std::string& name, - const std::map & options) const override; + InferenceEngine::Parameter GetMetric(const std::string& name, const std::map & options) const override; - InferenceEngine::Parameter GetConfig(const std::string& name, - const std::map & options) const override; - - IE_SUPPRESS_DEPRECATED_START - - PluginEntry GetDevicePlugin(const std::string& device) const; - - static Configs GetSupportedConfig(const Configs& globalConfig, const Configs& localConfig, const InferenceEngine::InferencePlugin& plugin); - - IE_SUPPRESS_DEPRECATED_END - - Plugins GetDevicePlugins(const std::string& targetFallback); - - Plugins GetDevicePlugins(const std::string& targetFallback) const; + InferenceEngine::Parameter GetConfig(const std::string& name, const std::map & options) const override; ExecutableNetwork ImportNetworkImpl(std::istream& heteroModel, const Configs& config) override; - Plugins _plugins; - std::vector _extensions; + + void SetAffinity(InferenceEngine::ICNNNetwork& network, const Configs &config); + + DeviceMetaInformationMap GetDevicePlugins(const std::string& targetFallback, + const Configs & localConfig) const; + +private: + Configs GetSupportedConfig(const Configs& config, const std::string & deviceName) const; }; struct HeteroLayerColorer { diff --git a/inference-engine/src/inference_engine/CMakeLists.txt b/inference-engine/src/inference_engine/CMakeLists.txt index 832d69c495f..091e68f1972 100644 --- a/inference-engine/src/inference_engine/CMakeLists.txt +++ b/inference-engine/src/inference_engine/CMakeLists.txt @@ -131,10 +131,6 @@ if(ENABLE_PROFILING_ITT AND INTEL_ITT_LIBS) target_compile_definitions(${TARGET_NAME}_obj PRIVATE $) endif() -if(ENABLE_IR_READER) - target_compile_definitions(${TARGET_NAME}_obj PRIVATE ENABLE_IR_READER) -endif() - target_include_directories(${TARGET_NAME}_obj PRIVATE $) if(ENABLE_MKL_DNN) @@ -172,7 +168,7 @@ endif() target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_ENGINE_API) ie_register_plugins(MAIN_TARGET ${TARGET_NAME} - POSSIBLE_PLUGINS HeteroPlugin clDNNPlugin GNAPlugin MKLDNNPlugin myriadPlugin) + POSSIBLE_PLUGINS MultiDevicePlugin HeteroPlugin clDNNPlugin GNAPlugin MKLDNNPlugin myriadPlugin) # Static library used for unit tests which are always built diff --git a/inference-engine/src/inference_engine/cnn_network_ngraph_impl.cpp b/inference-engine/src/inference_engine/cnn_network_ngraph_impl.cpp index 9b460bcbb04..16190983511 100644 --- a/inference-engine/src/inference_engine/cnn_network_ngraph_impl.cpp +++ b/inference-engine/src/inference_engine/cnn_network_ngraph_impl.cpp @@ -23,6 +23,7 @@ #include #include #include +#include #include #include "ngraph_ops/eltwise.hpp" @@ -76,6 +77,10 @@ static std::shared_ptr copyFunction(const std::shared_ptr& graph) { + if (graph == nullptr) { + THROW_IE_EXCEPTION << "CNNNetwork was not initialized: 'graph' object is empty"; + } + // Copy nGraph function network = std::make_shared(copyFunction(graph, false, {})); actual = network.get(); @@ -141,8 +146,10 @@ CNNNetworkNGraphImpl::CNNNetworkNGraphImpl(const std::shared_ptr& nGra keep_input_info(*this, ptr); } for (auto& output : _outputData) { - // Convert precision into native format. Be consistent with possible convertation to CNNNetwork later. - if (output.second->getPrecision() != Precision::FP32 && + // Convert precision into native format. Be consistent with possible conversion to CNNNetwork later. + if (output.second->getPrecision() == Precision::I64) { + output.second->setPrecision(Precision::I32); + } else if (output.second->getPrecision() != Precision::FP32 && output.second->getPrecision() != Precision::I32) { output.second->setPrecision(Precision::FP32); } @@ -456,6 +463,7 @@ StatusCode CNNNetworkNGraphImpl::serialize(const std::string& xmlPath, const std ::ngraph::op::GenericIE::DisableReshape noReshape(graph); ::ngraph::pass::CommonOptimizations().run_on_function(graph); + ::ngraph::pass::ConvertOpSet3ToOpSet2().run_on_function(graph); ::ngraph::pass::ConvertOpSet2ToOpSet1().run_on_function(graph); ::ngraph::pass::ConvertOpSet1ToLegacy().run_on_function(graph); network = InferenceEngine::details::convertFunctionToICNNNetwork(graph, *this); @@ -519,6 +527,7 @@ void CNNNetworkNGraphImpl::convertToCNNNetworkImpl() { ::ngraph::op::GenericIE::DisableReshape noReshape(graph); ::ngraph::pass::CommonOptimizations().run_on_function(graph); + ::ngraph::pass::ConvertOpSet3ToOpSet2().run_on_function(graph); ::ngraph::pass::ConvertOpSet2ToOpSet1().run_on_function(graph); ::ngraph::pass::ConvertOpSet1ToLegacy().run_on_function(graph); cnnNetwork = InferenceEngine::details::convertFunctionToICNNNetwork(graph, *this); diff --git a/inference-engine/src/inference_engine/dll_main.hpp b/inference-engine/src/inference_engine/dll_main.hpp deleted file mode 100644 index d433e136be2..00000000000 --- a/inference-engine/src/inference_engine/dll_main.hpp +++ /dev/null @@ -1,22 +0,0 @@ -// Copyright (C) 2018-2020 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -#pragma once - -#ifdef _WIN32 -#define _WINSOCKAPI_ -#include - -BOOL APIENTRY DllMain(HMODULE hModule, DWORD ul_reason_for_call, LPVOID lpReserved) { - switch (ul_reason_for_call) { - case DLL_PROCESS_ATTACH: - case DLL_THREAD_ATTACH: - case DLL_THREAD_DETACH: - case DLL_PROCESS_DETACH: - break; - } - return TRUE; -} - -#endif diff --git a/inference-engine/src/inference_engine/ie_core.cpp b/inference-engine/src/inference_engine/ie_core.cpp index cfb68abba21..7508696029b 100644 --- a/inference-engine/src/inference_engine/ie_core.cpp +++ b/inference-engine/src/inference_engine/ie_core.cpp @@ -14,6 +14,7 @@ #include #include #include +#include #include #include "cpp/ie_cnn_net_reader.h" @@ -70,6 +71,62 @@ IInferencePluginAPI* getInferencePluginAPIInterface(InferencePlugin plugin) { return getInferencePluginAPIInterface(static_cast(plugin)); } +template +struct Parsed { + std::string _deviceName; + std::map _config; +}; + +template +Parsed parseDeviceNameIntoConfig(const std::string& deviceName, const std::map& config = {}) { + auto config_ = config; + auto deviceName_ = deviceName; + if (deviceName_.find("HETERO:") == 0) { + deviceName_ = "HETERO"; + config_["TARGET_FALLBACK"] = deviceName.substr(7); + } else if (deviceName_.find("MULTI:") == 0) { + deviceName_ = "MULTI"; + config_[InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES] = deviceName.substr(6); + } else { + DeviceIDParser parser(deviceName_); + deviceName_ = parser.getDeviceName(); + std::string deviceIDLocal = parser.getDeviceID(); + + if (!deviceIDLocal.empty()) { + config_[KEY_DEVICE_ID] = deviceIDLocal; + } + } + return {deviceName_, config_}; +} + +Parameter copyParameterValue(const Parameter & value) { + if (value.is()) { + return { value.as() }; + } else if (value.is()) { + return { value.as() }; + } else if (value.is()) { + return { value.as() }; + } else if (value.is()) { + return { value.as() }; + } else if (value.is()) { + return { value.as() }; + } else if (value.is >()) { + return { value.as >() }; + } else if (value.is >()) { + return { value.as >() }; + } else if (value.is >()) { + return { value.as >() }; + } else if (value.is >()) { + return { value.as >() }; + } else if (value.is >()) { + return { value.as >() }; + } else if (value.is >()) { + return { value.as >() }; + } + + return std::move(value); +} + } // namespace CNNNetReaderPtr CreateCNNNetReaderPtr() noexcept { @@ -151,7 +208,8 @@ class Core::Impl : public ICore { }; /** - * Hold original blob in order to avoid situations when original blob is allocated on stack + * @brief Holds original blob in order to avoid situations + * when original blob is allocated on stack */ class WeightsHolderBlob : public TBlob { Blob::CPtr originBlob; @@ -167,6 +225,7 @@ class Core::Impl : public ICore { std::vector extensions; std::map pluginRegistry; + mutable std::mutex pluginsMutex; // to lock parallel access to pluginRegistry and plugins public: Impl(); @@ -174,9 +233,11 @@ public: /** * @brief Register plugins for devices which are located in .xml configuration file. The function supports UNICODE path - * @param xmlConfigFile - an .xml configuraion with device / plugin information + * @param xmlConfigFile An .xml configuraion with device / plugin information */ void RegisterPluginsInRegistry(const std::string& xmlConfigFile) { + std::lock_guard lock(pluginsMutex); + auto parse_result = ParseXml(xmlConfigFile.c_str()); if (!parse_result.error_msg.empty()) { THROW_IE_EXCEPTION << parse_result.error_msg; @@ -256,7 +317,8 @@ public: StatusCode rt = cnnReader->ReadNetwork(modelPath.c_str(), &desc); if (rt != OK) THROW_IE_EXCEPTION << desc.msg; if (cnnReader->getVersion(&desc) >= 10) { - cnnReader->addExtensions(getExtensions()); + std::lock_guard lock(pluginsMutex); + cnnReader->addExtensions(GetExtensions()); } std::string bPath = binPath; if (bPath.empty()) { @@ -289,7 +351,8 @@ public: StatusCode rt = cnnReader->ReadNetwork(model.data(), model.length(), &desc); if (rt != OK) THROW_IE_EXCEPTION << desc.msg; if (cnnReader->getVersion(&desc) >= 10) { - cnnReader->addExtensions(getExtensions()); + std::lock_guard lock(pluginsMutex); + cnnReader->addExtensions(GetExtensions()); } TBlob::Ptr weights_ptr; if (weights) { @@ -302,11 +365,90 @@ public: return CNNNetwork(cnnReader); } + ExecutableNetwork LoadNetwork(const CNNNetwork& network, const std::string& deviceName, + const std::map& config) override { + IE_PROFILING_AUTO_SCOPE(Core::LoadNetwork) + auto parsed = parseDeviceNameIntoConfig(deviceName, config); + IE_SUPPRESS_DEPRECATED_START + return GetCPPPluginByName(parsed._deviceName).LoadNetwork(network, parsed._config); + IE_SUPPRESS_DEPRECATED_END + } + IE_SUPPRESS_DEPRECATED_START + ExecutableNetwork ImportNetwork(std::istream& networkModel, const std::string& deviceName, + const std::map& config) override { + auto parsed = parseDeviceNameIntoConfig(deviceName, config); + + if (parsed._deviceName.empty()) { + ExportMagic magic = {}; + auto currentPos = networkModel.tellg(); + networkModel.read(magic.data(), magic.size()); + auto exportedWithName = (exportMagic == magic); + if (exportedWithName) { + std::getline(networkModel, parsed._deviceName); + } + networkModel.seekg(currentPos, networkModel.beg); + } + + auto cppPlugin = GetCPPPluginByName(parsed._deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); + if (pluginAPIInterface == nullptr) { + THROW_IE_EXCEPTION << parsed._deviceName << " does not implement the ImportNetwork method"; + } + + return pluginAPIInterface->ImportNetwork(networkModel, parsed._config); + } + + QueryNetworkResult QueryNetwork(const ICNNNetwork& network, const std::string& deviceName, + const std::map& config) const override { + QueryNetworkResult res; + auto parsed = parseDeviceNameIntoConfig(deviceName, config); + IE_SUPPRESS_DEPRECATED_START + GetCPPPluginByName(parsed._deviceName).QueryNetwork(network, parsed._config, res); + IE_SUPPRESS_DEPRECATED_END + return res; + } + + Parameter GetMetric(const std::string& deviceName, const std::string& name) const override { + // HETERO case + { + if (deviceName.find("HETERO:") == 0) { + THROW_IE_EXCEPTION + << "You can get specific metrics with the GetMetric only for the HETERO itself (without devices). " + "To get individual devices's metrics call GetMetric for each device separately"; + } + } + + // MULTI case + { + if (deviceName.find("MULTI:") == 0) { + THROW_IE_EXCEPTION + << "You can get specific metrics with the GetMetric only for the MULTI itself (without devices). " + "To get individual devices's metrics call GetMetric for each device separately"; + } + } + + auto parsed = parseDeviceNameIntoConfig(deviceName); + IE_SUPPRESS_DEPRECATED_START + InferencePlugin cppPlugin = GetCPPPluginByName(parsed._deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); + IE_SUPPRESS_DEPRECATED_END + + if (pluginAPIInterface == nullptr) { + THROW_IE_EXCEPTION << parsed._deviceName << " does not implement the GetMetric method"; + } + + // we need to return a copy of Parameter object which is created on Core side, + // not in InferenceEngine plugin side, which can be unloaded from Core in a parallel thread + // TODO: remove this WA after *-31417 is resolved + return copyParameterValue(pluginAPIInterface->GetMetric(name, parsed._config)); + } + /** + * @deprecated Use ICore::LoadNetwork, ICore::QueryNetwork, ICore::GetMetric instead * @brief Returns reference to plugin by a device name - * @param deviceName - a name of device + * @param deviceName A name of device * @return Reference to a plugin */ InferenceEnginePluginPtr GetPluginByName(const std::string& deviceName) const override { @@ -314,11 +456,14 @@ public: } /** + * @deprecated * @brief Returns reference to CPP plugin wrapper by a device name - * @param deviceName - a name of device + * @param deviceName A name of device * @return Reference to a CPP plugin wrapper */ InferencePlugin GetCPPPluginByName(const std::string& deviceName) const { + std::lock_guard lock(pluginsMutex); + IE_SUPPRESS_DEPRECATED_START auto it = pluginRegistry.find(deviceName); @@ -379,10 +524,11 @@ public: IE_SUPPRESS_DEPRECATED_END /** - * @brief Unregisters plugin for specified device - * @param deviceName - a name of device + * @brief Unload plugin for specified device, but plugin meta-data is still in plugin registry + * @param deviceName A name of device */ - void UnregisterPluginByName(const std::string& deviceName) { + void UnloadPluginByName(const std::string& deviceName) { + std::lock_guard lock(pluginsMutex); auto it = plugins.find(deviceName); if (it == plugins.end()) { THROW_IE_EXCEPTION << "Device with \"" << deviceName << "\" name is not registered in the InferenceEngine"; @@ -392,10 +538,12 @@ public: } /** - * @brief Registers plugin in registry for specified device - * @param deviceName - a name of device + * @brief Registers plugin meta-data in registry for specified device + * @param deviceName A name of device */ void RegisterPluginByName(const std::string& pluginName, const std::string& deviceName) { + std::lock_guard lock(pluginsMutex); + auto it = pluginRegistry.find(deviceName); if (it != pluginRegistry.end()) { THROW_IE_EXCEPTION << "Device with \"" << deviceName << "\" is already registered in the InferenceEngine"; @@ -418,7 +566,13 @@ public: pluginRegistry[deviceName] = desc; } + /** + * @brief Porvides a list of plugin names in registry; physically such plugins may not be created + * @return A list of plugin names + */ std::vector GetListOfDevicesInRegistry() const { + std::lock_guard lock(pluginsMutex); + std::vector listOfDevices; for (auto&& pluginDesc : pluginRegistry) { listOfDevices.push_back(pluginDesc.first); @@ -427,7 +581,14 @@ public: return listOfDevices; } + /** + * @brief Sets config values for a plugin or set of plugins + * @param deviceName A device name to set config to + * If empty, config is set for all the plugins / plugin's meta-data + */ void SetConfigForPlugins(const std::map& config, const std::string& deviceName) { + std::lock_guard lock(pluginsMutex); + // set config for plugins in registry bool configIsSet = false; for (auto& desc : pluginRegistry) { @@ -453,7 +614,13 @@ public: } } - void addExtension(const IExtensionPtr& extension) { + /** + * @brief Registers the extension in a Core object + * Such extensions can be used for both CNNNetwork readers and device plugins + */ + void AddExtension(const IExtensionPtr& extension) { + std::lock_guard lock(pluginsMutex); + std::map opsets = extension->getOpSets(); for (const auto& it : opsets) { if (opsetNames.find(it.first) != opsetNames.end()) @@ -461,6 +628,7 @@ public: opsetNames.insert(it.first); } + // add extensions for already created plugins for (auto& plugin : plugins) { IE_SUPPRESS_DEPRECATED_START try { @@ -471,7 +639,11 @@ public: extensions.emplace_back(extension); } - const std::vector& getExtensions() const { + /** + * @brief Provides a list of extensions + * @return A list of registered extensions + */ + const std::vector& GetExtensions() const { return extensions; } }; @@ -525,7 +697,8 @@ std::map Core::GetVersions(const std::string& deviceName) std::string deviceNameLocal = parser.getDeviceName(); IE_SUPPRESS_DEPRECATED_START - const Version* version = _impl->GetCPPPluginByName(deviceNameLocal).GetVersion(); + InferenceEngine::InferencePlugin cppPlugin = _impl->GetCPPPluginByName(deviceNameLocal); + const Version * version = cppPlugin.GetVersion(); IE_SUPPRESS_DEPRECATED_END versions[deviceNameLocal] = *version; } @@ -538,36 +711,6 @@ void Core::SetLogCallback(IErrorListener&) const { } IE_SUPPRESS_DEPRECATED_END -namespace { -template -struct Parsed { - std::string _deviceName; - std::map _config; -}; - -template -Parsed parseDeviceNameIntoConfig(const std::string& deviceName, const std::map& config = {}) { - auto config_ = config; - auto deviceName_ = deviceName; - if (deviceName_.find("HETERO:") == 0) { - deviceName_ = "HETERO"; - config_["TARGET_FALLBACK"] = deviceName.substr(7); - } else if (deviceName_.find("MULTI:") == 0) { - deviceName_ = "MULTI"; - config_[InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES] = deviceName.substr(6); - } else { - DeviceIDParser parser(deviceName_); - deviceName_ = parser.getDeviceName(); - std::string deviceIDLocal = parser.getDeviceID(); - - if (!deviceIDLocal.empty()) { - config_[KEY_DEVICE_ID] = deviceIDLocal; - } - } - return {deviceName_, config_}; -} -} // namespace - CNNNetwork Core::ReadNetwork(const std::string& modelPath, const std::string& binPath) const { return _impl->ReadNetwork(modelPath, binPath); } @@ -576,20 +719,16 @@ CNNNetwork Core::ReadNetwork(const std::string& model, const Blob::CPtr& weights return _impl->ReadNetwork(model, weights); } -ExecutableNetwork Core::LoadNetwork(const CNNNetwork network, const std::string& deviceName, +ExecutableNetwork Core::LoadNetwork(const CNNNetwork& network, const std::string& deviceName, const std::map& config) { - IE_PROFILING_AUTO_SCOPE(Core::LoadNetwork) - auto parsed = parseDeviceNameIntoConfig(deviceName, config); - IE_SUPPRESS_DEPRECATED_START - return _impl->GetCPPPluginByName(parsed._deviceName).LoadNetwork(network, parsed._config); - IE_SUPPRESS_DEPRECATED_END + return _impl->LoadNetwork(network, deviceName, config); } void Core::AddExtension(const IExtensionPtr& extension) { - _impl->addExtension(extension); + _impl->AddExtension(extension); } -ExecutableNetwork Core::LoadNetwork(const CNNNetwork network, RemoteContext::Ptr context, +ExecutableNetwork Core::LoadNetwork(const CNNNetwork& network, RemoteContext::Ptr context, const std::map& config) { IE_PROFILING_AUTO_SCOPE(Core::LoadNetwork) std::map config_ = config; @@ -603,7 +742,8 @@ ExecutableNetwork Core::LoadNetwork(const CNNNetwork network, RemoteContext::Ptr std::string deviceName = device.getDeviceName(); IE_SUPPRESS_DEPRECATED_START - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(deviceName)); + auto cppPlugin = _impl->GetCPPPluginByName(deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); if (pluginAPIInterface == nullptr) { THROW_IE_EXCEPTION << deviceName << " does not implement the LoadNetwork method"; @@ -625,7 +765,8 @@ RemoteContext::Ptr Core::CreateContext(const std::string& deviceName_, const Par std::string deviceName = device.getDeviceName(); IE_SUPPRESS_DEPRECATED_START - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(deviceName)); + auto cppPlugin = _impl->GetCPPPluginByName(deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); if (pluginAPIInterface == nullptr) { THROW_IE_EXCEPTION << deviceName << " does not implement the CreateContext method"; @@ -647,7 +788,8 @@ RemoteContext::Ptr Core::GetDefaultContext(const std::string& deviceName_) { std::string deviceName = device.getDeviceName(); IE_SUPPRESS_DEPRECATED_START - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(deviceName)); + auto cppPlugin = _impl->GetCPPPluginByName(deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); if (pluginAPIInterface == nullptr) { THROW_IE_EXCEPTION << deviceName << " does not implement the CreateContext method"; @@ -667,13 +809,7 @@ void Core::AddExtension(IExtensionPtr extension, const std::string& deviceName_) << "MULTI device does not support extensions. Please, set extensions directly to fallback devices"; } - DeviceIDParser parser(deviceName_); - std::string deviceName = parser.getDeviceName(); - - IE_SUPPRESS_DEPRECATED_START - _impl->GetCPPPluginByName(deviceName).AddExtension(extension); - _impl->addExtension(extension); - IE_SUPPRESS_DEPRECATED_END + _impl->AddExtension(extension); } ExecutableNetwork Core::ImportNetwork(const std::string& modelFileName, const std::string& deviceName, @@ -692,32 +828,11 @@ ExecutableNetwork Core::ImportNetwork(const std::string& modelFileName, const st IE_SUPPRESS_DEPRECATED_END } -IE_SUPPRESS_DEPRECATED_START - ExecutableNetwork Core::ImportNetwork(std::istream& networkModel, const std::string& deviceName, const std::map& config) { - auto parsed = parseDeviceNameIntoConfig(deviceName, config); - - if (parsed._deviceName.empty()) { - ExportMagic magic = {}; - networkModel.read(magic.data(), magic.size()); - auto exportedWithName = (exportMagic == magic); - if (exportedWithName) { - std::getline(networkModel, parsed._deviceName); - } - networkModel.seekg(0, networkModel.beg); - } - - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(parsed._deviceName)); - if (pluginAPIInterface == nullptr) { - THROW_IE_EXCEPTION << parsed._deviceName << " does not implement the ImportNetwork method"; - } - - return pluginAPIInterface->ImportNetwork(networkModel, parsed._config); + return _impl->ImportNetwork(networkModel, deviceName, config); } -IE_SUPPRESS_DEPRECATED_END - ExecutableNetwork Core::ImportNetwork(std::istream& networkModel, const RemoteContext::Ptr& context, const std::map& config) { @@ -734,7 +849,8 @@ ExecutableNetwork Core::ImportNetwork(std::istream& networkModel, auto parsed = parseDeviceNameIntoConfig(deviceName, config); IE_SUPPRESS_DEPRECATED_START - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(parsed._deviceName)); + auto cppPlugin = _impl->GetCPPPluginByName(deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); if (pluginAPIInterface == nullptr) { THROW_IE_EXCEPTION << deviceName << " does not implement the ImportNetwork method"; @@ -745,12 +861,7 @@ ExecutableNetwork Core::ImportNetwork(std::istream& networkModel, QueryNetworkResult Core::QueryNetwork(const ICNNNetwork& network, const std::string& deviceName, const std::map& config) const { - QueryNetworkResult res; - auto parsed = parseDeviceNameIntoConfig(deviceName, config); - IE_SUPPRESS_DEPRECATED_START - _impl->GetCPPPluginByName(parsed._deviceName).QueryNetwork(network, parsed._config, res); - IE_SUPPRESS_DEPRECATED_END - return res; + return _impl->QueryNetwork(network, deviceName, config); } void Core::SetConfig(const std::map& config, const std::string& deviceName) { @@ -798,42 +909,22 @@ Parameter Core::GetConfig(const std::string& deviceName, const std::string& name auto parsed = parseDeviceNameIntoConfig(deviceName); IE_SUPPRESS_DEPRECATED_START - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(parsed._deviceName)); + auto cppPlugin = _impl->GetCPPPluginByName(parsed._deviceName); + auto pluginAPIInterface = getInferencePluginAPIInterface(cppPlugin); IE_SUPPRESS_DEPRECATED_END + if (pluginAPIInterface == nullptr) { THROW_IE_EXCEPTION << parsed._deviceName << " does not implement the GetConfig method"; } - return pluginAPIInterface->GetConfig(name, parsed._config); + + // we need to return a copy of Parameter object which is created on Core side, + // not in InferenceEngine plugin side, which can be unloaded from Core in a parallel thread + // TODO: remove this WA after *-31417 is resolved + return copyParameterValue(pluginAPIInterface->GetConfig(name, parsed._config)); } Parameter Core::GetMetric(const std::string& deviceName, const std::string& name) const { - // HETERO case - { - if (deviceName.find("HETERO:") == 0) { - THROW_IE_EXCEPTION - << "You can get specific metrics with the GetMetric only for the HETERO itself (without devices). " - "To get individual devices's metrics call GetMetric for each device separately"; - } - } - - // MULTI case - { - if (deviceName.find("MULTI:") == 0) { - THROW_IE_EXCEPTION - << "You can get specific metrics with the GetMetric only for the MULTI itself (without devices). " - "To get individual devices's metrics call GetMetric for each device separately"; - } - } - - auto parsed = parseDeviceNameIntoConfig(deviceName); - IE_SUPPRESS_DEPRECATED_START - auto pluginAPIInterface = getInferencePluginAPIInterface(_impl->GetCPPPluginByName(parsed._deviceName)); - IE_SUPPRESS_DEPRECATED_END - if (pluginAPIInterface == nullptr) { - THROW_IE_EXCEPTION << parsed._deviceName << " does not implement the GetMetric method"; - } - - return pluginAPIInterface->GetMetric(name, parsed._config); + return _impl->GetMetric(deviceName, name); } std::vector Core::GetAvailableDevices() const { @@ -842,11 +933,11 @@ std::vector Core::GetAvailableDevices() const { std::string propertyName = METRIC_KEY(AVAILABLE_DEVICES); for (auto&& deviceName : _impl->GetListOfDevicesInRegistry()) { - Parameter p; std::vector devicesIDs; + IE_SUPPRESS_DEPRECATED_START try { - p = GetMetric(deviceName, propertyName); + Parameter p = GetMetric(deviceName, propertyName); devicesIDs = p.as>(); } catch (details::InferenceEngineException&) { // plugin is not created by e.g. invalid env @@ -857,6 +948,7 @@ std::vector Core::GetAvailableDevices() const { THROW_IE_EXCEPTION << "Unknown exception is thrown while trying to create the " << deviceName << " device and call GetMetric"; } + IE_SUPPRESS_DEPRECATED_END if (devicesIDs.size() > 1) { for (auto&& deviceID : devicesIDs) { @@ -882,7 +974,7 @@ void Core::UnregisterPlugin(const std::string& deviceName_) { DeviceIDParser parser(deviceName_); std::string deviceName = parser.getDeviceName(); - _impl->UnregisterPluginByName(deviceName); + _impl->UnloadPluginByName(deviceName); } } // namespace InferenceEngine diff --git a/inference-engine/src/inference_engine/threading/ie_cpu_streams_executor.cpp b/inference-engine/src/inference_engine/threading/ie_cpu_streams_executor.cpp index 6467b6bccf0..6ba200246ae 100644 --- a/inference-engine/src/inference_engine/threading/ie_cpu_streams_executor.cpp +++ b/inference-engine/src/inference_engine/threading/ie_cpu_streams_executor.cpp @@ -23,159 +23,215 @@ #include "threading/ie_cpu_streams_executor.hpp" namespace InferenceEngine { -#if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO -struct PinningObserver: public tbb::task_scheduler_observer { - CpuSet& _mask; - int _ncpus = 0; - int _streamId = 0; - int _threadsPerStream = 0; - int _threadBindingStep = 0; - int _threadBindingOffset = 0; - - PinningObserver(tbb::task_arena& arena, - CpuSet& mask, - int ncpus, - const int streamId, - const int threadsPerStream, - const int threadBindingStep, - const int threadBindingOffset) : - tbb::task_scheduler_observer(arena), - _mask(mask), - _ncpus(ncpus), - _streamId(streamId), - _threadsPerStream(threadsPerStream), - _threadBindingStep(threadBindingStep), - _threadBindingOffset(threadBindingOffset) { - observe(true); - } - - void on_scheduler_entry(bool) override { - int threadIdx = tbb::task_arena::current_thread_index(); - int thrIdx = _streamId * _threadsPerStream + threadIdx + _threadBindingOffset; - // pin thread to the vacant slot - PinThreadToVacantCore(thrIdx, _threadBindingStep, _ncpus, _mask); - } - - void on_scheduler_exit(bool) override { - // reset the thread's mask (to the original process mask) - PinCurrentThreadByMask(_ncpus, _mask); - } - - ~PinningObserver() { - observe(false); - } -}; -#endif // IE_THREAD != IE_THREAD_TBB - -struct Stream { - int _streamId = 0; - int _numaNodeId = 0; -#if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO - std::unique_ptr _taskArena; - std::unique_ptr _pinningObserver; -#endif -}; - struct CPUStreamsExecutor::Impl { - std::string _name; - std::vector _threads; - std::mutex _mutex; - std::condition_variable _queueCondVar; - std::queue _taskQueue; - bool _isStopped = false; - int _ncpus = 0; - CpuSet _processMask; - ThreadLocal _localStream; -}; - -int CPUStreamsExecutor::GetStreamId() { - auto stream = _impl->_localStream.local(); - if (nullptr == stream) THROW_IE_EXCEPTION << "Not in the stream thread"; - return stream->_streamId; -} - -int CPUStreamsExecutor::GetNumaNodeId() { - auto stream = _impl->_localStream.local(); - if (nullptr == stream) THROW_IE_EXCEPTION << "Not in the stream thread"; - return stream->_numaNodeId; -} - -CPUStreamsExecutor::CPUStreamsExecutor(const IStreamsExecutor::Config& config) : - _impl{new Impl} { - IE_ASSERT(config._streams > 0); - _impl->_name = config._name; - auto numaNodes = getAvailableNUMANodes(); - IE_ASSERT(!numaNodes.empty()); - if (ThreadBindingType::CORES == config._threadBindingType) { - std::tie(_impl->_processMask, _impl->_ncpus) = GetProcessMask(); - } - for (auto streamId = 0; streamId < config._streams; ++streamId) { - _impl->_threads.emplace_back([=] { - annotateSetThreadName((_impl->_name + "_" + std::to_string(streamId)).c_str()); - Stream stream; - stream._streamId = streamId; - stream._numaNodeId = numaNodes.at(streamId/((config._streams + numaNodes.size() - 1)/numaNodes.size())); + struct Stream { #if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO - auto concurrency = (0 == config._threadsPerStream) ? tbb::task_arena::automatic : config._threadsPerStream; - if (ThreadBindingType::NUMA == config._threadBindingType) { - stream._taskArena.reset(new tbb::task_arena(tbb::task_arena::constraints(stream._numaNodeId, concurrency))); - } else if ((0 != config._threadsPerStream) || ThreadBindingType::CORES == config._threadBindingType) { - stream._taskArena.reset(new tbb::task_arena(concurrency)); - if (ThreadBindingType::CORES == config._threadBindingType) { - if (nullptr != _impl->_processMask) { - stream._pinningObserver.reset(new PinningObserver{*stream._taskArena, - _impl->_processMask, - _impl->_ncpus, - stream._streamId, - config._threadsPerStream, - config._threadBindingStep, - config._threadBindingOffset}); + struct Observer: public tbb::task_scheduler_observer { + CpuSet _mask; + int _ncpus = 0; + int _threadBindingStep = 0; + int _offset = 0; + Observer(tbb::task_arena& arena, + CpuSet mask, + int ncpus, + const int streamId, + const int threadsPerStream, + const int threadBindingStep, + const int threadBindingOffset) : + tbb::task_scheduler_observer(arena), + _mask{std::move(mask)}, + _ncpus(ncpus), + _threadBindingStep(threadBindingStep), + _offset{streamId * threadsPerStream + threadBindingOffset} { + } + void on_scheduler_entry(bool) override { + PinThreadToVacantCore(_offset + tbb::task_arena::current_thread_index(), _threadBindingStep, _ncpus, _mask); + } + void on_scheduler_exit(bool) override { + PinCurrentThreadByMask(_ncpus, _mask); + } + ~Observer() override = default; + }; +#endif + explicit Stream(Impl* impl) : + _impl(impl) { + { + std::lock_guard lock{_impl->_streamIdMutex}; + if (_impl->_streamIdQueue.empty()) { + _streamId = _impl->_streamId++; + } else { + _streamId = _impl->_streamIdQueue.front(); + _impl->_streamIdQueue.pop(); + } + } + _numaNodeId = _impl->_usedNumaNodes.at( + (_streamId % _impl->_config._streams)/ + ((_impl->_config._streams + _impl->_usedNumaNodes.size() - 1)/_impl->_usedNumaNodes.size())); +#if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO + auto concurrency = (0 == _impl->_config._threadsPerStream) ? tbb::task_arena::automatic : _impl->_config._threadsPerStream; + if (ThreadBindingType::NUMA == _impl->_config._threadBindingType) { + _taskArena.reset(new tbb::task_arena{tbb::task_arena::constraints{_numaNodeId, concurrency}}); + } else if ((0 != _impl->_config._threadsPerStream) || (ThreadBindingType::CORES == _impl->_config._threadBindingType)) { + _taskArena.reset(new tbb::task_arena{concurrency}); + if (ThreadBindingType::CORES == _impl->_config._threadBindingType) { + CpuSet processMask; + int ncpus = 0; + std::tie(processMask, ncpus) = GetProcessMask(); + if (nullptr != processMask) { + _observer.reset(new Observer{*_taskArena, + std::move(processMask), + ncpus, + _streamId, + _impl->_config._threadsPerStream, + _impl->_config._threadBindingStep, + _impl->_config._threadBindingOffset}); + _observer->observe(true); } } } #elif IE_THREAD == IE_THREAD_OMP - omp_set_num_threads(config._threadsPerStream); - if (!checkOpenMpEnvVars(false) && (ThreadBindingType::NONE != config._threadBindingType)) { - if (nullptr != _impl->_processMask) { - parallel_nt(config._threadsPerStream, [&] (int threadIndex, int threadsPerStream) { - int thrIdx = stream._streamId * threadsPerStream + threadIndex + config._threadBindingOffset; - PinThreadToVacantCore(thrIdx, config._threadBindingStep, _impl->_ncpus, _impl->_processMask); + omp_set_num_threads(_impl->_config._threadsPerStream); + if (!checkOpenMpEnvVars(false) && (ThreadBindingType::NONE != _impl->_config._threadBindingType)) { + CpuSet processMask; + int ncpus = 0; + std::tie(processMask, ncpus) = GetProcessMask(); + if (nullptr != processMask) { + parallel_nt(_impl->_config._threadsPerStream, [&] (int threadIndex, int threadsPerStream) { + int thrIdx = _streamId * _impl->_config._threadsPerStream + threadIndex + _impl->_config._threadBindingOffset; + PinThreadToVacantCore(thrIdx, _impl->_config._threadBindingStep, ncpus, processMask); }); } } #elif IE_THREAD == IE_THREAD_SEQ - if (ThreadBindingType::NUMA == config._threadBindingType) { - PinCurrentThreadToSocket(stream._numaNodeId); - } else if (ThreadBindingType::CORES == config._threadBindingType) { - PinThreadToVacantCore(stream._streamId + config._threadBindingOffset, config._threadBindingStep, _impl->_ncpus, _impl->_processMask); + if (ThreadBindingType::NUMA == _impl->_config._threadBindingType) { + PinCurrentThreadToSocket(_numaNodeId); + } else if (ThreadBindingType::CORES == _impl->_config._threadBindingType) { + CpuSet processMask; + int ncpus = 0; + std::tie(processMask, ncpus) = GetProcessMask(); + if (nullptr != processMask) { + PinThreadToVacantCore(_streamId + _impl->_config._threadBindingOffset, _impl->_config._threadBindingStep, ncpus, processMask); + } } #endif - _impl->_localStream.local() = &stream; - for (bool stopped = false; !stopped;) { - Task currentTask; - { // waiting for the new task or for stop signal - std::unique_lock lock(_impl->_mutex); - _impl->_queueCondVar.wait(lock, [&] { return !_impl->_taskQueue.empty() || (stopped = _impl->_isStopped); }); - if (!_impl->_taskQueue.empty()) { - currentTask = std::move(_impl->_taskQueue.front()); - _impl->_taskQueue.pop(); - } - } - - if (currentTask) { + } + ~Stream() { + { + std::lock_guard lock{_impl->_streamIdMutex}; + _impl->_streamIdQueue.push(_streamId); + } #if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO - if (nullptr != stream._taskArena) { - stream._taskArena->execute(std::move(currentTask)); - } else { - currentTask(); - } -#else - currentTask(); -#endif - } + if (nullptr != _observer) { + _observer->observe(false); } - }); +#endif + } + + Impl* _impl = nullptr; + int _streamId = 0; + int _numaNodeId = 0; + bool _execute = false; + std::queue _taskQueue; +#if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO + std::unique_ptr _taskArena; + std::unique_ptr _observer; +#endif + }; + + explicit Impl(const Config& config) : + _config{config}, + _streams([this] { + return std::make_shared(this); + }) { + auto numaNodes = getAvailableNUMANodes(); + std::copy_n(std::begin(numaNodes), + std::min(std::max(static_cast(1), + static_cast(_config._streams)), + numaNodes.size()), + std::back_inserter(_usedNumaNodes)); + for (auto streamId = 0; streamId < _config._streams; ++streamId) { + _threads.emplace_back([this, streamId] { + annotateSetThreadName((_config._name + "_" + std::to_string(streamId)).c_str()); + for (bool stopped = false; !stopped;) { + Task task; + { + std::unique_lock lock(_mutex); + _queueCondVar.wait(lock, [&] { return !_taskQueue.empty() || (stopped = _isStopped); }); + if (!_taskQueue.empty()) { + task = std::move(_taskQueue.front()); + _taskQueue.pop(); + } + } + if (task) { + Execute(task, *(_streams.local())); + } + } + }); + } } + + void Enqueue(Task task) { + { + std::lock_guard lock(_mutex); + _taskQueue.emplace(std::move(task)); + } + _queueCondVar.notify_one(); + } + + void Execute(const Task& task, Stream& stream) { +#if IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO + auto& arena = stream._taskArena; + if (nullptr != arena) { + arena->execute(std::move(task)); + } else { + task(); + } +#else + task(); +#endif + } + + void Defer(Task task) { + auto& stream = *(_streams.local()); + stream._taskQueue.push(std::move(task)); + if (!stream._execute) { + stream._execute = true; + try { + while (!stream._taskQueue.empty()) { + Execute(stream._taskQueue.front(), stream); + stream._taskQueue.pop(); + } + } catch(...) {} + stream._execute = false; + } + } + + Config _config; + std::mutex _streamIdMutex; + int _streamId = 0; + std::queue _streamIdQueue; + std::vector _threads; + std::mutex _mutex; + std::condition_variable _queueCondVar; + std::queue _taskQueue; + bool _isStopped = false; + std::vector _usedNumaNodes; + ThreadLocal> _streams; +}; + + +int CPUStreamsExecutor::GetStreamId() { + auto stream = _impl->_streams.local(); + return stream->_streamId; +} + +int CPUStreamsExecutor::GetNumaNodeId() { + auto stream = _impl->_streams.local(); + return stream->_numaNodeId; +} + +CPUStreamsExecutor::CPUStreamsExecutor(const IStreamsExecutor::Config& config) : + _impl{new Impl{config}} { } CPUStreamsExecutor::~CPUStreamsExecutor() { @@ -191,12 +247,16 @@ CPUStreamsExecutor::~CPUStreamsExecutor() { } } +void CPUStreamsExecutor::Execute(Task task) { + _impl->Defer(std::move(task)); +} + void CPUStreamsExecutor::run(Task task) { - { - std::lock_guard lock(_impl->_mutex); - _impl->_taskQueue.emplace(std::move(task)); + if (0 == _impl->_config._streams) { + _impl->Defer(std::move(task)); + } else { + _impl->Enqueue(std::move(task)); } - _impl->_queueCondVar.notify_one(); } } // namespace InferenceEngine diff --git a/inference-engine/src/inference_engine/threading/ie_istreams_executor.cpp b/inference-engine/src/inference_engine/threading/ie_istreams_executor.cpp index c2ca71e25e4..81b26cc057f 100644 --- a/inference-engine/src/inference_engine/threading/ie_istreams_executor.cpp +++ b/inference-engine/src/inference_engine/threading/ie_istreams_executor.cpp @@ -126,4 +126,4 @@ Parameter IStreamsExecutor::Config::GetConfig(const std::string& key) { return {}; } -} // namespace InferenceEngine +} // namespace InferenceEngine \ No newline at end of file diff --git a/inference-engine/src/inference_engine/xml_parse_utils.cpp b/inference-engine/src/inference_engine/xml_parse_utils.cpp index df75d6453e8..1a5cfd8a266 100644 --- a/inference-engine/src/inference_engine/xml_parse_utils.cpp +++ b/inference-engine/src/inference_engine/xml_parse_utils.cpp @@ -76,7 +76,7 @@ unsigned int XMLParseUtils::GetUIntAttr(const pugi::xml_node& node, const char* std::string XMLParseUtils::GetStrAttr(const pugi::xml_node& node, const char* str) { auto attr = node.attribute(str); if (attr.empty()) - THROW_IE_EXCEPTION << "node <" << node.name() << "> is missing mandatory attribute: " << str << " at offset " + THROW_IE_EXCEPTION << "node <" << node.name() << "> is missing mandatory attribute: '" << str << "' at offset " << node.offset_debug(); return attr.value(); } diff --git a/inference-engine/src/ir_readers/ie_ir_parser.cpp b/inference-engine/src/ir_readers/ie_ir_parser.cpp index 04cb4ef059f..d33ebb4b3fc 100644 --- a/inference-engine/src/ir_readers/ie_ir_parser.cpp +++ b/inference-engine/src/ir_readers/ie_ir_parser.cpp @@ -344,29 +344,40 @@ std::shared_ptr V10Parser::createNode(const std::vector ngraphNode; - if (opsets.count(params.version)) { - auto opset = opsets.at(params.version); - - for (const auto& creator : creators) { - if (creator->shouldCreate(params.type)) { + // Try to create operation from creators + for (const auto& creator : creators) { + if (creator->shouldCreate(params.type)) { + bool useCreator = false; + // Check that opset is registered + useCreator |= opsets.find(params.version) == opsets.end(); + if (!useCreator) { + // Check that creator can create operation with the version from opset + const auto opset = opsets.at(params.version); + // Opset should contains the same version of operation or doesn't contain operation with current type + useCreator |= opset.contains_type(creator->getNodeType()) || !opset.contains_type(params.type); + } + if (useCreator) ngraphNode = creator->createLayer(inputs, node, weights, params); - break; - } - } - - if (!ngraphNode) { - if (!opset.contains_type(params.type)) { - THROW_IE_EXCEPTION << "Opset " << params.version << " doesn't contain the operation with type: " << params.type; - } - - ngraphNode = std::shared_ptr(opset.create(params.type)); - ngraphNode->set_arguments(inputs); - XmlDeserializer visitor(node); - if (ngraphNode->visit_attributes(visitor)) - ngraphNode->constructor_validate_and_infer_types(); + break; } } + // Try to create operation from loaded opsets + if (!ngraphNode && opsets.count(params.version)) { + auto opset = opsets.at(params.version); + + if (!opset.contains_type(params.type)) { + THROW_IE_EXCEPTION << "Opset " << params.version << " doesn't contain the operation with type: " << params.type; + } + + ngraphNode = std::shared_ptr(opset.create(params.type)); + ngraphNode->set_arguments(inputs); + XmlDeserializer visitor(node); + if (ngraphNode->visit_attributes(visitor)) + ngraphNode->constructor_validate_and_infer_types(); + } + + // Create GenericIE operation for backward compatibility if (!ngraphNode && (params.version == "experimental" || params.version == "extension")) { // Try to create Generic node for backward compatibility std::map parameters; diff --git a/inference-engine/src/ir_readers/ie_ir_parser.hpp b/inference-engine/src/ir_readers/ie_ir_parser.hpp index 9e095be42ac..95256ddb52d 100644 --- a/inference-engine/src/ir_readers/ie_ir_parser.hpp +++ b/inference-engine/src/ir_readers/ie_ir_parser.hpp @@ -140,8 +140,7 @@ private: const GenericLayerParams& layerParsePrms) = 0; bool shouldCreate(const std::string& nodeType) const; - - std::shared_ptr createOptionalParameter(const GenericLayerParams::LayerPortData& port); + virtual ngraph::NodeTypeInfo getNodeType() const = 0; }; template @@ -151,6 +150,9 @@ private: std::shared_ptr createLayer(const ngraph::OutputVector& inputs, const pugi::xml_node& node, const Blob::CPtr& weights, const GenericLayerParams& layerParsePrms) override; + ngraph::NodeTypeInfo getNodeType() const override { + return T::type_info; + } }; std::shared_ptr createNode(const ngraph::OutputVector& inputs, const pugi::xml_node& node, @@ -203,6 +205,12 @@ private: std::vector shape; if (!getParameters(node.child("data"), name, shape)) return; static_cast(*a) = ngraph::Strides(shape); + } else if (auto a = ngraph::as_type>(&adapter)) { + if (!getStrAttribute(node.child("data"), name, val)) return; + static_cast(*a) = ngraph::as_enum(val); + } else if (auto a = ngraph::as_type>(&adapter)) { + if (!getStrAttribute(node.child("data"), name, val)) return; + static_cast(*a) = ngraph::as_enum(val); } } void on_adapter(const std::string& name, ngraph::ValueAccessor& adapter) override { diff --git a/inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp b/inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp index 941b3e8154a..adc66807b22 100644 --- a/inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp +++ b/inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp @@ -334,6 +334,15 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr res->params = params; return res; }); + + addSpecificCreator({"ScatterElementsUpdate"}, [](const std::shared_ptr<::ngraph::Node>& node, + const std::map params) -> CNNLayerPtr { + LayerParams attrs = {node->get_friendly_name(), node->description(), + details::convertPrecision(node->get_output_element_type(0))}; + auto res = std::make_shared(attrs); + res->params = params; + return res; + }); } CNNLayerPtr InferenceEngine::details::CNNLayerCreator::create() { @@ -599,8 +608,10 @@ std::shared_ptr convertFunctionToICNNNetwork(const std::shared_p } size_t inputCount(0); for (size_t i = 0; i < layer->get_input_size(); i++) { - const auto &input = layer->get_inputs()[i]; - if (isInternalLayer(input.get_output().get_node(), op_names, keep_constants)) continue; + const auto &constant = ngraph::as_type_ptr(layer->get_inputs()[i].get_output().get_node()); + if (constant && isInternalConstLayer(constant, layer, keep_constants)) { + continue; + } inputCount++; } cnnLayer->insData.resize(inputCount); diff --git a/inference-engine/src/legacy_api/src/ie_deprecated.cpp b/inference-engine/src/legacy_api/src/ie_deprecated.cpp index 8084da35c19..dc886c9038f 100644 --- a/inference-engine/src/legacy_api/src/ie_deprecated.cpp +++ b/inference-engine/src/legacy_api/src/ie_deprecated.cpp @@ -13,6 +13,7 @@ IE_SUPPRESS_DEPRECATED_START namespace InferenceEngine { Precision CNNNetwork::getPrecision() const { + if (actual == nullptr) THROW_IE_EXCEPTION << "CNNNetwork was not initialized."; return actual->getPrecision(); } @@ -41,7 +42,7 @@ StatusCode ICNNNetwork::AddExtension(const IShapeInferExtensionPtr& extension, R }; void CNNNetwork::AddExtension(InferenceEngine::IShapeInferExtensionPtr extension) { - CALL_STATUS_FNC(AddExtension, extension); + CALL_STATUS_FNC(AddExtension, extension); } CNNLayer::CNNLayer(const LayerParams& prms) diff --git a/inference-engine/src/legacy_api/src/ie_layer_validators.cpp b/inference-engine/src/legacy_api/src/ie_layer_validators.cpp index d1266e1146b..a2030f92f6e 100644 --- a/inference-engine/src/legacy_api/src/ie_layer_validators.cpp +++ b/inference-engine/src/legacy_api/src/ie_layer_validators.cpp @@ -3163,12 +3163,12 @@ void ScatterElementsUpdateValidator::checkShapes(const CNNLayer* layer, const ve THROW_IE_EXCEPTION << layer->name << " Incorrect number of 'updates' tensors dimension"; Precision inIdxPrecision = layer->insData[INDICES].lock()->getTensorDesc().getPrecision(); - if (inIdxPrecision != Precision::FP32 && inIdxPrecision != Precision::I32) - THROW_IE_EXCEPTION << layer->name << " Incorrect input 'Indices' precision. Only FP32 or I32 are supported!"; + if (inIdxPrecision != Precision::FP32 && inIdxPrecision != Precision::I32 && inIdxPrecision != Precision::I64) + THROW_IE_EXCEPTION << layer->name << " Incorrect input 'Indices' precision. Only FP32 or I32 or I64 are supported!"; Precision inAxisPrecision = layer->insData[AXIS].lock()->getTensorDesc().getPrecision(); - if (inAxisPrecision != Precision::FP32 && inAxisPrecision != Precision::I32) - THROW_IE_EXCEPTION << layer->name << " Incorrect input 'Axis' precision. Only FP32 or I32 are supported!"; + if (inAxisPrecision != Precision::FP32 && inAxisPrecision != Precision::I32 && inIdxPrecision != Precision::I64) + THROW_IE_EXCEPTION << layer->name << " Incorrect input 'Axis' precision. Only FP32 or I32 or I64 are supported!"; if (layer->insData[DATA].lock()->getTensorDesc().getPrecision() != layer->insData[UPDATES].lock()->getTensorDesc().getPrecision()) diff --git a/inference-engine/src/legacy_api/src/shape_infer/built-in/ie_tensor_iterator_shape_infer.hpp b/inference-engine/src/legacy_api/src/shape_infer/built-in/ie_tensor_iterator_shape_infer.hpp index 257cec36ce3..169296a06cd 100644 --- a/inference-engine/src/legacy_api/src/shape_infer/built-in/ie_tensor_iterator_shape_infer.hpp +++ b/inference-engine/src/legacy_api/src/shape_infer/built-in/ie_tensor_iterator_shape_infer.hpp @@ -92,6 +92,7 @@ public: if (!_body_reshaper) THROW_IE_EXCEPTION << "Request of apply reshape results while shape infer was not finished"; _body_reshaper->apply(); + _body_reshaper.reset(); // WA: reset _body_reshaper to release ownership for input data } private: diff --git a/inference-engine/src/legacy_api/src/shape_infer/ie_reshaper.hpp b/inference-engine/src/legacy_api/src/shape_infer/ie_reshaper.hpp index b9d904521a8..144e25f775c 100644 --- a/inference-engine/src/legacy_api/src/shape_infer/ie_reshaper.hpp +++ b/inference-engine/src/legacy_api/src/shape_infer/ie_reshaper.hpp @@ -80,7 +80,7 @@ public: /** * @brief Perform shape inference for the given input shapes but not apply it. - * In case of cusses call apply() method. + * In case of success call apply() method. * @param inputShapes - Map of input names (data) to their input shapes. * @throws exception if shape infer failed without corruption of original shapes */ diff --git a/inference-engine/src/low_precision_transformations/include/low_precision_transformations/power.hpp b/inference-engine/src/low_precision_transformations/include/low_precision_transformations/power.hpp new file mode 100644 index 00000000000..b7edd023b19 --- /dev/null +++ b/inference-engine/src/low_precision_transformations/include/low_precision_transformations/power.hpp @@ -0,0 +1,24 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include "ie_layers.h" +#include "low_precision_transformations/transformation_context.hpp" +#include "low_precision_transformations/layer_transformation.hpp" + +namespace InferenceEngine { +namespace details { + +class INFERENCE_ENGINE_API_CLASS(PowerTransformation) : public LayerTransformation { +public: + PowerTransformation(const Params& params) : LayerTransformation(params) {} + ~PowerTransformation() override {} + void transform(TransformationContext& context, CNNLayer& layer) const override; + bool canBeTransformed(const TransformationContext& context, const CNNLayer& layer) const override; +}; + +} // namespace details +} // namespace InferenceEngine diff --git a/inference-engine/src/low_precision_transformations/src/power.cpp b/inference-engine/src/low_precision_transformations/src/power.cpp new file mode 100644 index 00000000000..2f9824018a1 --- /dev/null +++ b/inference-engine/src/low_precision_transformations/src/power.cpp @@ -0,0 +1,75 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "low_precision_transformations/power.hpp" + +#include +#include
+#include +#include +#include + +#include "low_precision_transformations/common/ie_lpt_exception.hpp" +#include "low_precision_transformations/network_helper.hpp" + +using namespace InferenceEngine; +using namespace InferenceEngine::details; + +bool PowerTransformation::canBeTransformed(const TransformationContext& context, const CNNLayer& layer) const { + if (!LayerTransformation::canBeTransformed(context, layer)) { + return false; + } + + if (layer.insData.size() != 1) { + THROW_IE_LPT_EXCEPTION(layer) << "layer inputs '" << layer.insData.size() << "' is not correct"; + } + + if (!CaselessEq()(layer.type, "Power")) { + THROW_IE_LPT_EXCEPTION(layer) << "layer '" << layer.name << "' is not correct"; + } + + const PowerLayer* powerLayer = dynamic_cast(&layer); + if (powerLayer == nullptr) { + THROW_IE_LPT_EXCEPTION(layer) << "unexpected Power layer type"; + } + if (powerLayer->power != 1.f) { + return false; + } + + const CNNLayerPtr parent = CNNNetworkHelper::getParent(layer, 0); + return !(parent->type != "ScaleShift"); +} + +void PowerTransformation::transform(TransformationContext& context, CNNLayer& layer) const { + if (!canBeTransformed(context, layer)) { + return; + } + + const PowerLayer* powerLayer = dynamic_cast(&layer); + if (powerLayer == nullptr) { + THROW_IE_LPT_EXCEPTION(layer) << "unexpected Power layer type"; + } + + const CNNLayerPtr parent = CNNNetworkHelper::getParent(layer, 0); + + Blob::Ptr weightsBlob = CNNNetworkHelper::getBlob(parent, "weights"); + auto wBuffer = weightsBlob->buffer().as(); + for (size_t channel = 0ul; channel < weightsBlob->size(); ++channel) { + wBuffer[channel] = wBuffer[channel] * powerLayer->scale; + } + + Blob::Ptr shiftsBlob = CNNNetworkHelper::getBlob(parent, "biases"); + auto sBuffer = shiftsBlob->buffer().as(); + for (size_t channel = 0ul; channel < shiftsBlob->size(); ++channel) { + sBuffer[channel] = sBuffer[channel] * powerLayer->scale + powerLayer->offset; + } + + const std::vector children = CNNNetworkHelper::getChildren(layer); + CNNNetworkHelper::removeLayer(context.network, std::make_shared(layer)); + context.removeLayer(layer); + if (children.empty()) { + const std::string originalName = layer.name; + CNNNetworkHelper::renameLayer(context.network, parent->name, layer.name); + } +} diff --git a/inference-engine/src/low_precision_transformations/src/scaleshift_to_convolution.cpp b/inference-engine/src/low_precision_transformations/src/scaleshift_to_convolution.cpp index 70b7ae7fc45..e712803a874 100644 --- a/inference-engine/src/low_precision_transformations/src/scaleshift_to_convolution.cpp +++ b/inference-engine/src/low_precision_transformations/src/scaleshift_to_convolution.cpp @@ -17,7 +17,7 @@ using namespace InferenceEngine; using namespace InferenceEngine::details; -static const std::unordered_set defaultIgnoreWithParents = { +static const char * defaultIgnoreWithParents[] = { "Convolution", "FakeQuantize" }; @@ -25,7 +25,8 @@ static const std::unordered_set defaultIgnoreWithParents = { ScaleShiftToConvolutionTransformation::ScaleShiftToConvolutionTransformation(const Params& params) : WeightableLayerTransformation(params), groupSize(1ul), - ignoreWithParents(defaultIgnoreWithParents) { + ignoreWithParents(defaultIgnoreWithParents, defaultIgnoreWithParents + + sizeof(defaultIgnoreWithParents) / sizeof(defaultIgnoreWithParents[0])) { } void ScaleShiftToConvolutionTransformation::transform(TransformationContext& context, CNNLayer& layer) const { diff --git a/inference-engine/src/low_precision_transformations/src/transformer.cpp b/inference-engine/src/low_precision_transformations/src/transformer.cpp index 100f5a8a65d..c5910499f5f 100644 --- a/inference-engine/src/low_precision_transformations/src/transformer.cpp +++ b/inference-engine/src/low_precision_transformations/src/transformer.cpp @@ -34,6 +34,7 @@ #include "low_precision_transformations/permute.hpp" #include "low_precision_transformations/pooling.hpp" #include "low_precision_transformations/resample.hpp" +#include "low_precision_transformations/power.hpp" #include "low_precision_transformations/reshape.hpp" #include "low_precision_transformations/scaleshift_to_convolution.hpp" #include "low_precision_transformations/squeeze.hpp" @@ -186,7 +187,8 @@ LowPrecisionTransformations LowPrecisionTransformer::getAllTransformations(const { "ReLU", LayerTransformationPtr(new ActivationTransformation(params)) }, { "MVN", LayerTransformationPtr(new MvnTransformation(params)) }, { "Eltwise", LayerTransformationPtr(new EltwiseTransformation(params)) }, - { "Resample", LayerTransformationPtr(new ResampleTransformation(params)) } + { "Resample", LayerTransformationPtr(new ResampleTransformation(params)) }, + { "Power", LayerTransformationPtr(new PowerTransformation(params)) } }), std::map({ { "FakeQuantize", LayerTransformationPtr(new FuseFakeQuantizeAndScaleShiftTransformation(params)) }, diff --git a/inference-engine/src/mkldnn_plugin/CMakeLists.txt b/inference-engine/src/mkldnn_plugin/CMakeLists.txt index 40abb8cd530..465be27eaf3 100644 --- a/inference-engine/src/mkldnn_plugin/CMakeLists.txt +++ b/inference-engine/src/mkldnn_plugin/CMakeLists.txt @@ -133,15 +133,15 @@ file(GLOB HEADERS addVersionDefines(mkldnn_plugin.cpp CI_BUILD_NUMBER MKL_VERSION) include_directories( - ${IE_MAIN_SOURCE_DIR}/include $ - ${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/mkldnn + ${CMAKE_CURRENT_SOURCE_DIR} + ${CMAKE_BINARY_DIR}/include) + +include_directories(SYSTEM ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/src/common ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/src/cpu - ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/include - ${CMAKE_BINARY_DIR}/include/ -) + ${IE_MAIN_SOURCE_DIR}/thirdparty/mkl-dnn/include) if (GEMM STREQUAL "MKL") log_rpath_from_dir(MKL "${MKL}/lib") @@ -167,8 +167,8 @@ add_library(mkldnn_plugin_layers_no_opt_s OBJECT ${CROSS_COMPILED_SOURCES}) set_ie_threading_interface_for(mkldnn_plugin_layers_no_opt_s) target_compile_definitions(mkldnn_plugin_layers_no_opt_s PRIVATE "USE_STATIC_IE;IMPLEMENT_INFERENCE_ENGINE_PLUGIN") -set(object_libraries mkldnn_plugin_layers_no_opt) -set(mkldnn_plugin_object_libraries mkldnn_plugin_layers_no_opt_s) +list(APPEND object_libraries mkldnn_plugin_layers_no_opt) +list(APPEND mkldnn_plugin_object_libraries mkldnn_plugin_layers_no_opt_s) # SSE 4.2 optimized layers diff --git a/inference-engine/src/mkldnn_plugin/bf16transformer.cpp b/inference-engine/src/mkldnn_plugin/bf16transformer.cpp index 0558bd1e51a..d1d27d4a52b 100644 --- a/inference-engine/src/mkldnn_plugin/bf16transformer.cpp +++ b/inference-engine/src/mkldnn_plugin/bf16transformer.cpp @@ -55,6 +55,9 @@ void BF16Transformer::convertToBFloat16(InferenceEngine::CNNNetwork &network) { InputsDataMap inputs = network.getInputsInfo(); OutputsDataMap outputs = network.getOutputsInfo(); for (auto iter : sortedLayers) { + if (_skipmarking.find(iter->type) != _skipmarking.end()) { + continue; + } for (size_t o = 0; o < iter->outData.size(); o++) { if (inputs.find(iter->outData[o]->getName()) == inputs.end() && outputs.find(iter->outData[o]->getName()) == outputs.end() @@ -109,6 +112,9 @@ void BF16Transformer::optimizeToFloat(InferenceEngine::CNNNetwork &network) { // 2b. go over all unknown layers for this algo and mark them as fp32 and add to the toAnalyzeTensors // 2c. go over all inputs to _initbf16 and if they are fp32 - add them to the toAnalyzeTensors for (auto iter : sortedLayers) { + if (_skipmarking.find(iter->type) != _skipmarking.end()) { + continue; + } if (_initbf16.find(iter->type) == _initbf16.end() && _complementbf16.find(iter->type) == _complementbf16.end() && _multiinput.find(iter->type) == _multiinput.end()) { diff --git a/inference-engine/src/mkldnn_plugin/bf16transformer.h b/inference-engine/src/mkldnn_plugin/bf16transformer.h index 22becc6e860..536338316df 100644 --- a/inference-engine/src/mkldnn_plugin/bf16transformer.h +++ b/inference-engine/src/mkldnn_plugin/bf16transformer.h @@ -15,9 +15,12 @@ class BF16Transformer { const InferenceEngine::details::caseless_set _initbf16 = { "convolution", "fullyconnected", "innerproduct" }; const InferenceEngine::details::caseless_set _complementbf16 = - { "relu", "pooling", "norm", "gather" }; + { "relu", "tanh", "elu", "square", "abs", "sqrt", "linear", "bounded_relu", "soft_relu", "logistic", + "exp", "gelu", "clamp", "swish", "prelu", "pooling", "norm", "gather" }; const InferenceEngine::details::caseless_set _multiinput = { "concat", "eltwise" }; + const InferenceEngine::details::caseless_set _skipmarking = + { "const" }; /** * Tries to mark tensor as FP32 by analyzing of local consumers of the tensor. Do not mark if diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp index f444ff56074..f6f2284c51f 100644 --- a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp +++ b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp @@ -41,25 +41,7 @@ MKLDNNExecNetwork::CreateInferRequestImpl(InferenceEngine::InputsDataMap network MKLDNNExecNetwork::MKLDNNExecNetwork(const InferenceEngine::ICNNNetwork &network, const Config &cfg, const MKLDNNExtensionManager::Ptr& extMgr) : - InferenceEngine::ExecutableNetworkThreadSafeDefault([&] ()->ITaskExecutor::Ptr { - ExecutorManager *executorManager = ExecutorManager::getInstance(); - - if (cfg.exclusiveAsyncRequests) { - // special case when all InferRequests are muxed into a single queue - return executorManager->getExecutor("CPU");; - } else { - const int env_threads = parallel_get_env_threads(); - const auto& numa_nodes = getAvailableNUMANodes(); - const auto numa_nodes_num = numa_nodes.size(); - auto streamExecutorConfig = cfg.streamExecutorConfig; - // use logical cores only for single-socket targets in throughput mode - const int hw_cores = streamExecutorConfig._streams > 1 && numa_nodes_num == 1 ? parallel_get_max_threads() : getNumberOfCPUCores(); - const int threads = streamExecutorConfig._threads ? streamExecutorConfig._threads : (env_threads ? env_threads : hw_cores); - streamExecutorConfig._threadsPerStream = std::max(1, threads/streamExecutorConfig._streams); - streamExecutorConfig._name = "CPUStreamsExecutor"; - return executorManager->getIdleCPUStreamsExecutor(streamExecutorConfig); - } - } ()), + InferenceEngine::ExecutableNetworkThreadSafeDefault{nullptr, nullptr}, extensionManager(extMgr), _cfg{cfg}, _name{network.getName()} { @@ -101,7 +83,21 @@ MKLDNNExecNetwork::MKLDNNExecNetwork(const InferenceEngine::ICNNNetwork &network LayerTransformation::Params(params).setPrecisionsOnActivations({ Precision::U8 }), "ScaleShift")); transformer.transform(*_clonedNetwork); - if (with_cpu_x86_bfloat16()) { + + // Check if network is INT8 or Binary. + // BF16 transformations were disabled since CPU plug-in doesn't support mixed precision execution: + // BF16 + INT8 or BF16 + BIN. + bool isFloatModel = true; + CNNNetworkIterator i(&network); + while (i != CNNNetworkIterator()) { + if (CaselessEq()((*i)->type, "FakeQuantize")) { + isFloatModel = false; + break; + } + i++; + } + + if (with_cpu_x86_bfloat16() && isFloatModel) { BF16Transformer bf16Transformer; CNNNetwork cnnetwork(_clonedNetwork); if (cfg.enforceBF16 == true) { @@ -126,6 +122,30 @@ MKLDNNExecNetwork::MKLDNNExecNetwork(const InferenceEngine::ICNNNetwork &network } } + if (cfg.exclusiveAsyncRequests) { + // special case when all InferRequests are muxed into a single queue + _taskExecutor = ExecutorManager::getInstance()->getExecutor("CPU"); + } else { + const int env_threads = parallel_get_env_threads(); + const auto& numa_nodes = getAvailableNUMANodes(); + const auto numa_nodes_num = numa_nodes.size(); + auto streamExecutorConfig = cfg.streamExecutorConfig; + // use logical cores only for single-socket targets in throughput mode + const int hw_cores = streamExecutorConfig._streams > 1 && numa_nodes_num == 1 ? parallel_get_max_threads() : getNumberOfCPUCores(); + const int threads = streamExecutorConfig._threads ? streamExecutorConfig._threads : (env_threads ? env_threads : hw_cores); + streamExecutorConfig._threadsPerStream = streamExecutorConfig._streams + ? std::max(1, threads/streamExecutorConfig._streams) + : threads; + streamExecutorConfig._name = "CPUStreamsExecutor"; + _taskExecutor = ExecutorManager::getInstance()->getIdleCPUStreamsExecutor(streamExecutorConfig); + } + if (0 != cfg.streamExecutorConfig._streams) { + _callbackExecutor = ExecutorManager::getInstance()->getIdleCPUStreamsExecutor( + IStreamsExecutor::Config{"CPUCallbackExecutor", 1, 0, IStreamsExecutor::ThreadBindingType::NONE}); + } else { + _callbackExecutor = _taskExecutor; + } + _graphs = decltype(_graphs){[&] { // TODO: Remove `cloneNet` to `localNetwork` when `MKLDNNGraph::CreateGraph` // is fixed and does not change content of network passed (CVS-26420) @@ -230,7 +250,9 @@ void MKLDNNExecNetwork::GetMetric(const std::string &name, Parameter &result, Re Config engConfig = _graphs.begin()->get()->getProperty(); auto option = engConfig._config.find(CONFIG_KEY(CPU_THROUGHPUT_STREAMS)); IE_ASSERT(option != engConfig._config.end()); - result = IE_SET_METRIC(OPTIMAL_NUMBER_OF_INFER_REQUESTS, static_cast(std::stoi(option->second))); + auto streams = std::stoi(option->second); + result = IE_SET_METRIC(OPTIMAL_NUMBER_OF_INFER_REQUESTS, static_cast( + streams ? streams : 1)); } else { THROW_IE_EXCEPTION << "Unsupported ExecutableNetwork metric: " << name; } diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp index 83435236563..6442c5bf757 100644 --- a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp +++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp @@ -704,7 +704,6 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndActivation(MKLDNNGraph &graph) { return activationNode && (activationNode->getAlgorithm() == eltwise_relu || (conv->getCnnLayer()->precision == Precision::FP32 && - conv->getCnnLayer()->insData[0].lock()->getPrecision() != Precision::BF16 && isOneOf(activationNode->getAlgorithm(), {eltwise_elu, eltwise_logistic, eltwise_bounded_relu, eltwise_clamp, eltwise_swish}))); }; @@ -778,7 +777,6 @@ void MKLDNNGraphOptimizer::FuseFullyConnectedAndSimpleOperation(MKLDNNGraph &gra auto isSutableParentNode = [](MKLDNNNodePtr node) { return node->getType() == FullyConnected && - node->getCnnLayer()->insData[0].lock()->getPrecision() != Precision::BF16 && node->getChildEdges().size() == 1; }; @@ -850,9 +848,7 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndDepthwise(MKLDNNGraph &graph) { bool isSutableConv = (node->getType() == Convolution) && node->getCnnLayer()->precision == Precision::FP32; bool isSutableBinConv = node->getType() == BinaryConvolution; - return (isSutableConv || isSutableBinConv) && node->getChildEdges().size() == 1 && - !(node->getCnnLayer()->insData[0].lock()->getPrecision() == Precision::BF16 && - node->getCnnLayer()->outData[0]->getPrecision() == Precision::FP32); + return (isSutableConv || isSutableBinConv) && node->getChildEdges().size() == 1; }; auto isSutableChildNode = [](MKLDNNNodePtr node) { @@ -1125,9 +1121,7 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndSimpleOperation(MKLDNNGraph &graph) auto isSutableParentNode = [](MKLDNNNodePtr node) { return node->getType() == Convolution && node->getChildEdges().size() == 1 && - node->getCnnLayer()->precision == Precision::FP32 && - !(node->getCnnLayer()->insData[0].lock()->getPrecision() == Precision::BF16 && - node->getCnnLayer()->outData[0]->getPrecision() == Precision::FP32); + node->getCnnLayer()->precision == Precision::FP32; }; auto isSutableChildNode = [&](MKLDNNNodePtr node) { diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp index f97d71fe625..35261ce2834 100644 --- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp +++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp @@ -19,6 +19,7 @@ #include #include #include +#include #include #include #include @@ -55,10 +56,11 @@ Engine::Engine() { Engine::~Engine() { ExecutorManager::getInstance()->clear("CPUStreamsExecutor"); + ExecutorManager::getInstance()->clear("CPUCallbackExecutor"); } InferenceEngine::ExecutableNetworkInternal::Ptr -Engine::LoadExeNetworkImpl(const ICore * /*core*/, const InferenceEngine::ICNNNetwork &network, const std::map &config) { +Engine::LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, const std::map &config) { // verification of supported input InferenceEngine::InputsDataMap _networkInputs; network.getInputsInfo(_networkInputs); @@ -100,6 +102,7 @@ Engine::LoadExeNetworkImpl(const ICore * /*core*/, const InferenceEngine::ICNNNe // Note: instead of running all Conversion Transformations you can make up your own transformation pipeline ngraph::pass::CommonOptimizations().run_on_function(nGraphFunc); + ngraph::pass::ConvertOpSet3ToOpSet2(transformations_callback).run_on_function(nGraphFunc); ngraph::pass::ConvertOpSet2ToOpSet1(transformations_callback).run_on_function(nGraphFunc); ngraph::pass::ConvertOpSet1ToLegacy(transformations_callback).run_on_function(nGraphFunc); clonedNetwork = InferenceEngine::details::convertFunctionToICNNNetwork(nGraphFunc, *clonedNetwork); diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.h b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.h index a95698a36d0..310aef43304 100644 --- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.h +++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.h @@ -71,14 +71,11 @@ public: ~Engine() override; InferenceEngine::ExecutableNetworkInternal::Ptr - LoadExeNetworkImpl(const InferenceEngine::ICore * core, const InferenceEngine::ICNNNetwork &network, + LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork &network, const std::map &config) override; void AddExtension(InferenceEngine::IExtensionPtr extension) override; - /** - * @deprecated - * @param config - */ + void SetConfig(const std::map &config) override; InferenceEngine::Parameter GetConfig(const std::string& name, const std::map& options) const override; diff --git a/inference-engine/src/mkldnn_plugin/nodes/list.hpp b/inference-engine/src/mkldnn_plugin/nodes/list.hpp index 7ffa7494c44..b83108cde4f 100644 --- a/inference-engine/src/mkldnn_plugin/nodes/list.hpp +++ b/inference-engine/src/mkldnn_plugin/nodes/list.hpp @@ -35,9 +35,6 @@ struct ExtensionsHolder { std::map si_list; }; -template -class TExtensionsHolder : public ExtensionsHolder {}; - template class MKLDNNExtensions : public IExtension { public: @@ -89,11 +86,11 @@ public: } static std::shared_ptr GetExtensionsHolder() { - static std::shared_ptr> localHolder; + static std::shared_ptr localHolder; if (localHolder == nullptr) { - localHolder = std::make_shared>(); + localHolder = std::make_shared(); } - return std::dynamic_pointer_cast(localHolder); + return localHolder; } private: diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.cpp index 025957133b5..ce62371cb08 100644 --- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.cpp +++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.cpp @@ -812,7 +812,7 @@ static void permute_to_04123(int MB, MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr }); } -std::multimap MKLDNNPermuteNode::OptimizedCases = { +const std::multimap MKLDNNPermuteNode::OptimizedCases = { {{0, 2, 3, 1}, MKLDNNPermuteNode::PermuteImpl(permute_to_0231, [](int MB, MKLDNNMemoryPtr& srcMemPtr, MKLDNNMemoryPtr& dstMemPtr) { return true; })}, // NCHW -> NHWC case diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.h index edfeb875690..2097b323190 100644 --- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.h +++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_permute_node.h @@ -68,7 +68,7 @@ private: isApplicable isValidParams; }; - static std::multimap OptimizedCases; + static const std::multimap OptimizedCases; std::shared_ptr permute_kernel; }; diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp index 870a8f67b1c..63e4e7b7387 100644 --- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp +++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp @@ -67,21 +67,23 @@ void MKLDNNPoolingNode::getSupportedDescriptors() { invertVectorCopyUtoI(poolingLayer->_stride, stride); invertVectorCopyUtoI(poolingLayer->_kernel, kernel); auto allPads = getPaddings(*poolingLayer); - invertVectorCopyUtoI(allPads.begin, paddingL); - invertVectorCopyUtoI(allPads.end, paddingR); + invertVectorCopyUtoI(allPads.begin, data_pad_begin); + invertVectorCopyUtoI(allPads.end, data_pad_end); + effective_pad_begin = data_pad_begin; + effective_pad_end.resize(data_pad_end.size()); auto parentDims = getParentEdgeAt(0)->getDims(); auto childDims = getChildEdgeAt(0)->getDims(); if ((parentDims.ndims() < 4) || (parentDims.ndims() > 5)) THROW_IE_EXCEPTION << "Pooling layer. Unsupported mode. Only 4D and 5D blobs are supported as input."; - for (int i = 0; i < paddingR.size(); i++) { + for (int i = 0; i < effective_pad_end.size(); i++) { int krn = kernel[i]; int src = getParentEdgeAt(0)->getDims()[2 + i]; int dst = getChildEdgeAt(0)->getDims()[2 + i]; - int calc_dst = (src - krn + paddingL[i]) / stride[i] + 1; - paddingR[i] = (dst - calc_dst) * stride[i]; + int calc_dst = (src - krn + data_pad_begin[i]) / stride[i] + 1; + effective_pad_end[i] = (dst - calc_dst) * stride[i]; } if (inputPrecision == Precision::I8 || inputPrecision == Precision::U8) { // i8 layers supports only ndhwc and nhwc layouts @@ -138,13 +140,20 @@ void MKLDNNPoolingNode::createDescriptor(const std::vector desc_ptr( new pooling_forward::desc(prop_kind::forward_scoring, alg, in_candidate, out_candidate, - stride, kernel, paddingL, paddingR, + stride, kernel, effective_pad_begin, effective_pad_end, mkldnn::padding_kind::zero)); - bool not_zero_r = false; - for (auto pr : paddingR) { - if (pr) { - not_zero_r = true; - break; - } - } - if (alg == pooling_avg_include_padding && not_zero_r) { + if (alg == pooling_avg_include_padding) { // In case of AVG including paddings the norm coeff should be calculated // with tacking into account original pads. So we need to restore - // original values (R_padding = L_padding). + // original values for end paddings. // // WA. Because mkldnn uses different formula to calculate AVG norm coeff // in compare with Caffe. In mkldnn coeff is always 1/(KH*KW) - for (int i = 0; i < paddingL.size(); i++) desc_ptr->data.padding[1][i] = paddingL[i]; + for (int i = 0; i < data_pad_end.size(); i++) { + if (data_pad_end[i] != effective_pad_end[i]) + desc_ptr->data.padding[1][i] = static_cast(data_pad_end[i]); + } } descs.emplace_back(desc_ptr); diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h index 764af52bda6..92208d1671b 100644 --- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h +++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h @@ -34,10 +34,21 @@ private: InferenceEngine::PoolingLayer::PoolType type = InferenceEngine::PoolingLayer::MAX; bool exclude_pad = false; std::vector stride; - std::vector paddingL; - std::vector paddingR; std::vector kernel; + /// Effective padding. Used to define correct output shape by MKLDNN + /// reshape formula: (iw - kernel + pad_l + pad_r) / strides[i - 2] + 1 + /// should be passed into pooling desc constructor. + std::vector effective_pad_begin; + std::vector effective_pad_end; + + /// Effective pad value. Describe how much zero element added to input + /// data tensor. May be less than "Effective padding" values. + /// If pooling window is out of this padding, the region of averaging + /// is decreased. + std::vector data_pad_begin; + std::vector data_pad_end; + InferenceEngine::Precision inputPrecision = InferenceEngine::Precision::FP32; InferenceEngine::Precision outputPrecision = InferenceEngine::Precision::FP32; diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_resample_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_resample_node.cpp index 9d7c188411e..a85ba1a615e 100644 --- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_resample_node.cpp +++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_resample_node.cpp @@ -39,7 +39,7 @@ struct jit_uni_resample_nearest_kernel_f32 : public jit_uni_resample_nearest_ker DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_resample_nearest_kernel_f32) explicit jit_uni_resample_nearest_kernel_f32(jit_resample_config_params jcp, const mkldnn_primitive_attr &attr) - : jit_uni_resample_nearest_kernel(jcp, attr), jit_generator() { + : jit_uni_resample_nearest_kernel(jcp, attr), jit_generator() { const auto &p = attr_.post_ops_; for (int i = 0; i < p.len_; i++) { auto &post_op = p.entry_[i]; @@ -321,6 +321,14 @@ void MKLDNNResampleNode::initSupportedPrimitiveDescriptors() { } } + if (inputPrecision == Precision::BF16) { + inputPrecision = Precision::FP32; + } + + if (outputPrecision == Precision::BF16) { + outputPrecision = Precision::FP32; + } + auto inputDataType = MKLDNNExtensionUtils::IEPrecisionToDataType(inputPrecision); auto outputDataType = MKLDNNExtensionUtils::IEPrecisionToDataType(outputPrecision); @@ -587,7 +595,7 @@ void MKLDNNResampleNode::execute(mkldnn::stream strm) { // f32 and no fused, f32->input is f32, no fuse->output is f32 void MKLDNNResampleNode::NearestNeighbor_PLN(const float *in_ptr_, float *out_ptr_, int B, int C, int ID, int IH, int IW, - float fx, float fy, float fz, int OD, int OH, int OW) { + float fx, float fy, float fz, int OD, int OH, int OW) { std::vector index_buffer(OD * OH * OW); for (int oz = 0; oz < OD; oz++) { float iz = oz * fz; @@ -640,7 +648,7 @@ void MKLDNNResampleNode::NearestNeighbor_PLN(const float *in_ptr_, float *out_pt // int8->input may be int8, fused->output may be int8 template void MKLDNNResampleNode::NearestNeighbor_BLK(const in_data_t *in_ptr_, out_data_t *out_ptr_, int B, int C, int ID, int IH, int IW, - float fx, float fy, float fz, int OD, int OH, int OW) { + float fx, float fy, float fz, int OD, int OH, int OW) { std::vector index_d(OD); std::vector index_h(OH); std::vector index_w(OW); @@ -792,7 +800,7 @@ static inline float triangleCoeff(float x) { template void MKLDNNResampleNode::LinearInterpolation(const in_data_t *in_ptr_, out_data_t *out_ptr_, int B, int C, int ID, int IH, int IW, - float fx, float fy, float fz, int OD, int OH, int OW, int kernel_width, bool antialias) { + float fx, float fy, float fz, int OD, int OH, int OW, int kernel_width, bool antialias) { if (IW == OW && IH == OH && ID == OD) { size_t size = B * C * ID * IH * IW; if (input_prec == Precision::FP32) { diff --git a/inference-engine/src/multi_device/CMakeLists.txt b/inference-engine/src/multi_device/CMakeLists.txt new file mode 100644 index 00000000000..d38f95289ef --- /dev/null +++ b/inference-engine/src/multi_device/CMakeLists.txt @@ -0,0 +1,26 @@ +# Copyright (C) 2018-2020 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# + +set (TARGET_NAME "MultiDevicePlugin") + +if(ENABLE_LTO) + ie_enable_lto() +endif() + +file(GLOB SOURCES + ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp +) + +file(GLOB HEADERS + ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp +) + +ie_add_plugin(NAME ${TARGET_NAME} + DEVICE_NAME "MULTI" + SOURCES ${SOURCES} ${HEADERS} + VERSION_DEFINES_FOR multi_device.cpp) + +target_link_libraries(${TARGET_NAME} PRIVATE inference_engine) + +set_ie_threading_interface_for(${TARGET_NAME}) diff --git a/inference-engine/src/multi_device/multi_device.cpp b/inference-engine/src/multi_device/multi_device.cpp new file mode 100644 index 00000000000..2f09d28b11c --- /dev/null +++ b/inference-engine/src/multi_device/multi_device.cpp @@ -0,0 +1,548 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/////////////////////////////////////////////////////////////////////////////////////////////////// +#include +#include +#include +#include +#include +#include +#include + +#include "ie_metric_helpers.hpp" +#include +#include +#include +#include +#include +#include "multi_device.hpp" + +namespace MultiDevicePlugin { + using namespace InferenceEngine; +// ------------------------------MultiDeviceInferRequest---------------------------- +MultiDeviceInferRequest::MultiDeviceInferRequest(const InputsDataMap& networkInputs, + const OutputsDataMap& networkOutputs) + : InferRequestInternal(networkInputs, networkOutputs) { + // Allocate all input blobs + for (const auto &it : networkInputs) { + Layout l = it.second->getLayout(); + Precision p = it.second->getPrecision(); + SizeVector dims = it.second->getTensorDesc().getDims(); + + TensorDesc desc = TensorDesc(p, dims, l); + _inputs[it.first] = make_blob_with_precision(desc); + _inputs[it.first]->allocate(); + } + // Allocate all output blobs + for (const auto &it : networkOutputs) { + Layout l = it.second->getLayout(); + Precision p = it.second->getPrecision(); + SizeVector dims = it.second->getTensorDesc().getDims(); + + TensorDesc desc = TensorDesc(p, dims, l); + _outputs[it.first] = make_blob_with_precision(desc); + _outputs[it.first]->allocate(); + } +} + +void MultiDeviceInferRequest::SetBlobsToAnotherRequest(InferRequest& req) { + for (const auto &it : _networkInputs) { + Blob::Ptr blob; + auto &name = it.first; + // this request is already in BUSY state, so using the internal functions safely + GetBlob(name.c_str(), blob); + req.SetBlob(name.c_str(), blob); + } + for (const auto &it : _networkOutputs) { + Blob::Ptr blob; + auto &name = it.first; + // this request is already in BUSY state, so using the internal functions safely + GetBlob(name.c_str(), blob); + req.SetBlob(name.c_str(), blob); + } +} + +MultiDeviceAsyncInferRequest::MultiDeviceAsyncInferRequest( + const MultiDeviceInferRequest::Ptr& inferRequest, + const bool needPerfCounters, + const MultiDeviceExecutableNetwork::Ptr& multiDeviceExecutableNetwork, + const ITaskExecutor::Ptr& callbackExecutor) : + AsyncInferRequestThreadSafeDefault(inferRequest, nullptr, callbackExecutor), + _multiDeviceExecutableNetwork{multiDeviceExecutableNetwork}, + _inferRequest{inferRequest}, + _needPerfCounters{needPerfCounters} { + struct ThisRequestExecutor : public ITaskExecutor { + explicit ThisRequestExecutor(MultiDeviceAsyncInferRequest* _this_) : _this{_this_} {} + void run(Task task) override { + auto workerInferRequest = _this->_workerInferRequest; + workerInferRequest->_task = std::move(task); + workerInferRequest->_inferRequest.StartAsync(); + }; + MultiDeviceAsyncInferRequest* _this = nullptr; + }; + _pipeline = { + {_multiDeviceExecutableNetwork, [this] { + _workerInferRequest = MultiDeviceExecutableNetwork::_thisWorkerInferRequest; + _inferRequest->SetBlobsToAnotherRequest(_workerInferRequest->_inferRequest); + }}, + {std::make_shared(this), [this] { + auto status = _workerInferRequest->_status; + if (InferenceEngine::StatusCode::OK != status) { + if (nullptr != InferenceEngine::CurrentException()) { + std::rethrow_exception(InferenceEngine::CurrentException()); + } else { + THROW_IE_EXCEPTION << InferenceEngine::details::as_status << status; + } + } + if (_needPerfCounters) { + _perfMap = _workerInferRequest->_inferRequest.GetPerformanceCounts(); + } + }} + }; +} + +void MultiDeviceAsyncInferRequest::Infer_ThreadUnsafe() { + InferUsingAsync(); +} + +void MultiDeviceAsyncInferRequest::GetPerformanceCounts_ThreadUnsafe(std::map &perfMap) const { + perfMap = std::move(_perfMap); +} + +MultiDeviceAsyncInferRequest::~MultiDeviceAsyncInferRequest() { + StopAndWait(); +} + +// ------------------------------MultiDeviceExecutableNetwork---------------------------- + +thread_local MultiDeviceExecutableNetwork::WorkerInferRequest* MultiDeviceExecutableNetwork::_thisWorkerInferRequest = nullptr; + +struct IdleGuard { + explicit IdleGuard(MultiDeviceExecutableNetwork::WorkerInferRequest* workerInferRequestPtr, + MultiDeviceExecutableNetwork::NotBusyWorkerRequests& notBusyWorkerRequests) : + _workerInferRequestPtr{workerInferRequestPtr}, + _notBusyWorkerRequests{¬BusyWorkerRequests} { + } + ~IdleGuard() { + if (nullptr != _notBusyWorkerRequests) { + _notBusyWorkerRequests->push(_workerInferRequestPtr); + } + } + MultiDeviceExecutableNetwork::NotBusyWorkerRequests* Release() { + auto notBusyWorkerRequests = _notBusyWorkerRequests; + _notBusyWorkerRequests = nullptr; + return notBusyWorkerRequests; + } + MultiDeviceExecutableNetwork::WorkerInferRequest* _workerInferRequestPtr = nullptr; + MultiDeviceExecutableNetwork::NotBusyWorkerRequests* _notBusyWorkerRequests = nullptr; +}; + +MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const DeviceMap& networksPerDevice, + const DeviceMap& networkDevices, + const std::unordered_map& config, + const bool needPerfCounters) : + InferenceEngine::ExecutableNetworkThreadSafeDefault(nullptr, std::make_shared()), + _devicePriorities{networkDevices}, + _networksPerDevice{networksPerDevice}, + _config{config}, + _needPerfCounters{needPerfCounters} { + _taskExecutor.reset(); + for (auto&& networkValue : _networksPerDevice) { + auto& device = networkValue.first; + auto& network = networkValue.second; + + auto itNumRequests = _devicePriorities.find(device); + unsigned int optimalNum = 0; + try { + optimalNum = network.GetMetric(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)).as(); + } catch (const details::InferenceEngineException &iie) { + THROW_IE_EXCEPTION + << "Every device used with the Multi-Device should " + << "support OPTIMAL_NUMBER_OF_INFER_REQUESTS ExecutableNetwork metric. " + << "Failed to query the metric for the " << device << " with error:" << iie.what(); + } + const auto numRequests = (_devicePriorities.end() == itNumRequests || + itNumRequests->second.numRequestsPerDevices == -1) ? optimalNum : itNumRequests->second.numRequestsPerDevices; + auto& workerRequests = _workerRequests[device]; + auto& idleWorkerRequests = _idleWorkerRequests[device]; + workerRequests.resize(numRequests); + auto* idleWorkerRequestsPtr = &(idleWorkerRequests); + for (auto&& workerRequest : workerRequests) { + workerRequest._inferRequest = network.CreateInferRequest(); + auto* workerRequestPtr = &workerRequest; + idleWorkerRequests.push(workerRequestPtr); + workerRequest._inferRequest.SetCompletionCallback>( + [workerRequestPtr, this, device, idleWorkerRequestsPtr] (InferRequest , StatusCode status) mutable { + IdleGuard idleGuard{workerRequestPtr, *idleWorkerRequestsPtr}; + workerRequestPtr->_status = status; + { + auto capturedTask = std::move(workerRequestPtr->_task); + capturedTask(); + } + if (!_terminate) { + idleGuard.Release()->push(workerRequestPtr); + ScheduleToWorkerInferRequest(); + } + }); + } + } +} + +void MultiDeviceExecutableNetwork::ScheduleToWorkerInferRequest() { + auto devices = [&] { + std::lock_guard lock(_mutex); + return _devicePriorities; + }(); + for (auto&& device : devices) { + auto& idleWorkerRequests = _idleWorkerRequests[device.first]; + WorkerInferRequest* workerRequestPtr = nullptr; + if (idleWorkerRequests.try_pop(workerRequestPtr)) { + IdleGuard idleGuard{workerRequestPtr, idleWorkerRequests}; + Task inferPipelineTask; + if (_inferPipelineTasks.try_pop(inferPipelineTask)) { + _thisWorkerInferRequest = workerRequestPtr; + inferPipelineTask(); + idleGuard.Release(); + break; + } + } + } +} + +void MultiDeviceExecutableNetwork::run(Task inferPipelineTask) { + if (!_terminate) { + _inferPipelineTasks.push(std::move(inferPipelineTask)); + ScheduleToWorkerInferRequest(); + } +} + +MultiDeviceExecutableNetwork::~MultiDeviceExecutableNetwork() { + { + std::lock_guard lock(_mutex); + _devicePriorities.clear(); + } + _terminate = true; + /* NOTE: The only threads that use `MultiDeviceExecutableNetwork` Context are those that are used by Worker infer requests. + * But AsyncInferRequest destructor should waits for all asynchronous tasks that are used by the request + */ + _workerRequests.clear(); +} + +InferenceEngine::InferRequestInternal::Ptr MultiDeviceExecutableNetwork::CreateInferRequestImpl(InferenceEngine::InputsDataMap networkInputs, + InferenceEngine::OutputsDataMap networkOutputs) { + return std::make_shared(networkInputs, networkOutputs); +} + +void MultiDeviceExecutableNetwork::CreateInferRequest(IInferRequest::Ptr& asyncRequest) { + auto syncRequestImpl = CreateInferRequestImpl(_networkInputs, _networkOutputs); + syncRequestImpl->setPointerToExecutableNetworkInternal(shared_from_this()); + auto asyncTreadSafeImpl = std::make_shared(std::static_pointer_cast(syncRequestImpl), + _needPerfCounters, + std::static_pointer_cast(shared_from_this()), + _callbackExecutor); + asyncRequest.reset(new InferRequestBase(asyncTreadSafeImpl), [](IInferRequest *p) { p->Release(); }); + asyncTreadSafeImpl->SetPointerToPublicInterface(asyncRequest); +} + +void MultiDeviceExecutableNetwork::SetConfig(const std::map &config, + InferenceEngine::ResponseDesc * /* resp */) { + auto priorities = config.find(MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES); + if (priorities == config.end() || config.size() > 1) { + THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str << + "The only config supported for the Network's SetConfig is MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES"; + } else { + auto multiPlugin = std::dynamic_pointer_cast(this->_plugin); + assert(multiPlugin != nullptr); + auto metaDevices = multiPlugin->ParseMetaDevices(priorities->second, {}); + + if (std::any_of(metaDevices.begin(), metaDevices.end(), [](const std::pair & kvp) { + return kvp.second.numRequestsPerDevices != -1; + })) { + THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str << "You can only change device priorities but not number of requests" + <<" with the Network's SetConfig(MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES!"; + } + + { + std::lock_guard lock{_mutex}; + for (auto && device : metaDevices) { + if (_devicePriorities.find(device.first) == _devicePriorities.end()) { + THROW_IE_EXCEPTION << NOT_FOUND_str << "You can only change device priorities but not add new devices with" + << " the Network's SetConfig(MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES." << device.first << + " device was not in the original device list!"; + } + } + _devicePriorities = metaDevices; + + // update value in config + _config[MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES] = priorities->second; + } + } +} + +void MultiDeviceExecutableNetwork::GetConfig(const std::string &name, InferenceEngine::Parameter &result, + InferenceEngine::ResponseDesc * /* resp */) const { + auto res = _config.find(name); + if (res != _config.end()) { + result = res->second; + } else { + THROW_IE_EXCEPTION << NOT_FOUND_str << name <<" not found in the ExecutableNetwork config"; + } +} + +void MultiDeviceExecutableNetwork::GetMetric(const std::string &name, Parameter &result, ResponseDesc *resp) const { + if (name == METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)) { + unsigned int res = 0u; + for (auto n : _networksPerDevice) { + try { + res += n.second.GetMetric(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)).as(); + } catch (const details::InferenceEngineException &iie) { + THROW_IE_EXCEPTION + << "Every device used with the Multi-Device should " + << "support OPTIMAL_NUMBER_OF_INFER_REQUESTS ExecutableNetwork metric. " + << "Failed to query the metric for the " << n.first << " with error:" << iie.what(); + } + } + result = IE_SET_METRIC(OPTIMAL_NUMBER_OF_INFER_REQUESTS, res); + } else if (name == METRIC_KEY(NETWORK_NAME)) { + auto it = _networksPerDevice.begin(); + IE_ASSERT(it != _networksPerDevice.end()); + result = IE_SET_METRIC(NETWORK_NAME, it->second.GetMetric( + METRIC_KEY(NETWORK_NAME)).as()); + } else if (name == METRIC_KEY(SUPPORTED_METRICS)) { + result = IE_SET_METRIC(SUPPORTED_METRICS, { + METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS), + METRIC_KEY(SUPPORTED_METRICS), + METRIC_KEY(NETWORK_NAME), + METRIC_KEY(SUPPORTED_CONFIG_KEYS) + }); + } else if (name == METRIC_KEY(SUPPORTED_CONFIG_KEYS)) { + std::vector configKeys = { MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES }; + result = IE_SET_METRIC(SUPPORTED_CONFIG_KEYS, configKeys); + } else { + THROW_IE_EXCEPTION << "Unsupported Network metric: " << name; + } +} + +// ------------------------------MultiDeviceInferencePlugin---------------------------- + +namespace { + +std::map mergeConfigs(std::map config, + const std::map & local) { + for (auto && kvp : local) { + config[kvp.first] = kvp.second; + } + return config; +} + +} // namespace + +std::map MultiDeviceInferencePlugin::GetSupportedConfig( + const std::map & config, const std::string & deviceName) const { + std::vector supportedConfigKeys = GetCore()->GetMetric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)); + std::map supportedConfig; + for (auto&& key : supportedConfigKeys) { + auto itKey = config.find(key); + if (config.end() != itKey) { + supportedConfig[key] = itKey->second; + } + } + return supportedConfig; +} + +DeviceMap MultiDeviceInferencePlugin::ParseMetaDevices(const std::string& priorities, + const std::map & config) const { + DeviceMap metaDevices; + + // parsing the string and splitting to tokens + std::vector devicesWithRequests; + // parsing the string and splitting the comma-separated tokens + std::string::size_type i = 0; + std::string::size_type idelimeter; + while ((idelimeter = priorities.find(',', i)) != std::string::npos) { + devicesWithRequests.push_back(priorities.substr(i, idelimeter - i)); + i = idelimeter + 1; + } + // last token in the string (which has no comma after that) + devicesWithRequests.push_back(priorities.substr(i, priorities.length() - i)); + + auto getDeviceConfig = [&] (const DeviceName & deviceWithID) { + DeviceIDParser deviceParser(deviceWithID); + std::string deviceName = deviceParser.getDeviceName(); + std::map tconfig = mergeConfigs(_config, config); + + // set device ID if any + std::string deviceIDLocal = deviceParser.getDeviceID(); + if (!deviceIDLocal.empty()) { + tconfig[PluginConfigParams::KEY_DEVICE_ID] = deviceIDLocal; + } + + return GetSupportedConfig(tconfig, deviceName); + }; + + for (auto && d : devicesWithRequests) { + auto openingBracket = d.find_first_of('('); + auto closingBracket = d.find_first_of(')', openingBracket); + auto device_name = d.substr(0, openingBracket); + + int numRequests = -1; + if (closingBracket != std::string::npos && openingBracket < closingBracket) { + numRequests = std::stol(d.substr(openingBracket + 1, closingBracket - 1)); + + if (numRequests <= 0) { + THROW_IE_EXCEPTION << "Priority value for '" << device_name << "' must be > 0, while " << numRequests + << "is passed"; + } + } + + // create meta device + metaDevices[device_name] = { getDeviceConfig(device_name), numRequests }; + } + + return metaDevices; +} + +Parameter MultiDeviceInferencePlugin::GetConfig(const std::string& name, + const std::map & options) const { + if (name == MULTI_CONFIG_KEY(DEVICE_PRIORITIES)) { + auto it = _config.find(MULTI_CONFIG_KEY(DEVICE_PRIORITIES)); + if (it == _config.end()) { + THROW_IE_EXCEPTION << "Value for KEY_MULTI_DEVICE_PRIORITIES is not set"; + } else { + return { it->second }; + } + } else { + THROW_IE_EXCEPTION << "Unsupported config key: " << name; + } +} + +void MultiDeviceInferencePlugin::SetConfig(const std::map & config) { + for (auto && kvp : config) { + _config[kvp.first] = kvp.second; + } +} + +IE_SUPPRESS_DEPRECATED_START + +INFERENCE_PLUGIN_API(InferenceEngine::StatusCode) CreatePluginEngine( + InferenceEngine::IInferencePlugin *&plugin, + InferenceEngine::ResponseDesc *resp) noexcept { + try { + plugin = make_ie_compatible_plugin( + {{2, 1}, + CI_BUILD_NUMBER, + "MultiDevicePlugin"}, std::make_shared()); + return OK; + } + catch (std::exception &ex) { + return DescriptionBuffer(GENERAL_ERROR, resp) << ex.what(); + } +} + +IE_SUPPRESS_DEPRECATED_END + +MultiDeviceInferencePlugin::MultiDeviceInferencePlugin() { + _pluginName = "MULTI"; +} + +InferenceEngine::Parameter MultiDeviceInferencePlugin::GetMetric(const std::string& name, + const std::map & options) const { + if (name == METRIC_KEY(SUPPORTED_METRICS)) { + std::vector metrics; + metrics.push_back(METRIC_KEY(SUPPORTED_METRICS)); + metrics.push_back(METRIC_KEY(FULL_DEVICE_NAME)); + metrics.push_back(METRIC_KEY(SUPPORTED_CONFIG_KEYS)); + IE_SET_METRIC_RETURN(SUPPORTED_METRICS, metrics); + } else if (name == METRIC_KEY(FULL_DEVICE_NAME)) { + std::string name = { "MULTI" }; + IE_SET_METRIC_RETURN(FULL_DEVICE_NAME, name); + } else if (name == METRIC_KEY(SUPPORTED_CONFIG_KEYS)) { + std::vector configKeys = { MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES }; + IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS, configKeys); + } else { + THROW_IE_EXCEPTION << "Unsupported metric key " << name; + } +} + +ExecutableNetworkInternal::Ptr MultiDeviceInferencePlugin::LoadExeNetworkImpl(const ICNNNetwork &network, + const std::map& config) { + if (GetCore() == nullptr) { + THROW_IE_EXCEPTION << "Please, work with MULTI device via InferencEngine::Core object"; + } + + // TODO: do we really need a clone? + ICNNNetwork::Ptr clonedNetwork = cloneNet(network); + + auto fullConfig = mergeConfigs(_config, config); + auto priorities = fullConfig.find(MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES); + if (priorities == fullConfig.end()) { + THROW_IE_EXCEPTION << "KEY_MULTI_DEVICE_PRIORITIES key is not set for MULTI device"; + } + + DeviceMap metaDevices = ParseMetaDevices(priorities->second, fullConfig); + + // collect the settings that are applicable to the devices we are loading the network to + std::unordered_map multiNetworkConfig; + multiNetworkConfig.insert(*priorities); + + DeviceMap executableNetworkPerDevice; + for (auto& p : metaDevices) { + auto & deviceName = p.first; + auto & metaDevice = p.second; + auto & deviceConfig = metaDevice.config; + executableNetworkPerDevice.insert({ deviceName, GetCore()->LoadNetwork(CNNNetwork{clonedNetwork}, deviceName, deviceConfig) }); + multiNetworkConfig.insert(deviceConfig.begin(), deviceConfig.end()); + } + if (executableNetworkPerDevice.empty()) + THROW_IE_EXCEPTION << NOT_FOUND_str << "Failed to load Executable network to any device " + << "that the MULTI device is initialized to work with"; + + auto perfConfig = fullConfig.find(PluginConfigParams::KEY_PERF_COUNT); + bool enablePerfCounters = (fullConfig.end() != perfConfig) && (perfConfig->second == PluginConfigParams::YES); + + return std::make_shared(executableNetworkPerDevice, + metaDevices, + multiNetworkConfig, + enablePerfCounters); +} + +void MultiDeviceInferencePlugin::QueryNetwork(const ICNNNetwork& network, + const std::map& config, + QueryNetworkResult& queryResult) const { + if (GetCore() == nullptr) { + THROW_IE_EXCEPTION << "Please, work with MULTI device via InferencEngine::Core object"; + } + + queryResult.rc = StatusCode::OK; + queryResult.supportedLayersMap.clear(); + + auto fullConfig = mergeConfigs(_config, config); + auto priorities = fullConfig.find(MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES); + if (priorities == fullConfig.end()) { + THROW_IE_EXCEPTION << "KEY_MULTI_DEVICE_PRIORITIES key is not set for MULTI device"; + } + + DeviceMap metaDevices = ParseMetaDevices(priorities->second, fullConfig); + std::map queryResults; + + for (auto&& value : metaDevices) { + auto& deviceName = value.first; + auto& metaDevice = value.second; + queryResults[deviceName] = GetCore()->QueryNetwork(network, deviceName, metaDevice.config); + } + + details::CNNNetworkIterator i(&network); + while (i != details::CNNNetworkIterator()) { + CNNLayer::Ptr layer = *i; + bool layerIsInQueryResultsForAllDevices = std::all_of(std::begin(queryResults), std::end(queryResults), + [&](const std::map::value_type& qr) { + return qr.second.supportedLayersMap.end() != qr.second.supportedLayersMap.find(layer->name);}); + if (layerIsInQueryResultsForAllDevices) { + queryResult.supportedLayersMap[layer->name] = GetName(); + } + i++; + } +} +} // namespace MultiDevicePlugin diff --git a/inference-engine/src/multi_device/multi_device.hpp b/inference-engine/src/multi_device/multi_device.hpp new file mode 100644 index 00000000000..29a40218a9f --- /dev/null +++ b/inference-engine/src/multi_device/multi_device.hpp @@ -0,0 +1,176 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +/////////////////////////////////////////////////////////////////////////////////////////////////// +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include "ie_iinfer_request.hpp" +#include "details/ie_exception_conversion.hpp" +#include + +#if (IE_THREAD == IE_THREAD_TBB || IE_THREAD == IE_THREAD_TBB_AUTO) +#include +#endif + +namespace MultiDevicePlugin { + +using DeviceName = std::string; + +struct DeviceInformation { + std::map config; + int numRequestsPerDevices; +}; + +template +using DeviceMap = std::unordered_map; + +class MultiDeviceInferRequest : public InferenceEngine::InferRequestInternal { +public: + using Ptr = std::shared_ptr; + explicit MultiDeviceInferRequest(const InferenceEngine::InputsDataMap& networkInputs, + const InferenceEngine::OutputsDataMap& networkOutputs); + void GetPerformanceCounts(std::map&) const override { + THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str; + } + void InferImpl() override { + THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str; + } + // Multi-Device impl specific: sets the data (blobs from the device-less requets to the specific device request) + void SetBlobsToAnotherRequest(InferenceEngine::InferRequest& req); +}; + +#if ((IE_THREAD == IE_THREAD_TBB) || (IE_THREAD == IE_THREAD_TBB_AUTO)) +template +using ThreadSafeQueue = tbb::concurrent_queue; +#else +template +class ThreadSafeQueue { +public: + void push(T value) { + std::lock_guard lock(_mutex); + _queue.push(std::move(value)); + } + + bool try_pop(T& value) { + std::lock_guard lock(_mutex); + if (!_queue.empty()) { + value = std::move(_queue.front()); + _queue.pop(); + return true; + } else { + return false; + } + } + + bool empty() { + std::lock_guard lock(_mutex); + return _queue.empty(); + } + +protected: + std::queue _queue; + std::mutex _mutex; +}; +#endif + +class MultiDeviceExecutableNetwork : public InferenceEngine::ExecutableNetworkThreadSafeDefault, + public ITaskExecutor { +public: + using Ptr = std::shared_ptr; + struct WorkerInferRequest { + InferenceEngine::InferRequest _inferRequest; + Task _task; + InferenceEngine::StatusCode _status = InferenceEngine::StatusCode::OK; + }; + using NotBusyWorkerRequests = ThreadSafeQueue; + + explicit MultiDeviceExecutableNetwork(const DeviceMap& networksPerDevice, + const DeviceMap& networkDevices, + const std::unordered_map& config, + const bool needPerfCounters = false); + + void SetConfig(const std::map &config, InferenceEngine::ResponseDesc *resp) override; + void GetConfig(const std::string &name, InferenceEngine::Parameter &result, InferenceEngine::ResponseDesc *resp) const override; + void GetMetric(const std::string &name, InferenceEngine::Parameter &result, InferenceEngine::ResponseDesc *resp) const override; + void run(Task inferTask) override; + void CreateInferRequest(InferenceEngine::IInferRequest::Ptr& asyncRequest) override; + InferenceEngine::InferRequestInternal::Ptr CreateInferRequestImpl(InferenceEngine::InputsDataMap networkInputs, + InferenceEngine::OutputsDataMap networkOutputs) override; + ~MultiDeviceExecutableNetwork() override; + + void ScheduleToWorkerInferRequest(); + + static thread_local WorkerInferRequest* _thisWorkerInferRequest; + std::atomic_bool _terminate = {false}; + std::mutex _mutex; + DeviceMap _devicePriorities; + DeviceMap _networksPerDevice; + ThreadSafeQueue _inferPipelineTasks; + DeviceMap _idleWorkerRequests; + DeviceMap> _workerRequests; + std::unordered_map _config; + bool _needPerfCounters = false; +}; + +class MultiDeviceAsyncInferRequest : public InferenceEngine::AsyncInferRequestThreadSafeDefault { +public: + using Ptr = std::shared_ptr; + + explicit MultiDeviceAsyncInferRequest(const MultiDeviceInferRequest::Ptr& inferRequest, + const bool needPerfCounters, + const MultiDeviceExecutableNetwork::Ptr& multiDeviceExecutableNetwork, + const InferenceEngine::ITaskExecutor::Ptr& callbackExecutor); + void Infer_ThreadUnsafe() override; + void GetPerformanceCounts_ThreadUnsafe(std::map &_perfMap) const override; + ~MultiDeviceAsyncInferRequest() override; + +protected: + MultiDeviceExecutableNetwork::Ptr _multiDeviceExecutableNetwork; + MultiDeviceInferRequest::Ptr _inferRequest; + std::map _perfMap; + bool _needPerfCounters = false; + MultiDeviceExecutableNetwork::WorkerInferRequest* _workerInferRequest = nullptr; +}; + +class MultiDeviceInferencePlugin : public InferenceEngine::InferencePluginInternal { +public: + MultiDeviceInferencePlugin(); + ~MultiDeviceInferencePlugin() override = default; + + InferenceEngine::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::ICNNNetwork& network, + const std::map& config) override; + + void SetConfig(const std::map& config) override; + Parameter GetConfig(const std::string& name, + const std::map & options) const override; + void QueryNetwork(const InferenceEngine::ICNNNetwork& network, + const std::map& config, + InferenceEngine::QueryNetworkResult& res) const override; + InferenceEngine::Parameter GetMetric(const std::string& name, + const std::map& options) const override; + + DeviceMap ParseMetaDevices(const std::string & devicesRequestsCfg, + const std::map & config) const; + +protected: + std::map GetSupportedConfig(const std::map& config, + const DeviceName & deviceName) const; +}; + +} // namespace MultiDevicePlugin diff --git a/inference-engine/src/plugin_api/cpp_interfaces/impl/ie_plugin_internal.hpp b/inference-engine/src/plugin_api/cpp_interfaces/impl/ie_plugin_internal.hpp index 907ff109b69..fb0221b025f 100644 --- a/inference-engine/src/plugin_api/cpp_interfaces/impl/ie_plugin_internal.hpp +++ b/inference-engine/src/plugin_api/cpp_interfaces/impl/ie_plugin_internal.hpp @@ -102,7 +102,7 @@ public: _core = core; } - const ICore* GetCore() const noexcept override { + ICore* GetCore() const noexcept override { return _core; } @@ -137,7 +137,6 @@ public: THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str; } - RemoteContext::Ptr GetDefaultContext() override { THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str; } @@ -154,7 +153,7 @@ protected: * @param config string-string map of config parameters relevant only for this load operation * @return Shared pointer to the ExecutableNetwork object */ - virtual ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const ICore* core, const ICNNNetwork& network, + virtual ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const ICNNNetwork& network, const std::map& config) = 0; /** @@ -163,16 +162,13 @@ protected: * @note The function is used in * InferencePluginInternal::LoadNetwork(const ICNNNetwork&, const std::map&, RemoteContext::Ptr) * which performs common steps first and calls this plugin-dependent method implementation after. - * @param core A pointer to ICore interface. * @param network A network object * @param context A remote context * @param config string-string map of config parameters relevant only for this load operation * @return Shared pointer to the ExecutableNetwork object */ - virtual ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const ICore* core, const ICNNNetwork& network, - RemoteContext::Ptr context, + virtual ExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const ICNNNetwork& network, RemoteContext::Ptr context, const std::map& config) { - (void)core; (void)network; (void)context; (void)config; @@ -190,21 +186,21 @@ protected: void cloneAndCreateExecutableNetwork(IExecutableNetwork::Ptr& executableNetwork, const ICNNNetwork& network, const std::map& config, RemoteContext::Ptr context = nullptr) { - InputsDataMap networkInputs; - OutputsDataMap networkOutputs; + InputsDataMap networkInputs, networkInputsCloned; + OutputsDataMap networkOutputs, networkOutputsCloned; network.getInputsInfo(networkInputs); network.getOutputsInfo(networkOutputs); - copyInputOutputInfo(networkInputs, networkOutputs, _networkInputs, _networkOutputs); + copyInputOutputInfo(networkInputs, networkOutputs, networkInputsCloned, networkOutputsCloned); ExecutableNetworkInternal::Ptr impl; if (nullptr == context) { - impl = LoadExeNetworkImpl(GetCore(), network, config); + impl = LoadExeNetworkImpl(network, config); } else { - impl = LoadExeNetworkImpl(GetCore(), network, context, config); + impl = LoadExeNetworkImpl(network, context, config); } - impl->setNetworkInputs(_networkInputs); - impl->setNetworkOutputs(_networkOutputs); + impl->setNetworkInputs(networkInputsCloned); + impl->setNetworkOutputs(networkOutputsCloned); impl->SetPointerToPluginInternal(shared_from_this()); executableNetwork.reset(new ExecutableNetworkBase(impl), [](details::IRelease* p) { @@ -243,8 +239,6 @@ protected: } std::string _pluginName; //!< A device name that plugins enables - InferenceEngine::InputsDataMap _networkInputs; //!< Holds information about network inputs info - InferenceEngine::OutputsDataMap _networkOutputs; //!< Holds information about network outputs data std::map _config; //!< A map config keys -> values ICore* _core = nullptr; //!< A pointer to ICore interface }; diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp index bf80615f0c9..b82758be4a4 100644 --- a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp +++ b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp @@ -217,7 +217,7 @@ public: * @brief Gets reference to ICore interface * @return Reference to ICore interface */ - virtual const ICore* GetCore() const noexcept = 0; + virtual ICore* GetCore() const noexcept = 0; /** * @brief Queries a plugin about support layers in network diff --git a/inference-engine/src/plugin_api/ie_icore.hpp b/inference-engine/src/plugin_api/ie_icore.hpp index 40dcf887f49..d773a3e42d5 100644 --- a/inference-engine/src/plugin_api/ie_icore.hpp +++ b/inference-engine/src/plugin_api/ie_icore.hpp @@ -32,6 +32,7 @@ public: virtual std::shared_ptr GetTaskExecutor() const = 0; /** + * @deprecated Use ICore::GetMetric, ICore::LoadNetwork, ICore::QueryNetwork instead * @brief Returns reference to plugin by a device name * @param deviceName - a name of device * @return Reference to plugin @@ -55,6 +56,55 @@ public: */ virtual CNNNetwork ReadNetwork(const std::string& modelPath, const std::string& binPath) const = 0; + /** + * @brief Creates an executable network from a network object. + * + * Users can create as many networks as they need and use + * them simultaneously (up to the limitation of the hardware resources) + * + * @param network CNNNetwork object acquired from Core::ReadNetwork + * @param deviceName Name of device to load network to + * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load + * operation + * @return An executable network reference + */ + virtual ExecutableNetwork LoadNetwork(const CNNNetwork& network, const std::string& deviceName, + const std::map& config = {}) = 0; + + /** + * @brief Creates an executable network from a previously exported network + * @param deviceName Name of device load executable network on + * @param networkModel network model stream + * @param config Optional map of pairs: (config parameter name, config parameter value) relevant only for this load + * operation* + * @return An executable network reference + */ + virtual ExecutableNetwork ImportNetwork(std::istream& networkModel, const std::string& deviceName = {}, + const std::map& config = {}) = 0; + + /** + * @brief Query device if it supports specified network with specified configuration + * + * @param deviceName A name of a device to query + * @param network Network object to query + * @param config Optional map of pairs: (config parameter name, config parameter value) + * @return An object containing a map of pairs a layer name -> a device name supporting this layer. + */ + virtual QueryNetworkResult QueryNetwork(const ICNNNetwork& network, const std::string& deviceName, + const std::map& config) const = 0; + + /** + * @brief Gets general runtime metric for dedicated hardware. + * + * The method is needed to request common device properties + * which are executable network agnostic. It can be device name, temperature, other devices-specific values. + * + * @param deviceName - A name of a device to get a metric value. + * @param name - metric name to request. + * @return Metric value corresponding to metric key. + */ + virtual Parameter GetMetric(const std::string& deviceName, const std::string& name) const = 0; + /** * @brief Default virtual destructor */ diff --git a/inference-engine/src/plugin_api/ie_profiling.hpp b/inference-engine/src/plugin_api/ie_profiling.hpp index 74ee2f2b811..aee0bbe732f 100644 --- a/inference-engine/src/plugin_api/ie_profiling.hpp +++ b/inference-engine/src/plugin_api/ie_profiling.hpp @@ -138,7 +138,7 @@ inline static void annotateEnd(IttTaskHandles& h, IttBlock&) { #define IE_STR(x) IE_STR_(x) #define IE_STR_(x) #x -class ProfilingTask; +struct ProfilingTask; struct IttStatic {}; @@ -179,8 +179,8 @@ struct ProfilingTask { } private: -friend void annotateBegin(IttStatic&, IttProfilingTask& t); -friend void annotateEnd(IttStatic&, IttProfilingTask& t); + friend void annotateBegin(IttStatic&, IttProfilingTask& t); + friend void annotateEnd(IttStatic&, IttProfilingTask& t); std::string name; #ifdef ENABLE_PROFILING_ITT diff --git a/inference-engine/src/plugin_api/threading/ie_cpu_streams_executor.hpp b/inference-engine/src/plugin_api/threading/ie_cpu_streams_executor.hpp index 6f1525bbb5a..f5e53cc6917 100644 --- a/inference-engine/src/plugin_api/threading/ie_cpu_streams_executor.hpp +++ b/inference-engine/src/plugin_api/threading/ie_cpu_streams_executor.hpp @@ -44,6 +44,8 @@ public: void run(Task task) override; + void Execute(Task task) override; + int GetStreamId() override; int GetNumaNodeId() override; diff --git a/inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp b/inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp index d53c6e48ccc..449ab526d11 100644 --- a/inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp +++ b/inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp @@ -121,6 +121,12 @@ public: * @return `ID` of current NUMA Node, or throws exceptions if called not from stream thread */ virtual int GetNumaNodeId() = 0; + + /** + * @brief Execute the task in the current thread using streams executor configuration and constraints + * @param task A task to start + */ + virtual void Execute(Task task) = 0; }; diff --git a/inference-engine/src/preprocessing/CMakeLists.txt b/inference-engine/src/preprocessing/CMakeLists.txt index 5943b295a4b..4c78228e3c5 100644 --- a/inference-engine/src/preprocessing/CMakeLists.txt +++ b/inference-engine/src/preprocessing/CMakeLists.txt @@ -17,15 +17,15 @@ file(GLOB LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp) # function(ie_avx512_core_optimization_flags flags) if(WIN32) - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "/QxCORE-AVX512" PARENT_SCOPE) - elseif(CMAKE_CXX_COMPILER_ID MATCHES MSVC) + elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(${flags} "/arch:AVX512" PARENT_SCOPE) else() message(WARNING "Unsupported CXX compiler ${CMAKE_CXX_COMPILER_ID}") endif() else() - if(CMAKE_CXX_COMPILER_ID STREQUAL Intel) + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") set(${flags} "-xCORE-AVX512" PARENT_SCOPE) else() set(${flags} "-mavx512f -mavx512bw -mavx512dq -mfma" PARENT_SCOPE) diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/common_optimizations_tbl.hpp b/inference-engine/src/transformations/include/transformations/common_optimizations/common_optimizations_tbl.hpp index 53103c4f6a2..c0a53fcf5f7 100644 --- a/inference-engine/src/transformations/include/transformations/common_optimizations/common_optimizations_tbl.hpp +++ b/inference-engine/src/transformations/include/transformations/common_optimizations/common_optimizations_tbl.hpp @@ -14,4 +14,14 @@ // #include // #undef NGRAPH_PASS -NGRAPH_PASS(NopElimination, ::ngraph::pass) +// This pass must be called first in pipeline +NGRAPH_PASS(InitNodeInfo, ::ngraph::pass) +NGRAPH_PASS(ConvertPriorBox, ::ngraph::pass) // WA: ConvertPriorBox must be executed before CF +NGRAPH_PASS(ConstantFolding, ::ngraph::pass) +NGRAPH_PASS(RemoveFilteringBoxesBySize, ::ngraph::pass) // Resolves dynamism (replaces NonZero), CF needed +NGRAPH_PASS(ConstantFolding, ::ngraph::pass) +NGRAPH_PASS(StridedSliceOptimization, ::ngraph::pass) // depends on CF +NGRAPH_PASS(NopElimination, ::ngraph::pass) // may introduce fake dynamism +NGRAPH_PASS(AlgebraicSimplification, ::ngraph::pass) // may introduce fake dynamism +NGRAPH_PASS(ConstantFolding, ::ngraph::pass) +NGRAPH_PASS(ConvertScatterElementsToScatter, ::ngraph::pass) // partially depends on CF diff --git a/inference-engine/src/transformations/include/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy_tbl.hpp b/inference-engine/src/transformations/include/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy_tbl.hpp index 8fc8839afbc..49f115d90f0 100644 --- a/inference-engine/src/transformations/include/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy_tbl.hpp +++ b/inference-engine/src/transformations/include/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy_tbl.hpp @@ -14,8 +14,6 @@ // #include // #undef NGRAPH_PASS -NGRAPH_PASS(InitNodeInfo, ::ngraph::pass) -NGRAPH_PASS(ConvertPriorBox, ::ngraph::pass) NGRAPH_PASS(ConstantFolding, ::ngraph::pass) NGRAPH_PASS(ConvertReduceToPooling, ::ngraph::pass) NGRAPH_PASS(ConvertMod, ::ngraph::pass) diff --git a/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_broadcast3.hpp b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_broadcast3.hpp new file mode 100644 index 00000000000..45ca68c78ac --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_broadcast3.hpp @@ -0,0 +1,31 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include +#include "transformations/utils/pass_param.hpp" + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(ConvertBroadcast3); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::ConvertBroadcast3: public ngraph::pass::GraphRewrite, public ngraph::pass::PassParam { +public: + ConvertBroadcast3() : GraphRewrite() { + convert_broadcast3(); + } + +private: + void convert_broadcast3(); +}; diff --git a/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_nms3.hpp b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_nms3.hpp new file mode 100644 index 00000000000..22df5fcaf28 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_nms3.hpp @@ -0,0 +1,31 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include +#include "transformations/utils/pass_param.hpp" + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(ConvertNMS3); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::ConvertNMS3: public ngraph::pass::GraphRewrite, public ngraph::pass::PassParam { +public: + ConvertNMS3() : GraphRewrite() { + convert_nms3(); + } + +private: + void convert_nms3(); +}; diff --git a/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.hpp b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.hpp new file mode 100644 index 00000000000..b7c6b983167 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.hpp @@ -0,0 +1,26 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include +#include +#include "transformations/utils/pass_param.hpp" + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(ConvertOpSet3ToOpSet2); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::ConvertOpSet3ToOpSet2: public ngraph::pass::FunctionPass, public ngraph::pass::PassParam { +public: + explicit ConvertOpSet3ToOpSet2(const PassParam::param_callback & callback = PassParam::getDefaultCallback()) + : FunctionPass(), PassParam(callback) {} + + bool run_on_function(std::shared_ptr f) override; +}; diff --git a/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2_tbl.hpp b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2_tbl.hpp new file mode 100644 index 00000000000..796642e3488 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2_tbl.hpp @@ -0,0 +1,21 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#ifndef NGRAPH_PASS +#warning "NGRAPH_PASS is not defined" +#define NGRAPH_PASS(A, B) +#endif + +// To register new pass you need to define NGRAPH_PASS +// Usage example: +// ngraph::pass:Manager pm; +// #define NGRAPH_PASS(NAME, NAMESPACE) pm.register_pass(); +// #include +// #undef NGRAPH_PASS + +NGRAPH_PASS(ConvertBroadcast3, ::ngraph::pass) +NGRAPH_PASS(ConvertNMS3, ::ngraph::pass) +NGRAPH_PASS(ConvertShapeOf3, ::ngraph::pass) +NGRAPH_PASS(ConvertTopK3, ::ngraph::pass) + diff --git a/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_shapeof3.hpp b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_shapeof3.hpp new file mode 100644 index 00000000000..781c7103d46 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_shapeof3.hpp @@ -0,0 +1,30 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(ConvertShapeOf3); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::ConvertShapeOf3: public ngraph::pass::GraphRewrite { +public: + ConvertShapeOf3() : GraphRewrite() { + convert_shapeof3(); + } + +private: + void convert_shapeof3(); +}; diff --git a/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_topk3.hpp b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_topk3.hpp new file mode 100644 index 00000000000..0fff6edd097 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_opset3_to_opset2/convert_topk3.hpp @@ -0,0 +1,31 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include +#include "transformations/utils/pass_param.hpp" + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(ConvertTopK3); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::ConvertTopK3: public ngraph::pass::GraphRewrite, public ngraph::pass::PassParam { +public: + ConvertTopK3() : GraphRewrite() { + convert_topk3(); + } + +private: + void convert_topk3(); +}; diff --git a/inference-engine/src/transformations/include/transformations/convert_scatter_elements_to_scatter.hpp b/inference-engine/src/transformations/include/transformations/convert_scatter_elements_to_scatter.hpp new file mode 100644 index 00000000000..f89d3c490b6 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/convert_scatter_elements_to_scatter.hpp @@ -0,0 +1,30 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include + +namespace ngraph { +namespace pass { + +class INFERENCE_ENGINE_API_CLASS(ConvertScatterElementsToScatter); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::ConvertScatterElementsToScatter: public ngraph::pass::GraphRewrite { +public: + ConvertScatterElementsToScatter() : GraphRewrite() { + convert_scatter_elements_to_scatter(); + } + +private: + void convert_scatter_elements_to_scatter(); +}; diff --git a/inference-engine/src/transformations/include/transformations/optimize_strided_slice.hpp b/inference-engine/src/transformations/include/transformations/optimize_strided_slice.hpp new file mode 100644 index 00000000000..13741adeaec --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/optimize_strided_slice.hpp @@ -0,0 +1,69 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include +#include +#include + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(StridedSliceOptimization); + class INFERENCE_ENGINE_API_CLASS(UselessStridedSliceEraser); + class INFERENCE_ENGINE_API_CLASS(SharedStridedSliceEraser); + class INFERENCE_ENGINE_API_CLASS(GroupedStridedSliceOptimizer); + } // namespace pass +} // namespace ngraph + + +/* + * Description: + * UselessStridedSliceEraser transformation removes StridedSlice operations + * with equal input and output shapes. + */ + +class ngraph::pass::UselessStridedSliceEraser: public ngraph::pass::FunctionPass { +public: + bool run_on_function(std::shared_ptr f) override; +}; + +/* + * Description: + * SharedStridedSliceEraser replaces group of StridedSlice operations with first + * StridedSlice in this group. All SrtideSluces in this group must be equal and + * consume the same output port. + */ + +class ngraph::pass::SharedStridedSliceEraser: public ngraph::pass::FunctionPass { +public: + bool run_on_function(std::shared_ptr f) override; +}; + +/* + * Description: + * GroupedStridedSliceOptimizer replaces group of StridedSlice operations with VariadicSplit + * All StridedSlice operations must slice data with the same axis and stride = 1. + */ + +class ngraph::pass::GroupedStridedSliceOptimizer: public ngraph::pass::FunctionPass { +public: + bool run_on_function(std::shared_ptr f) override; +}; + +class ngraph::pass::StridedSliceOptimization: public ngraph::pass::FunctionPass { +public: + bool run_on_function(std::shared_ptr f) override { + bool rewritten = UselessStridedSliceEraser().run_on_function(f); + rewritten |= SharedStridedSliceEraser().run_on_function(f); + rewritten |= GroupedStridedSliceOptimizer().run_on_function(f); + return rewritten; + } +}; diff --git a/inference-engine/src/transformations/include/transformations/remove_filtering_boxes_by_size.hpp b/inference-engine/src/transformations/include/transformations/remove_filtering_boxes_by_size.hpp new file mode 100644 index 00000000000..316c7995307 --- /dev/null +++ b/inference-engine/src/transformations/include/transformations/remove_filtering_boxes_by_size.hpp @@ -0,0 +1,30 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include + +#include + +namespace ngraph { +namespace pass { + + class INFERENCE_ENGINE_API_CLASS(RemoveFilteringBoxesBySize); + +} // namespace pass +} // namespace ngraph + +class ngraph::pass::RemoveFilteringBoxesBySize: public ngraph::pass::GraphRewrite { +public: + RemoveFilteringBoxesBySize() : GraphRewrite() { + remove_filtering_boxes_by_size(); + } + +private: + void remove_filtering_boxes_by_size(); +}; diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp index f791470a7bb..467f0766d59 100644 --- a/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp +++ b/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp @@ -2,12 +2,20 @@ // SPDX-License-Identifier: Apache-2.0 // -#include "transformations/common_optimizations/common_optimizations.hpp" - #include +#include "transformations/common_optimizations/common_optimizations.hpp" +#include "transformations/convert_opset1_to_legacy/convert_prior_to_ie_prior.hpp" +#include "transformations/optimize_strided_slice.hpp" +#include "transformations/convert_scatter_elements_to_scatter.hpp" +#include "transformations/remove_filtering_boxes_by_size.hpp" +#include "transformations/init_node_info.hpp" + #include #include +#include +#include + bool ngraph::pass::CommonOptimizations::run_on_function(std::shared_ptr f) { ngraph::pass::Manager CommonOptimizations; diff --git a/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp b/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp index 09857f080b6..28dbb08c70e 100644 --- a/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp +++ b/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.cpp @@ -27,7 +27,6 @@ #include #include #include -#include #include #include #include @@ -47,7 +46,6 @@ #include #include #include -#include #include #include diff --git a/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_prior_to_ie_prior.cpp b/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_prior_to_ie_prior.cpp index 01954ec632c..d0bbcc1307a 100644 --- a/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_prior_to_ie_prior.cpp +++ b/inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/convert_prior_to_ie_prior.cpp @@ -7,6 +7,7 @@ #include #include +#include #include #include @@ -108,12 +109,31 @@ void ngraph::pass::ConvertPriorBox::convert_prior_box() { input_2 = convert2->input_value(0).get_node_shared_ptr(); } - auto shape_of1 = std::dynamic_pointer_cast (input_1); - auto shape_of2 = std::dynamic_pointer_cast (input_2); + // the input can be either ShapeOf-1 or ShapeOf-3 + std::shared_ptr shape_of1 = std::dynamic_pointer_cast (input_1); + std::shared_ptr shape_of2 = std::dynamic_pointer_cast (input_2); + if (!shape_of1 || !shape_of2) { + shape_of1 = std::dynamic_pointer_cast(input_1); + shape_of2 = std::dynamic_pointer_cast(input_2); + } if (!shape_of1 || !shape_of2) { return false; } + // keep this code for a while if will decide to run this transformation again in the opset1->legacy + // the input can be either ShapeOf or Convert(ShapeOf) +// if (!shape_of1 || !shape_of2) { +// auto shapeof1_convert = std::dynamic_pointer_cast (input_1); +// auto shapeof2_convert = std::dynamic_pointer_cast (input_2); +// if (!shapeof1_convert || !shapeof2_convert) +// return false; +// shape_of1 = std::dynamic_pointer_cast(shapeof1_convert->input_value(0).get_node_shared_ptr()); +// shape_of2 = std::dynamic_pointer_cast(shapeof2_convert->input_value(0).get_node_shared_ptr()); +// if (!shape_of1 || !shape_of2) +// return false; +// ops_to_replace.push_back(shapeof1_convert); +// ops_to_replace.push_back(shapeof2_convert); +// } ops_to_replace.push_back(shape_of1); ops_to_replace.push_back(shape_of2); @@ -228,12 +248,31 @@ void ngraph::pass::ConvertPriorBox::convert_prior_box_clustered() { input_2 = convert2->input_value(0).get_node_shared_ptr(); } - auto shape_of1 = std::dynamic_pointer_cast (input_1); - auto shape_of2 = std::dynamic_pointer_cast (input_2); + // the input can be either ShapeOf-1 or ShapeOf-3 + std::shared_ptr shape_of1 = std::dynamic_pointer_cast (input_1); + std::shared_ptr shape_of2 = std::dynamic_pointer_cast (input_2); + if (!shape_of1 || !shape_of2) { + shape_of1 = std::dynamic_pointer_cast(input_1); + shape_of2 = std::dynamic_pointer_cast(input_2); + } if (!shape_of1 || !shape_of2) { return false; } + // keep this code for a while if will decide to run this transformation again in the opset1->legacy + // the input can be either ShapeOf or Convert(ShapeOf) +// if (!shape_of1 || !shape_of2) { +// auto shapeof1_convert = std::dynamic_pointer_cast (input_1); +// auto shapeof2_convert = std::dynamic_pointer_cast (input_2); +// if (!shapeof1_convert || !shapeof2_convert) +// return false; +// shape_of1 = std::dynamic_pointer_cast(shapeof1_convert->input_value(0).get_node_shared_ptr()); +// shape_of2 = std::dynamic_pointer_cast(shapeof2_convert->input_value(0).get_node_shared_ptr()); +// if (!shape_of1 || !shape_of2) +// return false; +// ops_to_replace.push_back(shapeof1_convert); +// ops_to_replace.push_back(shapeof2_convert); +// } ops_to_replace.push_back(shape_of1); ops_to_replace.push_back(shape_of2); diff --git a/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_broadcast3.cpp b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_broadcast3.cpp new file mode 100644 index 00000000000..7e78ba6901a --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_broadcast3.cpp @@ -0,0 +1,61 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/convert_opset3_to_opset2/convert_broadcast3.hpp" + +#include +#include + +#include + +#include +#include + +void ngraph::pass::ConvertBroadcast3::convert_broadcast3() { + auto weights = std::make_shared(element::f32, Shape {1}); + auto shp = std::make_shared(element::i64, Shape {1}); + auto axes = std::make_shared(element::i64, Shape {1}); + auto broadcast = std::make_shared(weights, shp, axes); + + auto broadcast_no_axes = std::make_shared(weights, shp); + + ngraph::graph_rewrite_callback callback = [](pattern::Matcher& m) { + auto broadcast = std::dynamic_pointer_cast(m.get_match_root()); + if (!broadcast) { + return false; + } + + auto input = broadcast->input_value(0); + auto target_shape = broadcast->input_value(1); + + auto last_node = input.get_node_shared_ptr(); + auto broadcast_type = broadcast->get_broadcast_spec(); + + if (broadcast_type == op::BroadcastType::NUMPY) { + last_node = std::make_shared(input, target_shape, op::AutoBroadcastType::NUMPY); + ngraph::copy_runtime_info(broadcast, last_node); + } else if (broadcast_type == op::BroadcastType::PDPD) { + last_node = std::make_shared(input, target_shape, op::AutoBroadcastType::PDPD); + ngraph::copy_runtime_info(broadcast, last_node); + } else if (broadcast_type == op::BroadcastType::NONE) { + last_node = std::make_shared(input, target_shape, broadcast->input_value(2), op::AutoBroadcastType::NONE); + ngraph::copy_runtime_info(broadcast, last_node); + } else if (broadcast_type == op::BroadcastType::BIDIRECTIONAL) { + auto constant_one = std::make_shared(input.get_element_type(), Shape({1}), std::vector{1}); + auto broadcast_ones = std::make_shared(constant_one, target_shape, op::AutoBroadcastType::NUMPY); + last_node = std::make_shared(input, broadcast_ones); + ngraph::copy_runtime_info(broadcast, {last_node, broadcast_ones, constant_one}); + } + + last_node->set_friendly_name(broadcast->get_friendly_name()); + + ngraph::replace_node(m.get_match_root(), last_node); + return true; + }; + + auto m = std::make_shared(broadcast, "ConvertBroadcast3"); + auto m_no_axes = std::make_shared(broadcast_no_axes, "ConvertBroadcast3NoAxes"); + this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE); + this->add_matcher(m_no_axes, callback, PassProperty::CHANGE_DYNAMIC_STATE); +} diff --git a/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_nms3.cpp b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_nms3.cpp new file mode 100644 index 00000000000..eebef00df32 --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_nms3.cpp @@ -0,0 +1,55 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/convert_opset3_to_opset2/convert_nms3.hpp" + +#include +#include + +#include +#include +#include +#include + +void ngraph::pass::ConvertNMS3::convert_nms3() { + auto boxes = std::make_shared(element::f32, Shape{1, 1000, 4}); + auto scores = std::make_shared(element::f32, Shape{1, 1, 1000}); + auto max_output_boxes_per_class = ngraph::opset3::Constant::create(element::i64, Shape{}, {10}); + auto iou_threshold = ngraph::opset3::Constant::create(element::f32, Shape{}, {0.75}); + auto score_threshold = ngraph::opset3::Constant::create(element::f32, Shape{}, {0.7}); + auto nms = std::make_shared(boxes, scores, max_output_boxes_per_class, + iou_threshold, score_threshold); + + ngraph::graph_rewrite_callback callback = [](pattern::Matcher &m) { + auto nms = std::dynamic_pointer_cast(m.get_match_root()); + if (!nms) { + return false; + } + + Output last; + ngraph::NodeVector new_ops; + + auto new_nms = std::make_shared(nms->input_value(0), nms->input_value(1), + nms->input_value(2), nms->input_value(3), nms->input_value(4), + static_cast(nms->get_box_encoding()), + nms->get_sort_result_descending()); + + new_ops.push_back(new_nms); + // if the output is the i32 then it matches behavior of the v1::NonMaxSuppression otherwise need to insert Convert + if (nms->get_output_type() == element::i32) { + last = new_nms; + } else { + last = std::make_shared(new_nms, nms->get_output_type()); + new_ops.push_back(last.get_node_shared_ptr()); + } + + last.get_node_shared_ptr()->set_friendly_name(nms->get_friendly_name()); + ngraph::copy_runtime_info(nms, new_ops); + ngraph::replace_node(nms, last.get_node_shared_ptr()); + return true; + }; + + auto m = std::make_shared(nms, "ConvertNMS3"); + this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE); +} diff --git a/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.cpp b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.cpp new file mode 100644 index 00000000000..0e6af43fc44 --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.cpp @@ -0,0 +1,32 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/convert_opset3_to_opset2/convert_opset3_to_opset2.hpp" + +#include "transformations/convert_opset3_to_opset2/convert_broadcast3.hpp" +#include "transformations/convert_opset3_to_opset2/convert_nms3.hpp" +#include "transformations/convert_opset3_to_opset2/convert_shapeof3.hpp" +#include "transformations/convert_opset3_to_opset2/convert_topk3.hpp" + +#include +#include + +#include + +bool ngraph::pass::ConvertOpSet3ToOpSet2::run_on_function(std::shared_ptr f) { + ngraph::pass::Manager OpSet3ToOpSet2; + std::vector > transforms; + +#define NGRAPH_PASS(NAME, NAMESPACE) transforms.push_back(OpSet3ToOpSet2.register_pass()); +#include +#undef NGRAPH_PASS + + for (auto & t : transforms) { + if (auto t_param = std::dynamic_pointer_cast(t)) { + t_param->setCallback(transformation_callback); + } + } + OpSet3ToOpSet2.run_passes(f); + return true; +} diff --git a/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_shapeof3.cpp b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_shapeof3.cpp new file mode 100644 index 00000000000..609ac2002ac --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_shapeof3.cpp @@ -0,0 +1,45 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/convert_opset3_to_opset2/convert_shapeof3.hpp" + +#include +#include + +#include +#include +#include + +void ngraph::pass::ConvertShapeOf3::convert_shapeof3() { + auto input = std::make_shared(element::i64, Shape{1, 1, 1, 1}); + auto shapeof = std::make_shared(input); + + ngraph::graph_rewrite_callback callback = [](pattern::Matcher& m) { + auto shapeof = std::dynamic_pointer_cast (m.get_match_root()); + if (!shapeof) { + return false; + } + + Output last; + ngraph::NodeVector new_ops; + + auto new_shapeof = std::make_shared(shapeof->input_value(0)); + new_ops.push_back(new_shapeof); + // if the output is the i64 then it matches behavior of the v1::ShapeOf otherwise need to insert Convert + if (shapeof->get_output_type() == element::i64) { + last = new_shapeof; + } else { + last = std::make_shared(new_shapeof, shapeof->get_output_type()); + new_ops.push_back(last.get_node_shared_ptr()); + } + + last.get_node_shared_ptr()->set_friendly_name(shapeof->get_friendly_name()); + ngraph::copy_runtime_info(shapeof, new_ops); + ngraph::replace_node(shapeof, last.get_node_shared_ptr()); + return true; + }; + + auto m = std::make_shared(shapeof, "ConvertShapeOf3"); + this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE); +} \ No newline at end of file diff --git a/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_topk3.cpp b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_topk3.cpp new file mode 100644 index 00000000000..08197aadcdb --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/convert_opset3_to_opset2/convert_topk3.cpp @@ -0,0 +1,56 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/convert_opset3_to_opset2/convert_topk3.hpp" + +#include +#include + +#include +#include +#include +#include + +void ngraph::pass::ConvertTopK3::convert_topk3() { + auto input = std::make_shared(element::i64, Shape{1, 1, 1, 1}); + auto k = ngraph::opset3::Constant::create(element::i64, Shape{}, {10}); + auto topk = std::make_shared(input, k, 0, "min", "value", element::i64); + // this is a temporary workaround to avoid bug that TopK-3 does not have clone_with_new_inputs so the TopK-3 clone + // generates TopK-1 operation + auto topk_v1 = std::make_shared(input, k, 0, "min", "value", element::i64); + + ngraph::graph_rewrite_callback callback = [](pattern::Matcher& m) { + std::shared_ptr topk = std::dynamic_pointer_cast (m.get_match_root()); + if (!topk) { + topk = std::dynamic_pointer_cast (m.get_match_root()); + } + if (!topk) { + return false; + } + Output last; + ngraph::NodeVector new_ops; + + auto new_topk = std::make_shared(topk->input_value(0), topk->input_value(1), + topk->get_axis(), topk->get_mode(), topk->get_sort_type(), element::i32); + new_ops.push_back(new_topk); + // if the output is the i32 then it matches behavior of the v1::TopK otherwise need to insert Convert + if (topk->get_index_element_type() == element::i32) { + last = new_topk->output(1); + } else { + last = std::make_shared(new_topk->output(1), topk->get_index_element_type()); + new_ops.push_back(last.get_node_shared_ptr()); + } + + new_topk->set_friendly_name(topk->get_friendly_name()); + ngraph::copy_runtime_info(topk, new_ops); + topk->output(0).replace(new_topk->output(0)); + topk->output(1).replace(last); + return true; + }; + + auto m = std::make_shared(topk, "ConvertTopK3"); + this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE); + auto m2 = std::make_shared(topk_v1, "ConvertTopK3"); + this->add_matcher(m2, callback, PassProperty::CHANGE_DYNAMIC_STATE); +} diff --git a/inference-engine/src/transformations/src/transformations/convert_scatter_elements_to_scatter.cpp b/inference-engine/src/transformations/src/transformations/convert_scatter_elements_to_scatter.cpp new file mode 100644 index 00000000000..42154e4868e --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/convert_scatter_elements_to_scatter.cpp @@ -0,0 +1,211 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/convert_scatter_elements_to_scatter.hpp" + +#include +#include +#include + +#include +#include +#include + +void ngraph::pass::ConvertScatterElementsToScatter::convert_scatter_elements_to_scatter() { + auto data = std::make_shared(element::f32, Shape{1}); + auto indices = std::make_shared(element::i64, Shape{1}); + auto updates = std::make_shared(element::f32, Shape{1}); + auto axis = ngraph::opset3::Constant::create(element::i64, {1}, {0}); + + auto broadcast_shape = std::make_shared(element::i64, Shape{1}); + auto broadcast = std::make_shared(indices, broadcast_shape); + + auto scatter = std::make_shared(data, broadcast, updates, axis); + + ngraph::graph_rewrite_callback callback = [](pattern::Matcher& m) { + auto scatter = m.get_match_root(); + auto broadcast = scatter->input_value(1).get_node_shared_ptr(); + auto axis_const = std::dynamic_pointer_cast(scatter->input_value(3).get_node_shared_ptr()); + + if (!axis_const) { + return false; + } + + auto indices_input = broadcast->input_value(0); + + const auto data_pshape = scatter->input(0).get_partial_shape(); + const auto indices_pshape = indices_input.get_partial_shape(); + const auto updates_pshape = scatter->input(2).get_partial_shape(); + + // Check that ScatterElementsUpdate and Broadcast inputs has static shapes + if (data_pshape.rank().is_dynamic() || indices_pshape.rank().is_dynamic() || updates_pshape.rank().is_dynamic()) { + return false; + } + + const uint64_t data_rank = data_pshape.rank().get_length(); + const uint64_t updates_rank = updates_pshape.rank().get_length(); + const uint64_t indices_rank = indices_pshape.rank().get_length(); + + + // Check that axis Constant has {} or {1} shape + if (shape_size(axis_const->get_shape()) > 1) { + return false; + } + + const size_t axis = ngraph::normalize_axes(scatter->get_friendly_name(), + axis_const->cast_vector(), + data_pshape.rank())[0]; + + struct Range { + uint64_t l, r; + Range(const uint64_t & l, const uint64_t & r) : l(l), r(r) { + if (l > r) throw ngraph_error("Range values are inconsistent"); + } + + uint64_t size() const { + return r - l; + } + + bool operator!= (const Range & rhs) const { + return (r - l != rhs.r - rhs.l); + } + + static + bool is_valid(const int64_t & l, const int64_t & r) { + return (l >= 0 && l <= r); + } + + static + bool is_empty(const uint64_t & l, const uint64_t & r) { + return l == r; + } + }; + + auto compare_shapes_ranges = [](const PartialShape & lhsShape, const PartialShape & rhsShape, const Range & lhsRange, const Range & rhsRange) -> bool { + // Check that ranges are equal and suits to Shapes sizes + if (lhsRange != rhsRange || + lhsRange.r > lhsShape.rank().get_length() || + rhsRange.r > rhsShape.rank().get_length()) { + return false; + } + + // Check that Shape values in ranges are equal + for (size_t lhsIndex = lhsRange.l, rhsIndex = rhsRange.l; lhsIndex < lhsRange.r; ++lhsIndex, ++rhsIndex) { + if (lhsShape[lhsIndex].is_dynamic() || rhsShape[rhsIndex].is_dynamic() || + lhsShape[lhsIndex] != rhsShape[rhsIndex]) { + return false; + } + } + + return true; + }; + + auto product = [](const Shape & shape, const Range & range) -> uint64_t { + uint64_t prod(1); + for (size_t dim = range.l; dim < range.r; ++dim) { + prod *= shape[dim]; + } + return prod; + }; + + /* To transform ScatterElementsUpdate to ScatterUpdate input shapes must match this rules: + * + * data_shape[d_0, d_1, ... , d_n] + * + * indices_shape[i_0, i_1, ... , i_n] + * + * updates_shape[d_0, d_1, i_0(axis), i_1, ... , i_n, d_axis + 1, ... , d_n] + * + * EXAMPLE: + * In this example the input shapes are suits the rules above and ScatterElementsUpdate can be replaced with ScatterUpdate + * + * axis = 1 | (axis) + * \/ + * + * data_shape [1000, 256, 10, 15] + * + * index_shape [ 125, 2 ] + * + * updates_shape [1000, 125, 2, 10, 15] + * + */ + + // data_shape and updates_shape dims must be equal up to axis dimension + if (!compare_shapes_ranges(data_pshape, updates_pshape, {0, axis}, {0, axis})) { + return false; + } + + // data_shape dims starting right after axis dim must match last updates_shape dimensions + if (!Range::is_valid(updates_rank - (data_rank - (axis + 1)), updates_rank)) { + return false; + } + + const Range updates_last{updates_rank - (data_rank - (axis + 1)), updates_rank}; + if (!compare_shapes_ranges(data_pshape, updates_pshape, {axis + 1, data_rank}, updates_last)) { + return false; + } + + // indices_shape dims product must match updates_shape dims starting from axis dimension + if (!Range::is_valid(axis, updates_last.l) && !Range::is_empty(axis, updates_last.l)) { + return false; + } + + NodeVector new_ops; + + // In case of static shapes we check that indices dims product match with updates dims + if (updates_pshape.is_static() && indices_pshape.is_static()) { + const auto updated_range_prod = product(updates_pshape.get_shape(), {axis, updates_last.l}); + const auto indices_range_prod = product(indices_pshape.get_shape(), {0, indices_rank}); + + if (updated_range_prod != indices_range_prod) { + return false; + } + + // if indices_shape do not match updates_shape dims{axis, updates_last.l} + // we reshape indices to updates_shape + const auto updates_shape = updates_pshape.get_shape(); + const auto indices_shape = indices_pshape.get_shape(); + Shape indices_new_shape(updates_shape.begin() + axis, updates_shape.begin() + updates_last.l); + if (indices_shape != indices_new_shape) { + indices_input = std::make_shared(indices_input, + opset3::Constant::create(element::i64, Shape{indices_new_shape.size()}, indices_new_shape), false); + new_ops.push_back(indices_input.get_node_shared_ptr()); + } + } else { + // Tight constrain for dynamic case: + // 1. indices_pshape 1...N dimensions must be equal to 1 + // 2. updates_pshape axis interval size = 1 + + for (size_t dim = 1; dim < indices_rank; ++dim) { + if (indices_pshape[dim] != 1) return false; + } + + if (Range(axis, updates_last.l).size() != 1) { + return false; + } + + // Squeeze 1 dims for indices input + if (indices_rank > 1) { + std::vector squeeze_axes(indices_rank - 1ul); + std::iota(squeeze_axes.begin(), squeeze_axes.end(), 1); + indices_input = std::make_shared(indices_input, + opset3::Constant::create(element::i64, Shape{squeeze_axes.size()}, squeeze_axes)); + new_ops.push_back(indices_input.get_node_shared_ptr()); + } + } + + auto scatter_update = std::make_shared(scatter->input_value(0), + indices_input, + scatter->input_value(2), + scatter->input_value(3)); + new_ops.push_back(scatter_update); + scatter_update->set_friendly_name(scatter->get_friendly_name()); + ngraph::copy_runtime_info({scatter, broadcast}, {new_ops}); + ngraph::replace_node(scatter, scatter_update); + return true; + }; + + auto m = std::make_shared(scatter, "ConvertScatterElementsToScatter"); + this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE); +} \ No newline at end of file diff --git a/inference-engine/src/transformations/src/transformations/optimize_strided_slice.cpp b/inference-engine/src/transformations/src/transformations/optimize_strided_slice.cpp new file mode 100644 index 00000000000..c03d5f4129c --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/optimize_strided_slice.cpp @@ -0,0 +1,203 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include + +#include +#include +#include +#include + +bool ngraph::pass::UselessStridedSliceEraser::run_on_function(std::shared_ptr f) { + bool rewritten = false; + for (auto & node : f->get_ordered_ops()) { + auto ss = std::dynamic_pointer_cast(node); + if (!ss || ss->get_output_partial_shape(0).is_dynamic() || ss->get_input_partial_shape(0).is_dynamic()) + continue; + if (ss->input(0).get_shape() != ss->output(0).get_shape()) + continue; + rewritten |= replace_output_update_name(ss->output(0), ss->input_value(0)); + } + return rewritten; +} + +ngraph::SlicePlan get_slice_plan(std::shared_ptr slice) { + auto convert_mask_to_axis_set = [](const std::vector& mask) { + ngraph::AxisSet axis_set{}; + for (size_t i = 0; i < static_cast(mask.size()); ++i) { + if (mask[i] == 1) + axis_set.emplace(i); + } + return axis_set; + }; + + auto data = slice->input_value(0).get_node_shared_ptr(); + auto begin = std::dynamic_pointer_cast(slice->input_value(1).get_node_shared_ptr()); + auto end = std::dynamic_pointer_cast(slice->input_value(2).get_node_shared_ptr()); + auto strides = std::dynamic_pointer_cast(slice->input_value(3).get_node_shared_ptr()); + if (!begin || !end || !strides || slice->input(0).get_partial_shape().is_dynamic()) + return ngraph::SlicePlan(); + + auto begin_vec = begin->cast_vector(); + auto end_vec = end->cast_vector(); + auto strides_vec = strides->cast_vector(); + const auto begin_mask = convert_mask_to_axis_set(slice->get_begin_mask()); + const auto end_mask = convert_mask_to_axis_set(slice->get_end_mask()); + + ngraph::SlicePlan plan = ngraph::make_slice_plan(slice->input(0).get_shape(), + begin_vec, + end_vec, + strides_vec, + begin_mask, + end_mask, + convert_mask_to_axis_set(slice->get_new_axis_mask()), + convert_mask_to_axis_set(slice->get_shrink_axis_mask()), + convert_mask_to_axis_set(slice->get_ellipsis_mask())); + return plan; +} + + +bool strided_slices_perform_the_same(std::shared_ptr lhs, + std::shared_ptr rhs) { + auto lhs_plan = get_slice_plan(lhs); + auto rhs_plan = get_slice_plan(rhs); + + auto empty_plan = ngraph::SlicePlan(); + if (lhs_plan == empty_plan || rhs_plan == empty_plan) + return false; + return lhs_plan == rhs_plan; +} + +bool ngraph::pass::SharedStridedSliceEraser::run_on_function(std::shared_ptr f) { + bool graph_rewritten = false; + + std::map, std::vector>> source_to_ss; + for (const auto & node : f->get_ordered_ops()) { + if (auto ss = std::dynamic_pointer_cast(node)) { + source_to_ss[ss->input_value(0)].push_back(ss); + } + } + + for (auto& pair : source_to_ss) { + if (pair.second.size() < 2) + continue; + auto root_ss = pair.second[0]; + for (auto& child_ss : pair.second) { + if (root_ss->get_instance_id() != child_ss->get_instance_id() && strided_slices_perform_the_same(root_ss, child_ss)) { + graph_rewritten |= replace_output_update_name(child_ss->output(0), root_ss->output(0)); + } + } + } + return graph_rewritten; +} + +bool ngraph::pass::GroupedStridedSliceOptimizer::run_on_function(std::shared_ptr f) { + bool graph_rewritten = false; + using planned_slice = std::pair, ngraph::SlicePlan>; + + std::map, std::vector> source_to_ss_with_plan; + for (const auto & node : f->get_ordered_ops()) { + if (auto ss = std::dynamic_pointer_cast(node)) { + auto slice_plan = get_slice_plan(ss); + if (slice_plan == ngraph::SlicePlan()) + continue; + source_to_ss_with_plan[ss->input_value(0)].push_back({ss, slice_plan}); + } + } + + for (auto& pair : source_to_ss_with_plan) { + if (pair.second.size() < 2) + continue; + + bool valid_for_replacement = true; + + auto root_plan = pair.second[0].second; + for (const auto & ss_plan : pair.second) { + valid_for_replacement &= (ss_plan.second.begins.size() == root_plan.begins.size()); + valid_for_replacement &= (ss_plan.first->get_ellipsis_mask().empty() && + ss_plan.first->get_new_axis_mask().empty() && + ss_plan.first->get_shrink_axis_mask().empty()); + } + + if (!valid_for_replacement) continue; + + auto input_shape = pair.first.get_shape(); + auto axis = -1; + + using OutputToPatrition = struct { + Output output; + int64_t begin; + int64_t end; + }; + + std::vector output_to_partition; + for (size_t i = 0; i < input_shape.size(); ++i) { + for (const auto & ss_plan : pair.second) { + if (ss_plan.second.begins[i] != 0 || ss_plan.second.ends[i] != input_shape[i]) { + if (axis == -1 || axis == i) + axis = i; + else + valid_for_replacement = false; + if (ss_plan.second.strides[i] != 1) + valid_for_replacement = false; + output_to_partition.push_back({ss_plan.first->output(0), ss_plan.second.begins[i], ss_plan.second.ends[i]}); + } + if (!valid_for_replacement) break; + } + if (!valid_for_replacement) break; + } + + if (!valid_for_replacement) continue; + if (output_to_partition.size() < 2) continue; + + std::sort(output_to_partition.begin(), output_to_partition.end(), + [](OutputToPatrition lhs, OutputToPatrition rhs) + {return lhs.begin < rhs.begin;}); + + std::vector, uint64_t>> output_to_size; + uint64_t prev_r = 0; + for (auto & record : output_to_partition) { + valid_for_replacement &= (record.begin >= prev_r); + prev_r = record.end; + } + valid_for_replacement &= (prev_r <= input_shape[axis]); + if (!valid_for_replacement) continue; + + prev_r = 0; + Output fake_output; + for (auto & record : output_to_partition) { + if (record.begin > prev_r) + output_to_size.emplace_back(fake_output, record.begin - prev_r); + prev_r = record.end; + output_to_size.emplace_back(record.output, record.end - record.begin); + } + if (prev_r < input_shape[axis]) { + output_to_size.emplace_back(fake_output, input_shape[axis] - prev_r); + } + + auto axis_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{}, {axis}); + + std::vector size_splits; + for (const auto & item : output_to_size) + size_splits.push_back(item.second); + auto size_splits_const = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{size_splits.size()}, size_splits); + auto variadic_split = std::make_shared(pair.first, axis_const, size_splits_const); + + auto i = 0; + NodeVector ops_to_replace; + for (auto & record : output_to_size) { + if (record.first == fake_output) { + std::make_shared(variadic_split->output(i)); + } else { + record.first.replace(variadic_split->output(i)); + ops_to_replace.push_back(record.first.get_node_shared_ptr()); + } + ++i; + } + copy_runtime_info(ops_to_replace, variadic_split); + } + return graph_rewritten; +} + diff --git a/inference-engine/src/transformations/src/transformations/remove_filtering_boxes_by_size.cpp b/inference-engine/src/transformations/src/transformations/remove_filtering_boxes_by_size.cpp new file mode 100644 index 00000000000..58e2d61971d --- /dev/null +++ b/inference-engine/src/transformations/src/transformations/remove_filtering_boxes_by_size.cpp @@ -0,0 +1,106 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include + +#include +#include + +#include "transformations/remove_filtering_boxes_by_size.hpp" + +void ngraph::pass::RemoveFilteringBoxesBySize::remove_filtering_boxes_by_size() { + // variadic split + auto data = std::make_shared(element::f32, Shape{1000, 4}); + auto sizes = opset3::Constant::create(element::i64, Shape{4}, std::vector({1, 1, 1, 1})); + auto axis = opset3::Constant::create(element::i64, Shape{1}, std::vector({1})); + auto split = std::make_shared(data, axis, sizes); + + // sub -> add + auto sub_2_0 = std::make_shared(split->output(2), split->output(0)); + auto term_1 = std::make_shared(element::f32, Shape{1}); + auto add_1 = std::make_shared(sub_2_0, term_1); + + auto sub_3_1 = std::make_shared(split->output(3), split->output(1)); + auto term_2 = std::make_shared(element::f32, Shape{1}); + auto add_2 = std::make_shared(sub_3_1, term_2); + + // concat + auto concat = std::make_shared(ngraph::OutputVector({split->output(0), split->output(1), add_1->output(0), add_2->output(0)}), 1); + + // second variadic split + auto sizes_1 = opset3::Constant::create(element::i64, Shape{4}, std::vector({1, 1, 1, 1})); + auto axis_1 = opset3::Constant::create(element::i64, Shape{1}, std::vector({1})); + auto split_1 = std::make_shared(concat, axis_1, sizes_1); + + // squeeze + auto squeeze_1_axis = opset3::Constant::create(element::i64, Shape{1}, std::vector({1})); + auto squeeze_1 = std::make_shared(split_1->output(2), squeeze_1_axis); + + auto squeeze_2_axis = opset3::Constant::create(element::i64, Shape{1}, std::vector({1})); + auto squeeze_2 = std::make_shared(split_1->output(3), squeeze_2_axis); + + // less + auto less_1_constant = opset3::Constant::create(element::f32, Shape{1}, std::vector({0})); + auto less_1 = std::make_shared(squeeze_1, less_1_constant); + + auto less_2_constant = opset3::Constant::create(element::f32, Shape{1}, std::vector({0})); + auto less_2 = std::make_shared(squeeze_2, less_2_constant); + + // Logical Not + auto not_1 = std::make_shared(less_1); + auto not_2 = std::make_shared(less_2); + + // cast + auto cast_11 = std::make_shared(not_1, ngraph::element::u8); + auto cast_12 = std::make_shared(cast_11, ngraph::element::boolean); + + auto cast_21 = std::make_shared(not_2, ngraph::element::u8); + auto cast_22 = std::make_shared(cast_21, ngraph::element::boolean); + + // logical and + auto and_1 = std::make_shared(cast_12, cast_22); + + // cast + auto cast_31 = std::make_shared(and_1, ngraph::element::u8); + auto cast_32 = std::make_shared(cast_31, ngraph::element::f32); + + // nonzero + auto non_zero = std::make_shared(cast_32); + + auto order = opset3::Constant::create(element::i64, Shape{2}, std::vector({1, 0})); + auto transpose = std::make_shared(non_zero, order); + + auto squeeze_3_axis = opset3::Constant::create(element::i64, Shape{1}, std::vector({1})); + auto squeeze_3 = std::make_shared(transpose, squeeze_3_axis); + + auto cast = std::make_shared(squeeze_3, ngraph::element::i64); + + ngraph::graph_rewrite_callback callback = [data](pattern::Matcher& m) { + auto start = opset3::Constant::create(element::i64, Shape{}, std::vector({0})); + auto step = opset3::Constant::create(element::i64, Shape{}, std::vector({1})); + + auto pattern_map = m.get_pattern_map(); + + auto input = pattern_map[data]; + auto output = m.get_match_root(); + + auto input_shape = std::make_shared(input); + + auto axis = opset3::Constant::create(element::i64, Shape{}, std::vector({0})); + auto index = opset3::Constant::create(element::i64, Shape{}, std::vector({0})); + auto stop = std::make_shared(input_shape, index, axis); + + auto range = std::make_shared(start, stop, step); + + range->set_friendly_name(output->get_friendly_name()); + // TODO: add copy_runtime_info + ngraph::replace_node(output, range); + + return true; + }; + + auto m = std::make_shared(cast, "RemoveFilteringBoxesBySize"); + this->add_matcher(m, callback, PassProperty::CHANGE_DYNAMIC_STATE); +} \ No newline at end of file diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/operations/out_shape_of_reshape.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/operations/out_shape_of_reshape.hpp new file mode 100644 index 00000000000..6bb56c16f02 --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/operations/out_shape_of_reshape.hpp @@ -0,0 +1,37 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +namespace ngraph { namespace vpu { namespace op { + +class OutShapeOfReshape : public ngraph::op::Op { +public: + static constexpr NodeTypeInfo type_info{"OutShapeOfReshape", 1}; + const NodeTypeInfo& get_type_info() const override { return type_info; } + + OutShapeOfReshape( + const Output& inDataShape, + const Output& outShapeDescriptor, + bool specialZero); + + void validate_and_infer_types() override; + + std::shared_ptr clone_with_new_inputs(const OutputVector& new_args) const override; + + bool visit_attributes(ngraph::AttributeVisitor& visitor) override; + + bool getSpecialZero() const { return m_specialZero; } + void setSpecialZero(bool special_zero) { m_specialZero = special_zero; } + +private: + bool m_specialZero; +}; + +} // namespace op +} // namespace vpu +} // namespace ngraph diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_broadcast.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_broadcast.hpp new file mode 100644 index 00000000000..9fc45c71caf --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_broadcast.hpp @@ -0,0 +1,47 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ngraph/node.hpp" +#include "ngraph/op/op.hpp" +#include "ngraph/op/util/broadcast_base.hpp" +#include "ngraph/op/util/attr_types.hpp" + +#include +#include + +namespace ngraph { namespace vpu { namespace op { + +class StaticShapeBroadcast : public ::ngraph::op::util::BroadcastBase { +public: + static constexpr NodeTypeInfo type_info{"StaticShapeBroadcast", 0}; + + const NodeTypeInfo& get_type_info() const override { return type_info; } + + StaticShapeBroadcast(const Output& arg, + const Output& targetShape, + const Output& axesMapping, + const ngraph::op::BroadcastModeSpec& broadcastSpec = ngraph::op::BroadcastType::EXPLICIT); + + StaticShapeBroadcast(const Output& arg, + const Output& targetShape, + const ngraph::op::BroadcastModeSpec& broadcastSpec = ngraph::op::BroadcastType::NUMPY); + + void validate_and_infer_types() override; + + std::shared_ptr clone_with_new_inputs(const OutputVector& newInputs) const override; + + bool visit_attributes(ngraph::AttributeVisitor& visitor) override; + + PartialShape getEvaluatedShape() const { return m_evaluatedOutputShape; } + void setEvaluatedShape(const PartialShape& shape) { m_evaluatedOutputShape = shape; } + +private: + PartialShape m_evaluatedOutputShape; +}; + +} // namespace op +} // namespace vpu +} // namespace ngraph diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_nonzero.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_nonzero.hpp index eb28ba452a4..5a9c75240c9 100644 --- a/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_nonzero.hpp +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/operations/static_shape_nonzero.hpp @@ -18,13 +18,24 @@ public: const NodeTypeInfo& get_type_info() const override { return type_info; } - explicit StaticShapeNonZero(const Output& input); + explicit StaticShapeNonZero(const Output& input, const element::Type& output_type = element::i64); void validate_and_infer_types() override; std::shared_ptr copy_with_new_args(const NodeVector& new_args) const override; bool visit_attributes(ngraph::AttributeVisitor& visitor) override; + + bool evaluate(const HostTensorVector& output_values, + const HostTensorVector& input_values) override; + + element::Type get_output_type() const { return m_output_type; } + void set_output_type(element::Type output_type) { m_output_type = output_type; } + // Overload collision with method on Node + using Node::set_output_type; + +protected: + element::Type m_output_type; }; } // namespace op diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape.hpp index 0ab93a4cb8e..f61ad57f2b1 100644 --- a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape.hpp +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape.hpp @@ -16,7 +16,7 @@ using Transformations = std::unordered_map function) const; private: Transformations transformations; diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_broadcast.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_broadcast.hpp new file mode 100644 index 00000000000..2ef7bbbecde --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_broadcast.hpp @@ -0,0 +1,15 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ngraph/node.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeBroadcast(std::shared_ptr target); + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_concat.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_concat.hpp new file mode 100644 index 00000000000..43dd69ee0ac --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_concat.hpp @@ -0,0 +1,15 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ngraph/node.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeConcat(std::shared_ptr target); + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_gather.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_gather.hpp new file mode 100644 index 00000000000..467f3ff1753 --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_gather.hpp @@ -0,0 +1,15 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ngraph/node.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeGather(std::shared_ptr node); + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_reshape.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_reshape.hpp new file mode 100644 index 00000000000..6771ade69fe --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_reshape.hpp @@ -0,0 +1,15 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ngraph/node.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeReshape(std::shared_ptr transpose); + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_shapeof.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_shapeof.hpp new file mode 100644 index 00000000000..a8be57aee02 --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_shapeof.hpp @@ -0,0 +1,16 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include + +namespace vpu { + +class DynamicToStaticShapeShapeOf : public ngraph::pass::GraphRewrite { +public: + DynamicToStaticShapeShapeOf(); +}; + +} //namespace vpu diff --git a/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_variadic_split.hpp b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_variadic_split.hpp new file mode 100644 index 00000000000..6c89afa6281 --- /dev/null +++ b/inference-engine/src/vpu/common/include/vpu/ngraph/transformations/dynamic_to_static_shape_variadic_split.hpp @@ -0,0 +1,15 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ngraph/node.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeVariadicSplit(std::shared_ptr node); + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/include/vpu/utils/simple_math.hpp b/inference-engine/src/vpu/common/include/vpu/utils/simple_math.hpp index e2df86e9a2c..aafe6888561 100644 --- a/inference-engine/src/vpu/common/include/vpu/utils/simple_math.hpp +++ b/inference-engine/src/vpu/common/include/vpu/utils/simple_math.hpp @@ -11,6 +11,8 @@ #include #include +#include "error.hpp" +#include #include // @@ -22,30 +24,107 @@ namespace vpu { -class SimpleMathExpression final { +template +Optional parseNumber(const std::string& s) { + T value; + if ((std::istringstream(s) >> value >> std::ws).eof()) { + return {value}; + } + return {}; +} + +namespace details { + +#define OPERATOR(OP) \ + IntOrFloat operator OP(const IntOrFloat &other) const { \ + if (isInt && other.isInt) { \ + return IntOrFloat{value.i OP other.value.i}; \ + } \ + const float lhs = isInt ? value.i : value.f; \ + const float rhs = other.isInt ? other.value.i : other.value.f; \ + return IntOrFloat{lhs OP rhs}; \ + } + +class IntOrFloat final { + union { + int i; + float f; + } value{}; + bool isInt = true; + public: - void setVariables(const std::map& vars) { _vars = vars; } + explicit IntOrFloat(int x) : isInt{true} { + value.i = x; + } + explicit IntOrFloat(float x) : isInt{false} { + value.f = x; + } + explicit IntOrFloat(const std::string& x) { + const auto integer = parseNumber(x); + if (integer.hasValue()) { + *this = IntOrFloat(integer.get()); + return; + } + const auto fp = parseNumber(x); + if (fp.hasValue()) { + *this = IntOrFloat(fp.get()); + return; + } + VPU_THROW_FORMAT("Failed to convert string to number: '%s'", x); + } + + explicit operator std::string() const { + return isInt ? std::to_string(value.i) : std::to_string(value.f); + } + + float toFloat() const { return isInt ? static_cast(value.i) : value.f; } + + OPERATOR(+) + OPERATOR(-) + OPERATOR(*) + OPERATOR(/) + + IntOrFloat operator %(const IntOrFloat & other) const { + if (isInt && other.isInt) { + return IntOrFloat{value.i % other.value.i}; + } + THROW_IE_EXCEPTION << "Can't apply modulus operation to floating point value"; + } +}; + +} // namespace details + +class MathExpression final { +public: + void setVariables(const std::map& variables) { + for (const auto& var : variables) { + // if string converts to float, it also will be able to convert to int + if (parseNumber(var.second).hasValue()) { + _vars.emplace(var.first, details::IntOrFloat{var.second}); + } + } + } void parse(const std::string& expression); - int evaluate() const; private: - struct Token final { - enum TokenType { - Value, - Operator, - }; - - TokenType type; - int value; - char op; - - explicit Token(TokenType t = Value, int v = 0, char o = 0) : type(t), value(v), op(o) {} + enum class TokenType { + Value, + Operator, + Function }; -private: - std::map _vars; + struct Token { + TokenType type; + details::IntOrFloat value; + std::string opName; + + explicit Token(TokenType type, details::IntOrFloat value, std::string name) + : type(type), value(value), opName(std::move(name)) {} + }; + + std::map _vars; SmallVector _parsedTokens; }; diff --git a/inference-engine/src/vpu/common/src/ngraph/operations/dynamic_shape_resolver.cpp b/inference-engine/src/vpu/common/src/ngraph/operations/dynamic_shape_resolver.cpp index a33c37e00ee..1a29f8efb4d 100644 --- a/inference-engine/src/vpu/common/src/ngraph/operations/dynamic_shape_resolver.cpp +++ b/inference-engine/src/vpu/common/src/ngraph/operations/dynamic_shape_resolver.cpp @@ -26,8 +26,9 @@ void DynamicShapeResolver::validate_and_infer_types() { const auto& dataElementType = get_input_element_type(0); NODE_VALIDATION_CHECK(this, dataElementType.is_static(), "(", get_friendly_name(), ") does not support dynamic element type for data tensor"); const auto& dimsElementType = get_input_element_type(1); - NODE_VALIDATION_CHECK(this, dimsElementType.is_static() && dimsElementType.compatible(ngraph::element::i64), "(", get_friendly_name(), - ") supports only i64 number type for dims tensor, but ", dimsElementType, " provided"); + NODE_VALIDATION_CHECK(this, dimsElementType.is_static() && (dimsElementType.compatible(ngraph::element::i64) || + dimsElementType.compatible(ngraph::element::i32)), + "(", get_friendly_name(), ") supports only i64 and i32 number type for dims tensor, but ", dimsElementType, " provided"); const auto& dataShape = get_input_shape(0); const auto& dimsShape = get_input_shape(1); diff --git a/inference-engine/src/vpu/common/src/ngraph/operations/out_shape_of_reshape.cpp b/inference-engine/src/vpu/common/src/ngraph/operations/out_shape_of_reshape.cpp new file mode 100644 index 00000000000..4688e7c1852 --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/operations/out_shape_of_reshape.cpp @@ -0,0 +1,72 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/operations/out_shape_of_reshape.hpp" + +namespace ngraph { namespace vpu { namespace op { + +constexpr NodeTypeInfo OutShapeOfReshape::type_info; + +OutShapeOfReshape::OutShapeOfReshape( + const Output& inDataShape, + const Output& outShapeDescriptor, + bool specialZero) : Op({inDataShape, outShapeDescriptor}), m_specialZero(specialZero) { + constructor_validate_and_infer_types(); +} + +void OutShapeOfReshape::validate_and_infer_types() { + NODE_VALIDATION_CHECK(this, get_input_size() == 2, + "OutShapeOfReshape (", get_friendly_name(), + ") must have only 2 inputs, provided: ", get_input_size()); + + const auto& inDataShapeTensorShape = get_input_partial_shape(0); + NODE_VALIDATION_CHECK(this, inDataShapeTensorShape.is_static(), + "OutShapeOfReshape (", get_friendly_name(), + ") doesn't support dynamic input data shape"); + NODE_VALIDATION_CHECK(this, inDataShapeTensorShape.rank().get_length() == 1, + "OutShapeOfReshape (", get_friendly_name(), + ") must have input data shape tensor with rank 1, provided: ", + inDataShapeTensorShape.rank().get_length()); + + const auto& outShapeDescriptorTensorShape = get_input_partial_shape(1); + NODE_VALIDATION_CHECK(this, outShapeDescriptorTensorShape.is_static(), + "OutShapeOfReshape (", get_friendly_name(), + ") doesn't support dynamic output shape descriptor"); + NODE_VALIDATION_CHECK(this, outShapeDescriptorTensorShape.rank().get_length() == 1, + "OutShapeOfReshape (", get_friendly_name(), + ") must have output shape descriptor tensor with rank 1, provided: ", + outShapeDescriptorTensorShape.rank().get_length()); + + const auto& inDataShapeTensorType = get_input_element_type(0); + NODE_VALIDATION_CHECK(this, + inDataShapeTensorType.is_static() && + inDataShapeTensorType.is_integral_number(), + "OutShapeOfReshape (", get_friendly_name(), + ") input data type needs to be an integral type. Got: ", + inDataShapeTensorType); + const auto& outShapeDescriptorTensorType = get_input_element_type(1); + NODE_VALIDATION_CHECK(this, + outShapeDescriptorTensorType.is_static() && + outShapeDescriptorTensorType.is_integral_number(), + "OutShapeOfReshape (", get_friendly_name(), + ") shape descriptor type needs to be an integral type. Got: ", + outShapeDescriptorTensorType); + + set_output_type(0, element::i64, outShapeDescriptorTensorShape); +} + +std::shared_ptr OutShapeOfReshape::clone_with_new_inputs(const OutputVector& new_args) const { + check_new_args_count(this, new_args); + return std::make_shared(new_args.at(0), new_args.at(1), m_specialZero); +} + +bool OutShapeOfReshape::visit_attributes(ngraph::AttributeVisitor& visitor) { + visitor.on_attribute("special_zero", m_specialZero); + return true; +} + + +} // namespace op +} // namespace vpu +} // namespace ngraph diff --git a/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_broadcast.cpp b/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_broadcast.cpp new file mode 100644 index 00000000000..333947c7589 --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_broadcast.cpp @@ -0,0 +1,147 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/operations/static_shape_broadcast.hpp" + +#include "vpu/utils/error.hpp" + +#include "ngraph/opsets/opset3.hpp" +#include "ngraph/evaluator.hpp" + +namespace ngraph { namespace vpu { namespace op { + +namespace { + +HostTensorVector evaluateShapeOf(Node* node, const HostTensorVector&) { + auto shapeOf = as_type(node); + const auto inputValue = shapeOf->input_value(0); + const auto outputValue = shapeOf->output(0); + const auto inputTensors = + HostTensorVector{std::make_shared(inputValue)}; + const auto outputTensors = + HostTensorVector{std::make_shared(outputValue)}; + + shapeOf->evaluate(outputTensors, inputTensors); + return outputTensors; +} + +HostTensorVector evaluateConstant(Node* node, const HostTensorVector&) { + const auto constantNode = as_type(node); + const auto constant = std::make_shared(*constantNode); + + const auto outputTensor = std::make_shared(constant); + + return {outputTensor}; +} + +HostTensorVector evaluateOp(Node* node, const HostTensorVector& inputTensors) { + HostTensorVector outputTensors; + for (const auto& output : node->outputs()) { + outputTensors.push_back(std::make_shared(output)); + } + + node->evaluate(outputTensors, inputTensors); + return outputTensors; +} + +PartialShape evaluateTargetShape(const Output& value) { + static Evaluator::op_handler_map handlers = { + {opset3::ShapeOf::type_info, evaluateShapeOf}, + {opset3::Constant::type_info, evaluateConstant}, + {opset3::Gather::type_info, evaluateOp}, + {opset3::Concat::type_info, evaluateOp}}; + Evaluator::value_map value_map; + Evaluator evaluator(handlers, value_map); + + const auto shapeTensor = evaluator.evaluate(value); + if (!shapeTensor || !shapeTensor->get_is_allocated()) { + return PartialShape::dynamic(); + } + const auto shapeConstNode = std::make_shared(shapeTensor); + const auto resultShape = Shape{shapeConstNode->cast_vector()}; + + return resultShape; +} + +} // namespace + +constexpr NodeTypeInfo StaticShapeBroadcast::type_info; + +StaticShapeBroadcast::StaticShapeBroadcast(const Output& arg, + const Output& targetShape, + const Output& axesMapping, + const ngraph::op::BroadcastModeSpec& broadcastSpec) + : ::ngraph::op::util::BroadcastBase{arg, targetShape, axesMapping, broadcastSpec}, + m_evaluatedOutputShape{PartialShape::dynamic()} { + constructor_validate_and_infer_types(); +} + +StaticShapeBroadcast::StaticShapeBroadcast(const Output& arg, + const Output& targetShape, + const ngraph::op::BroadcastModeSpec& broadcastSpec) + : ::ngraph::op::util::BroadcastBase{arg, targetShape, broadcastSpec}, + m_evaluatedOutputShape{PartialShape::dynamic()} { + constructor_validate_and_infer_types(); +} + +void StaticShapeBroadcast::validate_and_infer_types() { + if (m_mode.m_type == ngraph::op::BroadcastType::EXPLICIT) { + NODE_VALIDATION_CHECK(this, get_input_size() == 3, + "StaticShapeBroadcast (", get_friendly_name(), ") ", + "with explicit mode must have 3 inputs, provided: ", + get_input_size()); + } else if (m_mode.m_type == ngraph::op::BroadcastType::NUMPY) { + NODE_VALIDATION_CHECK(this, get_input_size() == 2, + "StaticShapeBroadcast (", get_friendly_name(), ") ", + "with numpy mode must have 2 inputs, provided: ", + get_input_size()); + } else { + NODE_VALIDATION_CHECK(this, false, + "StaticShapeBroadcast (", get_friendly_name(), ") ", + "doesn't support ", m_mode.m_type, " mode"); + } + + ::ngraph::op::util::BroadcastBase::validate_and_infer_types(); + + if (get_output_partial_shape(0).is_dynamic()) { + // Try to evaluate output shape. After some transformations further, we may not be able + // to evaluate the target shape again, then we will leave the evaluated shape unchanged. + // For example, DynamicToStaticShapeShapeOf remove ShapeOf and pass the second input of DSR. + const auto evaluatedTargetShape = evaluateTargetShape(input_value(1)); + if (evaluatedTargetShape.is_static()) { + m_evaluatedOutputShape = evaluatedTargetShape; + } + NODE_VALIDATION_CHECK(this, m_evaluatedOutputShape.is_static(), + "StaticShapeBroadcast (", get_friendly_name(), ") ", + "can't evaluate output shape, got: ", m_evaluatedOutputShape); + set_output_type(0, get_input_element_type(0), m_evaluatedOutputShape); + } +} + +std::shared_ptr StaticShapeBroadcast::clone_with_new_inputs(const OutputVector& newInputs) const { + check_new_args_count(this, newInputs); + if (newInputs.size() == 2) { + return std::make_shared( + newInputs.at(0), newInputs.at(1), m_mode); + } else { + return std::make_shared( + newInputs.at(0), newInputs.at(1), newInputs.at(2), m_mode); + } +} + +bool StaticShapeBroadcast::visit_attributes(ngraph::AttributeVisitor& visitor) { + std::string mode; + if (m_mode.m_type == ngraph::op::BroadcastType::EXPLICIT) { + mode = "explicit"; + } else if (m_mode.m_type == ngraph::op::BroadcastType::NUMPY) { + mode = "numpy"; + } + visitor.on_attribute("mode", mode); + + return true; +} + +} // namespace op +} // namespace vpu +} // namespace ngraph diff --git a/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_nonzero.cpp b/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_nonzero.cpp index a694274e2cd..eff8a6dba0b 100644 --- a/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_nonzero.cpp +++ b/inference-engine/src/vpu/common/src/ngraph/operations/static_shape_nonzero.cpp @@ -4,12 +4,14 @@ #include "vpu/ngraph/operations/static_shape_nonzero.hpp" +#include "ngraph/runtime/host_tensor.hpp" + namespace ngraph { namespace vpu { namespace op { constexpr NodeTypeInfo StaticShapeNonZero::type_info; -StaticShapeNonZero::StaticShapeNonZero(const Output& input) - : Op({input}) { +StaticShapeNonZero::StaticShapeNonZero(const Output& input, const element::Type& output_type) + : Op({input}), m_output_type(output_type) { constructor_validate_and_infer_types(); } @@ -28,21 +30,132 @@ void StaticShapeNonZero::validate_and_infer_types() { "StaticShapeNonZero input data type needs to be a numeric type. Got: ", input_et); + NODE_VALIDATION_CHECK(this, + m_output_type == element::i32 || m_output_type == element::i64, + "StaticShapeNonZero output data type can be either i32 or i64"); + const auto total_dim_size = Dimension(shape_size(arg_shape.to_shape())); - set_output_type(0, element::i64, {arg_shape.rank(), total_dim_size}); - set_output_type(1, element::i64, {Dimension(2)}); + set_output_type(0, m_output_type, {arg_shape.rank(), total_dim_size}); + set_output_type(1, m_output_type, {Dimension(2)}); } std::shared_ptr StaticShapeNonZero::copy_with_new_args( const NodeVector& new_args) const { check_new_args_count(this, new_args); - return std::make_shared(new_args.at(0)); + return std::make_shared(new_args.at(0), m_output_type); } bool StaticShapeNonZero::visit_attributes(ngraph::AttributeVisitor& visitor) { + visitor.on_attribute("output_type", m_output_type); return true; } +namespace { + +template +void staticShapeNonZeroReference(const InType* input, OutType* outIndices, OutType* outShape, const Shape& inputShape) { + auto strides = row_major_strides(inputShape); + auto totalDimSize = shape_size(inputShape); + + const auto getCoord = [&strides](int offset){ + std::vector coord; + for (const size_t& stride : strides) { + coord.insert(coord.begin(), offset / stride); + offset %= stride; + } + + return coord; + }; + + const auto addCoordToIndices = [&outIndices, &totalDimSize](const std::vector &coord, + size_t nonZeroCount) { + for (int j = 0; j < coord.size(); ++j) { + outIndices[j * totalDimSize + nonZeroCount] = coord[j]; + } + }; + + const InType zeroValue = InType{0}; + const auto isNonZero = [&input, &zeroValue](size_t i) { + return input[i] != zeroValue; + }; + + size_t nonZeroCount = 0; + for (size_t i = 0; i < totalDimSize; ++i) { + if (isNonZero(i)) { + addCoordToIndices(getCoord(i), nonZeroCount++); + } + } + + outShape[0] = nonZeroCount; + outShape[1] = inputShape.size(); +} + +template +bool evaluate(const HostTensorPtr& input, + const HostTensorPtr& outIndices, + const HostTensorPtr& outShape) { + bool rc = true; + + switch (outIndices->get_element_type()) { + case element::Type_t::i64: + staticShapeNonZeroReference(input->get_data_ptr(), + outIndices->get_data_ptr(), + outShape->get_data_ptr(), + input->get_shape()); + break; + case element::Type_t::i32: + staticShapeNonZeroReference(input->get_data_ptr(), + outIndices->get_data_ptr(), + outShape->get_data_ptr(), + input->get_shape()); + break; + default: rc = false; break; + } + + return rc; +} + +bool evaluateStaticShapeNonZero(const HostTensorPtr& input, + const HostTensorPtr& outIndices, + const HostTensorPtr& outShape) { + bool rc = true; + + switch (input->get_element_type()) { + TYPE_CASE(i8)(input, outIndices, outShape); + break; + TYPE_CASE(i16)(input, outIndices, outShape); + break; + TYPE_CASE(i32)(input, outIndices, outShape); + break; + TYPE_CASE(i64)(input, outIndices, outShape); + break; + TYPE_CASE(u8)(input, outIndices, outShape); + break; + TYPE_CASE(u16)(input, outIndices, outShape); + break; + TYPE_CASE(u32)(input, outIndices, outShape); + break; + TYPE_CASE(u64)(input, outIndices, outShape); + break; + TYPE_CASE(bf16)(input, outIndices, outShape); + break; + TYPE_CASE(f32)(input, outIndices, outShape); + break; + TYPE_CASE(f64)(input, outIndices, outShape); + break; + default: rc = false; break; + } + + return rc; +} + +} // namespace + +bool StaticShapeNonZero::evaluate(const HostTensorVector& outputs, + const HostTensorVector& inputs) { + return evaluateStaticShapeNonZero(inputs[0], outputs[0], outputs[1]); +} + } // namespace op } // namespace vpu } // namespace ngraph diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape.cpp index b7d4a8af364..a412e773d7a 100644 --- a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape.cpp +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape.cpp @@ -2,15 +2,22 @@ // SPDX-License-Identifier: Apache-2.0 // +#include "vpu/ngraph/transformations/dynamic_to_static_shape_broadcast.hpp" +#include "vpu/ngraph/transformations/dynamic_to_static_shape_concat.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_unary_elementwise.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_roialign.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_transpose.hpp" +#include "vpu/ngraph/transformations/dynamic_to_static_shape_variadic_split.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_non_max_suppression.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_nonzero.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_binary_elementwise.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_squeeze.hpp" #include "vpu/ngraph/transformations/dynamic_to_static_shape_unsqueeze.hpp" +#include "vpu/ngraph/transformations/dynamic_to_static_shape_gather.hpp" +#include "vpu/ngraph/transformations/dynamic_to_static_shape_shapeof.hpp" +#include "vpu/ngraph/transformations/dynamic_to_static_shape_reshape.hpp" + #include "vpu/utils/error.hpp" #include "ngraph/opsets/opset3.hpp" @@ -27,7 +34,13 @@ using namespace ngraph; bool isDynamic(const Node& node) { const auto& outputs = node.outputs(); - return std::any_of(outputs.cbegin(), outputs.cend(), [](const Output& output) { return output.get_partial_shape().is_dynamic(); }); + return std::any_of(outputs.cbegin(), outputs.cend(), [](const Output& output) { + VPU_THROW_UNLESS(output.get_partial_shape().rank() != ngraph::Rank::dynamic(), + "DynamicToStaticShape transformation: got dynamic rank for {} with type {} while only static is supported", + output.get_node_shared_ptr()->get_friendly_name(), output.get_node_shared_ptr()->get_type_name()); + + return output.get_partial_shape().is_dynamic(); + }); } bool validateStaticShapes(const ngraph::Function& function) { @@ -44,12 +57,14 @@ const Transformations& getDefaultTransformations() { {ngraph::opset3::Add::type_info, dynamicToStaticShapeBinaryEltwise}, {ngraph::opset3::Multiply::type_info, dynamicToStaticShapeBinaryEltwise}, {ngraph::opset3::Subtract::type_info, dynamicToStaticShapeBinaryEltwise}, + {ngraph::opset3::VariadicSplit::type_info, dynamicToStaticShapeVariadicSplit}, {ngraph::opset3::Divide::type_info, dynamicToStaticShapeBinaryEltwise}, {ngraph::opset3::Equal::type_info, dynamicToStaticShapeBinaryEltwise}, {ngraph::opset3::Power::type_info, dynamicToStaticShapeBinaryEltwise}, {ngraph::opset3::NonMaxSuppression::type_info, dynamicToStaticNonMaxSuppression}, {ngraph::opset3::NonZero::type_info, dynamicToStaticShapeNonZero}, {ngraph::opset3::Transpose::type_info, dynamicToStaticShapeTranspose}, + {ngraph::opset3::Concat::type_info, dynamicToStaticShapeConcat}, {ngraph::opset3::Convert::type_info, dynamicToStaticUnaryElementwise}, {ngraph::opset3::Clamp::type_info, dynamicToStaticUnaryElementwise}, {ngraph::opset3::Floor::type_info, dynamicToStaticUnaryElementwise}, @@ -59,8 +74,11 @@ const Transformations& getDefaultTransformations() { {ngraph::opset3::Sigmoid::type_info, dynamicToStaticUnaryElementwise}, {ngraph::opset3::Sqrt::type_info, dynamicToStaticUnaryElementwise}, {ngraph::opset3::Squeeze::type_info, dynamicToStaticShapeSqueeze}, + {ngraph::opset3::Gather::type_info, dynamicToStaticShapeGather}, {ngraph::opset3::Unsqueeze::type_info, dynamicToStaticShapeUnsqueeze}, {ngraph::opset3::ROIAlign::type_info, dynamicToStaticShapeROIAlign}, + {ngraph::opset3::Reshape::type_info, dynamicToStaticShapeReshape}, + {ngraph::opset3::Broadcast::type_info, dynamicToStaticShapeBroadcast}, }; return transformations; } @@ -80,8 +98,8 @@ DynamicToStaticShape::DynamicToStaticShape(const Transformations& specificTransf transformations.emplace(ngraph::opset3::Result::type_info, [](const std::shared_ptr&){}); } -void DynamicToStaticShape::transform(ngraph::Function& function) const { - for (const auto& operation : function.get_ordered_ops()) { +void DynamicToStaticShape::transform(std::shared_ptr function) const { + for (const auto& operation : function->get_ordered_ops()) { if (!isDynamic(*operation)) { continue; } @@ -94,8 +112,11 @@ void DynamicToStaticShape::transform(ngraph::Function& function) const { transformation->second(operation); } - function.validate_nodes_and_infer_types(); - validateStaticShapes(function); + // Should be executed after all dynamic-to-static transformations + DynamicToStaticShapeShapeOf().run_on_function(function); + + function->validate_nodes_and_infer_types(); + validateStaticShapes(*function); } } // namespace vpu diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_broadcast.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_broadcast.cpp new file mode 100644 index 00000000000..122002591c6 --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_broadcast.cpp @@ -0,0 +1,47 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_broadcast.hpp" + +#include "vpu/ngraph/operations/static_shape_broadcast.hpp" +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" +#include "vpu/utils/error.hpp" + +#include "ngraph/graph_util.hpp" +#include "ngraph/opsets/opset3.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeBroadcast(std::shared_ptr target) { + const auto broadcast = ngraph::as_type_ptr(target); + VPU_THROW_UNLESS(broadcast, + "dynamicToStaticShapeBroadcast transformation is not applicable for {}, " + "it should be {} instead", + target, ngraph::opset3::Broadcast::type_info.name); + + std::shared_ptr staticShapeBroadcast; + if (broadcast->get_broadcast_spec() == ngraph::op::BroadcastType::EXPLICIT) { + staticShapeBroadcast = std::make_shared( + broadcast->input_value(0), + broadcast->input_value(1), + broadcast->input_value(2)); + } else if (broadcast->get_broadcast_spec() == ngraph::op::BroadcastType::NUMPY) { + staticShapeBroadcast = std::make_shared( + broadcast->input_value(0), + broadcast->input_value(1)); + } else { + VPU_THROW_FORMAT("dynamicToStaticShapeBroadcast supports only explicit and numpy modes," + "provided {}", broadcast->get_broadcast_spec().m_type); + } + + auto dsr = std::make_shared( + staticShapeBroadcast->output(0), broadcast->input_value(1)); + + ngraph::replace_node(std::move(target), std::move(dsr)); +} + +} // namespace vpu + diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_concat.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_concat.cpp new file mode 100644 index 00000000000..ae111b13b81 --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_concat.cpp @@ -0,0 +1,116 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_concat.hpp" + +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" +#include + +#include "ngraph/graph_util.hpp" +#include "ngraph/opsets/opset3.hpp" + +#include +#include +#include + +namespace vpu { + +void dynamicToStaticShapeConcat(std::shared_ptr target) { + const auto inputs = target->input_values(); + + ngraph::OutputVector dsrInputs; + ngraph::OutputVector staticInputs; + for (const auto& input : inputs) { + const auto inputNode = input.get_node_shared_ptr(); + if (ngraph::as_type_ptr(inputNode)) { + dsrInputs.emplace_back(input); + } else { + staticInputs.emplace_back(input); + } + } + + VPU_THROW_UNLESS(!dsrInputs.empty(), + "DynamicToStaticShape transformation for {} of type {} expects at least " + "one {} as input, actual types: {}", target->get_friendly_name(), + target->get_type_info().name, ngraph::vpu::op::DynamicShapeResolver::type_info.name, + std::accumulate(inputs.begin(), inputs.end(), std::string(), []( + const std::string& typesStr, const ngraph::Output& input) { + return typesStr + input.get_node_shared_ptr()->get_type_info().name + ", "; + })); + + const auto firstDSRInputNode = dsrInputs.front().get_node_shared_ptr(); + const auto shapeDataType = firstDSRInputNode->input(1).get_element_type(); + const auto dataRank = firstDSRInputNode->get_output_partial_shape(0).rank().get_length(); + const auto axis = ngraph::as_type_ptr(target)->get_concatenation_axis(); + + const auto shapeToConstant = [&shapeDataType, &dataRank](const ngraph::Shape& shape) { + return ngraph::opset3::Constant::create( + shapeDataType, {static_cast(dataRank)}, shape)->output(0); + }; + + const auto getShapeFromDSR = [&target, &shapeDataType](const ngraph::Output& dsrOutput) { + const auto dsrNode = dsrOutput.get_node_shared_ptr(); + const auto dsrShapeInputValue = dsrNode->input_value(1); + VPU_THROW_UNLESS(dsrShapeInputValue.get_element_type() == shapeDataType, + "DynamicToStaticShape transformation for {} of type {} expects input " + "shape with {} type from {} argument of type {}, provided {}", + target->get_friendly_name(), target->get_type_info().name, + shapeDataType, dsrNode->get_friendly_name(), dsrNode->get_type_info().name, + dsrShapeInputValue.get_element_type()); + return dsrShapeInputValue; + }; + + const auto sumOfShapes = [](const ngraph::Output& shape1, + const ngraph::Output& shape2) { + const auto shapeAccumulatorOp = std::make_shared(shape1, shape2); + return shapeAccumulatorOp->output(0); + }; + + const auto divideDimsByNumOfInputsExceptAxis = [&target, &dataRank, &axis, + &shapeDataType, &shapeToConstant]( + const ngraph::Output& shape) { + ngraph::Shape dividerValues(dataRank, target->get_input_size()); + dividerValues[axis] = 1; + const auto divider = shapeToConstant(dividerValues); + const auto divide = std::make_shared(shape, divider); + return divide->output(0); + }; + + const auto getAdditionalShapeFromStatic = [&target, &dataRank, &axis]( + const ngraph::OutputVector& staticInputs) { + ngraph::Shape accumulatedStaticShapeValue(dataRank, 0); + for (const auto& staticInput : staticInputs) { + const auto& staticInputPartialShape = staticInput.get_partial_shape(); + VPU_THROW_UNLESS(staticInputPartialShape.is_static(), + "DynamicToStaticShape transformation for {} of type {} expects static " + "shape on inputs without DSR", target->get_friendly_name(), + target->get_type_info().name); + accumulatedStaticShapeValue[axis] += static_cast(staticInputPartialShape[axis]); + } + return accumulatedStaticShapeValue; + }; + + auto accumulatedShape = getShapeFromDSR(dsrInputs.front()); + for (size_t dsrInputIdx = 1; dsrInputIdx < dsrInputs.size(); ++dsrInputIdx) { + const auto dsrInputShape = getShapeFromDSR(dsrInputs[dsrInputIdx]); + accumulatedShape = sumOfShapes(accumulatedShape, dsrInputShape); + } + + if (dsrInputs.size() > 1) { + accumulatedShape = divideDimsByNumOfInputsExceptAxis(accumulatedShape); + } + + if (!staticInputs.empty()) { + const auto accumulatedStaticShape = shapeToConstant(getAdditionalShapeFromStatic(staticInputs)); + accumulatedShape = sumOfShapes(accumulatedShape, accumulatedStaticShape); + } + + const auto copied = target->clone_with_new_inputs(target->input_values()); + const auto outDsr = std::make_shared( + copied, accumulatedShape); + + ngraph::replace_node(std::move(target), outDsr); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_gather.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_gather.cpp new file mode 100644 index 00000000000..88e30df027c --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_gather.cpp @@ -0,0 +1,78 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_gather.hpp" + +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" +#include + +#include "ngraph/graph_util.hpp" +#include "ngraph/opsets/opset3.hpp" + +#include +#include + +namespace vpu { + +void dynamicToStaticShapeGather(std::shared_ptr target) { + const auto gather = ngraph::as_type_ptr(target); + VPU_THROW_UNLESS(gather, "dynamicToStaticShapeGather transformation is not applicable for {}, it should be {} instead", + target, ngraph::opset3::Gather::type_info); + + int64_t axis = gather->get_axis(); + VPU_THROW_UNLESS(axis != std::numeric_limits::max() && axis >= 0, + "dynamicToStaticShapeGather: Unsupported Gather axis {} for node {}", axis, gather); + + auto shapeToConstant = [&gather](const ngraph::Output & output) -> std::shared_ptr { + VPU_THROW_UNLESS(output.get_partial_shape().is_static(), + "DynamicToStaticShape transformation for {} of type {} expects static shape on inputs without DSR", + gather->get_friendly_name(), gather->get_type_info()); + return ngraph::opset3::Constant::create(ngraph::element::i64, {output.get_shape().size()}, output.get_shape()); + }; + + const auto dataDSR = ngraph::as_type_ptr(gather->input_value(0).get_node_shared_ptr()); + const auto idxDSR = ngraph::as_type_ptr(gather->input_value(1).get_node_shared_ptr()); + + VPU_THROW_UNLESS(dataDSR || idxDSR, "DynamicToStaticShape transformation for {} of type {} expects at least one DSR as input", + gather->get_friendly_name(), gather->get_type_info()); + + const auto data_shape = dataDSR ? dataDSR->input_value(1) : shapeToConstant(gather->input_value(0)); + const auto indices_shape = idxDSR ? idxDSR->input_value(1) : shapeToConstant(gather->input_value(1)); + + const auto copied = target->clone_with_new_inputs(target->input_values()); + + + const auto & data_rank = data_shape.get_partial_shape(); + const auto & indices_rank = indices_shape.get_partial_shape(); + VPU_THROW_UNLESS(data_rank.is_static() && indices_rank.is_static(), + "DynamicToStaticShape transformation for {} doesn't support dynamic rank", gather); + + const auto data_rank_value = data_rank[0].get_length(); + const auto indices_rank_value = indices_rank[0].get_length(); + ngraph::OutputVector output_dims; + if (axis) { + std::vector first_data_shape_part_indices(axis); + std::iota(first_data_shape_part_indices.begin(), first_data_shape_part_indices.end(), 0); + const auto first_data_shape_part = std::make_shared( + data_shape, + ngraph::opset3::Constant::create(ngraph::element::i64, {first_data_shape_part_indices.size()}, first_data_shape_part_indices), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0})); + output_dims.push_back(first_data_shape_part); + } + if (indices_rank_value) + output_dims.push_back(indices_shape); + if (axis + 1 < data_rank_value) { + std::vector second_data_shape_part_indices(data_rank_value - axis - 1); + std::iota(second_data_shape_part_indices.begin(), second_data_shape_part_indices.end(), axis + 1); + const auto second_data_shape_part = std::make_shared( + data_shape, + ngraph::opset3::Constant::create(ngraph::element::i64, {second_data_shape_part_indices.size()}, second_data_shape_part_indices), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0})); + output_dims.push_back(second_data_shape_part); + } + const auto output_shape = std::make_shared(output_dims, 0); + ngraph::replace_node(target, std::make_shared(copied, output_shape)); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp index 570a68b164e..abc82a02cb6 100644 --- a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp @@ -6,15 +6,21 @@ #include "vpu/ngraph/operations/static_shape_nonzero.hpp" #include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" +#include "vpu/utils/error.hpp" #include "ngraph/graph_util.hpp" +#include "ngraph/ops.hpp" #include namespace vpu { -void dynamicToStaticShapeNonZero(std::shared_ptr nonZero) { - auto staticShapeNonZero = std::make_shared(nonZero->input(0).get_source_output()); +void dynamicToStaticShapeNonZero(std::shared_ptr node) { + auto nonZero = std::dynamic_pointer_cast(node); + VPU_THROW_UNLESS(nonZero, "dynamicToStaticShapeNonZero transformation for {} of type {} expects {} as node for replacement", + node->get_friendly_name(), node->get_type_info(), ngraph::op::v3::NonZero::type_info); + + auto staticShapeNonZero = std::make_shared(nonZero->input(0).get_source_output(), nonZero->get_output_type()); staticShapeNonZero->set_friendly_name(nonZero->get_friendly_name() + "/static_shape"); auto dynamicShapeResolver = std::make_shared( diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_reshape.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_reshape.cpp new file mode 100644 index 00000000000..c1267f581a3 --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_reshape.cpp @@ -0,0 +1,40 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_reshape.hpp" + +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" +#include "vpu/ngraph/operations/out_shape_of_reshape.hpp" +#include + +#include "ngraph/graph_util.hpp" +#include "ngraph/opsets/opset3.hpp" + +#include + +namespace vpu { + +void dynamicToStaticShapeReshape(std::shared_ptr target) { + const auto dsr = target->get_argument(0); + VPU_THROW_UNLESS(ngraph::as_type_ptr(dsr), + "DynamicToStaticShape transformation for {} of type {} expects {} as input with index {}", + target->get_friendly_name(), target->get_type_info(), ngraph::vpu::op::DynamicShapeResolver::type_info, 0); + + const auto outShapeDescriptor = target->get_argument(1); + VPU_THROW_UNLESS(ngraph::as_type_ptr(outShapeDescriptor), + "DynamicToStaticShape transformation for {] of type {} expects {} as input with index {}", + target->get_friendly_name(), target->get_type_info(), ngraph::opset3::Constant::type_info, 1); + + const auto reshape = std::dynamic_pointer_cast(target); + const auto copied = reshape->clone_with_new_inputs(target->input_values()); + const auto inDataShape = dsr->input(1).get_source_output(); + + const auto outShapeOfReshape = std::make_shared( + inDataShape, outShapeDescriptor, reshape->get_special_zero()); + + ngraph::replace_node(std::move(target), std::make_shared( + copied, outShapeOfReshape)); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_shapeof.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_shapeof.cpp new file mode 100644 index 00000000000..2157883fe76 --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_shapeof.cpp @@ -0,0 +1,41 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_shapeof.hpp" + +#include +#include + +#include + +namespace vpu { + +DynamicToStaticShapeShapeOf::DynamicToStaticShapeShapeOf() : GraphRewrite() { + // We don't set strict_mode when use pattern Matcher, + // so we can set any type and shape for input. + auto inputWithAnyTypeAndShape = std::make_shared( + ngraph::element::dynamic, ngraph::PartialShape{}); + auto shapeOfPattern = std::make_shared(inputWithAnyTypeAndShape); + + ngraph::graph_rewrite_callback callback = [](ngraph::pattern::Matcher &m) { + auto shapeOfNode = std::dynamic_pointer_cast(m.get_match_root()); + if (!shapeOfNode) { + return false; + } + + auto dsr = shapeOfNode->input_value(0).get_node_shared_ptr(); + if (!ngraph::as_type_ptr(dsr)) { + return false; + } + + ngraph::replace_node(shapeOfNode, dsr->input_value(1).get_node_shared_ptr()); + + return true; + }; + + auto m = std::make_shared(shapeOfPattern, "DynamicToStaticShapeShapeOf"); + this->add_matcher(m, callback, ngraph::pass::PassProperty::CHANGE_DYNAMIC_STATE); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_transpose.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_transpose.cpp index 3fa3dad6aa8..ac1bb3a6c1d 100644 --- a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_transpose.cpp +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_transpose.cpp @@ -30,9 +30,9 @@ void dynamicToStaticShapeTranspose(std::shared_ptr target) { const auto shape = dsr->input(1).get_source_output(); const auto axis = std::make_shared( - ngraph::element::u64, + ngraph::element::i64, ngraph::Shape{std::initializer_list{1}}, - std::vector{0}); + std::vector{0}); const auto scatterElementsUpdate = std::make_shared(shape, transposition, shape, axis); ngraph::replace_node(std::move(target), std::make_shared(copied, scatterElementsUpdate)); } diff --git a/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_variadic_split.cpp b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_variadic_split.cpp new file mode 100644 index 00000000000..9828c00c04f --- /dev/null +++ b/inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_variadic_split.cpp @@ -0,0 +1,77 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_variadic_split.hpp" + +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" +#include + +#include "ngraph/graph_util.hpp" +#include "ngraph/opsets/opset3.hpp" + +#include +#include +#include + +namespace vpu { + +void dynamicToStaticShapeVariadicSplit(std::shared_ptr target) { + const auto dsr = ngraph::as_type_ptr(target->input_value(0).get_node_shared_ptr()); + VPU_THROW_UNLESS(dsr, "DynamicToStaticShape transformation for {} of type {} expects {} as input with index {}", + target->get_friendly_name(), target->get_type_info(), ngraph::vpu::op::DynamicShapeResolver::type_info, 0); + + const auto axis_node = ngraph::as_type_ptr(target->input_value(1).get_node_shared_ptr()); + VPU_THROW_UNLESS(axis_node, "dynamicToStaticShapeVariadic transformation is not applicable for {}, dynamic axis is not supported", target); + + const auto data_rank = target->get_input_partial_shape(0).rank(); + VPU_THROW_UNLESS(data_rank.is_static(), "dynamicToStaticShapeVariadic transformation for {} doesn't support dynamic rank", target); + + int64_t axis = ngraph::normalize_axis(target->description(), axis_node->cast_vector()[0], data_rank); + + const auto split_lengths_node = ngraph::as_type_ptr(target->input_value(2).get_node_shared_ptr()); + VPU_THROW_UNLESS(split_lengths_node, "dynamicToStaticShapeVariadic transformation is not applicable for {}, dynamic split_length is not supported", target); + const auto split_lengths = split_lengths_node->cast_vector(); + + for (const auto & i : split_lengths) { + VPU_THROW_UNLESS(i != -1, "dynamicToStaticShapeVariadic transformation is not applicable for {}, split_length with -1 is not supported", target); + VPU_THROW_UNLESS(i > 0, "dynamicToStaticShapeVariadic transformation is not applicable for {}, non-positive split_length is not supported", target); + } + + const auto data_shape = dsr->input_value(1).get_node_shared_ptr(); + const auto copied = target->clone_with_new_inputs(target->input_values()); + const auto data_rank_value = data_rank.get_length(); + ngraph::OutputVector first_shape_part, second_shape_part; + if (axis) { + std::vector first_data_shape_part_indices(axis); + std::iota(first_data_shape_part_indices.begin(), first_data_shape_part_indices.end(), 0); + const auto first_data_shape_part = std::make_shared( + data_shape, + ngraph::opset3::Constant::create(ngraph::element::i64, {first_data_shape_part_indices.size()}, first_data_shape_part_indices), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0})); + first_shape_part.push_back(first_data_shape_part); + } + if (axis + 1 < data_rank_value) { + std::vector second_data_shape_part_indices(data_rank_value - axis - 1); + std::iota(second_data_shape_part_indices.begin(), second_data_shape_part_indices.end(), axis + 1); + const auto second_data_shape_part = std::make_shared( + data_shape, + ngraph::opset3::Constant::create(ngraph::element::i64, {second_data_shape_part_indices.size()}, second_data_shape_part_indices), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0})); + second_shape_part.push_back(second_data_shape_part); + } + for (auto i = 0; i < split_lengths.size(); ++i) { + const auto dim = ngraph::opset3::Constant::create(data_shape->get_element_type(), {1}, {split_lengths[i]}); + if (!first_shape_part.empty() || !second_shape_part.empty()) { + ngraph::OutputVector output_dims{dim}; + output_dims.insert(output_dims.begin(), first_shape_part.begin(), first_shape_part.end()); + output_dims.insert(output_dims.end(), second_shape_part.begin(), second_shape_part.end()); + const auto output_shape = std::make_shared(output_dims, 0); + target->output(i).replace(std::make_shared(copied->output(i), output_shape)); + } else { + target->output(i).replace(std::make_shared(copied->output(i), dim)); + } + } +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/common/src/utils/simple_math.cpp b/inference-engine/src/vpu/common/src/utils/simple_math.cpp index 4b9917c0bf2..79a8179cefb 100644 --- a/inference-engine/src/vpu/common/src/utils/simple_math.cpp +++ b/inference-engine/src/vpu/common/src/utils/simple_math.cpp @@ -10,8 +10,6 @@ #include #include #include -#include -#include #include #include @@ -20,102 +18,109 @@ namespace vpu { namespace { -const std::set whitespaces = { - ' ', - '\t', +using ValueType = details::IntOrFloat; + +struct Operator { + int priority; + std::function op; }; -// priority, function -using Operator = std::pair>; - -const std::map operators = { - { '+', { 0, std::plus() } }, - { '-', { 0, std::minus() } }, - { '*', { 1, std::multiplies() } }, - { '/', { 1, std::divides() } }, - { '%', { 1, std::modulus() } }, +static const std::map operators = { + { "+", { 0, std::plus() }}, + { "-", { 0, std::minus() }}, + { "*", { 1, std::multiplies() }}, + { "/", { 1, std::divides() }}, + { "%", { 1, std::modulus() }} }; +static const std::map> function = { + {"floor", [](ValueType x) { return ValueType{std::floor(x.toFloat())}; }}, + {"ceil" , [](ValueType x) { return ValueType{std::ceil(x.toFloat())}; }}, + {"round", [](ValueType x) { return ValueType{std::round(x.toFloat())}; }}, + {"abs" , [](ValueType x) { return ValueType{std::abs(x.toFloat())}; }}, + {"sqrt" , [](ValueType x) { return ValueType{std::sqrt(x.toFloat())}; }} +}; + +bool isFunction(const std::string& token) { + return function.find(token) != function.end(); +} +bool isOperator(const std::string& token) { + return operators.find(token) != operators.end(); +} +int opPriority(const std::string& token) { + return operators.at(token).priority; +} + } // namespace -void SimpleMathExpression::parse(const std::string& expression) { +void MathExpression::parse(const std::string& expression) { _parsedTokens.clear(); + std::stack tokenStack; - std::stack operatorStack; - - // While there are tokens to be read. - for (size_t i = 0; i != expression.length(); i++) { - // Ignore whitespaces; - while (whitespaces.find(expression[i]) != whitespaces.end()) { - i++; + for (auto it = begin(expression); it != end(expression); ++it) { + if (*it == ' ' || *it == '\t') { + continue; } - // Read a token. - auto curr = expression[i]; - - // If the token is a number, then push it to the output queue. - if (std::isdigit(curr)) { + // parse number + if (std::isdigit(*it)) { size_t len = 0; - auto value = std::stoi(expression.substr(i), &len); + const auto value = std::stof(&*it, &len); - _parsedTokens.emplace_back(Token(Token::Value, value, 0)); - - i += (len - 1); + _parsedTokens.emplace_back(TokenType::Value, ValueType{value}, ""); + std::advance(it, len - 1); continue; } - // If the token is a variable, then push it's value to the output queue. - if (_vars.find(curr) != _vars.end()) { - _parsedTokens.emplace_back(Token(Token::Value, _vars.at(curr), 0)); + // parse variable/function + if (std::isalpha(*it)) { + const auto end_token = std::find_if_not(it, end(expression), + [](char c) { return std::isalnum(c) || c == '_'; }); + const auto token = std::string(it, end_token); + std::advance(it, token.length() - 1); - continue; + if (isFunction(token)) { + tokenStack.push(token); + continue; + } + if (_vars.find(token) != _vars.end()) { + _parsedTokens.emplace_back(TokenType::Value, ValueType{_vars.at(token)}, ""); + continue; + } } - // If the token is an operator, then: - if (operators.find(curr) != operators.end()) { - // While there is an operator at the top of the operator stack with - // greater than or equal to precedence: - // pop operators from the operator stack, onto the output queue; - while (!operatorStack.empty() && - (operators.find(operatorStack.top()) != operators.end()) && - (operators.at(operatorStack.top()).first >= operators.at(curr).first)) { - auto op = operatorStack.top(); - operatorStack.pop(); - - _parsedTokens.emplace_back(Token(Token::Operator, 0, op)); + // parse operator + if (isOperator(std::string(1, *it))) { + while (!tokenStack.empty() + && (isFunction(tokenStack.top()) + || (isOperator(tokenStack.top()) + && opPriority(tokenStack.top()) >= opPriority(std::string(1, *it))))) { + const auto tokenType = isOperator(tokenStack.top()) ? TokenType::Operator + : TokenType::Function; + _parsedTokens.emplace_back(tokenType, ValueType{0}, tokenStack.top()); + tokenStack.pop(); } - // push the read operator onto the operator stack. - operatorStack.push(curr); - + tokenStack.push(std::string(1, *it)); continue; } - // If the token is a left bracket (i.e. "("), then: - // push it onto the operator stack. - if (curr == '(') { - operatorStack.push(curr); - + if (*it == '(') { + tokenStack.push("("); continue; } - // If the token is a right bracket (i.e. ")"), then: - if (curr == ')') { - // While the operator at the top of the operator stack is not a left bracket: - // pop operators from the operator stack onto the output queue; - while (!operatorStack.empty() && - operatorStack.top() != '(') { - _parsedTokens.emplace_back(Token(Token::Operator, 0, operatorStack.top())); - - operatorStack.pop(); + if (*it == ')') { + while (!tokenStack.empty() && tokenStack.top() != "(") { + const auto tokenType = isOperator(tokenStack.top()) ? TokenType::Operator + : TokenType::Function; + _parsedTokens.emplace_back(tokenType, ValueType{0}, tokenStack.top()); + tokenStack.pop(); } - // pop the left bracket from the stack. - // If the stack runs out without finding a left bracket, then there are mismatched parentheses. - if (!operatorStack.empty() && - operatorStack.top() == '(') { - operatorStack.pop(); + if (!tokenStack.empty()) { + tokenStack.pop(); } else { VPU_THROW_EXCEPTION << "Mismatched parentheses in " << expression; } @@ -123,51 +128,54 @@ void SimpleMathExpression::parse(const std::string& expression) { continue; } - // Unknown token - VPU_THROW_EXCEPTION << "Unknown token " << curr << " in " << expression; + VPU_THROW_EXCEPTION << "Unknown token " << *it << " in " << expression; } - // If there are no more tokens to read: - // while there are still operator tokens on the stack: - // if the operator token on the top of the stack is a bracket, then - // there are mismatched parentheses; - // pop the operator onto the output queue. - while (!operatorStack.empty()) { - if (operatorStack.top() == '(') { + while (!tokenStack.empty()) { + if (tokenStack.top() == "(") { VPU_THROW_EXCEPTION << "Mismatched parentheses in " << expression; } - - _parsedTokens.emplace_back(Token(Token::Operator, 0, operatorStack.top())); - - operatorStack.pop(); + const auto tokenType = isOperator(tokenStack.top()) ? TokenType::Operator + : TokenType::Function; + _parsedTokens.emplace_back(tokenType, ValueType{0}, tokenStack.top()); + tokenStack.pop(); } } -int SimpleMathExpression::evaluate() const { - std::stack values; - for (const auto& t : _parsedTokens) { - switch (t.type) { - case Token::Value: - values.push(t.value); - break; - case Token::Operator: { - if (values.size() < 2) { - VPU_THROW_EXCEPTION << "Illegal expression: not enough values for operator evaluation"; +int MathExpression::evaluate() const { + std::stack values; + + for (const auto& token : _parsedTokens) { + switch (token.type) { + case TokenType::Value: + values.push(token.value); + break; + case TokenType::Operator: { + if (values.size() < 2) { + VPU_THROW_EXCEPTION << "Illegal expression: not enough values for operator evaluation"; + } + + auto val2 = values.top(); + values.pop(); + + auto val1 = values.top(); + values.pop(); + + values.push(operators.at(token.opName).op(val1, val2)); + break; } + case TokenType::Function: { + if (values.empty()) { + VPU_THROW_EXCEPTION << "Illegal expression: not enough values for function evaluation"; + } + auto val1 = values.top(); + values.pop(); - // pop last 2 values and apply operand - auto val2 = values.top(); - values.pop(); - - auto val1 = values.top(); - values.pop(); - - values.push(operators.at(t.op).second(val1, val2)); - - break; - } - default: - VPU_THROW_EXCEPTION << "Illegal expression: unhandled token"; + values.push(function.at(token.opName)(val1)); + break; + } + default: + VPU_THROW_EXCEPTION << "Illegal expression: unhandled token"; } } @@ -175,7 +183,7 @@ int SimpleMathExpression::evaluate() const { VPU_THROW_EXCEPTION << "Illegal expression: not enough operators"; } - return values.top(); + return values.top().toFloat(); } } // namespace vpu diff --git a/inference-engine/src/vpu/custom_kernels/binary_convolution1x1.cl b/inference-engine/src/vpu/custom_kernels/binary_convolution1x1.cl index 5954902cdfe..05bd7e75785 100644 --- a/inference-engine/src/vpu/custom_kernels/binary_convolution1x1.cl +++ b/inference-engine/src/vpu/custom_kernels/binary_convolution1x1.cl @@ -1,3 +1,7 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + #pragma OPENCL EXTENSION cl_khr_fp16 : enable ushort extract_weights(uchar val, int bit) diff --git a/inference-engine/src/vpu/custom_kernels/binary_convolution3x3.cl b/inference-engine/src/vpu/custom_kernels/binary_convolution3x3.cl index 6e242228bb2..db23c37f4dd 100644 --- a/inference-engine/src/vpu/custom_kernels/binary_convolution3x3.cl +++ b/inference-engine/src/vpu/custom_kernels/binary_convolution3x3.cl @@ -1,3 +1,6 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/binary_layers.cl b/inference-engine/src/vpu/custom_kernels/binary_layers.cl index 5a8122181f2..1924f335b22 100644 --- a/inference-engine/src/vpu/custom_kernels/binary_layers.cl +++ b/inference-engine/src/vpu/custom_kernels/binary_layers.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/convolution1x1.cl b/inference-engine/src/vpu/custom_kernels/convolution1x1.cl index b9e2bef158d..6ae0e2cfab4 100644 --- a/inference-engine/src/vpu/custom_kernels/convolution1x1.cl +++ b/inference-engine/src/vpu/custom_kernels/convolution1x1.cl @@ -1,3 +1,7 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + #pragma OPENCL EXTENSION cl_khr_fp16 : enable __kernel void Convolution1x1_NCHW( diff --git a/inference-engine/src/vpu/custom_kernels/convolution3x3.cl b/inference-engine/src/vpu/custom_kernels/convolution3x3.cl index c1d2970d0c2..5c054ed1c81 100644 --- a/inference-engine/src/vpu/custom_kernels/convolution3x3.cl +++ b/inference-engine/src/vpu/custom_kernels/convolution3x3.cl @@ -1,3 +1,7 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + #pragma OPENCL EXTENSION cl_khr_fp16 : enable __kernel void Convolution3x3(const __global half* in_param, diff --git a/inference-engine/src/vpu/custom_kernels/correlate.cl b/inference-engine/src/vpu/custom_kernels/correlate.cl new file mode 100644 index 00000000000..0a7b3aeeabe --- /dev/null +++ b/inference-engine/src/vpu/custom_kernels/correlate.cl @@ -0,0 +1,449 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +#define MAX_OPENCL_BUFF_SIZE 64*1024 + +// Define if runtime supports it. MX runtime is compatible, KMB is in WIP state +#define USE_MANUAL_DMA 1 + +#if defined (USE_MANUAL_DMA) +void dmacpyLineSrcStrideStart(global half* from, private half* to, int size, int src_width, int src_stride) +{ + item_dma_event_t copyEvent = WorkItemDmaCreateStrideTransaction(from, to, src_width, src_width, src_stride, src_width, size, 0); + WaitWorkItemDmaEvents(1, ©Event); +} + +void dmacpyLineDstStrideStart(private half* from, global half* to, int size, int src_width, int src_stride) +{ + item_dma_event_t copyEvent = WorkItemDmaCreateStrideTransaction(from, to, src_width, src_width, src_width, src_stride, size, 0); + WaitWorkItemDmaEvents(1, ©Event); +} +#endif + +void memzero(void * ptr, size_t num) +{ + float4* line0_ = (float4*) ptr; + #pragma unroll 16 + for (int i = 0; i < num/16; i++) + { + line0_[i] = (float4){0.f, 0.f, 0.f, 0.f}; + } + uchar* ptr_ = (uchar*) ptr; + for (int i = num/16*16; i < num; i++) + { + ptr_[i] = 0; + } +} + +void __attribute__((noinline)) crosscorrh(__private const half* restrict line0, + __private const half* restrict line1, + __private half* restrict dline, + int topwidth, + int max_displacement, + int neighborhood_grid_radius, + int kernel_size, + int padding, + int bottomwidth, + int stride1, + int stride2, + int max_channels, + int cur_subchannels) +{ + if (max_channels == 64) + { + for (int i = 0; i < kernel_size; i++) + { + int x1 = max_displacement - padding + i; + int offset1 = x1 >= 0 ? 0 : (-x1 + stride1 - 1)/stride1; + x1 += offset1*stride1; + + for (int blockIdx_x = offset1; blockIdx_x < topwidth && x1 < bottomwidth; blockIdx_x++, x1 += stride1) + { + int x2 = x1 - neighborhood_grid_radius*stride2; + int offset2 = x2 >= 0 ? 0 : (-x2 + stride2 - 1)/stride2; + x2 += offset2*stride2; + + for (int top_channel_x = offset2 - neighborhood_grid_radius; + top_channel_x <= neighborhood_grid_radius && x2 < bottomwidth; + top_channel_x++, x2 += stride2) + { + half8 sum4 = (half8){0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}; + + half8* src0 = (half8*)(line0 + x1*max_channels); + half8* src1 = (half8*)(line1 + x2*max_channels); + + #pragma unroll 8 + for (int ch = 0; ch < max_channels/8; ch++) + sum4 += (src0[ch])*(src1[ch]); + + half sum = __builtin_shave_sau_sumx_f16_r(sum4); + dline[(top_channel_x + neighborhood_grid_radius)*topwidth + blockIdx_x] += (sum); + } + } + } + } + else + { + int neighborhood_grid_width = 2*neighborhood_grid_radius + 1; + + for (int blockIdx_x = 0; blockIdx_x < topwidth; blockIdx_x++) + { + for (int i = 0; i < kernel_size; i++) + { + int x1 = blockIdx_x*stride1 + max_displacement + i - padding; + + if ((x1 >= 0) && (x1 < bottomwidth)) + { + int o_min = - neighborhood_grid_radius*stride2; + int o_max = neighborhood_grid_width*stride2 - neighborhood_grid_radius*stride2; + if ((o_min) < ( - x1)) o_min -= ((x1 + o_min - (stride2 - 1))/stride2)*stride2; + if ((o_max) >= (bottomwidth+stride2 - x1)) o_max -= ((x1 + o_max - bottomwidth )/stride2)*stride2; + + int o = o_min; + for (; o <= o_max - 4*stride2; o += 4*stride2) + { + half8* bottom0 = (half8*)(line0 + x1*max_channels); + half8* bottom1_0 = (half8*)(line1 + (x1 + o + 0*stride2)*max_channels); + half8* bottom1_1 = (half8*)(line1 + (x1 + o + 1*stride2)*max_channels); + half8* bottom1_2 = (half8*)(line1 + (x1 + o + 2*stride2)*max_channels); + half8* bottom1_3 = (half8*)(line1 + (x1 + o + 3*stride2)*max_channels); + + int c = 0; + + half8 sum40 = 0; + half8 sum41 = 0; + half8 sum42 = 0; + half8 sum43 = 0; + + for (; c <= cur_subchannels/8 - 4; c += 4) + { + sum40 += bottom0[c + 0] * bottom1_0[c + 0]; + sum40 += bottom0[c + 1] * bottom1_0[c + 1]; + sum40 += bottom0[c + 2] * bottom1_0[c + 2]; + sum40 += bottom0[c + 3] * bottom1_0[c + 3]; + + sum41 += bottom0[c + 0] * bottom1_1[c + 0]; + sum41 += bottom0[c + 1] * bottom1_1[c + 1]; + sum41 += bottom0[c + 2] * bottom1_1[c + 2]; + sum41 += bottom0[c + 3] * bottom1_1[c + 3]; + + sum42 += bottom0[c + 0] * bottom1_2[c + 0]; + sum42 += bottom0[c + 1] * bottom1_2[c + 1]; + sum42 += bottom0[c + 2] * bottom1_2[c + 2]; + sum42 += bottom0[c + 3] * bottom1_2[c + 3]; + + sum43 += bottom0[c + 0] * bottom1_3[c + 0]; + sum43 += bottom0[c + 1] * bottom1_3[c + 1]; + sum43 += bottom0[c + 2] * bottom1_3[c + 2]; + sum43 += bottom0[c + 3] * bottom1_3[c + 3]; + } + + for (; c < cur_subchannels/8; c++) + { + sum40 += bottom0[c] * bottom1_0[c]; + sum41 += bottom0[c] * bottom1_1[c]; + sum42 += bottom0[c] * bottom1_2[c]; + sum43 += bottom0[c] * bottom1_3[c]; + } + + half sum0 = __builtin_shave_sau_sumx_f16_r(sum40); + half sum1 = __builtin_shave_sau_sumx_f16_r(sum41); + half sum2 = __builtin_shave_sau_sumx_f16_r(sum42); + half sum3 = __builtin_shave_sau_sumx_f16_r(sum43); + + for (c = c*8; c < cur_subchannels; c++) + { + sum0 += line0[x1*max_channels + c] * line1[(x1 + o + 0*stride2)*max_channels + c]; + sum1 += line0[x1*max_channels + c] * line1[(x1 + o + 1*stride2)*max_channels + c]; + sum2 += line0[x1*max_channels + c] * line1[(x1 + o + 2*stride2)*max_channels + c]; + sum3 += line0[x1*max_channels + c] * line1[(x1 + o + 3*stride2)*max_channels + c]; + } + + dline[blockIdx_x + (((o/stride2) + 0)*topwidth + neighborhood_grid_radius*topwidth)] += sum0; + dline[blockIdx_x + (((o/stride2) + 1)*topwidth + neighborhood_grid_radius*topwidth)] += sum1; + dline[blockIdx_x + (((o/stride2) + 2)*topwidth + neighborhood_grid_radius*topwidth)] += sum2; + dline[blockIdx_x + (((o/stride2) + 3)*topwidth + neighborhood_grid_radius*topwidth)] += sum3; + } + + for (; o < o_max; o += 1*stride2) + { + half8* bottom0 = (half8*)(line0 + x1*max_channels); + half8* bottom1 = (half8*)(line1 + (x1 + o)*max_channels); + + int c = 0; + + half8 sum4 = 0; + for (; c <= cur_subchannels/8 - 4; c += 4) + { + sum4 += bottom0[c + 0] * bottom1[c + 0]; + sum4 += bottom0[c + 1] * bottom1[c + 1]; + sum4 += bottom0[c + 2] * bottom1[c + 2]; + sum4 += bottom0[c + 3] * bottom1[c + 3]; + } + for (; c < cur_subchannels/8; c++) + { + sum4 += bottom0[c] * bottom1[c]; + } + + half sum = __builtin_shave_sau_sumx_f16_r(sum4); + + for (c = c*8; c < cur_subchannels; c++) + { + sum += line0[x1*max_channels + c] * line1[(x1 + o)*max_channels + c]; + } + + dline[blockIdx_x + (((o + neighborhood_grid_radius*stride2)/stride2)*topwidth)] += sum; + } + } + } + } + } +} + + +__kernel void correlate2_half(__global const half* restrict bottom0, + __global const half* restrict bottom1, + __global half* restrict top, + int topwidth, + int topheight, + int bottomwidth, + int bottomheight, + int bottomchannels, + int max_displacement, + int padding, + int neighborhood_grid_radius, + int neighborhood_grid_width, + int kernel_size, + int stride1, + int stride2) +{ + int max_channels = (MAX_OPENCL_BUFF_SIZE/sizeof(half) - topwidth*neighborhood_grid_width) / (3*bottomwidth); + if (max_channels > 64) max_channels = 64; + int subchannels_count = (bottomchannels + max_channels - 1) / max_channels; + int subchannels = (bottomchannels + subchannels_count-1) / subchannels_count; + if (subchannels < max_channels) subchannels = max_channels; + + const int sumelems = kernel_size*kernel_size*bottomchannels; + + __private half cmx[MAX_OPENCL_BUFF_SIZE/sizeof(half)]; + + __private half* line0 = cmx; + __private half* line1 = line0 + bottomwidth*subchannels; + __private half* dline = line1 + bottomwidth*subchannels; + + int blockIdx_y = get_global_id(0); + +#if defined(USE_MANUAL_DMA) + __private half* dmabuf = dline + topwidth*neighborhood_grid_width; +#endif + + int y1 = blockIdx_y*stride1 + max_displacement; + + for (int j = 0; j < kernel_size; j++) + { + for (int bottomchannel = 0; bottomchannel < bottomchannels; bottomchannel += subchannels) + { + // configure channel batching + int startchannel = bottomchannel; + int endchannel = startchannel + subchannels > bottomchannels ? bottomchannels : startchannel + subchannels; + int deltachannels = endchannel-startchannel; + + // load line form blob 0 with repackaging + if (y1+j-padding >= 0 && y1+j-padding < bottomheight) + { +#if defined(USE_MANUAL_DMA) + __global const half* curr = bottom0 + startchannel*bottomheight*bottomwidth + (y1+j-padding)*bottomwidth; + dmacpyLineSrcStrideStart(curr, + dmabuf, + bottomwidth*deltachannels*sizeof(half), + bottomwidth*sizeof(half), + bottomwidth*bottomheight*sizeof(half)); + + for (int ch = 0; ch < deltachannels; ch++) + { + for (int blockIdx_x = 0; blockIdx_x < bottomwidth/8; blockIdx_x++) + { + half8 val = ((half8*)(dmabuf + ch*bottomwidth))[blockIdx_x]; + line0[(blockIdx_x*8 + 0)*max_channels+ch] = val[0]; + line0[(blockIdx_x*8 + 1)*max_channels+ch] = val[1]; + line0[(blockIdx_x*8 + 2)*max_channels+ch] = val[2]; + line0[(blockIdx_x*8 + 3)*max_channels+ch] = val[3]; + + line0[(blockIdx_x*8 + 4)*max_channels+ch] = val[4]; + line0[(blockIdx_x*8 + 5)*max_channels+ch] = val[5]; + line0[(blockIdx_x*8 + 6)*max_channels+ch] = val[6]; + line0[(blockIdx_x*8 + 7)*max_channels+ch] = val[7]; + } + + for (int blockIdx_x = bottomwidth/8*8; blockIdx_x < bottomwidth; blockIdx_x++) + { + line0[(blockIdx_x)*max_channels+ch] = dmabuf[blockIdx_x + ch*bottomwidth]; + } + } + + if (deltachannels < subchannels) + for (int blockIdx_x = 0; blockIdx_x < bottomwidth; blockIdx_x++) + memzero(line0 + blockIdx_x*max_channels+deltachannels, (subchannels-deltachannels)*sizeof(half)); +#else + for (int blockIdx_x = 0; blockIdx_x < bottomwidth; blockIdx_x++) + { + for (int ch = 0; ch < deltachannels; ch++) + line0[blockIdx_x*max_channels+ch] + = bottom0[(ch+startchannel)*bottomheight*bottomwidth + (y1+j-padding)*bottomwidth + blockIdx_x]; + + if (deltachannels < subchannels) + memzero(line0 + blockIdx_x*max_channels+deltachannels, (subchannels-deltachannels)*sizeof(half)); + } +#endif + } + else + memzero(line0, max_channels*bottomwidth*sizeof(half)); + + for (int top_channel_y = 0; top_channel_y < neighborhood_grid_width; top_channel_y++) + { + int y2 = y1 + (top_channel_y - neighborhood_grid_radius) * stride2; + + // load line form blob 1 with repackaging according to the line we work on now + if (y2+j-padding >= 0 && y2+j-padding < bottomheight) + { +#if defined(USE_MANUAL_DMA) + __global const half* curr = bottom1 + startchannel*bottomheight*bottomwidth + (y2+j-padding)*bottomwidth; + dmacpyLineSrcStrideStart(curr, + dmabuf, + bottomwidth*deltachannels*sizeof(half), + bottomwidth*sizeof(half), + bottomwidth*bottomheight*sizeof(half)); + + for (int ch = 0; ch < deltachannels; ch++) + { + for (int blockIdx_x = 0; blockIdx_x < bottomwidth/8; blockIdx_x++) + { + half8 val = ((half8*)(dmabuf + ch*bottomwidth))[blockIdx_x]; + line1[(blockIdx_x*8 + 0)*max_channels+ch] = val[0]; + line1[(blockIdx_x*8 + 1)*max_channels+ch] = val[1]; + line1[(blockIdx_x*8 + 2)*max_channels+ch] = val[2]; + line1[(blockIdx_x*8 + 3)*max_channels+ch] = val[3]; + + line1[(blockIdx_x*8 + 4)*max_channels+ch] = val[4]; + line1[(blockIdx_x*8 + 5)*max_channels+ch] = val[5]; + line1[(blockIdx_x*8 + 6)*max_channels+ch] = val[6]; + line1[(blockIdx_x*8 + 7)*max_channels+ch] = val[7]; + } + + for (int blockIdx_x = bottomwidth/8*8; blockIdx_x < bottomwidth; blockIdx_x++) + { + line1[(blockIdx_x)*max_channels+ch] = dmabuf[blockIdx_x + ch*bottomwidth]; + } + } +#else + for (int ch = 0; ch < deltachannels; ch++) + { + for (int blockIdx_x = 0; blockIdx_x < bottomwidth/8; blockIdx_x++) + { + half8 val = ((__global half8*)(bottom1 + (ch+startchannel)*bottomheight*bottomwidth + (y2+j-padding)*bottomwidth))[blockIdx_x]; + line1[(blockIdx_x*8 + 0)*max_channels+ch] = val[0]; + line1[(blockIdx_x*8 + 1)*max_channels+ch] = val[1]; + line1[(blockIdx_x*8 + 2)*max_channels+ch] = val[2]; + line1[(blockIdx_x*8 + 3)*max_channels+ch] = val[3]; + + line1[(blockIdx_x*8 + 4)*max_channels+ch] = val[4]; + line1[(blockIdx_x*8 + 5)*max_channels+ch] = val[5]; + line1[(blockIdx_x*8 + 6)*max_channels+ch] = val[6]; + line1[(blockIdx_x*8 + 7)*max_channels+ch] = val[7]; + } + for (int blockIdx_x = bottomwidth/8*8; blockIdx_x < bottomwidth; blockIdx_x++) + { + half val = (bottom1 + (ch+startchannel)*bottomheight*bottomwidth + (y2+j-padding)*bottomwidth)[blockIdx_x]; + line1[(blockIdx_x)*max_channels+ch] = val; + } + } +#endif + for (int blockIdx_x = 0; blockIdx_x < bottomwidth; blockIdx_x++) + { + if (deltachannels < subchannels) + memzero(line1 + blockIdx_x*max_channels+deltachannels, (subchannels-deltachannels)*sizeof(half)); + } + } + else + memzero(line1, max_channels*bottomwidth*sizeof(half)); + + if(j == 0 && startchannel == 0) + { + memzero(dline, neighborhood_grid_width*topwidth*sizeof(half)); + } + else + { +#if defined(USE_MANUAL_DMA) + dmacpyLineSrcStrideStart(top + top_channel_y*neighborhood_grid_width*topheight*topwidth + blockIdx_y*topwidth, + dline, + topwidth*neighborhood_grid_width*sizeof(half), + topwidth*sizeof(half), + topwidth*topheight*sizeof(half)); +#else + for (int top_channel_x = 0; top_channel_x < neighborhood_grid_width; top_channel_x++) + { + for (int blockIdx_x = 0; blockIdx_x < topwidth/8; blockIdx_x++) + { + half8 val = ((__global half8*)(top + ((top_channel_y*neighborhood_grid_width+top_channel_x)*topheight*topwidth + blockIdx_y*topwidth)))[blockIdx_x]; + ((half8*)(dline + top_channel_x*topwidth))[blockIdx_x] = val; + } + for (int blockIdx_x = (topwidth/8)*8; blockIdx_x < topwidth; blockIdx_x++) + { + dline[top_channel_x*topwidth+blockIdx_x] = + top[(top_channel_y*neighborhood_grid_width+top_channel_x)*topheight*topwidth + blockIdx_y*topwidth+blockIdx_x]; + } + } +#endif + } + + if (y1+j-padding >= 0 && y1+j-padding < bottomheight && y2+j-padding >= 0 && y2+j-padding < bottomheight) + { + crosscorrh(line0, line1, dline, topwidth, max_displacement, neighborhood_grid_radius, + kernel_size, padding, bottomwidth, stride1, stride2, max_channels, subchannels); + } + + if (j == kernel_size-1 && endchannel == bottomchannels) + { + half8 scale = (half8){(half)sumelems, (half)sumelems, (half)sumelems, (half)sumelems, (half)sumelems, (half)sumelems, (half)sumelems, (half)sumelems}; + for (int top_channel_x = 0; top_channel_x < neighborhood_grid_width; top_channel_x++) + { + for (int blockIdx_x = 0; blockIdx_x < topwidth/8; blockIdx_x++) + { + ((half8*)(dline + top_channel_x*topwidth))[blockIdx_x] = + ((half8*)(dline + top_channel_x*topwidth))[blockIdx_x] / scale; + } + for (int blockIdx_x = (topwidth/8)*8; blockIdx_x < topwidth; blockIdx_x++) + { + dline[top_channel_x*topwidth+blockIdx_x] = dline[top_channel_x*topwidth+blockIdx_x]/(half)sumelems; + } + } + } + +#if defined(USE_MANUAL_DMA) + dmacpyLineDstStrideStart(dline, + top + top_channel_y*neighborhood_grid_width*topheight*topwidth + blockIdx_y*topwidth, + topwidth*neighborhood_grid_width*sizeof(half), + topwidth*sizeof(half), + topwidth*topheight*sizeof(half)); +#else + for (int top_channel_x = 0; top_channel_x < neighborhood_grid_width; top_channel_x++) + { + for (int blockIdx_x = 0; blockIdx_x < topwidth/8; blockIdx_x++) + { + ((__global half8*)(top + ((top_channel_y*neighborhood_grid_width+top_channel_x)*topheight*topwidth + blockIdx_y*topwidth)))[blockIdx_x] = + ((half8*)(dline + top_channel_x*topwidth))[blockIdx_x] + (half8) {0, 0, 0, 0, 0, 0, 0, 0}; + } + for (int blockIdx_x = (topwidth/8)*8; blockIdx_x < topwidth; blockIdx_x++) + { + top[(top_channel_y*neighborhood_grid_width+top_channel_x)*topheight*topwidth + blockIdx_y*topwidth+blockIdx_x] + = dline[top_channel_x*topwidth+blockIdx_x] + (half)0; + } + } +#endif + } + } + } +} diff --git a/inference-engine/src/vpu/custom_kernels/ctc.cl b/inference-engine/src/vpu/custom_kernels/ctc.cl index 0197e702616..609fc00251e 100644 --- a/inference-engine/src/vpu/custom_kernels/ctc.cl +++ b/inference-engine/src/vpu/custom_kernels/ctc.cl @@ -1,137 +1,173 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable -__kernel void ctc_ocl(__global half* probabilities, - __global half* output_sequences, - int C_) -{ - size_t t = get_global_id(0); - - __global half* probs = probabilities + t * C_; - - int max_class_idx = 0; - half max_prob = probs[0]; - ++probs; - for (int c = 1 ; c < C_ ; c++, ++probs) - { - if (*probs > max_prob) - { - max_prob = *probs; - max_class_idx = c; +__global half *find(__global const half *begin, __global const half *end, half value) { + while (begin != end) { + if (*begin == value) { + return begin; } + ++begin; } - output_sequences[t] = (half)max_class_idx; + return end; } -__kernel void postProcess(__global half* input, - __global half* output, - __global half* seq_ind, - int height, - int width, - int classes) +#define USE_MANUAL_DMA + +#ifdef USE_MANUAL_DMA + +__kernel void __dma_preload_CTCDecoder(__global half *probabilities, + __global half *sequence_indicators, + __global half *output_sequences, + int width, + int height, + int channels, + __local half *local_src, + __local half *local_dst) { - int wr_index = 0; - int rd_index = 0; - - half update_data; - int update_index; - - for (int i = 0; i < classes; i++) - { - output[i] = (half)(-1); - } - - for (int n = 0; n < height; ++n) - { - int prev_class_id = -1; - for (int t = 0; t < classes; ++t) - { - int class_id = (int)input[rd_index++]; - update_index = wr_index; - update_data = output[update_index]; - - if ((class_id < (width - 1)) && !(1 && class_id == prev_class_id)) - { - update_data = (half)class_id; - wr_index++; - - } - output[update_index] = update_data; - prev_class_id = class_id; - - if (seq_ind[t + 1] == 0 ) { - break; - } - } - } + WorkGroupDmaCreateStrideTransaction( + probabilities, // src + local_src, // dst + width * sizeof(half), // src_width, + width * sizeof(half), // dst_width, + width * height * sizeof(half), // src_stride, + width * sizeof(half), // dst_stride, + width * height * channels * sizeof(half), // size + 0); } -__kernel void ctc_ref_fp16(__global half* probabilities, __global half* seq_ind, __global half* output_sequences, int C, int H, int W) +__kernel void __dma_postwrite_CTCDecoder(__global half *probabilities, + __global half *sequence_indicators, + __global half *output_sequences, + int width, + int height, + int channels, + __local half *local_src, + __local half *local_dst) { - int T_ = C; - int N_ = H; - int C_ = W; + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + output_sequences, // dst + channels * sizeof(half), // src_width, + channels * sizeof(half), // dst_width, + channels * sizeof(half), // src_stride, + channels * sizeof(half), // dst_stride, + channels * height * sizeof(half), // size + 0); +} - // Fill output_sequences with -1 - for (int i = 0; i < T_; i++) +__kernel void CTCDecoder(__global half *probabilities, + __global half *sequence_indicators, + __global half *output_sequences, + int width, + int height, + int channels, + __local half *local_src, + __local half *local_dst) +{ + const int T = channels; + const int B = height; + const int C = width; + + for (int i = 0; i < B*T; i++) { - output_sequences[i] = (half)(-1.0); + local_dst[i] = -1.h; } + int output_index = 0; - // Caffe impl - for(int n = 0; n < N_; ++n) + for (int b = 0; b < B; ++b) { + __global const half *seq_ind = sequence_indicators + b*T; + const int seq_len = find(seq_ind + 1, seq_ind + T, 0.h) - seq_ind; + const int time = min(seq_len, T); + int prev_class_idx = -1; - for (int t = 0; t < T_; ++t) + for (int t = 0; t < time; ++t) { - // get maximum probability and its index + __local const half *probs = local_src + b*C + t*C*B; int max_class_idx = 0; - __global half* probs; - half max_prob; + half max_prob = probs[0]; - probs = probabilities + t*C_; - max_prob = probs[0]; - ++probs; - - for (int c = 1; c < C_; ++c, ++probs) + for (int c = 1; c < C; ++c) { - if (*probs > max_prob) + const half prob = probs[c]; + if (prob > max_prob) { max_class_idx = c; - max_prob = *probs; + max_prob = prob; } } - //if (max_class_idx != blank_index_ - // && !(merge_repeated_&& max_class_idx == prev_class_idx)) - if (max_class_idx < C_-1 && !(1 && max_class_idx == prev_class_idx)) + if (max_class_idx < C-1 && max_class_idx != prev_class_idx) { - output_sequences[output_index] = (half)max_class_idx; + local_dst[b*T + output_index] = (half)max_class_idx; output_index++; } prev_class_idx = max_class_idx; - - // Assume sequence_indicators is always 1 - if (seq_ind[t + 1] == 0) - { - break; - } } } } + +#else + +__kernel void CTCDecoder(__global half *probabilities, + __global half *sequence_indicators, + __global half *output_sequences, + int width, + int height, + int channels, + __local half *local_src, + __local half *local_dst) +{ + const int T = channels; + const int B = height; + const int C = width; + + for (int i = 0; i < B*T; i++) + { + output_sequences[i] = -1.h; + } + + int output_index = 0; + + for (int b = 0; b < B; ++b) + { + __global const half *seq_ind = sequence_indicators + b*T; + const int seq_len = find(seq_ind + 1, seq_ind + T, 0.h) - seq_ind; + const int time = min(seq_len, T); + + int prev_class_idx = -1; + + for (int t = 0; t < time; ++t) + { + __global const half *probs = probabilities + b*C + t*C*B; + int max_class_idx = 0; + half max_prob = probs[0]; + + for (int c = 1; c < C; ++c) + { + const half prob = probs[c]; + if (prob > max_prob) + { + max_class_idx = c; + max_prob = prob; + } + } + + if (max_class_idx < C-1 && max_class_idx != prev_class_idx) + { + output_sequences[b*T + output_index] = (half)max_class_idx; + output_index++; + } + + prev_class_idx = max_class_idx; + } + } +} + +#endif diff --git a/inference-engine/src/vpu/custom_kernels/customLayerBindings.xml b/inference-engine/src/vpu/custom_kernels/customLayerBindings.xml index 78ade2b651a..929be758fdf 100644 --- a/inference-engine/src/vpu/custom_kernels/customLayerBindings.xml +++ b/inference-engine/src/vpu/custom_kernels/customLayerBindings.xml @@ -1,177 +1,188 @@ - - + - + + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - + + + + + + + + + - - - - - - - - - - - + + + + + + + + + + + + - - - - - - - - - - - - - + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + + + + + + + - - - - - - - - - @@ -179,16 +190,16 @@ + + + + + + + + + - - - - - - - - - @@ -196,25 +207,25 @@ + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - @@ -222,110 +233,110 @@ + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - + + + + + - + - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - - + + + + + @@ -333,183 +344,234 @@ + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + - - - - - - - - + + + + + + + + - - + + - - + + + - - - - - - - - - - - - - - + + + + + + + + + + + + + - - - - - - - - + + + + + + + + - - - - + + + + + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - + + + + + + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/inference-engine/src/vpu/custom_kernels/cvtf32f16.cl b/inference-engine/src/vpu/custom_kernels/cvtf32f16.cl index e592f14ae60..081a74ddc56 100644 --- a/inference-engine/src/vpu/custom_kernels/cvtf32f16.cl +++ b/inference-engine/src/vpu/custom_kernels/cvtf32f16.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/cvtu8f16.cl b/inference-engine/src/vpu/custom_kernels/cvtu8f16.cl index 622621dbf0b..33d7d2f891e 100644 --- a/inference-engine/src/vpu/custom_kernels/cvtu8f16.cl +++ b/inference-engine/src/vpu/custom_kernels/cvtu8f16.cl @@ -1,27 +1,90 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable -__kernel void cvtu8f16(const __global uchar* restrict src, - __global half* restrict dst, - float scale, - float bais) -{ - int idx = get_global_id(0) - + get_global_id(1) * get_global_size(0) - + get_global_id(2) * get_global_size(0) * get_global_size(1); +#define USE_MANUAL_DMA 1 - dst[idx] = convert_half(src[idx])*(half)scale+(half)bais; +#if defined (USE_MANUAL_DMA) + +__kernel void __dma_preload_cvtu8f16( + __global uchar* restrict src, + __global half* restrict dst, + float scale, + float bias, + __local uchar* restrict local_src, + __local half* restrict local_dst) +{ + WorkGroupDmaCreate3DTransaction( + src + get_group_id(0)*get_local_size(0) + + get_group_id(1)*get_local_size(1)*get_global_size(0) + + get_group_id(2)*get_local_size(2)*get_global_size(0)*get_global_size(1), // src + local_src, // dst + get_local_size(0) * sizeof(uchar), // src width + get_local_size(0) * sizeof(uchar), // dst width + get_global_size(0) * sizeof(uchar), // src stride + get_local_size(0) * sizeof(uchar), // dst stride + get_local_size(2), // num planes + get_global_size(0) * get_global_size(1) * sizeof(uchar), // src plane stride + get_local_size(0) * get_local_size(1) * sizeof(uchar), // dst plane stride + get_local_size(0) * get_local_size(1) * sizeof(uchar), // plane size + 0); } + +__kernel void __dma_postwrite_cvtu8f16( + __global uchar* restrict src, + __global half* restrict dst, + float scale, + float bias, + __local uchar* restrict local_src, + __local half* restrict local_dst) +{ + WorkGroupDmaCreate3DTransaction( + local_dst, // src + dst + get_group_id(0)*get_local_size(0) + + get_group_id(1)*get_local_size(1)*get_global_size(0) + + get_group_id(2)*get_local_size(2)*get_global_size(0)*get_global_size(1), // dst + get_local_size(0) * sizeof(half), // src width + get_local_size(0) * sizeof(half), // dst width + get_local_size(0) * sizeof(half), // src stride + get_global_size(0) * sizeof(half), // dst stride + get_local_size(2), // num planes + get_local_size(0) * get_local_size(1) * sizeof(half), // src plane stride + get_global_size(0) * get_global_size(1) * sizeof(half), // dst plane stride + get_local_size(0) * get_local_size(1) * sizeof(half), // plane size + 0); +} + +__kernel void cvtu8f16( + __global uchar* restrict src, + __global half* restrict dst, + float scale, + float bias, + __local uchar* restrict local_src, + __local half* restrict local_dst) +{ + size_t idx = get_local_id(0) + + get_local_id(1)*get_local_size(0) + + get_local_id(2)*get_local_size(0)*get_local_size(1); + local_dst[idx] = convert_half(local_src[idx])*(half)scale+(half)bias; +} + +#else // defined (USE_MANUAL_DMA) + +__kernel void cvtu8f16( + __global uchar* restrict src, + __global half* restrict dst, + float scale, + float bias, + __local uchar* restrict local_src, // unused, added for compatibility with DMA variant + __local half* restrict local_dst) // unused, added for compatibility with DMA variant +{ + int idx = get_global_id(0) + + get_global_id(1) * get_global_size(0) + + get_global_id(2) * get_global_size(0) * get_global_size(1); + dst[idx] = convert_half(src[idx])*(half)scale+(half)bias; +} + +#endif // defined (USE_MANUAL_DMA) + diff --git a/inference-engine/src/vpu/custom_kernels/detectron_prior_grid_gen.cl b/inference-engine/src/vpu/custom_kernels/detectron_prior_grid_gen.cl index ca0ad884a93..e92d3c6afb7 100644 --- a/inference-engine/src/vpu/custom_kernels/detectron_prior_grid_gen.cl +++ b/inference-engine/src/vpu/custom_kernels/detectron_prior_grid_gen.cl @@ -1,16 +1,6 @@ -// Copyright (C) 2019 Intel Corporation +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/grn.cl b/inference-engine/src/vpu/custom_kernels/grn.cl index 96df0417633..88cebb83caa 100644 --- a/inference-engine/src/vpu/custom_kernels/grn.cl +++ b/inference-engine/src/vpu/custom_kernels/grn.cl @@ -1,6 +1,9 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + #pragma OPENCL EXTENSION cl_khr_fp16 : enable -// Define if runtime supports it. MX runtime is compatible, KMB is in WIP state #define USE_MANUAL_DMA 1 #if defined (USE_MANUAL_DMA) @@ -52,10 +55,10 @@ __kernel void __dma_postwrite_grn_NCHW( } __kernel void grn_NCHW( - __global const half* restrict src_data, - __global half* restrict dst_data, - __local half* restrict src, - __local half* restrict dst, + __global const half* restrict src, + __global half* restrict dst, + __local half* restrict local_src, + __local half* restrict local_dst, int C, float bias) { @@ -64,7 +67,7 @@ __kernel void grn_NCHW( #pragma unroll 8 for (int c = 0; c < C; c++) { - float val = (float) src[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)]; + float val = (float) local_src[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)]; variance += val*val; } @@ -73,18 +76,18 @@ __kernel void grn_NCHW( #pragma unroll 8 for (int c = 0; c < C; c++) { - dst[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)] - = src[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)] * hvariance; + local_dst[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)] + = local_src[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)] * hvariance; } } #else // defined (USE_MANUAL_DMA) __kernel void grn_NCHW( - __global const half* restrict src_data, - __global half* restrict dst_data, - __local half* restrict src, // unused, added for compatibility with DMA variant - __local half* restrict dst, // unused, added for compatibility with DMA variant + __global const half* restrict src, + __global half* restrict dst, + __local half* restrict local_src, // unused, added for compatibility with DMA variant + __local half* restrict local_dst, // unused, added for compatibility with DMA variant int C, float bias) { @@ -93,7 +96,7 @@ __kernel void grn_NCHW( #pragma unroll 4 for (int c = 0; c < C; c++) { - float val = (float) src_data[c*get_global_size(1)*get_global_size(0) + get_global_id(1)*get_global_size(0) + get_global_id(0)]; + float val = (float) src[c*get_global_size(1)*get_global_size(0) + get_global_id(1)*get_global_size(0) + get_global_id(0)]; variance += val*val; } @@ -102,77 +105,9 @@ __kernel void grn_NCHW( #pragma unroll 4 for (int c = 0; c < C; c++) { - dst_data[c*get_global_size(1)*get_global_size(0) + get_global_id(1)*get_global_size(0) + get_global_id(0)] - = src_data[c*get_global_size(1)*get_global_size(0) + get_global_id(1)*get_global_size(0) + get_global_id(0)] * hvariance; + dst[c*get_global_size(1)*get_global_size(0) + get_global_id(1)*get_global_size(0) + get_global_id(0)] + = src[c*get_global_size(1)*get_global_size(0) + get_global_id(1)*get_global_size(0) + get_global_id(0)] * hvariance; } } #endif // defined (USE_MANUAL_DMA) - -// doesn't work right now due to compiler limitation -// ToDo: fix compiler -#if defined(IN_KERNEL_DMA) - -#define MAX_LOCAL_W 224 -#define MAX_LOCAL_H 2 -#define MAX_LOCAL_C 24 - -__kernel void grn_NCHW(__global const half* restrict src_data, - __global half* restrict dst_data, - int C, - float bias) -{ - __local half src[MAX_LOCAL_W*MAX_LOCAL_H*MAX_LOCAL_C]; // get_local_size(0)*get_local_size(1)*C - __local half dst[MAX_LOCAL_W*MAX_LOCAL_H*MAX_LOCAL_C]; // get_local_size(0)*get_local_size(1)*C - - const size_t index = get_group_id(0)*get_local_size(0) + get_group_id(1)*get_local_size(1)*get_global_size(0); - - event_t e1 = async_work_group_copy_3D3D( - src, // dst - src_data + index, // src - get_local_size(0), // num_elements_per_line, - get_local_size(1), // num_lines, - get_global_size(0) - get_local_size(0), // src_line_stride, - 0, // dst_line_stride, - C, // num_planes, - get_global_size(0)*get_global_size(1) - get_local_size(0) * get_local_size(1), // src_plane_stride - 0, // dst_plane_stride - 0); // event - wait_group_events(1, &e1); - - //////////////////////// - - float variance = bias + 1e-9f; - - #pragma unroll 8 - for (int c = 0; c < C; c++) - { - float val = (float) src[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)]; - variance += val*val; - } - - half hvariance = (half)(native_rsqrt((half)(variance/16.f))*0.25f); - - #pragma unroll 8 - for (int c = 0; c < C; c++) - { - dst[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)] - = src[c*get_local_size(1)*get_local_size(0) + get_local_id(1)*get_local_size(0) + get_local_id(0)] * hvariance; - } - - //////////////////// - - event_t e2 = async_work_group_copy_3D3D( - dst_data + index, // src - dst, // dst - get_local_size(0), // num_elements_per_line, - get_local_size(1), // num_lines, - 0, // src_line_stride, - get_global_size(0) - get_local_size(0), // dst_line_stride, - C, // num_planes, - 0, // src_plane_stride - get_global_size(0)*get_global_size(1) - get_local_size(0) * get_local_size(1), // dst_plane_stride - 0); // event - wait_group_events(1, &e2); -} -#endif diff --git a/inference-engine/src/vpu/custom_kernels/mvn.cl b/inference-engine/src/vpu/custom_kernels/mvn.cl index ab595bb0f32..9c5499c3848 100644 --- a/inference-engine/src/vpu/custom_kernels/mvn.cl +++ b/inference-engine/src/vpu/custom_kernels/mvn.cl @@ -1,3 +1,7 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + #pragma OPENCL EXTENSION cl_khr_fp16 : enable // Define if runtime supports it. MX runtime is compatible, KMB is in WIP state diff --git a/inference-engine/src/vpu/custom_kernels/quantize.cl b/inference-engine/src/vpu/custom_kernels/quantize.cl index 0b5d414260e..dd225877bff 100644 --- a/inference-engine/src/vpu/custom_kernels/quantize.cl +++ b/inference-engine/src/vpu/custom_kernels/quantize.cl @@ -1,154 +1,176 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + #pragma OPENCL EXTENSION cl_khr_fp16 : enable -__kernel void __dma_preload_quantize(const __global half* __restrict src, - const __global half* __restrict input_low, - const __global half* __restrict input_high, - const __global half* __restrict output_low, - const __global half* __restrict output_high, - const __global half* __restrict dst, - int levels, - int input_low_size, - int input_high_size, - int output_low_size, - int output_high_size, - int W, - int H, - __local half* __restrict src_local, - const __local half* __restrict dst_local) +__kernel void __dma_preload_quantize(__global half const *const restrict src, + __global half const *const restrict input_low, + __global half const *const restrict input_high, + __global half const *const restrict output_low, + __global half const *const restrict output_high, + __global half *const restrict dst, + int levels, + int input_low_size, + int input_high_size, + int output_low_size, + int output_high_size, + int W, + int C, + __local half *const restrict local_src, + __local half const *const restrict local_dst) { - const int sizePlane = W*H; - async_work_group_copy(src_local ,src + get_group_id(0)*sizePlane, sizePlane, 0); + WorkGroupDmaCreateStrideTransaction( + src + get_group_id(1) * get_local_size(1) * W, // src + local_src, // dst + W * sizeof(half), // src_width, + W * sizeof(half), // dst_width, + get_global_size(1) * W * sizeof(half), // src_stride, + W * sizeof(half), // dst_stride, + W * C * sizeof(half), // size + 0); } -__kernel void __dma_postwrite_quantize( const __global half* __restrict src, - const __global half* __restrict input_low, - const __global half* __restrict input_high, - const __global half* __restrict output_low, - const __global half* __restrict output_high, - __global half* __restrict dst, - int levels, - int input_low_size, - int input_high_size, - int output_low_size, - int output_high_size, - int W, - int H, - const __local half* __restrict src_local, - const __local half* __restrict dst_local) +__kernel void __dma_postwrite_quantize(__global half const *const restrict src, + __global half const *const restrict input_low, + __global half const *const restrict input_high, + __global half const *const restrict output_low, + __global half const *const restrict output_high, + __global half *const restrict dst, + int levels, + int input_low_size, + int input_high_size, + int output_low_size, + int output_high_size, + int W, + int C, + __local half const *const restrict local_src, + __local half const *const restrict local_dst) { - const int sizePlane = W*H; - async_work_group_copy(dst + get_group_id(0)*sizePlane ,dst_local, sizePlane, 0); + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + dst + get_group_id(1) * get_local_size(1) * W, // dst + W * sizeof(half), // src_width, + W * sizeof(half), // dst_width, + W * sizeof(half), // src_stride, + get_global_size(1) * W * sizeof(half), // dst_stride, + W * C * sizeof(half), // size + 0); } -__kernel void quantize(const __global half* __restrict src, - const __global half* __restrict input_low, - const __global half* __restrict input_high, - const __global half* __restrict output_low, - const __global half* __restrict output_high, - const __global half* __restrict dst, - int levels, - int input_low_size, - int input_high_size, - int output_low_size, - int output_high_size, - int W, - int H, - const __local half* __restrict src_local, - __local half* __restrict dst_local) +__kernel void quantize(__global half const *const restrict src, + __global half const *const restrict input_low, + __global half const *const restrict input_high, + __global half const *const restrict output_low, + __global half const *const restrict output_high, + __global half const *const restrict dst, + int levels, + int input_low_size, + int input_high_size, + int output_low_size, + int output_high_size, + int W, + int C, + __local half const *const restrict local_src, + __local half *const restrict local_dst) { + int h = get_global_id(1); + int H = get_global_size(1); - int c = get_global_id(0); + for (int c = 0; c < C; c++) + { + half h_ilow = (input_low_size == 1 ? input_low[0] : input_low[c]); + half h_ihigh = (input_high_size == 1 ? input_high[0] : input_high[c]); + half h_olow = (output_low_size == 1 ? output_low[0] : output_low[c]); + half h_ohigh = (output_high_size == 1 ? output_high[0] : output_high[c]); - int C = get_global_size(0); + half const1 = (half)(!(h_ihigh - h_ilow) ? 0.0f : convert_float(levels - 1) / (convert_float(h_ihigh) - convert_float(h_ilow))); + half const2 = (half)(!(levels - 1) ? 0.0f : (convert_float(h_ohigh) - convert_float(h_olow)) / convert_float(levels - 1)); - half h_ilow = (input_low_size == 1 ? input_low[0] : input_low[c]); - half h_ihigh = (input_high_size == 1 ? input_high[0] : input_high[c]); - half h_olow = (output_low_size == 1 ? output_low[0] : output_low[c]); - half h_ohigh = (output_high_size == 1 ? output_high[0] : output_high[c]); + __local const half* restrict addr_src = local_src + c*W; + __local half* restrict addr_dst = local_dst + c*W; - half8 h_ilow8 = h_ilow; - half8 h_ihigh8 = h_ihigh; - half8 h_olow8 = h_olow; - half8 h_ohigh8 = h_ohigh; + for (int w = 0; w < W / 8; w++) + { + half8 val = *((__local half8*)addr_src + w); +#if 1 + // round is too slow =( 902 b of code + //half8 aux = round((val - (half8)h_ilow) * (half8)const1); - float f_ilow = convert_float(h_ilow); - float f_ihigh = convert_float(h_ihigh); - float f_olow = convert_float(h_olow); - float f_ohigh = convert_float(h_ohigh); + half8 aux = (val - (half8)h_ilow) * (half8)const1 + (half8)0.5h; - float8 f_ilow8 = f_ilow; - float8 f_ihigh8 = f_ihigh; - float8 f_olow8 = f_olow; - float8 f_ohigh8 = f_ohigh; + aux = (half8){ + (half)(short)(aux.s0), + (half)(short)(aux.s1), + (half)(short)(aux.s2), + (half)(short)(aux.s3), + (half)(short)(aux.s4), + (half)(short)(aux.s5), + (half)(short)(aux.s6), + (half)(short)(aux.s7) + }; - float const1 = !(f_ihigh - f_ilow) ? 0.0f : convert_float(levels - 1) / (f_ihigh - f_ilow); - float const2 = !(levels - 1) ? 0.0f : (f_ohigh - f_olow) / convert_float(levels - 1); - - for (int h = 0; h < H; h++) { - int idx = h*W; + aux = aux * (half8)const2 + (half8)h_olow; - __local half* addr_src = (__local half*)src_local + idx; - __local half* addr_dst = (__local half*)dst_local + idx; + // vector comparison add 756 b of assembly, so do in manually + // short8 a = val <= (half8)h_olow; + // short8 b = val > (half8)h_ohigh; - for (int w = 0; w < W / 8; w++) { - half8 h_src_val8 = (*((__local half8*)addr_src + w)); - float8 f_src_val8 = convert_float8(h_src_val8); + short8 a; + short8 b; + a.s0 = (val.s0 <= h_ilow); + a.s1 = (val.s1 <= h_ilow); + a.s2 = (val.s2 <= h_ilow); + a.s3 = (val.s3 <= h_ilow); + a.s4 = (val.s4 <= h_ilow); + a.s5 = (val.s5 <= h_ilow); + a.s6 = (val.s6 <= h_ilow); + a.s7 = (val.s7 <= h_ilow); - short8 aux_cond1; - aux_cond1.s0 = (h_src_val8.s0 <= h_ilow); - aux_cond1.s1 = (h_src_val8.s1 <= h_ilow); - aux_cond1.s2 = (h_src_val8.s2 <= h_ilow); - aux_cond1.s3 = (h_src_val8.s3 <= h_ilow); - aux_cond1.s4 = (h_src_val8.s4 <= h_ilow); - aux_cond1.s5 = (h_src_val8.s5 <= h_ilow); - aux_cond1.s6 = (h_src_val8.s6 <= h_ilow); - aux_cond1.s7 = (h_src_val8.s7 <= h_ilow); - aux_cond1 *= aux_cond1; + b.s0 = (val.s0 > h_ihigh); + b.s1 = (val.s1 > h_ihigh); + b.s2 = (val.s2 > h_ihigh); + b.s3 = (val.s3 > h_ihigh); + b.s4 = (val.s4 > h_ihigh); + b.s5 = (val.s5 > h_ihigh); + b.s6 = (val.s6 > h_ihigh); + b.s7 = (val.s7 > h_ihigh); - short8 aux_cond2; - aux_cond2.s0 = (h_src_val8.s0 > h_ihigh); - aux_cond2.s1 = (h_src_val8.s1 > h_ihigh); - aux_cond2.s2 = (h_src_val8.s2 > h_ihigh); - aux_cond2.s3 = (h_src_val8.s3 > h_ihigh); - aux_cond2.s4 = (h_src_val8.s4 > h_ihigh); - aux_cond2.s5 = (h_src_val8.s5 > h_ihigh); - aux_cond2.s6 = (h_src_val8.s6 > h_ihigh); - aux_cond2.s7 = (h_src_val8.s7 > h_ihigh); - aux_cond2 *= aux_cond2; + a = ~(a-(short8)1); + b = ~(b-(short8)1); - short8 aux_cond3 = (!aux_cond1 & aux_cond2); - short8 aux_cond4 = (!aux_cond1 & !aux_cond2); - aux_cond3 *= aux_cond3; - aux_cond4 *= aux_cond4; + short8 c1 = (~a & b); + short8 c2 = (~a & ~b); - half8 cond1 = convert_half8(aux_cond1); - half8 cond2 = convert_half8(aux_cond2); - half8 cond3 = convert_half8(aux_cond3); - half8 cond4 = convert_half8(aux_cond4); + short8 res = a & as_short8((half8)h_olow) + | c1 & as_short8((half8)h_ohigh) + | c2 & as_short8(aux); - half8 aux; - aux = convert_half8(round(((f_src_val8 - f_ilow8) * (float8)const1)) * (float8)const2 + f_olow8); - half8 dst_val = ( - (h_olow8 * cond1) + - (h_ohigh8 * cond3) + - (aux * cond4) - ); - *((__local half8*)addr_dst + w) = dst_val; + *((__local half8*)addr_dst + w) = as_half8(res); +#else + *((__local half8*)addr_dst + w) = val; +#endif } - for (int w = W & (~0x7); w < W; w++) { - half h_src_val = addr_src[w]; - float f_src_val = convert_float(h_src_val); - half dst_val; + for (int w = W & (~0x7); w < W; w++) + //for (int w = 0 ; w < W; w++) + { + half val = addr_src[w]; +#if 1 + short a = val <= h_ilow; a = ~(a-1); + short b = val > h_ihigh; b = ~(b-1); - if (h_src_val <= h_ilow) { - dst_val = h_olow; - } else if (h_src_val > h_ihigh) { - dst_val = h_ohigh; - } else { - dst_val = convert_half(round((f_src_val - f_ilow) * const1) * const2 + f_olow); - } - addr_dst[w] = dst_val; + short c1 = (~a & b); + short c2 = (~a & ~b); + + short res = a & as_short(h_olow) + | c1 & as_short(h_ohigh) + | c2 & as_short(((half)(round( (val - h_ilow) * const1) * const2) + h_olow)); + + addr_dst[w] = as_half(res); +#else + addr_dst[w] = val; +#endif } } } diff --git a/inference-engine/src/vpu/custom_kernels/region.cl b/inference-engine/src/vpu/custom_kernels/region.cl new file mode 100644 index 00000000000..d04b7383c60 --- /dev/null +++ b/inference-engine/src/vpu/custom_kernels/region.cl @@ -0,0 +1,474 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +__constant static half log_2_e = (half)1.442695040888963; // log2(exp(1.0)) + +#define ALLOW_EARLY_RETURN 1 + +#define USE_MANUAL_DMA 1 + +#if USE_MANUAL_DMA + +static void inline logistic_activate(__local const half* restrict src, + __local half* restrict dst, + int offset) +{ + half val = src[offset]; + val = 1.0h / (1.0h + exp2(val * -log_2_e)); + dst[offset] = val; +} + +__kernel void __dma_preload_region_chw( + __global const half* restrict src, + __global half* restrict _0, + __local half* restrict local_src, + __local half* restrict _1, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int local_C = classes + coords + 1; + const int c = get_group_id(1)*local_C; + const int h = get_group_id(0); + + WorkGroupDmaCreateStrideTransaction( + src + c*H*W + h*W, // src + local_src, // dst + W*sizeof(half), // src_width, + W*sizeof(half), // dst_width, + W*H*sizeof(half), // src_stride, + W*sizeof(half), // dst_stride, + W*local_C*sizeof(half), // size + 0); +} + +__kernel void __dma_postwrite_region_chw( + __global half* restrict _0, + __global half* restrict dst, + __local half* restrict _1, + __local const half* restrict local_dst, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int local_C = classes + coords + 1; + const int c = get_group_id(1)*local_C; + const int h = get_group_id(0); + + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + dst + c*H*W + h*W, // dst + W*sizeof(half), // src_width, + W*sizeof(half), // dst_width, + W*sizeof(half), // src_stride, + W*H*sizeof(half), // dst_stride, + W*local_C*sizeof(half), // size + 0); +} + +__kernel void region_chw( + __global half* restrict src_data, + __global half* restrict dst_data, + __local const half* restrict local_src, + __local half* restrict local_dst, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int w = get_local_id(0); + +#if ALLOW_EARLY_RETURN + if (w >= W) return; +#endif + + __local const half *restrict src = local_src + w; + __local half *restrict dst = local_dst + w; + + const int stride = W; + logistic_activate(src, dst, 0*stride); + logistic_activate(src, dst, 1*stride); + + //copy plane 2 and 3 + dst[2*stride] = src[2*stride]; + dst[3*stride] = src[3*stride]; + + logistic_activate(src, dst, 4*stride); + + src += (coords + 1)*stride; + dst += (coords + 1)*stride; + + if (doSoftmax) + { + half max_val = src[0]; + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + max_val = max(max_val, src[c*stride]); + } + + half expSum = 0.0h; + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + const half e = src[c*stride] - max_val; + const half tmp = exp2(e * log_2_e); + dst[c*stride] = tmp; + expSum += tmp; + } + + const half invExpSum = 1.0h / expSum; + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + dst[c*stride] *= invExpSum; + } + } + else + { + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + logistic_activate(src, dst, c*stride); + } + } +} + +__kernel void __dma_preload_region_hwc( + __global const half* restrict src, + __global half* restrict _0, + __local half* restrict local_src, + __local half* restrict _1, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int local_C = classes + coords + 1; + const int c = get_group_id(1)*local_C; + const int h = get_group_id(0); + if (!doSoftmax) num = maskSize; + const int C = local_C*num; + + WorkGroupDmaCreateStrideTransaction( + src + h*W*C + c, // src + local_src, // dst + local_C*sizeof(half), // src_width, + local_C*sizeof(half), // dst_width, + C*sizeof(half), // src_stride, + local_C*sizeof(half), // dst_stride, + local_C*W*sizeof(half), // size + 0); +} + +__kernel void __dma_postwrite_region_hwc( + __global half* restrict _0, + __global half* restrict dst, + __local half* restrict _1, + __local const half* restrict local_dst, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + // Region always outputs in CHW layout; same as postwrite_chw + const int local_C = classes + coords + 1; + const int c = get_group_id(1)*local_C; + const int h = get_group_id(0); + + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + dst + c*H*W + h*W, // dst + W*sizeof(half), // src_width, + W*sizeof(half), // dst_width, + W*sizeof(half), // src_stride, + W*H*sizeof(half), // dst_stride, + W*local_C*sizeof(half), // size + 0); +} + +static void inline logistic_activate_hwc(__local const half* restrict src, + __local half* restrict dst, + int offset, + int stride) +{ + half val = src[offset]; + val = 1.0h / (1.0h + exp2(val * -log_2_e)); + dst[offset*stride] = val; +} + +__kernel void region_hwc( + __global half* restrict src_data, + __global half* restrict dst_data, + __local const half* restrict local_src, + __local half* restrict local_dst, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int w = get_local_id(0); + +#if ALLOW_EARLY_RETURN + if (w >= W) return; +#endif + + const int local_C = classes + coords + 1; + + __local const half *restrict src = local_src + w*local_C; + __local half *restrict dst = local_dst + w; + + const int stride = W; + logistic_activate_hwc(src, dst, 0, stride); + logistic_activate_hwc(src, dst, 1, stride); + + //copy plane 2 and 3 + dst[2*stride] = src[2]; + dst[3*stride] = src[3]; + + logistic_activate_hwc(src, dst, 4, stride); + + src += coords + 1; + dst += (coords + 1)*stride; + + if (doSoftmax) + { + half max_val = src[0]; + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + max_val = max(max_val, src[c]); + } + + half expSum = 0.0h; + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + const half e = src[c] - max_val; + const half tmp = exp2(e * log_2_e); + dst[c*stride] = tmp; + expSum += tmp; + } + + const half invExpSum = 1.0h / expSum; + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + dst[c*stride] *= invExpSum; + } + } + else + { + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + logistic_activate_hwc(src, dst, c, stride); + } + } +} + +#else // defined (USE_MANUAL_DMA) + +#define NUM_CLASSES 80 + +static void inline logistic_activate(__global const half* restrict src, + __global half* restrict dst, + int offset) +{ + half val = src[offset]; + val = 1.0h / (1.0h + exp2(val * -log_2_e)); + dst[offset] = val; +} + +__kernel void region_chw( + __global const half* restrict global_src, + __global half* restrict global_dst, + __local half* restrict _0, + __local half* restrict _1, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int w = get_local_id(0); + +#if ALLOW_EARLY_RETURN + if (w >= W) return; +#endif + + const int local_C = classes + coords + 1; + const int c = get_group_id(1)*local_C; + const int h = get_group_id(0); + + __global const half *restrict src = global_src + c*H*W + h*W + w; + __global half *restrict dst = global_dst + c*H*W + h*W + w; + + const int stride = H*W; + logistic_activate(src, dst, 0*stride); + logistic_activate(src, dst, 1*stride); + + //copy plane 2 and 3 + dst[2*stride] = src[2*stride]; + dst[3*stride] = src[3*stride]; + + logistic_activate(src, dst, 4*stride); + + src += (coords + 1)*stride; + dst += (coords + 1)*stride; + + if (doSoftmax) + { + __private half data[NUM_CLASSES]; + + half max_val = src[0]; + for (int c = 0; c < classes; c++) + { + half tmp = src[c*stride]; + data[c] = tmp; + max_val = max(max_val, tmp); + } + + half expSum = 0.0h; + for (int c = 0; c < classes; c++) + { + half tmp = half_exp(data[c] - max_val); + data[c] = tmp; + expSum += tmp; + } + + for (int c = 0; c < classes; c++) + { + dst[c*stride] = data[c] / expSum; + } + } + else + { + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + logistic_activate(src, dst, c*stride); + } + } +} + +static void inline logistic_activate_hwc(__global const half* restrict src, + __global half* restrict dst, + int offset, + int stride) +{ + half val = src[offset]; + val = 1.0h / (1.0h + exp2(val * -log_2_e)); + dst[offset*stride] = val; +} + + +__kernel void region_hwc( + __global const half* restrict global_src, + __global half* restrict global_dst, + __local half* restrict _0, + __local half* restrict _1, + int W, /* 13 */ + int H, /* 13 */ + int classes, /* 20 */ + int coords, /* 4 */ + int num, /* 5 */ + int maskSize, + int doSoftmax + ) +{ + const int w = get_local_id(0); + +#if ALLOW_EARLY_RETURN + if (w >= W) return; +#endif + + const int local_C = classes + coords + 1; + const int c = get_group_id(1)*local_C; + const int h = get_group_id(0); + const int C = num*local_C; + + __global const half *restrict src = global_src + h*W*C + w*C + c; + __global half *restrict dst = global_dst + c*H*W + h*W + w; + + const int stride = H*W; + logistic_activate_hwc(src, dst, 0, stride); + logistic_activate_hwc(src, dst, 1, stride); + + //copy plane 2 and 3 + dst[2*stride] = src[2]; + dst[3*stride] = src[3]; + + logistic_activate_hwc(src, dst, 4, stride); + + src += coords + 1; + dst += (coords + 1)*stride; + + if (doSoftmax) + { + __private half data[NUM_CLASSES]; + + half max_val = src[0]; + for (int c = 0; c < classes; c++) + { + half tmp = src[c]; + data[c] = tmp; + max_val = max(max_val, tmp); + } + + half expSum = 0.0h; + for (int c = 0; c < classes; c++) + { + half tmp = half_exp(data[c] - max_val); + data[c] = tmp; + expSum += tmp; + } + + for (int c = 0; c < classes; c++) + { + dst[c*stride] = data[c] / expSum; + } + } + else + { + #pragma unroll 4 + for (int c = 0; c < classes; c++) + { + logistic_activate_hwc(src, dst, c, stride); + } + } +} + +#endif // defined (USE_MANUAL_DMA) diff --git a/inference-engine/src/vpu/custom_kernels/region_chw.cl b/inference-engine/src/vpu/custom_kernels/region_chw.cl index 650d8f418de..c728042fe85 100644 --- a/inference-engine/src/vpu/custom_kernels/region_chw.cl +++ b/inference-engine/src/vpu/custom_kernels/region_chw.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch0.cl b/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch0.cl index 12fb6911cda..f83e8149cad 100644 --- a/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch0.cl +++ b/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch0.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch1.cl b/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch1.cl index f88df7815fb..16298d53beb 100644 --- a/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch1.cl +++ b/inference-engine/src/vpu/custom_kernels/region_chw_m7_branch1.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/reorg_chw.cl b/inference-engine/src/vpu/custom_kernels/reorg_chw.cl index 59df994f080..6cd2b7890e6 100644 --- a/inference-engine/src/vpu/custom_kernels/reorg_chw.cl +++ b/inference-engine/src/vpu/custom_kernels/reorg_chw.cl @@ -1,43 +1,121 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable -__kernel void reorg_NCHW(__global const half* restrict src, - __global half* restrict out, - int H, - int W, - int stride) +#define USE_MANUAL_DMA + +#if defined (USE_MANUAL_DMA) + +__kernel void __dma_preload_reorg_chw(__global half const *restrict src, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half *restrict local_src, + __local half *restrict local_dst + ) { - int h = min((int)get_global_id(0), H-1); + const int stride_y = get_group_id(1); - int c = get_global_id(1); - int C = get_global_size(1); - int C2 = C/(stride*stride); + const int srcIdx = stride_y*W*stride + W*stride*stride*get_group_id(0); - int offset = c / C2; + WorkGroupDmaCreateStrideTransaction( + src + srcIdx, // src + local_src, // dst + W * stride * sizeof(half), // src width + W * stride * sizeof(half), // dst width + W * stride * stride * get_num_groups(0) * sizeof(half), // src stride + W * stride * sizeof(half), // dst stride + W * stride * get_local_size(0) * sizeof(half), //total size + 0); +} - int c2 = c - C2 * offset; +__kernel void __dma_postwrite_reorg_chw(__global half const *restrict src, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half *restrict local_src, + __local half const *restrict local_dst + ) +{ + const int stride_y = get_group_id(1); - int H2 = H*stride; - int W2 = W*stride; + const int dstIdx = stride_y*W*stride*get_global_size(0) + get_group_id(0)*W; - for (int w = 0; w < W; ++w) - { - int h2 = h*stride + offset / stride; - int w2 = w*stride + offset - stride * (offset / stride); + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + dst + dstIdx, // dst + W * sizeof(half), // src width + W * sizeof(half), // dst width + W * sizeof(half), // src stride + W * get_num_groups(0) * sizeof(half), // dst stride + get_local_size(0) * W * stride * sizeof(half), //total size + 0); +} - out[W*H*c + W*h + w] = src[W2*H2*c2 + W2*h2 + w2]; +__kernel void reorg_chw(__global half const *restrict src, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half *restrict local_src, + __local half *restrict local_dst + ) +{ + const int c = get_local_id(0); + const int stride_x = get_local_id(1); + + const int srcIdx = stride_x + c*W*stride; + const int dstIdx = stride_x*W*get_local_size(0) + c*W; + + int x = 0; + for (; x <= W - 8; x += 8) { + half8 data = (half8) { + local_src[srcIdx + (x + 0)*stride], local_src[srcIdx + (x + 1)*stride], + local_src[srcIdx + (x + 2)*stride], local_src[srcIdx + (x + 3)*stride], + local_src[srcIdx + (x + 4)*stride], local_src[srcIdx + (x + 5)*stride], + local_src[srcIdx + (x + 6)*stride], local_src[srcIdx + (x + 7)*stride] + }; + + *((__local half8*)(&local_dst[dstIdx + x])) = data; + } + + for (; x < W; x++) { + local_dst[dstIdx + x] = local_src[srcIdx + x*stride]; } } + +#else + +__kernel void reorg_chw(__global half const *restrict src, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half const *restrict _0, + __local half *restrict _1 + ) +{ + const int stride_x = get_local_id(1); + const int stride_y = get_group_id(1); + const int N = get_global_size(0); + const int c = get_local_id(0)*get_num_groups(0) + get_group_id(0); + + const int srcIdx = c*W*stride*stride + stride_x + stride_y*W*stride; + const int dstIdx = c*W + stride_x*W*N + stride_y*W*N*stride; + + #pragma unroll 8 + for (int x = 0; x < W; x++) { + dst[dstIdx + x] = src[srcIdx + x*stride]; + } +} + +#endif + diff --git a/inference-engine/src/vpu/custom_kernels/reorg_chw_local.cl b/inference-engine/src/vpu/custom_kernels/reorg_chw_local.cl index 62b9c579ca7..35032cf9223 100644 --- a/inference-engine/src/vpu/custom_kernels/reorg_chw_local.cl +++ b/inference-engine/src/vpu/custom_kernels/reorg_chw_local.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/reorg_chw_stack.cl b/inference-engine/src/vpu/custom_kernels/reorg_chw_stack.cl index 3482649b5e7..3e0932e7cd6 100644 --- a/inference-engine/src/vpu/custom_kernels/reorg_chw_stack.cl +++ b/inference-engine/src/vpu/custom_kernels/reorg_chw_stack.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/reorg_hwc.cl b/inference-engine/src/vpu/custom_kernels/reorg_hwc.cl index 56b3c86f6aa..6bbddc08f9a 100644 --- a/inference-engine/src/vpu/custom_kernels/reorg_hwc.cl +++ b/inference-engine/src/vpu/custom_kernels/reorg_hwc.cl @@ -1,64 +1,139 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#define MIN(v1, v2) ((v1) < (v2) ? (v1) : (v2)) #pragma OPENCL EXTENSION cl_khr_fp16 : enable -__kernel void reorg(__global half* restrict src, - __global half* restrict out, - int h, - int w, - int stride) +__kernel void __dma_preload_reorg_hwc(__global half const *restrict src, + __global half *restrict _0, + int W, + int H, + int C, + int stride, + __local half *restrict local_src, + __local half *restrict _1 + ) { - int j = MIN(get_global_id(0), h-1); + const int stride_x = get_group_id(1); - int k = get_global_id(1); - int c = get_global_size(1); + WorkGroupDmaCreateStrideTransaction( + src + get_group_id(0) * stride + stride_x * C, // src + local_src, // dst + stride * sizeof(half), // src_width, + stride * sizeof(half), // dst_width, + C * stride * sizeof(half), // src_stride, + stride * sizeof(half), // dst_stride, + H * W * sizeof(half), // size + 0); +} - int out_c = c / (stride * stride); - int oc = c * (stride * stride); - int oh = h / stride; - int ow = w / stride; +__kernel void __dma_postwrite_reorg_hwc(__global half const *restrict _0, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half *restrict _1, + __local half *restrict local_dst + ) +{ + const int stride_x = get_group_id(1); - int in_index = w * (j + h*k); + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + dst + stride_x * C + get_group_id(0) * stride, // dst + stride * sizeof(half), // src_width, + stride * sizeof(half), // dst_width, + stride * sizeof(half), // src_stride, + C * stride * sizeof(half), // dst_stride, + W * H * sizeof(half), // size + 0); +} - int new_z = in_index / (oh*ow); - int new_y = (in_index %(oh*ow)) / ow; - int new_x = (in_index %(oh*ow)) % ow; - int new_index = new_z + new_x * oc + new_y * oc * ow; +__kernel void reorg_hwc(__global half const *restrict src, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half *restrict local_src, + __local half *restrict local_dst + ) +{ + const int stride_y = get_local_id(1); + const int blocks = get_local_size(0); + const int b = get_local_id(0); - in_index++; + const int OC = stride * stride; + const int OH = H / stride; + const int OW = W / stride; + const int IC = stride; + const int IH = H; + const int IW = W / stride; - int c2 = k % out_c; - int offset = k / out_c; - int w2 = 0 * stride + offset % stride; - int h2 = j * stride + offset / stride; - int out_index = w2 + w * stride * (h2 + h * stride * c2); + for (int block_h = 0; block_h < stride; block_h++) { + const int src_line = b * stride * stride + stride_y * stride + block_h; + const int c = src_line / IH; + const int h = src_line % IH; - for (int i = 0; i < w; ++i, out_index+=stride, in_index++) - { - // repacking coordinates - int k0 = out_index / (h*w); - int j0 = (out_index % (h*w)) / w; - int i0 = (out_index % (h*w)) % w; - int out_index_repack = k0 + c * i0 + c * w * j0; - out[new_index] = src[out_index_repack]; + const int dst_line = b * stride + stride_y * blocks * stride + block_h; + const int oc = dst_line / OH; + const int oh = dst_line % OH; - int new_z = in_index / (oh*ow); - int new_y = (in_index %(oh*ow)) / ow; - int new_x = (in_index %(oh*ow)) % ow; - new_index = new_z + new_x * oc + new_y * oc * ow; + for (int w = 0; w < W / stride; w++) { + local_dst[oh*OW*OC + w*OC + oc] = local_src[h*IW*IC + w*IC + c]; + } + } +} + +__kernel void reorg_hwc_naive(__global half const *restrict src, + __global half *restrict dst, + int W, + int H, + int C, + int stride, + __local half *restrict local_src, + __local half *restrict local_dst + ) +{ + const int out_c = C / (stride * stride); + const int oc = C * (stride * stride); + const int oh = H / stride; + const int ow = W / stride; + + const int c = get_global_id(0); + + for (int h = 0; h < H; ++h) + { + int in_index = W * (h + H*c) + (0); + int new_z = in_index / (oh*ow); + int new_y = (in_index %(oh*ow)) / ow; + int new_x = (in_index %(oh*ow)) % ow; + int new_index = new_z + new_x * oc + new_y * oc * ow; + + in_index++; + + int c2 = c % out_c; + int offset = c / out_c; + int w2 = 0 * stride + offset % stride; + int h2 = h * stride + offset / stride; + int out_index = w2 + W * stride * (h2 + H * stride * c2); + + #pragma unroll 2 + for(int i = 0; i < W; ++i, out_index+=stride, in_index++) + { + // repacking coordinates + int k0 = out_index / (H*W); + int j0 = (out_index % (H*W)) / W; + int i0 = (out_index % (H*W)) % W; + int out_index_repack = k0 + C * i0 + C * W * j0; + + dst[new_index] = src[out_index_repack]; + + int new_z = in_index / (oh*ow); + int new_y = (in_index %(oh*ow)) / ow; + int new_x = (in_index %(oh*ow)) % ow; + new_index = new_z + new_x * oc + new_y * oc * ow; + } } } diff --git a/inference-engine/src/vpu/custom_kernels/resample_nn.cl b/inference-engine/src/vpu/custom_kernels/resample_nn.cl index 49a741a0585..9584cb2518f 100644 --- a/inference-engine/src/vpu/custom_kernels/resample_nn.cl +++ b/inference-engine/src/vpu/custom_kernels/resample_nn.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable @@ -22,8 +12,126 @@ #define ROUND(x) (int)(round(x)) #endif +inline int out_to_in(float ox, float f) { + return (int)((ox + 0.5f) * f); +} + +#define USE_MANUAL_DMA + +#if defined (USE_MANUAL_DMA) + +void interpolationCHW_nn(__local half* psrc, __local half* pdst, int OW, int IW, int C, float rw, float rh) +{ + float alpha = rh / 2.0f - 0.5f; + + for (int w = 0; w < OW/8; w++) + { + float fw0 = rw*(w*8+0) + alpha; + float fw1 = rw*(w*8+1) + alpha; + float fw2 = rw*(w*8+2) + alpha; + float fw3 = rw*(w*8+3) + alpha; + + float fw4 = rw*(w*8+4) + alpha; + float fw5 = rw*(w*8+5) + alpha; + float fw6 = rw*(w*8+6) + alpha; + float fw7 = rw*(w*8+7) + alpha; + + int iw0 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw0), IW-1); + int iw1 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw1), IW-1); + int iw2 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw2), IW-1); + int iw3 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw3), IW-1); + + int iw4 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw4), IW-1); + int iw5 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw5), IW-1); + int iw6 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw6), IW-1); + int iw7 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw7), IW-1); + + for (int c = 0; c < C; c++) + { + half8 val = { + *((__local half*)(psrc + c * IW + iw0)), + *((__local half*)(psrc + c * IW + iw1)), + + *((__local half*)(psrc + c * IW + iw2)), + *((__local half*)(psrc + c * IW + iw3)), + + *((__local half*)(psrc + c * IW + iw4)), + *((__local half*)(psrc + c * IW + iw5)), + + *((__local half*)(psrc + c * IW + iw6)), + *((__local half*)(psrc + c * IW + iw7)), + }; + *((__local half8*)(pdst + c * OW + w*8)) = val; + } + } + + for (int w = OW/8*8; w < OW; w++) + { + float fw = rw*w + alpha; + int iw0 = __builtin_shave_cmu_min_i32_rr_int((int)ROUND(fw), IW-1); + + for (int c = 0; c < C; c++) + { + *((__local half*)(pdst + c * OW + w)) = *((__local half*)(psrc + c * IW + iw0)); + } + } +} + +__kernel void __dma_preload_resample_nearest(__global const half* restrict src, + __global half* restrict _0, + __local half* restrict local_src, + __local half* restrict _1, + int iw, + int ih, + float factor, + int ow, + int oh, + int channels) +{ + const int oy_first = get_group_id(1) * get_local_size(1); + const int oy_last = (get_group_id(1) + 1) * get_local_size(1) - 1; + const int iy_first = out_to_in(oy_first, 1.0 / factor); + const int iy_last = out_to_in(oy_last, 1.0 /factor); + const int iy_size = iy_last - iy_first + 1; + + WorkGroupDmaCreateStrideTransaction( + src + get_group_id(2)*channels*ih*iw + iy_first*iw, // src + local_src, // dst + iy_size * iw * sizeof(half), // src_width, + iy_size * iw * sizeof(half), // dst_width, + ih * iw * sizeof(half), // src_stride, + iy_size * iw * sizeof(half), // dst_stride, + channels * iy_size * iw * sizeof(half), // size + 0); +} + +__kernel void __dma_postwrite_resample_nearest(__global const half* restrict _0, + __global half* restrict dst, + __local half* restrict _1, + __local half* restrict local_dst, + int iw, + int ih, + float factor, + int ow, + int oh, + int channels) +{ + + WorkGroupDmaCreateStrideTransaction( + local_dst, // src + dst + get_group_id(2)*channels*get_global_size(1)*ow + get_group_id(1)*get_local_size(1)*ow, // dst + get_local_size(1) * ow * sizeof(half), // src_width, + get_local_size(1) * ow * sizeof(half), // dst_width, + get_local_size(1) * ow * sizeof(half), // src_stride, + get_global_size(1) * ow * sizeof(half), // dst_stride, + channels * get_local_size(1) * ow * sizeof(half), // size + 0); +} + kernel void resample_nearest(__global const half* restrict src, __global half* restrict dst, + __local half* restrict local_src, + __local half* restrict local_dst, int iw, int ih, float factor, @@ -31,30 +139,35 @@ kernel void resample_nearest(__global const half* restrict src, int oh, int channels) { - int oy = min((int)get_global_id(0), oh-1); - int c = get_global_id(1); - int b = get_global_id(2); + interpolationCHW_nn(local_src, local_dst, ow, iw, channels, 1.0 / factor, 1.0 / factor); +} - float fx = 1.f / factor; - float fy = 1.f / factor; +#else // defined (USE_MANUAL_DMA) - __global const half* start_src = src + b * iw * ih * channels + iw * ih * c; - __global half* start_dst = dst + b * ow * oh * channels + ow * oh * c; +kernel void resample_nearest(__global const half* restrict src, + __global half* restrict dst, + __local half* restrict local_src, + __local half* restrict local_dst, + int iw, + int ih, + float factor, + int ow, + int oh, + int channels) +{ + const float inv_factor = 1.0f / factor; + const int iy = out_to_in(get_global_id(1), inv_factor); - for (int ox = 0; ox < ow; ox++) + __global half* dst_data = dst + get_global_id(1)*ow; + __global half* src_data = src + iy*iw; + + for (int ox = 0; ox < ow; ++ox) { - float ix_r0 = ox*fx + fx / 2.0f - 0.5f; - float iy_r0 = oy*fy + fy / 2.0f - 0.5f; - - int ix_r1 = ROUND(ix_r0); - int iy_r1 = ROUND(iy_r0); - - ix_r1 = max(ix_r1, 0); - ix_r1 = min(ix_r1, iw - 1); - - iy_r1 = max(iy_r1, 0); - iy_r1 = min(iy_r1, ih - 1); - - start_dst[oy * ow + ox] = start_src[iy_r1 * iw + ix_r1]; + const int ix = out_to_in(ox, inv_factor); + for (int c = 0; c < channels; c++) { + dst_data[c*oh*ow + ox] = src_data[c*ih*iw + ix]; + } } } + +#endif // defined (USE_MANUAL_DMA) diff --git a/inference-engine/src/vpu/custom_kernels/resample_with_antialias.cl b/inference-engine/src/vpu/custom_kernels/resample_with_antialias.cl index 621633c7e41..26d310dc340 100644 --- a/inference-engine/src/vpu/custom_kernels/resample_with_antialias.cl +++ b/inference-engine/src/vpu/custom_kernels/resample_with_antialias.cl @@ -1,31 +1,110 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable +#define USE_OPTIMIZED_ROUND + +#ifdef USE_OPTIMIZED_ROUND + #define ROUND(x) ((int)((x) + 0.5f)) +#else + #define ROUND(x) (int)(round(x)) +#endif + + +inline int out_to_in(float ox, float f) { +#ifdef USE_OPTIMIZED_ROUND + return (int)((ox + 0.5f) / f); +#else + return ROUND((ox + 0.5f) / f - 0.5f); +#endif +} + static inline float triangleCoeff(float x) { - return 1.0f - fabs(x);//fmax(0.0f, 1 - fabs(x)); + return 1.0f - fabs(x); } static inline float4 triangleCoeff4(float4 x) { - return 1.0f - fabs(x);//fmax(0.0f, 1 - fabs(x)); + return 1.0f - fabs(x); +} + +static inline half triangleCoeffHalf(half x) +{ + return 1.0h - fabs(x); +} + +static inline half4 triangleCoeffHalf4(half4 x) +{ + return 1.0h - fabs(x); +} + +static inline half8 triangleCoeffHalf8(half8 x) +{ + return 1.0h - fabs(x); +} + +#define USE_MANUAL_DMA + +#if defined (USE_MANUAL_DMA) + +__kernel void __dma_preload_resample_with_antialias(__global const half* restrict src, + __global half* restrict _0, + __local half* restrict local_src, + __local half* restrict _1, + int iw, + int ih, + float factor, + int ow, + int oh, + int channels) +{ + const int r = (factor > 1.0f) ? 2 : ceil(1.0f / factor); + const int oy_first = get_group_id(1) * get_local_size(1); + const int oy_last = (get_group_id(1) + 1) * get_local_size(1) - 1; + const int iy_first = max(out_to_in(oy_first, factor) - r, 0); + const int iy_last = min(out_to_in(oy_last, factor) + r, ih - 1); + const int iy_size = iy_last - iy_first + 1; + + WorkGroupDmaCreateStrideTransaction( + src + get_group_id(2)*get_local_size(2)*ih*iw + iy_first*iw, // src + local_src, // dst + iy_size * iw * sizeof(half), // src_width, + iy_size * iw * sizeof(half), // dst_width, + ih * iw * sizeof(half), // src_stride, + iy_size * iw * sizeof(half), // dst_stride, + get_local_size(2) * iy_size * iw * sizeof(half), // size + 0); +} + +__kernel void __dma_postwrite_resample_with_antialias(__global const half* restrict _0, + __global half* restrict dst, + __local half* restrict _1, + __local half* restrict dst_local, + int iw, + int ih, + float factor, + int ow, + int oh, + int channels) +{ + WorkGroupDmaCreateStrideTransaction( + dst_local, // src + dst + get_group_id(2)*get_local_size(2)*get_global_size(1)*ow + get_group_id(1)*get_local_size(1)*ow, // dst + get_local_size(1) * ow * sizeof(half), // src_width, + get_local_size(1) * ow * sizeof(half), // dst_width, + get_local_size(1) * ow * sizeof(half), // src_stride, + get_global_size(1) * ow * sizeof(half), // dst_stride, + get_local_size(2) * get_local_size(1) * ow * sizeof(half), // size + 0); } __kernel void resample_with_antialias(const __global half* restrict src, __global half* restrict dst, + __local half* restrict local_src, + __local half* restrict local_dst, int iw, int ih, float factor, @@ -33,47 +112,39 @@ __kernel void resample_with_antialias(const __global half* restrict src, int oh, int channels) { - int oy = min((int)get_global_id(0), oh-1); - int c = get_global_id(1); - int b = get_global_id(2); + const int r = (factor > 1.0f) ? 2 : ceil(1.0f / factor); + const int oy_first = get_group_id(1) * get_local_size(1); + const int oy_last = (get_group_id(1) + 1) * get_local_size(1) - 1; + const int iy_first = max(out_to_in(oy_first, factor) - r, 0); + const int iy_last = min(out_to_in(oy_last, factor) + r, ih - 1); + const int iy_size = iy_last - iy_first + 1; + const int oy = get_global_id(1); + const float iy_f = ((oy + 0.5f) / factor - 0.5f) - iy_first; + const int iy = ROUND(iy_f); - float fx = 1.f / factor; - float fy = 1.f / factor; - - float ax = 1.0f / fx; - float ay = 1.0f / fy; - - int rx = (fx < 1.0f) ? 2 : ceil((1.0f)/ax); - int ry = (fy < 1.0f) ? 2 : ceil((1.0f)/ay); - - const __global half* restrict start_src = src + b * iw * ih * channels + iw * ih * c; - __global half* restrict start_dst = dst + b * ow * oh * channels + ow * oh * c; - - float iy_r0 = oy*fy + fy / 2.0f - 0.5f; - int iy_r1 = (int)(round(iy_r0)); + __local half const *restrict start_src = local_src + iw * get_local_id(1) + iw * iy_size * get_local_id(2); + __local half *restrict start_dst = local_dst + ow * get_local_id(1) + ow * get_local_size(1) * get_local_id(2); for (int ox = 0; ox < ow; ox++) { - float ix_r0 = ox*fx + fx / 2.0f - 0.5f; - int ix_r1 = (int)(round(ix_r0)); + const float ix_f = (float)((ox + 0.5f) / factor) - 0.5f; + const int ix_i = ROUND(ix_f); float4 v_sum = 0.f; float4 v_wsum = 0.f; - - for (int y = max(iy_r1 - ry, 0); - y <= min(iy_r1 + ry, (int)ih - 1); y++) + for (int y = 0; y < iy_size; y++) { - float dy = iy_r0 - y; - int x = max(ix_r1 - rx, 0); - int end_x = min(ix_r1 + rx, (int)iw - 1); + float dy = iy_f - y; + int x = max(ix_i - r, 0); + int end_x = min(ix_i + r, iw - 1); float4 dx; for (int i = 0; i < 4; i++) - dx[i] = ix_r0 - x - i; + dx[i] = ix_f - x - i; - for (; x <= end_x - 3; x += 4, dx -= 4) + for (; x < end_x - 3; x += 4, dx -= 4) { - float4 w = ax*triangleCoeff4(ax*dx) * ay*triangleCoeff(ay*dy); + float4 w = factor*triangleCoeff4(factor*dx) * factor*triangleCoeff(factor*dy); float4 src_vec = { start_src[y*iw + x + 0], start_src[y*iw + x + 1], start_src[y*iw + x + 2], @@ -85,8 +156,79 @@ __kernel void resample_with_antialias(const __global half* restrict src, for (; x <= end_x; x++) { - float dx = ix_r0 - x; - float w = ax*triangleCoeff(ax*dx) * ay*triangleCoeff(ay*dy); + float dx = ix_f - x; + float w = factor*triangleCoeff(factor*dx) * factor*triangleCoeff(factor*dy); + + v_sum[0] += w * start_src[y*iw + x]; + v_wsum[0] += w; + } + } + + v_sum[0] = v_sum[0] + v_sum[1] + v_sum[2] + v_sum[3]; + v_wsum[0] = v_wsum[0] + v_wsum[1] + v_wsum[2] + v_wsum[3]; + + start_dst[get_local_id(1)*ow + ox] = (!v_wsum[0]) ? 0.0f : (half)(v_sum[0] / v_wsum[0]); + } +} + +#else + +__kernel void resample_with_antialias(const __global half* restrict src, + __global half* restrict dst, + __local half* restrict _0, + __local half* restrict _1, + int iw, + int ih, + float factor, + int ow, + int oh, + int channels) +{ + int oy = get_global_id(1); + int c = get_global_id(2); + + int r = (factor > 1.0f) ? 2 : ceil((1.0f)/factor); + + const __global half* restrict start_src = src + iw * ih * c; + __global half* restrict start_dst = dst + ow * oh * c; + + float iy_f = (oy + 0.5) / factor - 0.5f; + int iy_i = ROUND(iy_f); + + for (int ox = 0; ox < ow; ox++) + { + float ix_f = (ox + 0.5) / factor - 0.5f; + int ix_i = ROUND(ix_f); + + float4 v_sum = 0.f; + float4 v_wsum = 0.f; + + for (int y = max(iy_i - r, 0); y <= min(iy_i + r, (int)ih - 1); y++) + { + float dy = iy_f - y; + int x = max(ix_i - r, 0); + int end_x = min(ix_i + r, (int)iw - 1); + + float4 dx; + for (int i = 0; i < 4; i++) + dx[i] = ix_f - x - i; + + for (; x <= end_x - 3; x += 4, dx -= 4) + { + float4 w = factor*triangleCoeff4(factor*dx) * factor*triangleCoeff(factor*dy); + float4 src_vec = { start_src[y*iw + x + 0], + start_src[y*iw + x + 1], + start_src[y*iw + x + 2], + start_src[y*iw + x + 3] }; + + v_sum += w * src_vec; + v_wsum += w; + } + + for (; x <= end_x; x++) + { + float dx = ix_f - x; + float w = factor*triangleCoeff(factor*dx) * factor*triangleCoeff(factor*dy); v_sum[0] += w * start_src[y*iw + x]; v_wsum[0] += w; @@ -99,3 +241,5 @@ __kernel void resample_with_antialias(const __global half* restrict src, start_dst[oy*ow + ox] = (!v_wsum[0]) ? (half)0.0f : (half)(v_sum[0] / v_wsum[0]); } } + +#endif diff --git a/inference-engine/src/vpu/custom_kernels/shuffle_channels.cl b/inference-engine/src/vpu/custom_kernels/shuffle_channels.cl index 48afd02f5a5..237e26fe4d6 100644 --- a/inference-engine/src/vpu/custom_kernels/shuffle_channels.cl +++ b/inference-engine/src/vpu/custom_kernels/shuffle_channels.cl @@ -1,16 +1,6 @@ // Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 // -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. #pragma OPENCL EXTENSION cl_khr_fp16 : enable diff --git a/inference-engine/src/vpu/custom_kernels/st.cl b/inference-engine/src/vpu/custom_kernels/st.cl new file mode 100644 index 00000000000..bac1606edbc --- /dev/null +++ b/inference-engine/src/vpu/custom_kernels/st.cl @@ -0,0 +1,253 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma OPENCL EXTENSION cl_khr_fp16 : enable + +#define MAX_WIDTH 512 +#define MIN(a, b) ((a) < (b)) ? (a) : (b); + +__kernel void __dma_postwrite_ocl_st(__global half const *const restrict src_data, + __global half const *const restrict theta, + __global half *const restrict dst_data, + int C, + int W, + __local half const *const restrict local_dst) +{ + const int x0 = get_global_id(0) * MAX_WIDTH; + const int x1 = MIN(x0 + MAX_WIDTH, W); + const int length = x1 - x0; + + WorkGroupDmaCreate3DTransaction( + local_dst, // src + dst_data + get_global_id(1) * W + x0, // dst + length * sizeof(half), // src width + length * sizeof(half), // dst width + length * sizeof(half), // src stride + W * sizeof(half), // dst stride + C, // num planes + get_local_size(1) * length * sizeof(half), // src plane stride + get_global_size(1) * W * sizeof(half), // dst plane stride + get_local_size(1) * length * sizeof(half), // plane size + 0); +} + +__attribute__((noinline)) +void calcInd(__global half const *const restrict theta, + half *const restrict weight, + int *const restrict ind, + int y, int H, int x0, int length, int step, int W) +{ + float a = (float)y * 1.0f / H * 2 - 1; + + int x = 0; + + float8 va = (float8) {a, a, a, a, a, a, a, a}; + float8 vxy = (float8) {x0 + 0, x0 + 1, x0 + 2, x0 + 3, + x0 + 4, x0 + 5, x0 + 6, x0 + 7}; + + for (; x <= length - 8; x += 8, vxy += 8) + { + float8 va1 = vxy * 1.0f / W * 2 - 1.f; + + float8 vx = (va * theta[0] + va1 * theta[1] + theta[2] + 1.f) / 2.f * H; + float8 vy = (va * theta[3] + va1 * theta[4] + theta[5] + 1.f) / 2.f * W; + + const int8 ix = convert_int8(vx) - ((vx < 0) & 1); + const int8 iy = convert_int8(vy) - ((vy < 0) & 1); + + float8 ax = vx - convert_float8(ix); + float8 ay = vy - convert_float8(iy); + float8 bx = 1.f - ax; + float8 by = 1.f - ay; + + union {int8 d; uint8 i; } check_x; + + check_x.d = ix; + int8 b01 = check_x.i < (uint8)H; + + check_x.d = ix + 1; + int8 b45 = check_x.i < (uint8)H; + + union {int8 d; uint8 i; } check_y; + + check_y.d = iy; + int8 b23 = check_y.i < (uint8)W; + + check_y.d = iy + 1; + int8 b67 = check_y.i < (uint8)W; + + int8 b0123 = b01 & b23; + int8 b0167 = b01 & b67; + int8 b4523 = b45 & b23; + int8 b4567 = b45 & b67; + + int8 TL_id = ((ix + 0) * W + (iy + 0)) * (b0123 & 1); + int8 BL_id = ((ix + 1) * W + (iy + 0)) * (b4523 & 1); + int8 TR_id = ((ix + 0) * W + (iy + 1)) * (b0167 & 1); + int8 BR_id = ((ix + 1) * W + (iy + 1)) * (b4567 & 1); + + union {float8 f; int8 i;} w0; w0.f = bx * by; + union {float8 f; int8 i;} w1; w1.f = ax * by; + union {float8 f; int8 i;} w2; w2.f = bx * ay; + union {float8 f; int8 i;} w3; w3.f = ax * ay; + + w0.i = w0.i & b0123; + w1.i = w1.i & b4523; + w2.i = w2.i & b0167; + w3.i = w3.i & b4567; + + *((half8*)(weight + x + 0*step)) = convert_half8(w0.f); + *((half8*)(weight + x + 1*step)) = convert_half8(w1.f); + *((half8*)(weight + x + 2*step)) = convert_half8(w2.f); + *((half8*)(weight + x + 3*step)) = convert_half8(w3.f); + + *((int8*)(ind + x + 0*step)) = TL_id; + *((int8*)(ind + x + 1*step)) = BL_id; + *((int8*)(ind + x + 2*step)) = TR_id; + *((int8*)(ind + x + 3*step)) = BR_id; + } + + for (; x < length; x++) + { + float a1 = (float)(x0 + x) * 1.0f / W * 2 - 1; + + float fx = (a * theta[0] + a1 * theta[1] + theta[2] + 1)/2 * H; + float fy = (a * theta[3] + a1 * theta[4] + theta[5] + 1)/2 * W; + + const int ix = (int)(fx) - (fx < 0); + const int iy = (int)(fy) - (fy < 0); + + float ax = fx - ix; + float ay = fy - iy; + float bx = 1 - ax; + float by = 1 - ay; + + int b0 = ix >= 0; + int b4 = ix >= -1; + int b1 = ix < H; + int b5 = ix < H-1; + + int b2 = iy >= 0; + int b6 = iy >= -1; + int b3 = iy < W; + int b7 = iy < W-1; + + int b01 = b0 & b1; + int b23 = b2 & b3; + int b45 = b4 & b5; + int b67 = b6 & b7; + + int b0123 = b01 & b23; + int b0167 = b01 & b67; + int b4523 = b45 & b23; + int b4567 = b45 & b67; + + int TL_id = ((ix + 0) * W + (iy + 0)) * b0123; + int BL_id = ((ix + 1) * W + (iy + 0)) * b4523; + int TR_id = ((ix + 0) * W + (iy + 1)) * b0167; + int BR_id = ((ix + 1) * W + (iy + 1)) * b4567; + + half w0 = bx*by*b0123; + half w1 = ax*by*b4523; + half w2 = bx*ay*b0167; + half w3 = ax*ay*b4567; + + weight[x + 0*step] = w0; + weight[x + 1*step] = w1; + weight[x + 2*step] = w2; + weight[x + 3*step] = w3; + + ind[x + 0*step] = TL_id; + ind[x + 1*step] = BL_id; + ind[x + 2*step] = TR_id; + ind[x + 3*step] = BR_id; + } +} + +__attribute__((noinline)) +void apply(__global half const *const restrict src, + half const *const restrict weight, + int const *const restrict ind, + __local half *const restrict dst, + int length, + int step) +{ + int x = 0; + for(; x <= length - 8; x += 8) + { + int8 TL_id = *((int8*)(ind + x + 0*step)); + int8 BL_id = *((int8*)(ind + x + 1*step)); + int8 TR_id = *((int8*)(ind + x + 2*step)); + int8 BR_id = *((int8*)(ind + x + 3*step)); + + half8 w00 = *((half8*)(weight + x + 0*step)); + half8 w01 = *((half8*)(weight + x + 1*step)); + half8 w02 = *((half8*)(weight + x + 2*step)); + half8 w03 = *((half8*)(weight + x + 3*step)); + + half8 TL = (half8){src[TL_id[0]], src[TL_id[1]], src[TL_id[2]], src[TL_id[3]], + src[TL_id[4]], src[TL_id[5]], src[TL_id[6]], src[TL_id[7]]}; + half8 TR = (half8){src[TR_id[0]], src[TR_id[1]], src[TR_id[2]], src[TR_id[3]], + src[TR_id[4]], src[TR_id[5]], src[TR_id[6]], src[TR_id[7]]}; + half8 BL = (half8){src[BL_id[0]], src[BL_id[1]], src[BL_id[2]], src[BL_id[3]], + src[BL_id[4]], src[BL_id[5]], src[BL_id[6]], src[BL_id[7]]}; + half8 BR = (half8){src[BR_id[0]], src[BR_id[1]], src[BR_id[2]], src[BR_id[3]], + src[BR_id[4]], src[BR_id[5]], src[BR_id[6]], src[BR_id[7]]}; + + half8 res = w00 * TL + w01 * BL + w02 * TR + w03 * BR; + + *((__local half8*)(dst + x)) = res; + } + + for (; x < length; x++) + { + int TL_id = ind[x + 0*step]; + int BL_id = ind[x + 1*step]; + int TR_id = ind[x + 2*step]; + int BR_id = ind[x + 3*step]; + + half w00 = weight[x + 0*step]; + half w01 = weight[x + 1*step]; + half w02 = weight[x + 2*step]; + half w03 = weight[x + 3*step]; + + half TL = src[TL_id]; + half TR = src[TR_id]; + half BL = src[BL_id]; + half BR = src[BR_id]; + + half res = w00 * TL + w01 * BL + w02 * TR + w03 * BR; + dst[x] = res; + } +} + +__kernel void ocl_st(__global half const *const restrict src_data, + __global half const *const restrict theta, + __global half const *const restrict dst_data, + int C, + int W, + __local half *const restrict local_dst) +{ + int w = get_group_id(0); + + int y = get_global_id(1); + int H = get_global_size(1); + + __private int ind[4][MAX_WIDTH] __attribute__((aligned(16))); + __private half weight[4][MAX_WIDTH] __attribute__((aligned(16))); + + const int x0 = w * MAX_WIDTH; + const int x1 = MIN(x0 + MAX_WIDTH, W); + const int length = x1 - x0; + + calcInd(theta, weight, ind, y, H, x0, length, MAX_WIDTH, W); + + for (int c = 0; c < C; c++) + { + __global half const *const restrict src = src_data + c*H*W; + __local half *const restrict dst = local_dst + c*get_local_size(1)*length + get_local_id(1)*length; + + apply(src, weight, ind, dst, length, MAX_WIDTH); + } +} diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_kernel.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_kernel.hpp new file mode 100644 index 00000000000..fd5e33984be --- /dev/null +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_kernel.hpp @@ -0,0 +1,83 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include + +#include +#include + +namespace vpu { + +namespace ie = InferenceEngine; + +VPU_DECLARE_ENUM(CustomParamType, + Input, + Output, + Data, + LocalData, + InputBuffer, + OutputBuffer, + Int, + Float) + +VPU_DECLARE_ENUM(CustomDataFormat, + BYXF = 0, // NHWC used in most software layers + BFYX = 1, // NCHW used if HW module is enabled + YXF = 2, // HWC used in most software layers + FYX = 3, // CHW used if HW module is enabled + BF = 4, // NC layout + Any = 5, // doesn't really matter + None = 6) + +VPU_DECLARE_ENUM(CustomDimSource, Input, Output) + +struct CustomKernel final { + struct KernelParam final { + CustomParamType type = CustomParamType::Input; + CustomDataFormat format = CustomDataFormat::Any; + std::string argName; + int portIndex = -1; + std::string irSource; + std::string bufferSizeRule; + CustomDimSource dimSource; + int dimIdx = -1; + }; + +private: + std::string _configDir; + int _maxShaves = 0; + std::string _kernelBinary; + SmallVector _kernelParams; + SmallVector _globalGridSizeRules; + SmallVector _localGridSizeRules; + SmallVector _parameters; + int _kernelId = 0; + + CustomDimSource _wgDimSource = CustomDimSource::Input; + int _wgDimIdx = -1; + + int _inputDataCount = 0; + +public: + explicit CustomKernel(const pugi::xml_node& node, std::string configDir); + + void processParametersNode(const pugi::xml_node& node); + void processWorkSizesNode(const pugi::xml_node& node); + + int maxShaves() const { return _maxShaves; } + const std::string& kernelBinary() const { return _kernelBinary; } + SmallVector bindings() const { return _kernelParams; } + SmallVector globalGridSizeRules() const { return _globalGridSizeRules; } + SmallVector localGridSizeRules() const { return _localGridSizeRules; } + SmallVector parameters() const { return _parameters; } + int kernelId() const { return _kernelId; } + CustomDimSource dimSource() const { return _wgDimSource; } + int dimSourceIndex() const { return _wgDimIdx; } + int inputDataCount() const { return _inputDataCount; } +}; + +} // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_layer.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_layer.hpp index 3f1e90df393..10b7b97b8cc 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_layer.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/custom_layer.hpp @@ -12,109 +12,45 @@ #include #include
- #include #include #include +#include + +#include namespace vpu { namespace ie = InferenceEngine; -VPU_DECLARE_ENUM(CustomDataFormat, - BYXF = 0, // NHWC used in most software layers - BFYX = 1, // NCHW used if HW module is enabled - YXF = 2, // HWC used in most software layers - FYX = 3, // CHW used if HW module is enabled - Any = 4, // doesn't really matter - None = 5 -) - -VPU_DECLARE_ENUM(CustomParamType, - Input, - Output, - Data, - LocalData, - InputBuffer, - OutputBuffer, - Int, - Float -) - -VPU_DECLARE_ENUM(CustomDimSource, - Input, - Output -) - class CustomLayer final { public: using Ptr = std::shared_ptr; + explicit CustomLayer(std::string configDir, const pugi::xml_node& customLayer); - struct KernelParam final { - CustomParamType type = CustomParamType::Input; - CustomDataFormat format = CustomDataFormat::Any; - std::string argName; - int portIndex = -1; - std::string irSource; - SmallVector bufferSizeRules; - CustomDimSource dimSource; - int dimIdx = -1; - }; + std::vector kernels() const { return _kernels; } + std::string layerName() const { return _layerName; } + std::map inputs() { return _inputs; } + std::map outputs() { return _outputs; } static ie::details::caseless_map> loadFromFile( const std::string& configFile, bool canBeMissed = false); - const std::string& kernelBinary() const { return _kernelBinary; } - - void setStageNumInputs(int id); - int stageNumInputs() const; - uint32_t kernelAddress(int idx = 1) const; - int kernelId() const; - int maxShaves() const; - const std::map& whereParams() const { return _whereParams; } - - const SmallVector& bindings() const { return _kernelParams; } - const SmallVector& parameters() const { return _parameters; } - - const SmallVector& globalSizeRules() const { return _globalSizeRules; } - const SmallVector& localSizeRules() const { return _localSizeRules; } - - CustomDimSource dimSource() const { return _wgDimSource; } - int dimSourceIndex() const { return _wgDimIdx; } - -private: - explicit CustomLayer(const std::string& dirname) : _configDir(dirname) {} - - void loadSingleLayer(const pugi::xml_node& node); - void processWhere(const pugi::xml_node& node); - void processKernelNode(const pugi::xml_node& node); - void processParametersNode(const pugi::xml_node& node); - void processWorkSizesNode(const pugi::xml_node& node); - - static bool isLegalSizeRule(const std::string& rule); - static CustomDataFormat formatFromString(const std::string& str); + bool meetsWhereRestrictions(const std::map& params) const; + static bool isLegalSizeRule(const std::string& rule, std::map layerParams); + static CustomDataFormat formatFromLayout(const InferenceEngine::Layout& layout); private: std::string _configDir; std::string _layerName; - std::string _kernelEntry; - std::string _kernelBinary; std::map _whereParams; - int _maxShaves = 0; - int _stageNumInputs = -1; + std::vector _kernels; - SmallVector _kernelParams; - SmallVector _globalSizeRules; - SmallVector _localSizeRules; - SmallVector _parameters; - - std::map> _kernelAddress; - - CustomDimSource _wgDimSource = CustomDimSource::Input; - int _wgDimIdx = -1; + std::map _inputs; + std::map _outputs; }; }; // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/frontend.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/frontend.hpp index 76ee331fdaa..462ecc9ac6b 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/frontend.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/frontend/frontend.hpp @@ -151,6 +151,8 @@ public: void parseExpTopKROIs(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const; void parseNonZero(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const; void parseROIAlign(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const; + void parseOutShapeOfReshape(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const; + void parseBroadcast(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const; // // Special layers @@ -172,9 +174,11 @@ public: void parseLSTMCell(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs); void parseTensorIterator(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs); -// -// Utility -// + // + // Utility + // + + static CustomLayer::Ptr getSuitableCustomLayer(const std::vector& customLayers, const ie::CNNLayerPtr&cnnLayer); private: Data getVpuData(const ie::DataPtr& ieData) const; diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp index d3449cf815c..2692e67c1a9 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/graph_transformer.hpp @@ -103,6 +103,7 @@ struct CompilationConfig final { bool dumpAllPasses; bool disableReorder = false; // TODO: rename to enableReorder and switch logic. + bool disableConvertStages = false; bool enablePermuteMerging = true; bool enableReplWithSCRelu = false; bool enableReplaceWithReduceMean = true; diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/pass_manager.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/pass_manager.hpp index bd734a32570..c8c26c5fa55 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/pass_manager.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/middleend/pass_manager.hpp @@ -110,6 +110,7 @@ public: Pass::Ptr splitHwDepthConv(); Pass::Ptr splitHwConvAndPool(); Pass::Ptr hwPadding(); + Pass::Ptr splitLargeKernelConv(); // // Batch support diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/model/data.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/model/data.hpp index b59bbfa2c25..8c33d522c4d 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/model/data.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/model/data.hpp @@ -264,7 +264,7 @@ private: void serializeDescImpl( BlobSerializer& serializer, const DataDesc& storedDesc, - const DimValues& storedStrides) const; + const ShapeLocation& shapeLocation) const; private: inline DataNode() : diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/model/model.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/model/model.hpp index ea11234bab7..4554fc6853d 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/model/model.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/model/model.hpp @@ -241,18 +241,25 @@ public: friend ModelObj; }; - inline DataToDataEdgeHelper connectDataWithData() { - return DataToDataEdgeHelper(this); - } - DataToShapeAllocation connectDataWithShape( const Data& parent, const Data& child); - void replaceParentData( + void replaceDataToShapeParent( + const DataToShapeAllocation& edge, + const Data& newParent); + void replaceDataToShapeChild( + const DataToShapeAllocation& edge, + const Data& newChild); + + inline DataToDataEdgeHelper connectDataWithData() { + return DataToDataEdgeHelper(this); + } + + void replaceDataToDataParent( const DataToDataAllocation& edge, const Data& newParent); - void replaceChildData( + void replaceDataToDataChild( const DataToDataAllocation& edge, const Data& newChild); diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/model/stage.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/model/stage.hpp index 0f6136f136f..76b5aa07f38 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/model/stage.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/model/stage.hpp @@ -53,7 +53,7 @@ VPU_DECLARE_ENUM(StageType, StubPriorBox, StubPriorBoxClustered, - Concat, + StubConcat, Split, Reshape, Expand, @@ -119,7 +119,6 @@ VPU_DECLARE_ENUM(StageType, Pad = 71, Resample = 72, Upsampling = 73, - ArgMax = 74, Div = 75, Min = 76, Squared_diff = 77, @@ -166,6 +165,9 @@ VPU_DECLARE_ENUM(StageType, ExpGenerateProposals = 124, ExpTopKROIs = 125, ScatterElementsUpdate = 126, + OutShapeOfReshape = 127, + Concat = 128, + Broadcast = 129, ) // @@ -240,6 +242,24 @@ VPU_DECLARE_ENUM(TopKOutputs, ValueOnly = 1, IndexOnly = 2) +// +// ConcatInferRequirement +// + +// Requirement whether to infer Concat stage on the device side +VPU_DECLARE_ENUM(ConcatInferRequirement, + NeedToInfer = 0, + CanBeReplaced = 1) + +// +// ConcatInferRequirement +// + +// Modes for Broadcast operation according to specification +VPU_DECLARE_ENUM(BroadcastMode, + NUMPY = 0, + EXPLICIT = 1) + // // StageDataInfo // diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/private_plugin_config.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/private_plugin_config.hpp index 06b7c95425f..253a995f47b 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/private_plugin_config.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/private_plugin_config.hpp @@ -56,6 +56,12 @@ DECLARE_VPU_CONFIG_KEY(DUMP_ALL_PASSES); */ DECLARE_VPU_CONFIG_KEY(DISABLE_REORDER); +/** + * @brief Used to disable convert stages in tests to be able to insert + * convert layer with desired precision. + */ +DECLARE_VPU_CONFIG_KEY(DISABLE_CONVERT_STAGES); + /** * @brief Used to disable permute merging pass (with setting "NO") in tests to check it preserves behaviour. Default = "YES" */ diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/stage_builder.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/stage_builder.hpp index 419179df5f9..29fb772c462 100644 --- a/inference-engine/src/vpu/graph_transformer/include/vpu/stage_builder.hpp +++ b/inference-engine/src/vpu/graph_transformer/include/vpu/stage_builder.hpp @@ -116,7 +116,8 @@ public: const ie::CNNLayerPtr& layer, Dim axis, const DataVector& inputs, - const Data& output); + const Data& output, + ConcatInferRequirement inferRequirement = ConcatInferRequirement::CanBeReplaced); Stage addConcatStage( const Model& model, diff --git a/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp b/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp index 783fd0db00f..c533a1411f7 100644 --- a/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/backend/serialize.cpp @@ -132,33 +132,33 @@ void BackEnd::serializeConstData(const Model& model, const mv_blob_header& blobH void BackEnd::serializeConstShapes(const Model& model, const mv_blob_header& blobHdr, std::vector& blob) { for (const auto& data : model->datas()) { - const auto serializeToBlob = [&data, &blob, &blobHdr](const BlobSerializer& serializer, int offset) { - std::copy_n(serializer.data(), data->desc().numDims() * sizeof(uint32_t), blob.data() + blobHdr.const_data_section_offset + offset); - }; - const auto dimsOrder = data->desc().dimsOrder(); const auto storedPerm = dimsOrder.toPermutation(); + const auto serializeToBlob = [&data, &blob, &blobHdr, &storedPerm](const DimValues& values, int offset) { + BlobSerializer serializer; + + for (const auto& d : storedPerm) { + serializer.append(checked_cast(values[d])); + } + + std::copy_n(serializer.data(), data->desc().numDims() * sizeof(uint32_t), blob.data() + blobHdr.const_data_section_offset + offset); + }; + const auto shapeLocation = data->shapeLocation(); if (shapeLocation.dimsLocation == Location::Blob) { - BlobSerializer dimsSerializer; - const auto dims = data->desc().dims(); - - for (const auto& d : storedPerm) { - dimsSerializer.append(checked_cast(dims[d])); - } - serializeToBlob(dimsSerializer, shapeLocation.dimsOffset); + serializeToBlob(data->desc().dims(), shapeLocation.dimsOffset); + } else if (data->usage() == DataUsage::Output) { + auto ioDimsUpperBoundOffset = data->attrs().get("ioDimsUpperBoundOffset"); + serializeToBlob(data->desc().dims(), ioDimsUpperBoundOffset); } if (shapeLocation.stridesLocation == Location::Blob) { - BlobSerializer stridesSerializer; - const auto strides = data->strides(); - - for (const auto& d : storedPerm) { - stridesSerializer.append(checked_cast(strides[d])); - } - serializeToBlob(stridesSerializer, shapeLocation.stridesOffset); + serializeToBlob(data->strides(), shapeLocation.stridesOffset); + } else if (data->usage() == DataUsage::Output) { + auto ioStridesUpperBoundOffset = data->attrs().get("ioStridesUpperBoundOffset"); + serializeToBlob(data->strides(), ioStridesUpperBoundOffset); } } } diff --git a/inference-engine/src/vpu/graph_transformer/src/frontend/custom_kernel.cpp b/inference-engine/src/vpu/graph_transformer/src/frontend/custom_kernel.cpp new file mode 100644 index 00000000000..c7ad6f6cc8f --- /dev/null +++ b/inference-engine/src/vpu/graph_transformer/src/frontend/custom_kernel.cpp @@ -0,0 +1,426 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include
+#include + +namespace vpu { + +VPU_PACKED(Elf32Ehdr { + uint8_t offs1[28]; + uint32_t ePhoff; // Program header offset + uint32_t eShoff; // Section header offset + uint8_t offs2[12]; + uint16_t eShnum; // Number of sections + uint16_t offs3; +};) + +VPU_PACKED(Elf32Section { + uint32_t shName; + uint32_t shType; + uint32_t shFlags; + uint32_t shAddr; + uint32_t shOffset; + uint32_t shSize; + uint32_t shLink; + uint32_t shInfo; + uint32_t shAddralign; + uint32_t shEntsize; +};) + +VPU_PACKED(Elf32Phdr { + uint32_t pType; // Identifies program segment type + uint32_t pOffset; // Segment file offset + uint32_t pVaddr; // Segment virtual address + uint32_t pPaddr; // Segment physical address + uint32_t pFilesz; // Segment size in file + uint32_t pMemsz; // Segment size in memory + uint32_t pFlags; // Flags position from ELF standard spec + uint32_t pAlign; // Segment alignment, file & memory +};) + +VPU_PACKED(Elf32Sym { + uint32_t stName; + uint32_t stValue; + uint32_t stSize; + uint8_t stInfo; + uint8_t stOther; + uint16_t stShndx; +};) + +VPU_PACKED(KernelHdr { + uint32_t address; // Kernel address + uint32_t flags; // Should be 0 for now + uint32_t sectionSize; // Section size, offset to the next kernel + uint32_t argOffset; // offset to arguments + uint32_t stackSize; // Size of the stack required for kernel + uint32_t stackSizeWI; // Size of the stack required for kernel per WI +};) + +VPU_PACKED(KernelArgHdr { + uint32_t stringOffset; + uint32_t addressSpace; + uint32_t typeOffset; + uint32_t size; + uint32_t laneSize; +};) + +std::pair findSymbolTable( + const char* ELFData) { + const uint32_t SYMTAB = 2; // Link editing symbol table + const uint32_t STRTAB = 3; // A string table + + IE_ASSERT(ELFData != nullptr); + + auto ehdr = reinterpret_cast(ELFData); + auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); + + const Elf32Section* strShdr = nullptr; + const Elf32Section* symShdr = nullptr; + for (size_t i = 0; i < ehdr->eShnum; i++) { + if (shdr[i].shType == STRTAB && strShdr == nullptr) { + strShdr = &shdr[i]; + } else if (shdr[i].shType == SYMTAB && symShdr == nullptr) { + symShdr = &shdr[i]; + } + + if (symShdr != nullptr && strShdr != nullptr) + break; + } + IE_ASSERT(symShdr != nullptr && strShdr != nullptr); + + return std::make_pair(strShdr, symShdr); +} + +SmallVector deduceKernelParameters( + const char* ELFData, + uint32_t kernelAddress) { + IE_ASSERT(ELFData != nullptr); + const auto cmp = ie::details::CaselessEq{}; + + auto ehdr = reinterpret_cast(ELFData); + auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); + auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); + + const Elf32Section* strShdr = nullptr; + const Elf32Section* symShdr = nullptr; + std::tie(strShdr, symShdr) = findSymbolTable(ELFData); + IE_ASSERT(symShdr != nullptr && strShdr != nullptr); + + auto numSymEntries = symShdr->shSize / symShdr->shEntsize; + auto sym = reinterpret_cast(ELFData + symShdr->shOffset); + auto firstStr = ELFData + strShdr->shOffset; + + const char* kernelArgStrings = nullptr; + for (size_t i = 0; i < numSymEntries; i++) { + if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.strings")) { + kernelArgStrings = ELFData + shdr[sym[i].stShndx].shOffset; + break; + } + } + IE_ASSERT(kernelArgStrings != nullptr); + + SmallVector parameters; + for (size_t i = 0; i < numSymEntries; i++) { + if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.info")) { + auto ptr = ELFData + shdr[sym[i].stShndx].shOffset; + auto numKernels = *reinterpret_cast(ptr); + + auto metaOffset = sizeof(int); + for (int k = 0; k < numKernels; k++) { + auto kHdr = reinterpret_cast(ptr + metaOffset); + + if (kHdr->address-phdr->pVaddr == kernelAddress) { + auto aHdr = reinterpret_cast( + reinterpret_cast(&(kHdr->argOffset)) + sizeof(kHdr->argOffset) + kHdr->argOffset); + + auto numArgs = reinterpret_cast(aHdr)[-1]; + for (int n = 0; n < numArgs; n++, aHdr++) { + parameters.push_back(kernelArgStrings + aHdr->stringOffset); + } + + break; + } + + metaOffset += kHdr->sectionSize + sizeof(kHdr->address) + sizeof(kHdr->flags); + } + } + } + + return parameters; +} + +int32_t getKernelId( + const char* ELFData, + uint32_t kernelAddress) { + IE_ASSERT(ELFData != nullptr); + const auto cmp = ie::details::CaselessEq{}; + + auto ehdr = reinterpret_cast(ELFData); + auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); + auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); + + const Elf32Section* strShdr = nullptr; + const Elf32Section* symShdr = nullptr; + std::tie(strShdr, symShdr) = findSymbolTable(ELFData); + IE_ASSERT(symShdr != nullptr && strShdr != nullptr); + + auto numSymEntries = symShdr->shSize / symShdr->shEntsize; + auto sym = reinterpret_cast(ELFData + symShdr->shOffset); + auto firstStr = ELFData + strShdr->shOffset; + + const char* kernelArgStrings = nullptr; + for (size_t i = 0; i < numSymEntries; i++) { + if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.strings")) { + kernelArgStrings = ELFData + shdr[sym[i].stShndx].shOffset; + break; + } + } + IE_ASSERT(kernelArgStrings != nullptr); + + for (size_t i = 0; i < numSymEntries; i++) { + if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.info")) { + auto ptr = ELFData + shdr[sym[i].stShndx].shOffset; + auto numKernels = *reinterpret_cast(ptr); + + auto metaOffset = sizeof(int); + for (int k = 0; k < numKernels; k++) { + auto kHdr = reinterpret_cast(ptr + metaOffset); + + if (kHdr->address-phdr->pVaddr == kernelAddress) { + return k; + } + + metaOffset += kHdr->sectionSize + sizeof(kHdr->address) + sizeof(kHdr->flags); + } + } + } + + return -1; +} + +uint32_t getKernelEntry(const char* ELFData, const std::string& kernelName) { + IE_ASSERT(ELFData != nullptr); + const auto cmp = ie::details::CaselessEq{}; + + auto ehdr = reinterpret_cast(ELFData); + auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); + + const Elf32Section* strShdr = nullptr; + const Elf32Section* symShdr = nullptr; + std::tie(strShdr, symShdr) = findSymbolTable(ELFData); + IE_ASSERT(symShdr != nullptr && strShdr != nullptr); + + auto numSymEntries = symShdr->shSize / symShdr->shEntsize; + auto sym = reinterpret_cast(ELFData + symShdr->shOffset); + auto firstStr = ELFData + strShdr->shOffset; + + for (size_t i = 0; i < numSymEntries; i++) { + if (cmp(firstStr + sym[i].stName, kernelName)) { + return sym[i].stValue - phdr->pVaddr; + } + } + + THROW_IE_EXCEPTION << "Cannot find kernel entry point for custom kernel " << kernelName; +} + +CustomKernel::CustomKernel(const pugi::xml_node& kernel, std::string configDir): _configDir {std::move(configDir)} { + _maxShaves = XMLParseUtils::GetIntAttr(kernel, "max-shaves", 0); + + for (auto source = kernel.child("Source"); !source.empty(); source = source.next_sibling("Source")) { + auto fileName = _configDir + "/" + XMLParseUtils::GetStrAttr(source, "filename", ""); + + std::ifstream inputFile(fileName, std::ios::binary); + if (!inputFile.is_open()) { + THROW_IE_EXCEPTION << "Couldn't open kernel file " << fileName; + } + + std::ostringstream contentStream; + contentStream << inputFile.rdbuf(); + _kernelBinary.append(contentStream.str()); + } + + const auto kernelEntryName = XMLParseUtils::GetStrAttr(kernel, "entry"); + const auto kernelEntry = getKernelEntry(&_kernelBinary[0], kernelEntryName); + _parameters = deduceKernelParameters(&_kernelBinary[0], kernelEntry); + _kernelId = getKernelId(&_kernelBinary[0], kernelEntry); + + processParametersNode(kernel); + processWorkSizesNode(kernel); + + const auto isInputData = [&](const CustomKernel::KernelParam& param) { + return param.type == CustomParamType::Input || param.type == CustomParamType::InputBuffer || + param.type == CustomParamType::Data; + }; + + _inputDataCount = std::count_if(begin(_kernelParams), end(_kernelParams), isInputData); +} + +std::pair parseDimSource(const std::string& dims) { + const auto cmp = ie::details::CaselessEq{}; + const auto pos = dims.find_first_of(','); + const auto source = dims.substr(0, pos); + const auto dimSource = [&] { + if (cmp(source, "input")) { + return CustomDimSource::Input; + } else if (cmp(source, "output")) { + return CustomDimSource::Output; + } else { + THROW_IE_EXCEPTION << "Invalid dim source argument" << source; + } + }(); + + const auto idx = [&] { + if (pos == std::string::npos) { + return -1; + } + const auto idxString = dims.substr(pos + 1, std::string::npos); + return std::stoi(idxString); + }(); + + return std::make_pair(dimSource, idx); +} + + +CustomDataFormat formatFromString(const std::string& str) { + static const ie::details::caseless_map FormatNameToType = { + { "BFYX" , CustomDataFormat::BFYX }, + { "BYXF" , CustomDataFormat::BYXF }, + { "FYX" , CustomDataFormat::FYX }, + { "YXF" , CustomDataFormat::YXF }, + { "BF" , CustomDataFormat::BF }, + { "ANY" , CustomDataFormat::Any } + }; + + auto it = FormatNameToType.find(str); + if (it != FormatNameToType.end()) { + return it->second; + } + + THROW_IE_EXCEPTION << "Tensor node has an invalid format '" << str << "'"; +} + +SmallVector parseSizeRule(const std::string& size) { + auto result = SmallVector(); + result.reserve(std::count(begin(size), end(size), ',') + 1); + std::stringstream sizeRules{size}; + std::string bufferSize; + + while (std::getline(sizeRules, bufferSize, ',')) { + result.push_back(bufferSize); + } + + return result; +} + +void CustomKernel::processParametersNode(const pugi::xml_node& node) { + const auto cmp = ie::details::CaselessEq {}; + const auto parameters = node.child("Parameters"); + + for (auto tensor = parameters.child("Tensor"); !tensor.empty(); tensor = tensor.next_sibling("Tensor")) { + KernelParam kp; + + auto typeStr = XMLParseUtils::GetStrAttr(tensor, "type"); + if (cmp(typeStr, "input")) { + kp.type = CustomParamType::Input; + } else if (cmp(typeStr, "output")) { + kp.type = CustomParamType::Output; + } else if (cmp(typeStr, "input_buffer")) { + kp.type = CustomParamType::InputBuffer; + } else if (cmp(typeStr, "output_buffer")) { + kp.type = CustomParamType::OutputBuffer; + } else if (cmp(typeStr, "data")) { + kp.type = CustomParamType::Data; + } else { + THROW_IE_EXCEPTION << "Tensor node has an invalid type '" << typeStr << "'"; + } + + if (kp.type == CustomParamType::InputBuffer || kp.type == CustomParamType::OutputBuffer) { + const auto sizeRule = XMLParseUtils::GetStrAttr(tensor, "size"); + kp.bufferSizeRule = parseSizeRule(sizeRule)[0]; + + const auto dimString = XMLParseUtils::GetStrAttr(tensor, "dim"); + std::tie(kp.dimSource, kp.dimIdx) = parseDimSource(dimString); + } + + kp.format = formatFromString(XMLParseUtils::GetStrAttr(tensor, "format", "BFYX")); + kp.argName = XMLParseUtils::GetStrAttr(tensor, "arg-name"); + kp.portIndex = XMLParseUtils::GetIntAttr(tensor, "port-index"); + + _kernelParams.push_back(std::move(kp)); + } + + for (auto data = parameters.child("Data"); !data.empty(); data = data.next_sibling("Data")) { + KernelParam kp; + + auto typeStr = XMLParseUtils::GetStrAttr(data, "type"); + if (cmp(typeStr, "data")) { + kp.type = CustomParamType::Data; + } else if (cmp(typeStr, "local_data")) { + kp.type = CustomParamType::LocalData; + } else { + THROW_IE_EXCEPTION << "Data node has an invalid type '" << typeStr << "'"; + } + + kp.argName = XMLParseUtils::GetStrAttr(data, "arg-name"); + + kp.irSource = XMLParseUtils::GetStrAttr(data, "source", ""); + const auto dimString = XMLParseUtils::GetStrAttr(data, "dim", ""); + + if (kp.irSource.empty() && dimString.empty()) { + THROW_IE_EXCEPTION << "Data node has no source or dim"; + } + + if (!kp.irSource.empty() && !dimString.empty()) { + THROW_IE_EXCEPTION << "Data node can only have source or dim"; + } + + if (kp.type == CustomParamType::LocalData) { + const auto bufferSize = XMLParseUtils::GetStrAttr(data, "size", ""); + kp.bufferSizeRule = bufferSize; + + if (!dimString.empty()) { + std::tie(kp.dimSource, kp.dimIdx) = parseDimSource(dimString); + } + } + + _kernelParams.push_back(std::move(kp)); + } + + for (auto scalar = parameters.child("Scalar"); !scalar.empty(); scalar = scalar.next_sibling("Scalar")) { + KernelParam kp; + + const auto type = XMLParseUtils::GetStrAttr(scalar, "type"); + if (cmp(type, "int")) { + kp.type = CustomParamType::Int; + } else if (cmp(type, "float")) { + kp.type = CustomParamType::Float; + } else { + THROW_IE_EXCEPTION << "Scalar node has an invalid type " << type; + } + + kp.argName = XMLParseUtils::GetStrAttr(scalar, "arg-name"); + kp.portIndex = XMLParseUtils::GetIntAttr(scalar, "port-index", -1); + kp.irSource = XMLParseUtils::GetStrAttr(scalar, "source", ""); + + _kernelParams.push_back(std::move(kp)); + } +} + +void CustomKernel::processWorkSizesNode(const pugi::xml_node& node) { + const auto workSizes = node.child("WorkSizes"); + + const auto dims = XMLParseUtils::GetStrAttr(workSizes, "dim"); + std::tie(_wgDimSource, _wgDimIdx) = parseDimSource(dims); + + const auto gwgs = XMLParseUtils::GetStrAttr(workSizes, "global"); + _globalGridSizeRules = parseSizeRule(gwgs); + + const auto lwgs = XMLParseUtils::GetStrAttr(workSizes, "local"); + _localGridSizeRules = parseSizeRule(lwgs); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/src/frontend/custom_layer.cpp b/inference-engine/src/vpu/graph_transformer/src/frontend/custom_layer.cpp index 6feed7781aa..ee1aa84c20f 100644 --- a/inference-engine/src/vpu/graph_transformer/src/frontend/custom_layer.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/frontend/custom_layer.cpp @@ -3,6 +3,7 @@ // #include +#include #include @@ -11,7 +12,6 @@ #include #include #include -#include #include #include @@ -31,289 +31,51 @@ #include #include -#include +#include namespace vpu { namespace { -VPU_PACKED(Elf32Ehdr { - uint8_t offs1[28]; - uint32_t ePhoff; // Program header offset - uint32_t eShoff; // Section header offset - uint8_t offs2[12]; - uint16_t eShnum; // Number of sections - uint16_t offs3; -};) - -VPU_PACKED(Elf32Section { - uint32_t shName; - uint32_t shType; - uint32_t shFlags; - uint32_t shAddr; - uint32_t shOffset; - uint32_t shSize; - uint32_t shLink; - uint32_t shInfo; - uint32_t shAddralign; - uint32_t shEntsize; -};) - -VPU_PACKED(Elf32Phdr { - uint32_t pType; // Identifies program segment type - uint32_t pOffset; // Segment file offset - uint32_t pVaddr; // Segment virtual address - uint32_t pPaddr; // Segment physical address - uint32_t pFilesz; // Segment size in file - uint32_t pMemsz; // Segment size in memory - uint32_t pFlags; // Flags position from ELF standard spec - uint32_t pAlign; // Segment alignment, file & memory -};) - -VPU_PACKED(Elf32Sym { - uint32_t stName; - uint32_t stValue; - uint32_t stSize; - uint8_t stInfo; - uint8_t stOther; - uint16_t stShndx; -};) - -VPU_PACKED(KernelHdr { - uint32_t address; // Kernel address - uint32_t flags; // Should be 0 for now - uint32_t sectionSize; // Section size, offset to the next kernel - uint32_t argOffset; // offset to arguments - uint32_t stackSize; // Size of the stack required for kernel - uint32_t stackSizeWI; // Size of the stack required for kernel per WI -};) - -VPU_PACKED(KernelArgHdr { - uint32_t stringOffset; - uint32_t addressSpace; - uint32_t typeOffset; - uint32_t size; - uint32_t laneSize; -};) - -enum Flags { - CL_Vecz = 0x01, - CL_Unrolled = 0x02, - CL_Predicated = 0x04, - CL_Dma = 0x08, - CL_VeczDma = 0x10 -}; - -std::pair findSymbolTable( - const char* ELFData) { - const uint32_t SYMTAB = 2; // Link editing symbol table - const uint32_t STRTAB = 3; // A string table - - IE_ASSERT(ELFData != nullptr); - - auto ehdr = reinterpret_cast(ELFData); - auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); - - const Elf32Section* strShdr = nullptr; - const Elf32Section* symShdr = nullptr; - for (size_t i = 0; i < ehdr->eShnum; i++) { - if (shdr[i].shType == STRTAB && strShdr == nullptr) { - strShdr = &shdr[i]; - } else if (shdr[i].shType == SYMTAB && symShdr == nullptr) { - symShdr = &shdr[i]; - } - - if (symShdr != nullptr && strShdr != nullptr) - break; +void assertExactlyOneOccurrence(const pugi::xml_node &node, const SmallVector& childs) { + for (const auto &name : childs) { + const auto& child = node.child(name.c_str()); + VPU_THROW_UNLESS(!child.empty(), "Required parameter %s is not found", name); + VPU_THROW_UNLESS(child.next_sibling(name.c_str()).empty(), + "Found several definitions of the parameter %s", name); } - IE_ASSERT(symShdr != nullptr && strShdr != nullptr); - - return std::make_pair(strShdr, symShdr); } -uint32_t getKernelEntry(const char* ELFData, const std::string& kernelName) { - ie::details::CaselessEq cmp; - - IE_ASSERT(ELFData != nullptr); - - auto ehdr = reinterpret_cast(ELFData); - auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); - - const Elf32Section* strShdr = nullptr; - const Elf32Section* symShdr = nullptr; - std::tie(strShdr, symShdr) = findSymbolTable(ELFData); - IE_ASSERT(symShdr != nullptr && strShdr != nullptr); - - auto numSymEntries = symShdr->shSize / symShdr->shEntsize; - auto sym = reinterpret_cast(ELFData + symShdr->shOffset); - auto firstStr = ELFData + strShdr->shOffset; - - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, kernelName)) { - return sym[i].stValue - phdr->pVaddr; - } +void assertOneOrMoreOccurrence(const pugi::xml_node &node, const SmallVector& childs) { + for (const auto& name : childs) { + const auto& child = node.child(name.c_str()); + VPU_THROW_UNLESS(!child.empty(), + "Required parameter %s is not found", name); } - - VPU_THROW_EXCEPTION << "Cannot find kernel entry point for custom kernel " << kernelName; } -SmallVector deduceKernelParameters( - const char* ELFData, - uint32_t kernelAddress) { - ie::details::CaselessEq cmp; - IE_ASSERT(ELFData != nullptr); - - auto ehdr = reinterpret_cast(ELFData); - auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); - auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); - - const Elf32Section* strShdr = nullptr; - const Elf32Section* symShdr = nullptr; - std::tie(strShdr, symShdr) = findSymbolTable(ELFData); - IE_ASSERT(symShdr != nullptr && strShdr != nullptr); - - auto numSymEntries = symShdr->shSize / symShdr->shEntsize; - auto sym = reinterpret_cast(ELFData + symShdr->shOffset); - auto firstStr = ELFData + strShdr->shOffset; - - const char* kernelArgStrings = nullptr; - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.strings")) { - kernelArgStrings = ELFData + shdr[sym[i].stShndx].shOffset; - break; - } +void assertZeroOrOneOccurrence(const pugi::xml_node& node, const SmallVector& childNames) { + for (const auto& name : childNames) { + const auto& child = node.child(name.c_str()); + VPU_THROW_UNLESS(!child.empty() || child.next_sibling(name.c_str()).empty(), + "Found several definitions of the parameter %s", name); } - IE_ASSERT(kernelArgStrings != nullptr); - - SmallVector parameters; - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.info")) { - auto ptr = ELFData + shdr[sym[i].stShndx].shOffset; - auto numKernels = *reinterpret_cast(ptr); - - auto metaOffset = sizeof(int); - for (int k = 0; k < numKernels; k++) { - auto kHdr = reinterpret_cast(ptr + metaOffset); - - if (kHdr->address-phdr->pVaddr == kernelAddress) { - auto aHdr = reinterpret_cast( - reinterpret_cast(&(kHdr->argOffset)) + sizeof(kHdr->argOffset) + kHdr->argOffset); - - auto numArgs = reinterpret_cast(aHdr)[-1]; - for (int n = 0; n < numArgs; n++, aHdr++) { - parameters.push_back(kernelArgStrings + aHdr->stringOffset); - } - - break; - } - - metaOffset += kHdr->sectionSize + sizeof(kHdr->address) + sizeof(kHdr->flags); - } - } - } - - return parameters; } -std::pair deduceVectorized( - const char* ELFData, - uint32_t kernelAddress) { - ie::details::CaselessEq cmp; - - IE_ASSERT(ELFData != nullptr); - - auto ehdr = reinterpret_cast(ELFData); - auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); - auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); - - const Elf32Section* strShdr = nullptr; - const Elf32Section* symShdr = nullptr; - std::tie(strShdr, symShdr) = findSymbolTable(ELFData); - IE_ASSERT(symShdr != nullptr && strShdr != nullptr); - - auto numSymEntries = symShdr->shSize / symShdr->shEntsize; - auto sym = reinterpret_cast(ELFData + symShdr->shOffset); - auto firstStr = ELFData + strShdr->shOffset; - - const char* kernelArgStrings = nullptr; - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.strings")) { - kernelArgStrings = ELFData + shdr[sym[i].stShndx].shOffset; - break; +void assertNoEmptyAttributes(const pugi::xml_node& customLayer) { + const auto checkAttributes = [&customLayer](const pugi::xml_node& node) { + for (const auto& attr : node.attributes()) { + VPU_THROW_UNLESS(strlen(attr.value()) != 0, + "Wrong custom layer XML: Custom layer %s has node <%s> with an empty attribute %s", + customLayer.attribute("name").value(), node.name(), attr.name()); } + }; + + checkAttributes(customLayer); + + for (const auto& child : customLayer.children()) { + assertNoEmptyAttributes(child); } - IE_ASSERT(kernelArgStrings != nullptr); - - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.info")) { - auto ptr = ELFData + shdr[sym[i].stShndx].shOffset; - auto numKernels = *reinterpret_cast(ptr); - - auto metaOffset = sizeof(int); - for (int k = 0; k < numKernels; k++) { - auto kHdr = reinterpret_cast(ptr + metaOffset); - - if (kHdr->address-phdr->pVaddr == kernelAddress && kHdr->flags == 1) { - auto vecInfo = reinterpret_cast(kHdr + 1); - return std::make_pair(vecInfo[1], vecInfo[0]-phdr->pVaddr); - } - - metaOffset += kHdr->sectionSize + sizeof(kHdr->address) + sizeof(kHdr->flags); - } - } - } - - return std::make_pair(0, 0); -} - -int32_t getKernelId( - const char* ELFData, - uint32_t kernelAddress) { - ie::details::CaselessEq cmp; - - IE_ASSERT(ELFData != nullptr); - - auto ehdr = reinterpret_cast(ELFData); - auto phdr = reinterpret_cast(ELFData + ehdr->ePhoff); - auto shdr = reinterpret_cast(ELFData + ehdr->eShoff); - - const Elf32Section* strShdr = nullptr; - const Elf32Section* symShdr = nullptr; - std::tie(strShdr, symShdr) = findSymbolTable(ELFData); - IE_ASSERT(symShdr != nullptr && strShdr != nullptr); - - auto numSymEntries = symShdr->shSize / symShdr->shEntsize; - auto sym = reinterpret_cast(ELFData + symShdr->shOffset); - auto firstStr = ELFData + strShdr->shOffset; - - const char* kernelArgStrings = nullptr; - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.strings")) { - kernelArgStrings = ELFData + shdr[sym[i].stShndx].shOffset; - break; - } - } - IE_ASSERT(kernelArgStrings != nullptr); - - for (size_t i = 0; i < numSymEntries; i++) { - if (cmp(firstStr + sym[i].stName, "opencl.kernelArgs.info")) { - auto ptr = ELFData + shdr[sym[i].stShndx].shOffset; - auto numKernels = *reinterpret_cast(ptr); - - auto metaOffset = sizeof(int); - for (int k = 0; k < numKernels; k++) { - auto kHdr = reinterpret_cast(ptr + metaOffset); - - if (kHdr->address-phdr->pVaddr == kernelAddress) { - return k; - } - - metaOffset += kHdr->sectionSize + sizeof(kHdr->address) + sizeof(kHdr->flags); - } - } - } - - return -1; } } // namespace @@ -321,20 +83,16 @@ int32_t getKernelId( ie::details::caseless_map> CustomLayer::loadFromFile( const std::string& configFile, bool canBeMissed) { - ie::details::caseless_map> out; - pugi::xml_document xmlDoc; pugi::xml_parse_result res = xmlDoc.load_file(configFile.c_str()); if (res.status != pugi::status_ok) { if (canBeMissed) { // Config file might not exist - like global config, for example. - return out; + return {}; } else { - VPU_THROW_EXCEPTION - << "Failed to load custom layer configuration file " << configFile - << " : " << res.description() - << " at offset " << res.offset; + VPU_THROW_FORMAT("Failed to load custom layer configuration file %s : %s at offset %s", + configFile, res.description(), res.offset); } } @@ -346,18 +104,15 @@ ie::details::caseless_map> CustomLaye auto abs_path_ptr = realpath(configFile.c_str(), path); #endif - if (abs_path_ptr == nullptr) { - VPU_THROW_EXCEPTION - << "Failed to load custom layer configuration file " << configFile - << " : can't get canonicalized absolute path"; - } + VPU_THROW_UNLESS(abs_path_ptr != nullptr, + "Failed to load custom layer configuration file %s : can't get canonicalized absolute path", configFile); std::string abs_file_name(path); // Try extracting directory from config path. auto dir_split_pos = abs_file_name.find_last_of("/\\"); - auto colon_pos = abs_file_name.find_first_of(":"); - auto first_slash_pos = abs_file_name.find_first_of("/"); + auto colon_pos = abs_file_name.find_first_of(':'); + auto first_slash_pos = abs_file_name.find_first_of('/'); // If path is absolute. std::string dir_path; @@ -369,411 +124,113 @@ ie::details::caseless_map> CustomLaye << " : path is not valid"; } + auto out = ie::details::caseless_map> {}; for (auto r = xmlDoc.document_element(); r; r = r.next_sibling()) { - CustomLayer::Ptr layer(new CustomLayer(dir_path)); - - layer->loadSingleLayer(r); - - out[layer->_layerName].push_back(layer); + auto layerPtr = std::make_shared(dir_path, r); + out[layerPtr->_layerName].push_back(std::move(layerPtr)); } return out; } -int CustomLayer::maxShaves() const { - return _maxShaves; -} -void CustomLayer::setStageNumInputs(int id) { - _stageNumInputs = id; -} +CustomLayer::CustomLayer(std::string configDir, const pugi::xml_node& customLayer) : _configDir(std::move(configDir)) { + const auto cmp = ie::details::CaselessEq{}; + const auto nodeName = customLayer.name(); + VPU_THROW_UNLESS(cmp(nodeName, "CustomLayer"), + "Wrong custom layer XML : Node is not CustomLayer, but %s", nodeName); -int CustomLayer::stageNumInputs() const { - return _stageNumInputs; -} + const auto nodeType = XMLParseUtils::GetStrAttr(customLayer, "type"); + VPU_THROW_UNLESS(cmp(nodeType, "MVCL"), + "Wrong custom layer XML : Type is not MVCL, but %s", nodeType); -uint32_t CustomLayer::kernelAddress(int idx) const { - for (const auto& x : _kernelAddress) { - if ((checked_cast(idx) % x.first) == 0) { - return x.second; + const auto version = XMLParseUtils::GetIntAttr(customLayer, "version"); + VPU_THROW_UNLESS(version == 1, "Wrong custom layer XML : only version 1 is supported"); + + _layerName = XMLParseUtils::GetStrAttr(customLayer, "name"); + + assertNoEmptyAttributes(customLayer); + + assertZeroOrOneOccurrence(customLayer, {"Where"}); + const auto whereNode = customLayer.child("Where"); + for (auto where : whereNode.attributes()) { + _whereParams[where.name()] = where.value(); + } + + assertOneOrMoreOccurrence(customLayer, {"Kernel"}); + auto kernelNodes = [&] { + auto nodes = SmallVector{}; + for (auto kernel = customLayer.child("Kernel"); !kernel.empty(); kernel = kernel.next_sibling("Kernel")) { + assertExactlyOneOccurrence(kernel, {"Parameters", "WorkSizes"}); + assertOneOrMoreOccurrence(kernel, {"Source"}); + nodes.push_back(kernel); + } + return nodes; + }(); + + if (kernelNodes.size() == 1) { + _kernels.emplace_back(kernelNodes.front(), _configDir); + } else { + auto stageOrder = std::map{}; + for (auto& kernel : kernelNodes) { + const auto stageAttr = kernel.attribute("stage"); + VPU_THROW_UNLESS(stageAttr, "Error while binding %s custom layer: for multi-kernel binding, " + "each kernel should be provided with 'stage' attribute.", _layerName); + + const auto stageNum = std::stod(stageAttr.value()); + VPU_THROW_UNLESS(stageOrder.find(stageNum) == stageOrder.end(), + "Error while binding %s custom layer: found duplicating stage id.", _layerName); + + stageOrder.emplace(stageNum, CustomKernel{kernel, _configDir}); + } + + VPU_THROW_UNLESS(stageOrder.begin()->first == 0, + "Error while binding %s custom layer: Stage 0 is not found.", _layerName); + VPU_THROW_UNLESS(stageOrder.rbegin()->first == stageOrder.size() - 1, + "Error while binding %s custom layer: Kernels should have stage id from 0 to N.", _layerName); + + for (auto& stage : stageOrder) { + _kernels.push_back(std::move(stage.second)); } } - auto it = _kernelAddress.find(1); - IE_ASSERT(it != _kernelAddress.end()); - - return it->second; -} - -int CustomLayer::kernelId() const { - uint32_t kernelAddress = getKernelEntry(&_kernelBinary[0], _kernelEntry); - return getKernelId(&_kernelBinary[0], kernelAddress); -} - -void CustomLayer::loadSingleLayer(const pugi::xml_node& node) { - ie::details::CaselessEq cmp; - - std::string nodeName(node.name()); - if (!cmp(nodeName, "CustomLayer")) { - VPU_THROW_EXCEPTION << "Wrong custom layer XML : Node is not CustomLayer, but " << nodeName; - } - - auto nodeType = XMLParseUtils::GetStrAttr(node, "type", ""); - if (!cmp(nodeType, "MVCL")) { - VPU_THROW_EXCEPTION << "Wrong custom layer XML : Type is not MVCL, but " << nodeType; - } - - auto version = XMLParseUtils::GetIntAttr(node, "version", -1); - IE_ASSERT(version == 1); - - auto layerStage = XMLParseUtils::GetStrAttr(node, "stage", ""); - auto layerName = XMLParseUtils::GetStrAttr(node, "name", ""); - if (layerName.empty()) { - VPU_THROW_EXCEPTION << "Missing Layer name in CustomLayer"; - } - _layerName = layerStage.empty() ? layerName : layerName + "@stage_" + layerStage; - - _maxShaves = XMLParseUtils::GetIntAttr(node, "max-shaves", 0); - - processWhere(node.child("Where")); - - processKernelNode(node.child("Kernel")); - - processParametersNode(node.child("Parameters")); - - processWorkSizesNode(node.child("WorkSizes")); -} - -void CustomLayer::processWhere(const pugi::xml_node& node) { - for (auto child : node.attributes()) { - _whereParams[child.name()] = child.value(); - } -} - -void CustomLayer::processKernelNode(const pugi::xml_node& node) { - ie::details::CaselessEq cmp; - - std::string nodeName(node.name()); - if (!cmp(nodeName, "Kernel")) { - VPU_THROW_EXCEPTION << "Wrong node, expected Kernel found " << nodeName; - } - - if (!_kernelBinary.empty()) { - VPU_THROW_EXCEPTION << "Multiple definition of Kernel"; - } - - _kernelEntry = XMLParseUtils::GetStrAttr(node, "entry", ""); - if (_kernelEntry.empty()) { - VPU_THROW_EXCEPTION << "No Kernel entry in custom layer"; - } - - _kernelBinary.clear(); - for (auto sourceNode = node.child("Source"); !sourceNode.empty(); sourceNode = sourceNode.next_sibling("Source")) { - auto fileName = _configDir + "/" + XMLParseUtils::GetStrAttr(sourceNode, "filename", ""); - - std::ifstream inputFile(fileName, std::ios::binary); - if (!inputFile.is_open()) { - VPU_THROW_EXCEPTION << "Couldn't open kernel file " << fileName; + const auto addPorts = [](std::map& ports, const CustomKernel::KernelParam& newEdge) { + const auto layerInput = ports.find(newEdge.portIndex); + if (layerInput == ports.end()) { + ports.emplace(newEdge.portIndex, newEdge.format); + } else if (newEdge.format == CustomDataFormat::Any) { + return; + } else if (layerInput->second == CustomDataFormat::Any) { + layerInput->second = newEdge.format; } + }; - std::ostringstream contentStream; - contentStream << inputFile.rdbuf(); - _kernelBinary.append(contentStream.str()); - } - - _kernelAddress[1] = getKernelEntry(&_kernelBinary[0], _kernelEntry); - - _parameters = deduceKernelParameters(&_kernelBinary[0], _kernelAddress[1]); - - auto vecInfo = deduceVectorized(&_kernelBinary[0], _kernelAddress[1]); - if (vecInfo.first != 0) { - _kernelAddress[vecInfo.first] = vecInfo.second; - } -} - -void CustomLayer::processParametersNode(const pugi::xml_node& node) { - ie::details::CaselessEq cmp; - - std::string nodeName(node.name()); - if (!cmp(nodeName, "Parameters")) { - VPU_THROW_EXCEPTION << "Wrong node, expected Parameters found " << nodeName; - } - - for (auto tensorNode = node.child("Tensor"); !tensorNode.empty(); tensorNode = tensorNode.next_sibling("Tensor")) { - KernelParam kp; - - auto typeStr = XMLParseUtils::GetStrAttr(tensorNode, "type"); - if (cmp(typeStr, "input")) { - kp.type = CustomParamType::Input; - } else if (cmp(typeStr, "output")) { - kp.type = CustomParamType::Output; - } else if (cmp(typeStr, "input_buffer")) { - kp.type = CustomParamType::InputBuffer; - } else if (cmp(typeStr, "output_buffer")) { - kp.type = CustomParamType::OutputBuffer; - } else if (cmp(typeStr, "data")) { - kp.type = CustomParamType::Data; - } else { - VPU_THROW_EXCEPTION << "Tensor node has an invalid type " << typeStr; - } - - kp.format = formatFromString(XMLParseUtils::GetStrAttr(tensorNode, "format", "BFYX")); - if (kp.format == CustomDataFormat::None) { - VPU_THROW_EXCEPTION << "Tensor node has an invalid format " << kp.format; - } - - kp.argName = XMLParseUtils::GetStrAttr(tensorNode, "arg-name"); - if (kp.argName.empty()) { - VPU_THROW_EXCEPTION << "Tensor node has no arg-name"; - } - - kp.portIndex = XMLParseUtils::GetIntAttr(tensorNode, "port-index", -1); - if (kp.portIndex == -1) { - VPU_THROW_EXCEPTION << "Tensor node has no port-index"; - } - - if (kp.type == CustomParamType::InputBuffer || kp.type == CustomParamType::OutputBuffer) { - std::string bufferSize(XMLParseUtils::GetStrAttr(tensorNode, "size", "")); - while (!bufferSize.empty()) { - auto pos = bufferSize.find_first_of(','); - auto rule = bufferSize.substr(0, pos); - if (!isLegalSizeRule(rule)) { - VPU_THROW_EXCEPTION << "Invalid BufferSize " << rule; - } - - kp.bufferSizeRules.emplace_back(std::move(rule)); - - if (pos == std::string::npos) { - bufferSize.clear(); - } else { - bufferSize = bufferSize.substr(pos + 1, std::string::npos); - } + for (const auto& kernel : _kernels) { + for (const auto& binding : kernel.bindings()) { + if (binding.type == CustomParamType::Input) { + addPorts(_inputs, binding); } - - kp.dimIdx = -1; - std::string dim_src_string(XMLParseUtils::GetStrAttr(tensorNode, "dim", "")); - if (!dim_src_string.empty()) { - // Try to locate index separator. - auto pos = dim_src_string.find_first_of(','); - auto flag = dim_src_string.substr(0, pos); - if (cmp(flag, "input")) { - kp.dimSource = CustomDimSource::Input; - } else if (cmp(flag, "output")) { - kp.dimSource = CustomDimSource::Output; - } else { - VPU_THROW_EXCEPTION << "Invalid WG dim source " << flag; - } - - int idx = 0; - if (pos != std::string::npos) { - // User explicitly set input index in config. - auto idx_string = dim_src_string.substr(pos + 1, std::string::npos); - idx = std::stoi(idx_string); - } - if (idx < 0) { - VPU_THROW_EXCEPTION << "Invalid tensor index " << idx; - } - - kp.dimIdx = idx; + if (binding.type == CustomParamType::Output) { + addPorts(_outputs, binding); } } - - kp.irSource.clear(); - - _kernelParams.emplace_back(std::move(kp)); - } - - for (auto dataNode = node.child("Data"); !dataNode.empty(); dataNode = dataNode.next_sibling("Data")) { - KernelParam kp; - - auto typeStr = XMLParseUtils::GetStrAttr(dataNode, "type"); - if (cmp(typeStr, "data")) { - kp.type = CustomParamType::Data; - } else if (cmp(typeStr, "local_data")) { - kp.type = CustomParamType::LocalData; - } else { - VPU_THROW_EXCEPTION << "Data node has an invalid type " << typeStr; - } - - kp.format = CustomDataFormat::Any; - - kp.argName = XMLParseUtils::GetStrAttr(dataNode, "arg-name"); - if (kp.argName.empty()) { - VPU_THROW_EXCEPTION << "Data node has no arg-name"; - } - - kp.portIndex = -1; - - kp.irSource = XMLParseUtils::GetStrAttr(dataNode, "source", ""); - std::string dim_src_string(XMLParseUtils::GetStrAttr(dataNode, "dim", "")); - - if (kp.irSource.empty() && dim_src_string.empty()) { - VPU_THROW_EXCEPTION << "Data node has no source or dim"; - } - - if (!kp.irSource.empty() && !dim_src_string.empty()) { - VPU_THROW_EXCEPTION << "Data node can only have source or dim"; - } - - kp.dimIdx = -1; - if (kp.type == CustomParamType::LocalData) { - std::string bufferSize(XMLParseUtils::GetStrAttr(dataNode, "size", "")); - while (!bufferSize.empty()) { - auto pos = bufferSize.find_first_of(','); - auto rule = bufferSize.substr(0, pos); - if (!isLegalSizeRule(rule)) { - VPU_THROW_EXCEPTION << "Invalid BufferSize " << rule; - } - - kp.bufferSizeRules.emplace_back(std::move(rule)); - - if (pos == std::string::npos) { - bufferSize.clear(); - } else { - bufferSize = bufferSize.substr(pos + 1, std::string::npos); - } - } - - kp.dimIdx = -1; - std::string dim_src_string(XMLParseUtils::GetStrAttr(dataNode, "dim", "")); - if (!dim_src_string.empty()) { - // Try to locate index separator. - auto pos = dim_src_string.find_first_of(','); - auto flag = dim_src_string.substr(0, pos); - if (cmp(flag, "input")) { - kp.dimSource = CustomDimSource::Input; - } else if (cmp(flag, "output")) { - kp.dimSource = CustomDimSource::Output; - } else { - VPU_THROW_EXCEPTION << "Invalid WG dim source " << flag; - } - - int idx = 0; - if (pos != std::string::npos) { - // User explicitly set input index in config. - auto idx_string = dim_src_string.substr(pos + 1, std::string::npos); - idx = std::stoi(idx_string); - } - if (idx < 0) { - VPU_THROW_EXCEPTION << "Invalid tensor index " << idx; - } - - kp.dimIdx = idx; - } - } - - _kernelParams.emplace_back(std::move(kp)); - } - - for (auto scalarNode = node.child("Scalar"); !scalarNode.empty(); scalarNode = scalarNode.next_sibling("Scalar")) { - KernelParam kp; - - std::string typeStr = XMLParseUtils::GetStrAttr(scalarNode, "type"); - if (cmp(typeStr, "int")) { - kp.type = CustomParamType::Int; - } else if (cmp(typeStr, "float")) { - kp.type = CustomParamType::Float; - } else { - VPU_THROW_EXCEPTION << "Scalar node has an invalid type " << typeStr; - } - - kp.format = CustomDataFormat::Any; - - kp.argName = XMLParseUtils::GetStrAttr(scalarNode, "arg-name"); - if (kp.argName.empty()) { - VPU_THROW_EXCEPTION << "Scalar node has no arg-name"; - } - - kp.portIndex = XMLParseUtils::GetIntAttr(scalarNode, "port-index", 0); - - kp.irSource = XMLParseUtils::GetStrAttr(scalarNode, "source", ""); - if (kp.irSource.empty()) { - VPU_THROW_EXCEPTION << "Scalar node has no source"; - } - - _kernelParams.emplace_back(std::move(kp)); } } -void CustomLayer::processWorkSizesNode(const pugi::xml_node & node) { - ie::details::CaselessEq cmp; +bool CustomLayer::isLegalSizeRule(const std::string& rule, std::map layerParams) { + { + auto sizes = SmallVector> { + { "b", "1" }, { "B", "1" }, + { "f", "1" }, { "F", "1" }, + { "y", "1" }, { "Y", "1" }, + { "x", "1" }, { "X", "1" }, + }; - std::string nodeName(node.name()); - if (!cmp(node.name(), "WorkSizes")) { - VPU_THROW_EXCEPTION << "Wrong node, expected WorkSizes found " << nodeName; + std::move(begin(sizes), end(sizes), inserter(layerParams, end(layerParams))); } - _wgDimIdx = -1; - std::string dim_src_string(node.attribute("dim").as_string("")); - if (!dim_src_string.empty()) { - // Try to locate index separator. - auto pos = dim_src_string.find_first_of(','); - auto flag = dim_src_string.substr(0, pos); - if (cmp(flag, "input")) { - _wgDimSource = CustomDimSource::Input; - } else if (cmp(flag, "output")) { - _wgDimSource = CustomDimSource::Output; - } else { - VPU_THROW_EXCEPTION << "Invalid WG dim source " << flag; - } - - int idx = 0; - if (pos != std::string::npos) { - // User explicitly set input index in config. - auto idx_string = dim_src_string.substr(pos + 1, std::string::npos); - idx = std::stoi(idx_string); - } - if (idx < 0) { - VPU_THROW_EXCEPTION << "Invalid tensor index " << idx; - } - - _wgDimIdx = idx; - } - - std::string gws(node.attribute("global").as_string("")); - while (!gws.empty()) { - auto pos = gws.find_first_of(','); - auto rule = gws.substr(0, pos); - if (!isLegalSizeRule(rule)) { - VPU_THROW_EXCEPTION << "Invalid WorkSize " << rule; - } - - _globalSizeRules.emplace_back(std::move(rule)); - - if (pos == std::string::npos) { - gws.clear(); - } else { - gws = gws.substr(pos + 1, std::string::npos); - } - } - - std::string lws(node.attribute("local").as_string("")); - while (!lws.empty()) { - auto pos = lws.find_first_of(','); - auto rule = lws.substr(0, pos); - if (!isLegalSizeRule(rule)) { - VPU_THROW_EXCEPTION << "Invalid WorkSize " << rule; - } - - _localSizeRules.emplace_back(std::move(rule)); - - if (pos == std::string::npos) { - lws.clear(); - } else { - lws = lws.substr(pos + 1, std::string::npos); - } - } -} - -bool CustomLayer::isLegalSizeRule(const std::string& rule) { - SimpleMathExpression expr; - - expr.setVariables({ - { 'b', 1 }, { 'B', 1 }, - { 'f', 1 }, { 'F', 1 }, - { 'y', 1 }, { 'Y', 1 }, - { 'x', 1 }, { 'X', 1 }, - }); + MathExpression expr; + expr.setVariables(layerParams); try { expr.parse(rule); @@ -784,21 +241,66 @@ bool CustomLayer::isLegalSizeRule(const std::string& rule) { return true; } -CustomDataFormat CustomLayer::formatFromString(const std::string & str) { - static const ie::details::caseless_map FormatNameToType = { - { "BFYX" , CustomDataFormat::BFYX }, - { "BYXF" , CustomDataFormat::BYXF }, - { "FYX" , CustomDataFormat::FYX }, - { "YXF" , CustomDataFormat::YXF }, - { "ANY" , CustomDataFormat::Any }, +CustomDataFormat CustomLayer::formatFromLayout(const InferenceEngine::Layout& layout) { + const auto layoutToFormat = std::map { + { ie::NCHW , CustomDataFormat::BFYX }, + { ie::NHWC , CustomDataFormat::BYXF }, + { ie::CHW , CustomDataFormat::FYX }, + { ie::NC , CustomDataFormat::BF }, + { ie::ANY , CustomDataFormat::Any } }; - auto it = FormatNameToType.find(str); - if (it != FormatNameToType.end()) { - return it->second; - } + const auto it = layoutToFormat.find(layout); + VPU_THROW_UNLESS(it != layoutToFormat.end(), "Tensor node has an invalid format %s", layout); + return it->second; +} - return CustomDataFormat::None; +bool CustomLayer::meetsWhereRestrictions(const std::map& params) const { + const auto cmp = ie::details::CaselessEq{}; + + for (const auto& where : _whereParams) { + const auto restrictedParam = [&](const std::pair& param) { + return param.first == where.first; + }; + + const auto param = std::find_if(begin(params), end(params), restrictedParam); + if (param == params.end()) { + return false; + } + + const auto& restriction = where.second; + const auto number = parseNumber(param->second); + + const auto meetsRestriction = [&] { + // compare non-number restrictions (ex. kernel="3,3") + if (!number.hasValue()) { + return cmp(param->second, restriction); + } else { + if (restriction[0] == '>' && restriction[1] == '=') { + const auto to_compare = std::stof(restriction.substr(2, std::string::npos)); + return number.get() >= to_compare; + } else if (restriction[0] == '<' && restriction[1] == '=') { + const auto to_compare = std::stof(restriction.substr(2, std::string::npos)); + return number.get() <= to_compare; + } else if (restriction[0] == '>') { + const auto to_compare = std::stof(restriction.substr(1, std::string::npos)); + return number.get() > to_compare; + } else if (restriction[0] == '<') { + const auto to_compare = std::stof(restriction.substr(1, std::string::npos)); + return number.get() < to_compare; + } else if (restriction[0] == '!' && restriction[1] == '=') { + const auto to_compare = std::stof(restriction.substr(2, std::string::npos)); + return number.get() != to_compare; + } + return number.get() == std::stof(restriction); + } + }(); + + if (!meetsRestriction) { + return false; + } + } + return true; } } // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp b/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp index 0902dd06d11..16f18e9240e 100644 --- a/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/frontend/frontend.cpp @@ -17,6 +17,13 @@ #include #include #include +#include + +#include +#include +#include +#include +#include namespace vpu { @@ -105,6 +112,8 @@ FrontEnd::FrontEnd(StageBuilder::Ptr stageBuilder) {"StaticShapeNonZero", LAYER_PARSER(parseNonZero)}, {"ROIAlign", LAYER_PARSER(parseROIAlign)}, {"DynamicShapeResolver", LAYER_PARSER(parseDSR)}, + {"OutShapeOfReshape", LAYER_PARSER(parseOutShapeOfReshape)}, + {"StaticShapeBroadcast", LAYER_PARSER(parseBroadcast)}, }} {} ModelPtr FrontEnd::buildInitialModel(ie::ICNNNetwork& network) { @@ -149,38 +158,110 @@ namespace { std::atomic g_counter(0); -bool hasSuitableCustom( - const std::vector& customLayers, - const ie::CNNLayerPtr& layer) { - const auto& env = CompileEnv::get(); - ie::details::CaselessEq cmp; +} // namespace - env.log->trace("Check for suitable custom implementation for layer %s:%s", layer->name, layer->type); +CustomLayer::Ptr FrontEnd::getSuitableCustomLayer(const std::vector& customLayers, + const ie::CNNLayerPtr& cnnLayer) { + const auto& env = CompileEnv::get(); + env.log->trace("Check for suitable custom implementation for layer %s:%s", + cnnLayer->name, cnnLayer->type); VPU_LOGGER_SECTION(env.log); - for (const auto& customLayer : customLayers) { - env.log->trace("Check next custom layer : %v", customLayer->whereParams()); + const auto cnnInputs = [&] { + auto inputs = SmallVector{}; + inputs.reserve(cnnLayer->insData.size()); + for (const auto& input : cnnLayer->insData) { + const auto layout = input.lock()->getLayout(); + const auto format = CustomLayer::formatFromLayout(layout); + inputs.push_back(format); + } + return inputs; + }(); + + const auto cnnOutputs = [&] { + auto outputs = SmallVector{}; + outputs.reserve(cnnLayer->outData.size()); + for (const auto& output : cnnLayer->outData) { + const auto layout = output->getLayout(); + const auto format = CustomLayer::formatFromLayout(layout); + outputs.push_back(format); + } + return outputs; + }(); + + const auto isSuitableLayer = [&env, &cnnLayer](const CustomLayer::Ptr& customLayer) { + env.log->trace("Check next custom layer : %v", customLayer->layerName()); VPU_LOGGER_SECTION(env.log); - bool suitable = true; - for (const auto& whereParam : customLayer->whereParams()) { - const auto iter = layer->params.find(whereParam.first); - if (iter == layer->params.end() || !cmp(iter->second, whereParam.second)) { - suitable = false; - break; + if (!customLayer->meetsWhereRestrictions(cnnLayer->params)) { + env.log->trace("Where restrictions are not met"); + return false; + } + + for (const auto& kernel : customLayer->kernels()) { + const auto& gws = kernel.globalGridSizeRules(); + const auto& lws = kernel.localGridSizeRules(); + + const auto validSizeRule = [&](const std::string& rule) { + return CustomLayer::isLegalSizeRule(rule, cnnLayer->params); + }; + + const auto validGridSizes = std::all_of(begin(gws), end(gws), validSizeRule) && + std::all_of(begin(lws), end(lws), validSizeRule); + + if (!validGridSizes) { + env.log->trace("Work group grid sizes are not valid"); + return false; } } - if (suitable) { - env.log->trace("Matches"); - return true; + return true; + }; + + auto suitableCustomLayers = SmallVector{}; + + std::copy_if(begin(customLayers), end(customLayers), + back_inserter(suitableCustomLayers), isSuitableLayer); + + if (suitableCustomLayers.empty()) { + return nullptr; + } + + const auto inputsLayoutMatch = [&](const SmallVector& cnnEdges, + const std::map& clEdges) { + for (const auto clEdge : clEdges) { + const auto port = clEdge.first; + VPU_THROW_UNLESS(port < cnnEdges.size(), + "Can't bind custom layer edge with port '%s' to CNNNetwork layer", port); + + const auto clFormat = clEdge.second; + const auto cnnFormat = cnnEdges[port]; + if (cnnFormat != clFormat && + cnnFormat != CustomDataFormat::Any && + clFormat != CustomDataFormat::Any) { + return false; + } + } + return true; + }; + + + for (const auto& customLayer : suitableCustomLayers) { + const auto clInputs = customLayer->inputs(); + + if (inputsLayoutMatch(cnnInputs, clInputs)) { + env.log->trace("Found suitable '%s' custom layer", customLayer->layerName()); + return customLayer; } } - return false; + const auto firstGoodLayer = suitableCustomLayers.front(); + env.log->trace("Found suitable custom layer '%s', but input layouts " + "have not matched with what CNNNetwork expected", + firstGoodLayer->layerName()); + return firstGoodLayer; } -} // namespace void FrontEnd::parseLayer(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) { parseLayer(model, layer, inputs, outputs, @@ -191,12 +272,8 @@ void FrontEnd::parseLayer(const Model& model, const ie::CNNLayerPtr& layer, cons void FrontEnd::parseLayer(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs, const FrontEnd::UnsupportedLayerCallback& onUnsupported, const FrontEnd::SupportedLayerCallback& onSupported) { - const auto customLayerByType = _customLayers.find(layer->type); - const auto customLayerAsStage = _customLayers.find(layer->type + "@stage_0"); - - const bool isCustomLayer = - ((customLayerByType != _customLayers.end()) && hasSuitableCustom(customLayerByType->second, layer)) || - ((customLayerAsStage != _customLayers.end()) && hasSuitableCustom(customLayerAsStage->second, layer)); + const auto customLayer = _customLayers.find(layer->type); + const bool isCustomLayer = customLayer != _customLayers.end() && getSuitableCustomLayer(customLayer->second, layer); const auto& type = isCustomLayer ? "Custom" : layer->type; if (parsers.count(type) == 0) { @@ -235,6 +312,15 @@ ModelPtr FrontEnd::runCommonPasses(ie::ICNNNetwork& network) { ModelPtr FrontEnd::runCommonPasses(ie::ICNNNetwork& network, const UnsupportedLayerCallback& unsupportedLayer, const SupportedLayerCallback& supportedLayer) { + // NGraph -> CNN conversion may be called in 2 different moments: at + // the beginning if conversion was forced by configuration or after detect + // network batch and precision conversions. Conversion utility + // returns std::shared_ptr. ICNNNetwork is neither copyable nor movable. + // As a result, it is impossible to overwrite given "network" argument. + // Do not use network parameter in this function to avoid using wrong network + // reference (e.g. original instead of converted). + auto* originalOrConvertNetwork = &network; + const auto& env = CompileEnv::get(); // @@ -268,7 +354,7 @@ ModelPtr FrontEnd::runCommonPasses(ie::ICNNNetwork& network, const UnsupportedLa // Create new VPU model // - const auto model = std::make_shared(network.getName()); + const auto model = std::make_shared(originalOrConvertNetwork->getName()); model->attrs().set("index", g_counter.fetch_add(1)); model->attrs().set("resources", env.resources); @@ -276,7 +362,7 @@ ModelPtr FrontEnd::runCommonPasses(ie::ICNNNetwork& network, const UnsupportedLa if (!env.config.ignoreIRStatistic) { ie::ICNNNetworkStats* stats = nullptr; // V10 IRs doesn't contain stats - if (network.getStats(&stats, nullptr) == InferenceEngine::OK && !stats->isEmpty()) { + if (originalOrConvertNetwork->getStats(&stats, nullptr) == InferenceEngine::OK && !stats->isEmpty()) { env.log->trace("Use node statistics from the IR"); model->setNodesStats(stats->getNodesStats()); } @@ -286,40 +372,50 @@ ModelPtr FrontEnd::runCommonPasses(ie::ICNNNetwork& network, const UnsupportedLa // Update IE Network // + std::shared_ptr convertedNetwork; + { env.log->trace("Update IE Network"); VPU_LOGGER_SECTION(env.log); - IE_SUPPRESS_DEPRECATED_START - // If we have NGraph network, but CNN compatibility is enabled, enforce conversion - if (network.getFunction() && env.config.forceDeprecatedCnnConversion) - network.addLayer(nullptr); - IE_SUPPRESS_DEPRECATED_END + auto convertNetwork = [&convertedNetwork, &originalOrConvertNetwork]() { + auto nGraphFunc = originalOrConvertNetwork->getFunction(); + // Disable shape inference (WA for generic operations) + ngraph::op::GenericIE::DisableReshape noReshape(nGraphFunc); - detectNetworkBatch(network, model); + ngraph::pass::ConvertOpSet3ToOpSet2().run_on_function(nGraphFunc); + ngraph::pass::ConvertOpSet2ToOpSet1().run_on_function(nGraphFunc); + ngraph::pass::ConvertOpSet1ToLegacy().run_on_function(nGraphFunc); + convertedNetwork = InferenceEngine::details::convertFunctionToICNNNetwork(nGraphFunc, *originalOrConvertNetwork); + originalOrConvertNetwork = convertedNetwork.get(); + }; - ie::NetPass::ConvertPrecision(network, ie::Precision::I64, ie::Precision::I32); - ie::NetPass::ConvertPrecision(network, ie::Precision::U64, ie::Precision::I32); - ie::NetPass::ConvertPrecision(network, ie::Precision::BOOL, ie::Precision::I32); + if (originalOrConvertNetwork->getFunction() && env.config.forceDeprecatedCnnConversion) { + convertNetwork(); + } - IE_SUPPRESS_DEPRECATED_START - // force conversion to CNNNetwork - if (network.getFunction()) - network.addLayer(nullptr); - IE_SUPPRESS_DEPRECATED_END + detectNetworkBatch(*originalOrConvertNetwork, model); - moveConstInputsToBlobs(network); + if (originalOrConvertNetwork->getFunction()) { + convertNetwork(); + } - removeConstLayers(network); + ie::NetPass::ConvertPrecision(*originalOrConvertNetwork, ie::Precision::I64, ie::Precision::I32); + ie::NetPass::ConvertPrecision(*originalOrConvertNetwork, ie::Precision::U64, ie::Precision::I32); + ie::NetPass::ConvertPrecision(*originalOrConvertNetwork, ie::Precision::BOOL, ie::Precision::I32); - unrollLoops(network); + moveConstInputsToBlobs(*originalOrConvertNetwork); + + removeConstLayers(*originalOrConvertNetwork); + + unrollLoops(*originalOrConvertNetwork); } // // Parse IR Network // - _ieParsedNetwork = parseNetwork(network); + _ieParsedNetwork = parseNetwork(*originalOrConvertNetwork); // // Process internal VPU Model @@ -331,7 +427,9 @@ ModelPtr FrontEnd::runCommonPasses(ie::ICNNNetwork& network, const UnsupportedLa parseInputAndOutputData(model); - addDataTypeConvertStages(model); + if (!CompileEnv::get().config.disableConvertStages) { + addDataTypeConvertStages(model); + } addPreProcessStages(model); } diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/allocator/allocator.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/allocator/allocator.cpp index 36a9a9e8e2e..5c9ee385b26 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/allocator/allocator.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/allocator/allocator.cpp @@ -291,6 +291,12 @@ ShapeLocation Allocator::allocateShape(Data& data) { shapeLocation.dimsLocation = dataLocation.location; shapeLocation.dimsOffset = dataLocation.offset; + + if (data->usage() == DataUsage::Output) { + // We need to allocate memory for maximum dims values also + data->attrs().set("ioDimsUpperBoundOffset", _blobMemOffset); + _blobMemOffset += dimsByteSize; + } } else { // Static allocation shapeLocation.dimsLocation = Location::Blob; @@ -308,21 +314,20 @@ ShapeLocation Allocator::allocateShape(Data& data) { } void Allocator::freeData(const Data& data, DeallocationMode mode) { - // - // Release the chunk - // + const auto getChunk = [this, &data](const Data& parent) { + VPU_THROW_UNLESS(_allocatedIntermData.count(parent) > 0, + "Allocator failed on freeData for {} with usage {}: parent data {} with usage {} is not allocated", + data->name(), data->usage(), parent->name(), parent->usage()); - if (const auto& parentDataToShapeEdge = data->parentDataToShapeEdge()) { - auto const& parent = parentDataToShapeEdge->parent(); - - auto it = _memChunksPerData.find(parentDataToShapeEdge->parent()); - auto chunk = it->second; + auto it = _memChunksPerData.find(parent); VPU_INTERNAL_CHECK(it != _memChunksPerData.end(), "Allocator failed on freeData for {} with usage {}: parent data {} with usage {} " "containing shape for current data wasn't yet allocated", data->name(), data->usage(), parent->name(), parent->usage()); + auto chunk = it->second; + VPU_INTERNAL_CHECK(chunk != nullptr, "Allocator failed on freeData for {} with usage {}: parent data {} with usage {} " "containing shape for current data has no memory chunk", @@ -333,6 +338,10 @@ void Allocator::freeData(const Data& data, DeallocationMode mode) { "containing shape for this data has zero usages, but it is using at least by current data", data->name(), data->usage(), parent->name(), parent->usage()); + return chunk; + }; + + const auto decreaseChunkUsage = [this](allocator::MemChunk* chunk, const Data& parent) { --chunk->inUse; if (chunk->inUse == 0) { @@ -341,32 +350,30 @@ void Allocator::freeData(const Data& data, DeallocationMode mode) { _memChunksPerData.erase(parent); _allocatedIntermData.erase(parent); } + }; + + // + // Release the chunk + // + + if (const auto& parentDataToShapeEdge = data->parentDataToShapeEdge()) { + auto const& parent = parentDataToShapeEdge->parent(); + + if (parent->usage() == DataUsage::Intermediate || parent->usage() == DataUsage::Temp) { + auto chunk = getChunk(parent); + decreaseChunkUsage(chunk, parent); + } } auto topParent = data->getTopParentData(); if (topParent->usage() == DataUsage::Intermediate || topParent->usage() == DataUsage::Temp) { - IE_ASSERT(_allocatedIntermData.count(topParent) > 0); - - auto it = _memChunksPerData.find(topParent); - IE_ASSERT(it != _memChunksPerData.end()); - - auto chunk = it->second; - IE_ASSERT(chunk != nullptr); - IE_ASSERT(chunk->inUse > 0); + auto chunk = getChunk(topParent); switch (mode) { case DeallocationMode::JustFree: { - --chunk->inUse; - - if (chunk->inUse == 0) { - freeMem(chunk); - - _memChunksPerData.erase(topParent); - _allocatedIntermData.erase(topParent); - } - + decreaseChunkUsage(chunk, topParent); break; } diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/pass_manager.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/pass_manager.cpp index 7e43fc5055c..7717d25de89 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/pass_manager.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/pass_manager.cpp @@ -87,6 +87,16 @@ PassSet::Ptr PassManager::buildMiddleEnd() { // initial dump pass must be the first dump ADD_DUMP_PASS("initial"); + // + // Replace Global AvgPooling with ReduceMean + // + + if (env.config.enableReplaceWithReduceMean) { + ADD_PASS(replaceWithReduceMean); + ADD_DUMP_PASS("replaceWithReduceMean"); + } + + if (!env.config.disableReorder && !env.config.hwOptimization) { ADD_PASS(reorderInputsToChannelMinor); ADD_DUMP_PASS("reorderInputsToChannelMinor"); @@ -127,15 +137,6 @@ PassSet::Ptr PassManager::buildMiddleEnd() { ADD_PASS(mergeParallelFC); ADD_DUMP_PASS("mergeParallelFC"); - // - // Replace Global AvgPooling with ReduceMean - // - - if (env.config.enableReplaceWithReduceMean) { - ADD_PASS(replaceWithReduceMean); - ADD_DUMP_PASS("replaceWithReduceMean"); - } - // // Model common adaptation // @@ -188,6 +189,11 @@ PassSet::Ptr PassManager::buildMiddleEnd() { ADD_PASS(hwPadding); ADD_DUMP_PASS("hwPadding"); + if (env.config.hwOptimization) { + ADD_PASS(splitLargeKernelConv); + ADD_DUMP_PASS("splitLargeKernelConv"); + } + // // Batch support // diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/adjust_data_location.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/adjust_data_location.cpp index 8538f344134..54e24461d14 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/adjust_data_location.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/adjust_data_location.cpp @@ -327,7 +327,7 @@ void PassImpl::adjustModelForMemReqs(const Model& model) { env.log->trace("Child data [%s] : mode [%v] offset [%v]", childData->name(), mode, offset); - model->replaceParentData(childDataEdge, ddrCopy); + model->replaceDataToDataParent(childDataEdge, ddrCopy); loopOverData(childData, [](const Data& subData) { subData->setMemReqs(MemoryType::DDR); diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/eliminate_const_concat.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/eliminate_const_concat.cpp index d465ce2adef..f21c4b4cc64 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/eliminate_const_concat.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/eliminate_const_concat.cpp @@ -18,7 +18,7 @@ namespace { class PassImpl final : public PerStagePass { public: - PassImpl() : PerStagePass({StageType::Concat}) {} + PassImpl() : PerStagePass({StageType::StubConcat}) {} void runForStage(const Model& model, const Stage& stage) override; }; diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/final_check.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/final_check.cpp index b76f0715e19..097e5f7708a 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/final_check.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/final_check.cpp @@ -112,7 +112,7 @@ void PassImpl::run(const Model& model) { // if (dataEdge->connectionMode() == SharedConnectionMode::SINGLE_STAGE) { - if (connectionStage->type() == StageType::Concat || + if (connectionStage->type() == StageType::StubConcat || connectionStage->type() == StageType::Expand) { IE_ASSERT(producer == child); IE_ASSERT(consumer == parent); diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/process_special_stages.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/process_special_stages.cpp index 0da6be9ab13..542586632a8 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/process_special_stages.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/process_special_stages.cpp @@ -105,7 +105,7 @@ void PassImpl::run(const Model& model) { continue; } - if (stage->type() == StageType::Concat) { + if (stage->type() == StageType::StubConcat) { _processor.processConcat(model, stage); } else if (stage->type() == StageType::Split) { _processor.processSplit(model, stage); diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/replace_with_screlu.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/replace_with_screlu.cpp index f1059f4c5a8..30200b9cbc0 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/replace_with_screlu.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/replace_with_screlu.cpp @@ -38,7 +38,7 @@ void PassImpl::run(const Model& model) { continue; } - const auto nextStages = getExactNextStages(convolutionStage, {StageType::Power, StageType::Concat}); + const auto nextStages = getExactNextStages(convolutionStage, {StageType::Power, StageType::StubConcat}); if (nextStages.size() != 2 || convolutionStage->type() != StageType::StubConv) { continue; } @@ -54,7 +54,7 @@ void PassImpl::run(const Model& model) { continue; } - auto concatAfterPowerStage = getOneOfSingleNextStage(powerStage, {StageType::Concat}); + auto concatAfterPowerStage = getOneOfSingleNextStage(powerStage, {StageType::StubConcat}); if (concatAfterPowerStage != concatStage) { continue; } diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/split_large_kernel_conv.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/split_large_kernel_conv.cpp new file mode 100644 index 00000000000..0ffdfe9347a --- /dev/null +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/split_large_kernel_conv.cpp @@ -0,0 +1,163 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include +#include + +#include +#include +#include + + +namespace vpu { + +namespace { + +class PassImpl final : public Pass { +public: + explicit PassImpl(const StageBuilder::Ptr& stageBuilder) : _stageBuilder(stageBuilder) {} + + void run(const Model& model) override; + +private: + StageBuilder::Ptr _stageBuilder; +}; + +void PassImpl::run(const Model& model) { + VPU_PROFILE(splitLargeKernelConv); + + for (const auto& stage : model->getStages()) { + if (stage->type() != StageType::StubConv) { + continue; + } + const auto tryHW = stage->attrs().getOrDefault("tryHW", false); + if (!tryHW) { + continue; + } + + IE_ASSERT(stage->numInputs() == 4); + IE_ASSERT(stage->numOutputs() == 1); + + const auto input = stage->input(0); + const auto weights = stage->input(1); + const auto biases = stage->input(2); + const auto scales = stage->input(3); + const auto output = stage->output(0); + + const auto kernelSizeX = stage->attrs().get("kernelSizeX"); + const auto kernelSizeY = stage->attrs().get("kernelSizeY"); + const auto groupSize = stage->attrs().get("groupSize"); + const auto inputC = input->desc().dim(Dim::C); + const auto outputC = output->desc().dim(Dim::C); + + //Only 1x16 convolution is supported now, could expand support up to 1x30 + if (kernelSizeX != 16 || kernelSizeY != 1 || groupSize != 1) { + continue; + } + + model->disconnectStage(stage); + + int kernelGroupSize = kernelSizeX / 16 + 1; + IE_ASSERT(kernelGroupSize == 2); + const auto newKernelSizeX = kernelSizeX / kernelGroupSize; + IE_ASSERT(newKernelSizeX * kernelGroupSize == kernelSizeX); + const auto inGroupDimX = input->desc().dim(Dim::W) - newKernelSizeX; + + DataVector subInputs(kernelGroupSize); + DataVector subOutputs(kernelGroupSize); + + for (int groupInd = 0; groupInd < kernelGroupSize; ++groupInd) { + auto postfix = formatString("@subkernel=%d/%d", groupInd + 1, kernelGroupSize); + + //subInput + auto subInputDesc = input->desc(); + subInputDesc.setDim(Dim::W, inGroupDimX); + + subInputs[groupInd] = model->duplicateData( + input, + postfix, + subInputDesc); + + //subShrinkStage + DimValues offsets({{Dim::W, groupInd * newKernelSizeX}}); + + _stageBuilder->addCropStage( + model, + stage->name() + postfix, + stage->origLayer(), + input, + subInputs[groupInd], + std::move(offsets)); + + // subWeights + Data subWeights; + { + const auto content = weights->content(); + IE_ASSERT(content != nullptr); + + const auto origWeights = content->get(); + IE_ASSERT(origWeights != nullptr); + + size_t newWeightsSize = newKernelSizeX * kernelSizeY * outputC * inputC; + + auto newWeightsBlob = ie::make_shared_blob(InferenceEngine::TensorDesc( + ie::Precision::FP16, + {newWeightsSize}, + ie::Layout::C)); + newWeightsBlob->allocate(); + + const auto newWeightsPtr = newWeightsBlob->buffer().as(); + auto src = origWeights + groupInd * newKernelSizeX; + auto dst = newWeightsPtr; + + for (int i = 0; i < kernelSizeY * inputC * outputC; ++i) { + std::copy_n(src + i * kernelSizeX, newKernelSizeX, dst + i * newKernelSizeX); + } + + subWeights = model->duplicateData( + weights, + postfix, + DataDesc({newKernelSizeX, kernelSizeY, inputC, outputC}), + ieBlobContent(newWeightsBlob)); + } + + // subOutput + auto subOutputDesc = output->desc(); + + subOutputs[groupInd] = model->duplicateData( + output, + postfix, + subOutputDesc); + + // subConvStage + auto subConvStage = model->duplicateStage( + stage, + postfix, + {subInputs[groupInd], subWeights, biases, scales}, + {subOutputs[groupInd]}); + + subConvStage->attrs().set("kernelSizeX", newKernelSizeX); + } + + _stageBuilder->addSumStage( + model, + stage->name() + "@sum", + stage->origLayer(), + subOutputs[0], + subOutputs[1], + output); + + model->removeStage(stage); + } +} + +} // namespace + +Pass::Ptr PassManager::splitLargeKernelConv() { + return std::make_shared(_stageBuilder); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/swap_concat_and_hw_ops.cpp b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/swap_concat_and_hw_ops.cpp index cd7715400b6..c5c19d8df3f 100644 --- a/inference-engine/src/vpu/graph_transformer/src/middleend/passes/swap_concat_and_hw_ops.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/middleend/passes/swap_concat_and_hw_ops.cpp @@ -27,7 +27,7 @@ void PassImpl::run(const Model& model) { if (concatStage == nullptr) continue; - if (concatStage->type() != StageType::Concat) + if (concatStage->type() != StageType::StubConcat) continue; IE_ASSERT(concatStage->numInputs() > 0); diff --git a/inference-engine/src/vpu/graph_transformer/src/model/data.cpp b/inference-engine/src/vpu/graph_transformer/src/model/data.cpp index ddf95985694..72d9968635c 100644 --- a/inference-engine/src/vpu/graph_transformer/src/model/data.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/model/data.cpp @@ -178,26 +178,36 @@ void DataNode::setShapeAllocationInfo(const ShapeLocation& shapeLocation) { void DataNode::serializeBuffer( BlobSerializer& serializer) { - serializeDescImpl(serializer, _desc, this->strides()); + serializeDescImpl(serializer, _desc, this->shapeLocation()); serializer.append(checked_cast(_dataLocation.location)); - if (_dataLocation.location == Location::Input || _dataLocation.location == Location::Output) { - auto topParent = getTopParentData(); + const auto serializeIOParams = [&serializer](const Data& parent) { + auto IOIdx = parent->attrs().get("ioIdx"); + serializer.append(checked_cast(IOIdx)); - auto ioIdx = topParent->attrs().get("ioIdx"); - serializer.append(checked_cast(ioIdx)); - - auto parentByteSize = topParent->totalByteSize(); + auto parentByteSize = parent->totalByteSize(); serializer.append(checked_cast(parentByteSize)); + }; + + if (_dataLocation.location == Location::Input || _dataLocation.location == Location::Output) { + serializeIOParams(getTopParentData()); + } + + if (_shapeLocation.dimsLocation == Location::Output) { + serializeIOParams(parentDataToShapeEdge()->parent()); + } + + if (_shapeLocation.stridesLocation == Location::Output) { + serializeIOParams(parentDataToShapeEdge()->parent()); } serializer.append(checked_cast(_dataLocation.offset)); } void DataNode::serializeIOInfo(BlobSerializer& serializer) const { - auto ioIdx = attrs().get("ioIdx"); - serializer.append(checked_cast(ioIdx)); + auto dataIOIdx = attrs().get("ioIdx"); + serializer.append(checked_cast(dataIOIdx)); auto ioBufferOffset = attrs().get("ioBufferOffset"); serializer.append(checked_cast(ioBufferOffset)); @@ -213,13 +223,25 @@ void DataNode::serializeIOInfo(BlobSerializer& serializer) const { serializer.append(uint8_t(0)); } - serializeDescImpl(serializer, _desc, strides()); + auto resShapeLocation = shapeLocation(); + if (resShapeLocation.dimsLocation != Location::Blob) { + auto ioDimsUpperBoundOffset = attrs().get("ioDimsUpperBoundOffset"); + resShapeLocation.dimsLocation = Location::Blob; + resShapeLocation.dimsOffset = ioDimsUpperBoundOffset; + } + if (resShapeLocation.stridesLocation != Location::Blob) { + auto ioStridesUpperBoundOffset = attrs().get("ioStridesUpperBoundOffset"); + resShapeLocation.stridesLocation = Location::Blob; + resShapeLocation.stridesOffset = ioStridesUpperBoundOffset; + } + + serializeDescImpl(serializer, _desc, resShapeLocation); } void DataNode::serializeDescImpl( BlobSerializer& serializer, const DataDesc& storedDesc, - const DimValues& storedStrides) const { + const ShapeLocation& shapeLocation) const { IE_ASSERT(storedDesc.numDims() <= MAX_DIMS_32); auto storedDimsOrder = storedDesc.dimsOrder(); @@ -232,12 +254,10 @@ void DataNode::serializeDescImpl( serializer.append(checked_cast(storedPerm.size())); - const auto& shape = shapeLocation(); - - serializer.append(checked_cast(shape.dimsLocation)); - serializer.append(checked_cast(shape.dimsOffset)); - serializer.append(checked_cast(shape.stridesLocation)); - serializer.append(checked_cast(shape.stridesOffset)); + serializer.append(checked_cast(shapeLocation.dimsLocation)); + serializer.append(checked_cast(shapeLocation.dimsOffset)); + serializer.append(checked_cast(shapeLocation.stridesLocation)); + serializer.append(checked_cast(shapeLocation.stridesOffset)); } void printTo(std::ostream& os, const Data& data) { diff --git a/inference-engine/src/vpu/graph_transformer/src/model/model.cpp b/inference-engine/src/vpu/graph_transformer/src/model/model.cpp index b0667f0887c..c2a2ed9ea91 100644 --- a/inference-engine/src/vpu/graph_transformer/src/model/model.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/model/model.cpp @@ -401,19 +401,12 @@ StageDependency ModelObj::addStageDependency(const Stage& stage, const Data& dat data->_dependentStagesEdges.push_back(edge); - VPU_THROW_UNLESS(data->usage() == DataUsage::Intermediate, - "Adding stage dependency for {} with type {} failed: only {} datas can be added as a dependency " - "while adding {} with usage {} was attempted", - stage->name(), stage->type(), DataUsage::Intermediate, data->name(), data->usage()); - VPU_THROW_UNLESS(data->_producerEdge != nullptr, "Adding stage dependency for {} with type {} failed: data {} with usage {} should have producer, " "but actually it doesn't", stage->name(), stage->type(), data->name(), data->usage()); - if (data->_producerEdge != nullptr) { - ++data->_producerEdge->_producer->_nextStages[stage]; - ++stage->_prevStages[data->_producerEdge->_producer]; - } + ++data->_producerEdge->_producer->_nextStages[stage]; + ++stage->_prevStages[data->_producerEdge->_producer]; return edge; } @@ -1392,7 +1385,7 @@ Stage getDataConnectionStage( // Check connection stage type and that parent has the largest buffer. // - if (connectionStage->type() == StageType::Concat || + if (connectionStage->type() == StageType::StubConcat || connectionStage->type() == StageType::Expand) { IE_ASSERT(producer == child); IE_ASSERT(consumer == parent); @@ -1538,7 +1531,11 @@ DataToShapeAllocation ModelObj::connectDataWithShape( const auto& parentStage = parent->producer(); const auto& childStage = child->producer(); - if (parentStage && childStage && parentStage != childStage && parent->usage() == DataUsage::Intermediate) { + const auto& areStagesDifferent = [](const Stage& lhs, const Stage& rhs) { + return lhs && rhs && lhs != rhs; + }; + + if (areStagesDifferent(parentStage, childStage)) { // Shape and data are produced from different stages, make sure that shape is calculated before data addStageDependency(childStage, parent); } @@ -1546,7 +1543,34 @@ DataToShapeAllocation ModelObj::connectDataWithShape( return edge; } -void ModelObj::replaceParentData( +void ModelObj::replaceDataToShapeParent( + const DataToShapeAllocation& edge, + const Data& newParent) { + auto oldParent = edge->parent(); + auto child = edge->child(); + + oldParent->_childDataToShapeEdges.erase(edge); + edge->_parent = newParent; + newParent->_childDataToShapeEdges.push_back(edge); +} + +void ModelObj::replaceDataToShapeChild( + const DataToShapeAllocation& edge, + const Data& newChild) { + auto parent = edge->parent(); + auto oldChild = edge->child(); + + oldChild->_parentDataToShapeEdge = nullptr; + edge->_child = newChild; + + VPU_THROW_UNLESS(newChild->_parentDataToShapeEdge == nullptr, + "replaceDataToShapeChild failed: newChild {} with usage {} already has parent {} with usage {}", + newChild->name(), newChild->usage(), newChild->_parentDataToShapeEdge->parent()->name(), newChild->_parentDataToShapeEdge->parent()->usage()); + + newChild->_parentDataToShapeEdge = edge; +} + +void ModelObj::replaceDataToDataParent( const DataToDataAllocation& edge, const Data& newParent) { auto oldParent = edge->parent(); @@ -1571,7 +1595,7 @@ void ModelObj::replaceParentData( } } -void ModelObj::replaceChildData( +void ModelObj::replaceDataToDataChild( const DataToDataAllocation& edge, const Data& newChild) { auto parent = edge->parent(); diff --git a/inference-engine/src/vpu/graph_transformer/src/parsed_config.cpp b/inference-engine/src/vpu/graph_transformer/src/parsed_config.cpp index 405bb65b27c..b00c95bc39a 100644 --- a/inference-engine/src/vpu/graph_transformer/src/parsed_config.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/parsed_config.cpp @@ -62,6 +62,7 @@ IE_SUPPRESS_DEPRECATED_START VPU_CONFIG_KEY(ENABLE_REPLACE_WITH_REDUCE_MEAN), VPU_CONFIG_KEY(ENABLE_TENSOR_ITERATOR_UNROLLING), VPU_CONFIG_KEY(FORCE_PURE_TENSOR_ITERATOR), + VPU_CONFIG_KEY(DISABLE_CONVERT_STAGES), // // Debug options @@ -168,6 +169,7 @@ void ParsedConfig::parse(const std::map& config) { setOption(_compileConfig.enableReplaceWithReduceMean, switches, config, VPU_CONFIG_KEY(ENABLE_REPLACE_WITH_REDUCE_MEAN)); setOption(_compileConfig.enableTensorIteratorUnrolling, switches, config, VPU_CONFIG_KEY(ENABLE_TENSOR_ITERATOR_UNROLLING)); setOption(_compileConfig.forcePureTensorIterator, switches, config, VPU_CONFIG_KEY(FORCE_PURE_TENSOR_ITERATOR)); + setOption(_compileConfig.disableConvertStages, switches, config, VPU_CONFIG_KEY(DISABLE_CONVERT_STAGES)); setOption(_compileConfig.irWithVpuScalesDir, config, VPU_CONFIG_KEY(IR_WITH_SCALES_DIRECTORY)); setOption(_compileConfig.noneLayers, config, VPU_CONFIG_KEY(NONE_LAYERS), parseStringSet); @@ -213,6 +215,9 @@ IE_SUPPRESS_DEPRECATED_END if (const auto envVar = std::getenv("IE_VPU_DUMP_ALL_PASSES")) { _compileConfig.dumpAllPasses = std::stoi(envVar) != 0; } + if (const auto envVar = std::getenv("IE_VPU_NUMBER_OF_SHAVES_AND_CMX_SLICES")) { + _compileConfig.numSHAVEs = _compileConfig.numCMXSlices = std::stoi(envVar); + } #endif } diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/broadcast.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/broadcast.cpp new file mode 100644 index 00000000000..a5a6582a07b --- /dev/null +++ b/inference-engine/src/vpu/graph_transformer/src/stages/broadcast.cpp @@ -0,0 +1,159 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include + +#include +#include + +#include +#include +#include +#include + +namespace vpu { + +namespace { + +class BroadcastStage final : public StageNode { +public: + using StageNode::StageNode; + +protected: + StagePtr cloneImpl() const override { + return std::make_shared(*this); + } + + void propagateDataOrderImpl(StageDataInfo& orderInfo) override { + const auto inputOrder = input(0)->desc().dimsOrder(); + auto outputOrder = DimsOrder::fromNumDims(output(0)->desc().numDims()); + + if (inputOrder.numDims() >= 3 && inputOrder.dimInd(Dim::C) == 0) { + outputOrder.moveDim(Dim::C, 0); + } + + orderInfo.setOutput(outputEdge(0), outputOrder); + } + + void getDataStridesRequirementsImpl(StageDataInfo& stridesInfo) override { + stridesInfo.setInput(inputEdge(0), StridesRequirement().remove(0)); + stridesInfo.setOutput(outputEdge(0), StridesRequirement().remove(0)); + } + + void finalizeDataLayoutImpl() override { + } + + void getBatchSupportInfoImpl(StageDataInfo& batchInfo) override { + } + + void initialCheckImpl() const override { + const auto mode = attrs().getOrDefault("mode", BroadcastMode::NUMPY); + const auto& dataPrecision = input(0)->desc().type(); + + VPU_THROW_UNLESS(numOutputs() == 1, + "{} stage with name {} must have only 1 output, actually provided {} outputs", + type(), name(), numOutputs()); + if (mode == BroadcastMode::NUMPY) { + VPU_THROW_UNLESS(numInputs() == 2, + "{} stage with name {} and numpy mode must have 2 inputs, actually " + "provided {} inputs", type(), name(), numInputs()); + assertInputsOutputsTypes(this, + {{dataPrecision}, {DataType::S32}}, + {{dataPrecision}}); + + } else { + VPU_THROW_UNLESS(numInputs() == 3, + "{} stage with name {} and explicit mode must have 3 inputs, actually " + "provided {} inputs", type(), name(), numInputs()); + assertInputsOutputsTypes(this, + {{dataPrecision}, {DataType::S32}, {DataType::S32}}, + {{dataPrecision}}); + } + } + + void serializeParamsImpl(BlobSerializer& serializer) const override { + const auto mode = attrs().getOrDefault("mode", BroadcastMode::NUMPY); + serializer.append(static_cast(mode == BroadcastMode::NUMPY ? 0 : 1)); + } + + void serializeDataImpl(BlobSerializer& serializer) const override { + const auto mode = attrs().getOrDefault("mode", BroadcastMode::NUMPY); + + input(0)->serializeBuffer(serializer); + input(1)->serializeBuffer(serializer); + if (mode == BroadcastMode::EXPLICIT) { + input(2)->serializeBuffer(serializer); + } + output(0)->serializeBuffer(serializer); + } +}; + +} // namespace + +void FrontEnd::parseBroadcast( + const Model& model, + const ie::CNNLayerPtr& layer, + const DataVector& inputs, + const DataVector& outputs) const { + VPU_THROW_UNLESS(layer != nullptr, + "parseBroadcast expects valid CNNLayerPtr, got nullptr"); + + VPU_THROW_UNLESS(outputs.size() == 1, + "{} layer with name {} must have only 1 output, actually provided {} outputs", + layer->type, layer->name, outputs.size()); + const auto output = outputs[0]; + + const auto modeString = layer->GetParamAsString("mode", "numpy"); + if (modeString == "numpy") { + VPU_THROW_UNLESS(inputs.size() == 2, + "{} layer with name {} and numpy mode must have 2 inputs, actually " + "provided {} inputs", layer->type, layer->name, inputs.size()); + } else if (modeString == "explicit") { + VPU_THROW_UNLESS(inputs.size() == 3, + "{} layer with name {} and explicit mode must have 3 inputs, actually " + "provided {} inputs", layer->type, layer->name, inputs.size()); + const auto axesMappingDesc = inputs[2]->desc(); + const auto axesMappingPerm = axesMappingDesc.dimsOrder().toPermutation(); + const auto axesMappingDim = axesMappingDesc.dim(axesMappingPerm.at(0)); + VPU_THROW_UNLESS(axesMappingDesc.numDims() == 1, + "{} layer with name {} and explicit mode must have 1D axesMapping tensor, " + "actually provided {}D tensor", + layer->type, layer->name, axesMappingDesc.numDims()); + VPU_THROW_UNLESS(axesMappingDim == output->desc().numDims(), + "{} layer with name {} and explicit mode must have axesMapping tensor with " + "size equals to number of output dims, expected [{}], provided [{}]", + layer->type, layer->name, output->desc().numDims(), axesMappingDim); + + } else { + VPU_THROW_FORMAT("{} layer with name {}: Graph Transformer doesn't support {} mode", + layer->type, layer->name, modeString); + } + + const auto shape = inputs[1]; + const auto shapeDesc = inputs[1]->desc(); + const auto shapeDim = shapeDesc.dim(shapeDesc.dimsOrder().toPermutation().at(0)); + VPU_THROW_UNLESS(shapeDesc.numDims() == 1, + "{} layer with name {} and explicit mode must have 1D target shape tensor, " + "actually provided {}D tensor", + layer->type, layer->name, shapeDesc.numDims()); + VPU_THROW_UNLESS(shapeDim == output->desc().numDims(), + "{} layer with name {} and explicit mode must have target shape tensor with " + "size equals to number of output dims, expected [{}], provided [{}]", + layer->type, layer->name, output->desc().numDims(), shapeDim); + + const auto mode = modeString == "numpy" ? BroadcastMode::NUMPY : BroadcastMode::EXPLICIT; + + auto stage = model->addNewStage( + layer->name, + StageType::Broadcast, + layer, + inputs, + outputs); + + stage->attrs().set("mode", mode); +} + +} //namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/concat.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/concat.cpp index 6371573937f..ee6a5385f40 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/concat.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/concat.cpp @@ -4,61 +4,74 @@ #include -#include -#include -#include -#include -#include -#include -#include -#include - #include +#include +#include + +#include +#include +#include +#include + namespace vpu { namespace { -class ConcatStage final : public StageNode { +using InputEdges = details::ContainerRange; + +DimsOrder getMostSuitableOrder(const InputEdges& inputEdges) { + DimsOrderMap dimsOrderVotes; + for (const auto& inEdge : inputEdges) { + dimsOrderVotes[inEdge->input()->desc().dimsOrder()]++; + } + + // Select DimsOrder with most votes. + // For equal votes : HCW > CHW > HWC. + + DimsOrder finalOrder; + int curVotes = -1; + for (const auto& p : dimsOrderVotes) { + if (p.second > curVotes) { + finalOrder = p.first; + curVotes = p.second; + } else if (p.second == curVotes) { + if (p.first.numDims() >= 3) { + if (p.first.dimInd(Dim::C) == 2) { + finalOrder = p.first; + } else if (p.first.dimInd(Dim::C) == 3 && + finalOrder.dimInd(Dim::C) != 2) { + finalOrder = p.first; + } + } + } + } + + VPU_INTERNAL_CHECK(finalOrder.numDims() > 0, + "getMostSuitableOrder must find order with rank which is grater than 0, " + "actually rank is {}", finalOrder.numDims()); + VPU_INTERNAL_CHECK(curVotes > 0, + "getMostSuitableOrder: final order must have at least 1 vote " + "actually votes number is {}", curVotes); + + return finalOrder; +} + +// +// StubConcatStage will be replaced with Data <-> Data edges on special stage processor +// + +class StubConcatStage final : public StageNode { public: using StageNode::StageNode; protected: StagePtr cloneImpl() const override { - return std::make_shared(*this); + return std::make_shared(*this); } void propagateDataOrderImpl(StageDataInfo& orderInfo) override { - auto output = outputEdge(0)->output(); - - DimsOrderMap dimsOrderVotes; - for (const auto& inEdge : inputEdges()) { - dimsOrderVotes[inEdge->input()->desc().dimsOrder()]++; - } - - // Select DimsOrder with most votes. - // For equal votes : HCW > CHW > HWC. - - DimsOrder finalOrder; - int curVotes = -1; - for (const auto& p : dimsOrderVotes) { - if (p.second > curVotes) { - finalOrder = p.first; - curVotes = p.second; - } else if (p.second == curVotes) { - if (p.first.numDims() >= 3) { - if (p.first.dimInd(Dim::C) == 2) { - finalOrder = p.first; - } else if (p.first.dimInd(Dim::C) == 3 && - finalOrder.dimInd(Dim::C) != 2) { - finalOrder = p.first; - } - } - } - } - - IE_ASSERT(finalOrder.numDims() > 0); - IE_ASSERT(curVotes > 0); + const auto finalOrder = getMostSuitableOrder(inputEdges()); for (const auto& inEdge : inputEdges()) { orderInfo.setInput(inEdge, finalOrder); @@ -88,7 +101,10 @@ protected: } } - IE_ASSERT(minConcatDimInd < dimsOrder.numDims()); + VPU_INTERNAL_CHECK(minConcatDimInd < dimsOrder.numDims(), + "{} stage with name {} must have minConcatDimInd no greater than number " + "of dimensions, actually index is {}, number of dimension is {}", + type(), name(), minConcatDimInd, dimsOrder.numDims()); // // Initial StridesRequirement for inputs and output. @@ -157,39 +173,133 @@ protected: } void initialCheckImpl() const override { - IE_ASSERT(numInputs() > 0); - IE_ASSERT(numOutputs() == 1); + VPU_INTERNAL_CHECK(numInputs() > 0, + "{} stage with name {} must have no less than 1 input, " + "actually provided {} inputs", type(), name(), numInputs()); + VPU_INTERNAL_CHECK(numOutputs() == 1, + "{} stage with name {} must have only 1 output, " + "actually provided {} outputs", type(), name(), numOutputs()); const auto& firstInputPrecision = input(0)->desc().type(); assertAllInputsOutputsTypes(this, {firstInputPrecision}, {firstInputPrecision}); } void serializeParamsImpl(BlobSerializer&) const override { - VPU_THROW_EXCEPTION << "Must never be called"; + VPU_THROW_FORMAT("{} stage with name {} must never call serializeParamsImpl", + type(), name()); } void serializeDataImpl(BlobSerializer&) const override { - VPU_THROW_EXCEPTION << "Must never be called"; + VPU_THROW_FORMAT("{} stage with name {} must never call serializeDataImpl", + type(), name()); + } +}; + +// +// ConcatStage will be inferred on device side +// + +class ConcatStage final : public StageNode { +public: + using StageNode::StageNode; + +protected: + StagePtr cloneImpl() const override { + return std::make_shared(*this); + } + + void propagateDataOrderImpl(StageDataInfo& orderInfo) override { + const auto finalOrder = getMostSuitableOrder(inputEdges()); + + for (const auto& inEdge : inputEdges()) { + orderInfo.setInput(inEdge, finalOrder); + } + + orderInfo.setOutput(outputEdge(0), finalOrder); + } + + void getDataStridesRequirementsImpl(StageDataInfo& stridesInfo) override { + for (const auto& inEdge : inputEdges()) { + stridesInfo.setInput(inEdge, StridesRequirement().remove(0)); + } + stridesInfo.setOutput(outputEdge(0), StridesRequirement().remove(0)); + } + + void finalizeDataLayoutImpl() override { + } + + void getBatchSupportInfoImpl(StageDataInfo& batchInfo) override { + } + + void initialCheckImpl() const override { + VPU_INTERNAL_CHECK(numInputs() > 0, + "{} stage with name {} must have no less than 1 input, " + "actually provided {} inputs", type(), name(), numInputs()); + VPU_INTERNAL_CHECK(numOutputs() == 1, + "{} stage with name {} must have only 1 output, " + "actually provided {} outputs", type(), name(), numOutputs()); + + const auto& firstInputPrecision = input(0)->desc().type(); + assertAllInputsOutputsTypes(this, {firstInputPrecision}, {firstInputPrecision}); + } + + void serializeParamsImpl(BlobSerializer& serializer) const override { + const auto axis = attrs().get("axis"); + const auto axisInd = input(0)->desc().dimsOrder().dimInd(axis); + + serializer.append(static_cast(axisInd)); + serializer.append(static_cast(numInputs())); + } + + void serializeDataImpl(BlobSerializer& serializer) const override { + for (const auto& input : inputs()) { + input->serializeBuffer(serializer); + } + output(0)->serializeBuffer(serializer); } }; } // namespace -void FrontEnd::parseConcat(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const { - IE_ASSERT(!inputs.empty()); - IE_ASSERT(outputs.size() == 1); +void FrontEnd::parseConcat( + const Model& model, + const ie::CNNLayerPtr& layer, + const DataVector& inputs, + const DataVector& outputs) const { + VPU_THROW_UNLESS(!inputs.empty(), + "{} layer with name {} must have no less than 1 input, " + "actually provided 0 inputs", layer->type, layer->name); + VPU_THROW_UNLESS(outputs.size() == 1, + "{} layer with name {} must have only 1 output, actually provided {} outputs", + layer->type, layer->name, outputs.size()); auto output = outputs[0]; auto concat = std::dynamic_pointer_cast(layer); - IE_ASSERT(layer != nullptr); + VPU_THROW_UNLESS(layer != nullptr, + "{} layer with name {} must be able to convert to ie::ConcatLayer", + layer->type, layer->name); - IE_ASSERT(concat->_axis < output->desc().numDims()); + VPU_THROW_UNLESS(concat->_axis < output->desc().numDims(), + "{} layer with name {} must have axis attribute no grater than number of " + "dimensions, actually provided axis = {}, numDims = {}", + layer->type, layer->name, concat->_axis, output->desc().numDims()); auto perm = DimsOrder::fromNumDims(output->desc().numDims()).toPermutation(); auto axis = perm[output->desc().numDims() - 1 - concat->_axis]; - _stageBuilder->addConcatStage(model, concat->name, concat, axis, inputs, output); + // If there is DSR as concat's output in the transformed graph, then we need to infer + // concat on the device side. In other cases StubConcat stage will be added and it will + // be replace with Data <-> Data edges. + auto inferRequirement = ConcatInferRequirement::CanBeReplaced; + if (auto concatOp = std::dynamic_pointer_cast(layer->getNode())) { + inferRequirement = concatOp->get_output_as_single_output_node(0)->get_type_info() == + ngraph::vpu::op::DynamicShapeResolver::type_info + ? ConcatInferRequirement::NeedToInfer + : ConcatInferRequirement::CanBeReplaced; + } + + _stageBuilder->addConcatStage(model, concat->name, concat, axis, inputs, output, inferRequirement); } Stage StageBuilder::addConcatStage( @@ -198,17 +308,28 @@ Stage StageBuilder::addConcatStage( const ie::CNNLayerPtr& layer, Dim axis, const DataVector& inputs, - const Data& output) { + const Data& output, + ConcatInferRequirement inferRequirement) { std::vector offsets; offsets.reserve(inputs.size()); - DimValues curOffset({{axis, 0}}); - for (const auto& input : inputs) { - offsets.emplace_back(curOffset); - curOffset.set(axis, curOffset[axis] + input->desc().dim(axis)); - } + Stage stage; + if (inferRequirement == ConcatInferRequirement::NeedToInfer) { + stage = model->addNewStage( + layer->name, + StageType::Concat, + layer, + inputs, + {output}); + } else { + DimValues curOffset({{axis, 0}}); + for (const auto& input : inputs) { + offsets.emplace_back(curOffset); + curOffset.set(axis, curOffset[axis] + input->desc().dim(axis)); + } - auto stage = addConcatStage(model, name, layer, std::move(offsets), inputs, output); + stage = addConcatStage(model, name, layer, std::move(offsets), inputs, output); + } stage->attrs().set("axis", axis); @@ -222,11 +343,13 @@ Stage StageBuilder::addConcatStage( std::vector&& offsets, const DataVector& inputs, const Data& output) { - IE_ASSERT(offsets.size() == inputs.size()); + VPU_INTERNAL_CHECK(offsets.size() == inputs.size(), + "offsets count (provided {}) must be equal to inputs count (provided {}) to " + "create Concat stage with name {}", offsets.size(), inputs.size(), name); - auto stage = model->addNewStage( + auto stage = model->addNewStage( name, - StageType::Concat, + StageType::StubConcat, layer, inputs, {output}); diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/convolution.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/convolution.cpp index 65d1fb812ea..1f8878e6434 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/convolution.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/convolution.cpp @@ -93,7 +93,14 @@ bool canTryHW(const int outputNumDims, tryHW = false; } - if (kernelSizeX > 15 || kernelSizeY > 15 || kernelStrideX > 8) { + // 1x16 convolution is split into two 1x8 convolutions in splitLargeKernelConv pass + const bool KernelSizeCantBeSplit = !(kernelSizeX == 16 && kernelSizeY == 1); + const bool KernelSizeTooLarge = (kernelSizeX > 15 || kernelSizeY > 15); + if (KernelSizeTooLarge && KernelSizeCantBeSplit) { + tryHW = false; + } + + if (kernelStrideX > 8) { tryHW = false; } @@ -139,6 +146,10 @@ void parseConv2D(const Model & model, int groupSize = convLayer->_group; + // kernelStrideY doesn't matter when kernelSizeY==InputSizeY, change it to try HW in 1D case + if (kernelSizeY == input->desc().dim(Dim::H) + padTop + padBottom) + kernelStrideY = kernelStrideX; + // // Check if HW is applicable // diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/custom.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/custom.cpp index 55844e00772..27cc40086a0 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/custom.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/custom.cpp @@ -13,14 +13,14 @@ #include #include #include -#include #include #include #include namespace vpu { -static void calcSizesFromParams(const DataDesc &desc, const SmallVector &bufferSizeRules, SmallVector &sizes); +static SmallVector calcSizesFromParams(const DataDesc& desc, const SmallVector& bufferSizeRules, + std::map layerParams); namespace { @@ -95,17 +95,14 @@ private: } void serializeParamsImpl(BlobSerializer& serializer) const override { - const auto& customLayer = attrs().get("customLayer"); - const auto& gws = attrs().get>("gws"); - const auto& lws = attrs().get>("lws"); + const auto& kernel = attrs().get("customKernel"); + const auto& gws = attrs().get>("gws"); + const auto& lws = attrs().get>("lws"); const auto& ports = attrs().get>("ports"); - - // - // GWG, LWG, Offs - // + const auto& localDataSizes = attrs().get>("localDataSizes"); for (int i = 0; i < gws.size(); ++i) { - serializer.append(static_cast(gws[i]/lws[i])); + serializer.append(static_cast(gws[i] / lws[i])); } for (auto x : lws) { @@ -116,172 +113,146 @@ private: serializer.append(static_cast(0)); } - serializer.append(static_cast(customLayer->maxShaves())); - - // - // Kernel Id - // - - serializer.append(static_cast(customLayer->kernelId())); - - // - // Number of inputs - // - - IE_ASSERT(customLayer->stageNumInputs() >= 0); - serializer.append(static_cast(customLayer->stageNumInputs())); - - // - // Total number of blobs - // - + serializer.append(static_cast(kernel.maxShaves())); + serializer.append(static_cast(kernel.kernelId())); + serializer.append(static_cast(kernel.inputDataCount())); serializer.append(static_cast(numInputs() + numOutputs())); + serializer.append(static_cast(kernel.parameters().size())); - // - // Number of kernel parameters - // - - serializer.append(static_cast(customLayer->parameters().size())); - - // - // Parameters & relocation info - // - - std::map b2b; - for (const auto& kp : customLayer->bindings()) { + std::map b2b; + for (const auto& kp : kernel.bindings()) { b2b[kp.argName] = kp; } IE_ASSERT(origLayer() != nullptr); - for (const auto& kp : customLayer->parameters()) { + for (const auto& kp : kernel.parameters()) { const auto& parameter = b2b[kp]; switch (parameter.type) { - case CustomParamType::Input: - case CustomParamType::Output: - case CustomParamType::InputBuffer: - case CustomParamType::OutputBuffer: - case CustomParamType::Data: - { - if (ports.find(kp) == ports.end()) { - VPU_THROW_EXCEPTION - << "Unable to bind parameter " << parameter.argName << " for " - << origLayer()->type <<" layer. Name is: " << origLayer()->name; - } - int id = ports.find(kp)->second; - serializer.append(static_cast(0)); - serializer.append(static_cast(id)); + case CustomParamType::Input: + case CustomParamType::Output: + case CustomParamType::InputBuffer: + case CustomParamType::OutputBuffer: + case CustomParamType::Data: { + VPU_THROW_UNLESS(ports.find(kp) != ports.end(), + "XML specification for %s layer has no definition for %s parameter. Layer name: %s", + origLayer()->type, kp, origLayer()->name); - break; - } - case CustomParamType::Int: - case CustomParamType::Float: - { - if (origLayer()->params.find(parameter.irSource) != origLayer()->params.end()) { - std::stringstream parameterStream(origLayer()->params[parameter.irSource]); - std::string param; - for (int i = 0; i <= parameter.portIndex; i++) { - getline(parameterStream, param, ','); + int id = ports.find(kp)->second; + serializer.append(static_cast(0)); + serializer.append(static_cast(id)); + break; + } + case CustomParamType::Int: + case CustomParamType::Float: { + const auto cnnParam = origLayer()->params.find(parameter.irSource); + if (cnnParam != origLayer()->params.end()) { + const auto param = [&]() -> std::string { + if (parameter.portIndex < 0) { + return cnnParam->second; } - if (parameter.type == CustomParamType::Int) { - serializer.append(static_cast(std::stoi(param))); + VPU_THROW_UNLESS(cnnParam->second.find(',') != std::string::npos, + "Error while parsing CNNetwork parameter '%s' for '%s' layer: port-index=%d is set, " + "but parameter is neither a tensor, nor an array type.", + cnnParam->first, origLayer()->type, parameter.portIndex); + + std::string value; + std::stringstream parameterStream{cnnParam->second}; + for (int i = 0; i <= parameter.portIndex; i++) { + getline(parameterStream, value, ','); + } + return value; + }(); + + if (parameter.type == CustomParamType::Int) { + serializer.append(static_cast(std::stoi(param))); + serializer.append(static_cast(-1)); + } else { + serializer.append(static_cast(std::stof(param))); + serializer.append(static_cast(-2)); + } + break; + } else { + auto pos = parameter.irSource.find_first_of('.'); + if (pos != std::string::npos) { + auto blob = parameter.irSource.substr(0, pos); + auto dim = parameter.irSource.substr(pos + 1, std::string::npos); + + VPU_THROW_UNLESS(dim.length() == 1, + "Unable to deduce parameter '%s' for '%s' layer. Name is: '%s'", + parameter.argName, origLayer()->type, origLayer()->name); + + char dimLetter = dim[0]; + + ie::DataPtr origData; + if (blob == "I") { + origData = origLayer()->insData[parameter.portIndex].lock(); + } else { + origData = origLayer()->outData[parameter.portIndex]; + } + IE_ASSERT(origData != nullptr); + + auto dims = origData->getDims(); + int ndims = dims.size(); + + if (ndims > 4) { + VPU_THROW_UNLESS(dim.length() == 1, + "Unable to deduce parameter '%s' for '%s' layer. Name is: '%s'", + parameter.argName, origLayer()->type, origLayer()->name); + } + const std::map vars = { + {'b', 0}, {'B', 0}, + {'f', 1}, {'F', 1}, + {'y', 2}, {'Y', 2}, + {'x', 3}, {'X', 3}, + }; + + auto var = vars.find(dimLetter); + if (var != vars.end()) { + auto res = dims.at(var->second - 4 + ndims); + + serializer.append(static_cast(res)); serializer.append(static_cast(-1)); } else { - serializer.append(static_cast(std::stof(param) )); - serializer.append(static_cast(-2)); + VPU_THROW_FORMAT("Unable to deduce parameter '%s' for '%s' layer. Name is: '%s'", + parameter.argName, origLayer()->type, origLayer()->name); } + break; } else { - auto pos = parameter.irSource.find_first_of('.'); - if (pos != std::string::npos) { - auto blob = parameter.irSource.substr(0, pos); - auto dim = parameter.irSource.substr(pos + 1, std::string::npos); - - IE_ASSERT(dim.length() == 1) - << "Unable to deduce parameter " << parameter.argName << " for " - << origLayer()->type <<" layer. Name is: " << origLayer()->name; - char dimLetter = dim[0]; - - ie::DataPtr origData; - if (blob == "I") { - origData = origLayer()->insData[parameter.portIndex].lock(); - } else { - origData = origLayer()->outData[parameter.portIndex]; - } - IE_ASSERT(origData != nullptr); - - auto dims = origData->getDims(); - int ndims = dims.size(); - - if (ndims > 4) - VPU_THROW_EXCEPTION - << "Unable to deduce parameter " << parameter.argName << " for " - << origLayer()->type <<" layer. Name is: " << origLayer()->name; - - const std::map vars = { - { 'b', 0 }, { 'B', 0 }, - { 'f', 1 }, { 'F', 1 }, - { 'y', 2 }, { 'Y', 2 }, - { 'x', 3 }, { 'X', 3 }, - }; - - auto var = vars.find(dimLetter); - if (var != vars.end()) { - auto res = dims.at(var->second-4+ndims); - - serializer.append(static_cast(res)); + VPU_THROW_UNLESS(parameter.portIndex < 0, + "Unable to deduce parameter '%s' for '%s' layer: port-index=%d is set, " + "but parameter is neither a tensor, nor an array type.", + parameter.argName, origLayer()->type, parameter.portIndex); + try { + if (parameter.type == CustomParamType::Int) { + serializer.append(static_cast(std::stoi(parameter.irSource))); serializer.append(static_cast(-1)); } else { - VPU_THROW_EXCEPTION - << "Unable to deduce parameter " << parameter.argName << " for " - << origLayer()->type <<" layer. Name is: " << origLayer()->name; + serializer.append(static_cast(std::stof(parameter.irSource))); + serializer.append(static_cast(-2)); } - break; - } else { - try { - if (parameter.type == CustomParamType::Int) { - serializer.append(static_cast(std::stoi(parameter.irSource))); - serializer.append(static_cast(-1)); - } else { - serializer.append(static_cast(std::stof(parameter.irSource) )); - serializer.append(static_cast(-2)); - } - break; - } - catch (const std::invalid_argument&) { - VPU_THROW_EXCEPTION - << "Unable to deduce parameter " << parameter.argName << " for " - << origLayer()->type <<" layer. Name is: " << origLayer()->name - <<", parameter is: " << parameter.irSource; - } + } catch (const std::invalid_argument&) { + VPU_THROW_FORMAT("Unable to deduce parameter '%s' for '%s' layer. " + "Name is: '%s', parameter is: '%s'", + parameter.argName, origLayer()->type, origLayer()->name, parameter.irSource); } } } - case CustomParamType::LocalData: - { - ie::DataPtr origData; - if (parameter.dimSource == CustomDimSource::Input) { - origData = origLayer()->insData[parameter.dimIdx].lock(); - } else { - origData = origLayer()->outData[parameter.dimIdx]; - } - IE_ASSERT(origData != nullptr); + } + case CustomParamType::LocalData: { + const auto size = localDataSizes.at(parameter.argName); + serializer.append(static_cast(size)); + serializer.append(static_cast(-3)); - SmallVector sizes; - calcSizesFromParams(DataDesc(origData->getTensorDesc()), parameter.bufferSizeRules, sizes); - - serializer.append(static_cast(sizes[0] * sizes[1] * sizes[2])); - serializer.append(static_cast(-3)); - - break; - } - default: - VPU_THROW_EXCEPTION - << "Unable to deduce parameter " << parameter.argName << " for " - << origLayer()->type <<" layer. Name is: " << origLayer()->name; + break; + } + default: + VPU_THROW_FORMAT("Unable to deduce parameter '%s' for '%s' layer. Name is: '%s'", + parameter.argName, origLayer()->type, origLayer()->name); } } } @@ -305,80 +276,59 @@ private: } // namespace -static void calcSizesFromParams(const DataDesc &desc, const SmallVector &bufferSizeRules, SmallVector &sizes) { - // assume output tensor is dimension source by default - auto batchDim = desc.dim(Dim::N, 1); - auto featureDim = desc.dim(Dim::C, 1); - auto yDim = desc.dim(Dim::H, 1); - auto xDim = desc.dim(Dim::W, 1); +static SmallVector calcSizesFromParams(const DataDesc& desc, const SmallVector& bufferSizeRules, + std::map layerParams) { + { + const auto B = std::to_string(desc.dim(Dim::N, 1)); + const auto F = std::to_string(desc.dim(Dim::C, 1)); + const auto Y = std::to_string(desc.dim(Dim::H, 1)); + const auto X = std::to_string(desc.dim(Dim::W, 1)); - const std::map vars = { - { 'b', batchDim }, { 'B', batchDim }, - { 'f', featureDim }, { 'F', featureDim }, - { 'y', yDim }, { 'Y', yDim }, - { 'x', xDim }, { 'X', xDim }, + auto sizes = std::vector> { + {"b", B}, {"B", B}, + {"f", F}, {"F", F}, + {"y", Y}, {"Y", Y}, + {"x", X}, {"X", X}, + }; + + std::move(begin(sizes), end(sizes), inserter(layerParams, end(layerParams))); + } + + MathExpression expr; + expr.setVariables(layerParams); + const auto parseSizeRule = [&expr](const std::string& rule) { + expr.parse(rule); + return expr.evaluate(); }; - sizes.reserve(std::max(bufferSizeRules.size(), 3)); - for (const auto& rule : bufferSizeRules) { - SimpleMathExpression expr; - expr.setVariables(vars); - expr.parse(rule); - sizes.emplace_back(expr.evaluate()); - } - while (sizes.size() < 3) { - sizes.emplace_back(1); - } -} + auto sizes = SmallVector{}; + sizes.reserve(bufferSizeRules.size()); + std::transform(begin(bufferSizeRules), end(bufferSizeRules), std::back_inserter(sizes), parseSizeRule); -static CustomLayer::Ptr chooseSuitable(const std::vector& customLayers, - const std::map& layerParams) { - ie::details::CaselessEq cmp; - - for (const auto& customLayer : customLayers) { - bool suitable = true; - for (const auto& whereParam : customLayer->whereParams()) { - if (layerParams.find(whereParam.first) == layerParams.end() || - !cmp(layerParams.find(whereParam.first)->second, whereParam.second)) { - suitable = false; - } - } - if (suitable) { - return customLayer; - } - } - - IE_ASSERT(false); - return CustomLayer::Ptr(nullptr); + return sizes; } void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) { IE_ASSERT(layer != nullptr); IE_ASSERT(outputs.size() == 1); - std::vector customLayersForType; - if (_customLayers.count(layer->type) > 0) { - customLayersForType.push_back(chooseSuitable(_customLayers.find(layer->type)->second, layer->params)); - } else if (_customLayers.count(layer->type + "@stage_0") > 0) { - int stageNum = 0; - while (_customLayers.count(layer->type + "@stage_" + std::to_string(stageNum)) > 0) { - customLayersForType.push_back(chooseSuitable(_customLayers.find(layer->type + "@stage_" + std::to_string(stageNum))->second, - layer->params)); - stageNum++; - } - } else { - IE_ASSERT(false); - } + const auto suitableLayer = [&] { + const auto customLayersForType = _customLayers.find(layer->type); + IE_ASSERT(customLayersForType != _customLayers.end()); + return getSuitableCustomLayer(customLayersForType->second, layer); + }(); + IE_ASSERT(suitableLayer); + const auto kernels = suitableLayer->kernels(); // Get all buffers, buffers must be unique associated by port index std::map tempBuffsMap; - for (size_t stageNum = 0; stageNum < customLayersForType.size(); stageNum++) { - for (auto& param : customLayersForType[stageNum]->bindings()) { + for (const auto& kernel : kernels) { + for (const auto& param : kernel.bindings()) { if (param.type == CustomParamType::InputBuffer || param.type == CustomParamType::OutputBuffer) { - SmallVector sizes; - auto desc = (param.dimSource == CustomDimSource::Input) ? inputs[param.dimIdx]->desc() : outputs[param.dimIdx]->desc(); - calcSizesFromParams(desc, param.bufferSizeRules, sizes); - auto buf = model->addNewData("custom_" + layer->type + "_buf", DataDesc({sizes[0], sizes[1], sizes[2], 1})); + const auto desc = (param.dimSource == CustomDimSource::Input) ? inputs[param.dimIdx]->desc() + : outputs[param.dimIdx]->desc(); + const auto sizes = calcSizesFromParams(desc, { param.bufferSizeRule }, layer->params); + const auto buf = model->addNewData("custom_" + layer->type + "_buf", DataDesc({sizes[0], 1, 1, 1})); if (tempBuffsMap.find(param.portIndex) == tempBuffsMap.end()) { tempBuffsMap[param.portIndex] = buf; } @@ -387,15 +337,15 @@ void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, con } // Gather inputs and outputs for each stage for the layer - for (int stage_num = 0; stage_num < customLayersForType.size(); stage_num++) { - auto customLayer = customLayersForType[stage_num]; + for (int stage_num = 0; stage_num < kernels.size(); stage_num++) { + const auto& kernel = kernels[stage_num]; std::map ports; std::vector formats; // Gather inputs DataVector stageInputs; - for (auto& param : customLayer->bindings()) { + for (auto& param : kernel.bindings()) { if (param.type == CustomParamType::Input) { ports[param.argName] = stageInputs.size(); formats.emplace_back(param.format); @@ -408,7 +358,7 @@ void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, con } // Gather data blobs - for (auto& param : customLayer->bindings()) { + for (auto& param : kernel.bindings()) { if (param.type == CustomParamType::Data) { auto blobIterator = layer->blobs.find(param.irSource); if (blobIterator != layer->blobs.end()) { @@ -424,27 +374,26 @@ void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, con } } - customLayer->setStageNumInputs(stageInputs.size()); formats.emplace_back(CustomDataFormat::Any); // Get kernel binary - auto kernelNode = _kernelNodes.find(customLayer->kernelBinary()); + auto kernelNode = _kernelNodes.find(kernel.kernelBinary()); if (kernelNode != _kernelNodes.end()) { stageInputs.emplace_back((kernelNode->second)); } else { - auto kernelBinaryDesc = DataDesc({customLayer->kernelBinary().length()}); + auto kernelBinaryDesc = DataDesc({kernel.kernelBinary().length()}); kernelBinaryDesc.setType(DataType::U8); auto kernelBinary = model->addConstData( layer->type + "@kernelBinary", kernelBinaryDesc, - std::make_shared(customLayer->kernelBinary())); + std::make_shared(kernel.kernelBinary())); stageInputs.emplace_back((kernelBinary)); - _kernelNodes[customLayer->kernelBinary()] = kernelBinary; + _kernelNodes[kernel.kernelBinary()] = kernelBinary; } DataVector stageOutputs; - for (auto& param : customLayer->bindings()) { + for (auto& param : kernel.bindings()) { if (param.type == CustomParamType::Output) { ports[param.argName] = stageInputs.size() + stageOutputs.size(); stageOutputs.emplace_back(outputs[param.portIndex]); @@ -455,30 +404,45 @@ void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, con } auto stage = model->addNewStage( - layer->name + ((customLayersForType.size() == 1) ? "" : "@stage_" + std::to_string(stage_num)), + layer->name + ((kernels.size() == 1) ? "" : "@stage_" + std::to_string(stage_num)), StageType::Custom, layer, stageInputs, stageOutputs); - stage->attrs().set("customLayer", customLayer); + stage->attrs().set("customKernel", suitableLayer->kernels()[stage_num]); stage->attrs().set("ports", ports); stage->attrs().set("formats", formats); - SmallVector gws; - SmallVector lws; - auto dimSource = (customLayer->dimSource() == CustomDimSource::Input) ? inputs : outputs; - calcSizesFromParams(dimSource[customLayer->dimSourceIndex()]->desc(), customLayer->globalSizeRules(), gws); - calcSizesFromParams(dimSource[customLayer->dimSourceIndex()]->desc(), customLayer->localSizeRules(), lws); + const auto& dimSource = (kernel.dimSource() == CustomDimSource::Input) ? inputs : outputs; + const auto& dataDesc = dimSource[kernel.dimSourceIndex()]->desc(); + + const auto gws = calcSizesFromParams(dataDesc, kernel.globalGridSizeRules(), layer->params); + const auto lws = calcSizesFromParams(dataDesc, kernel.localGridSizeRules(), layer->params); stage->attrs().set("gws", gws); stage->attrs().set("lws", lws); + const auto localDataSizes = [&] { + auto sizes = std::map{}; + for (const auto& bind : kernel.bindings()) { + if (bind.type == CustomParamType::LocalData) { + const auto& source = bind.dimSource == CustomDimSource::Input ? inputs : outputs; + const auto& desc = source[bind.dimIdx]->desc(); + const auto size = calcSizesFromParams(desc, { bind.bufferSizeRule }, layer->params); + sizes.emplace(bind.argName, size[0]); + } + } + return sizes; + }(); + + stage->attrs().set("localDataSizes", localDataSizes); + std::map inputOrders; std::map outputOrders; - std::map b2b; - for (const auto& kp : customLayer->bindings()) { + std::map b2b; + for (const auto& kp : kernel.bindings()) { b2b[kp.argName] = kp; } @@ -489,7 +453,7 @@ void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, con { CustomDataFormat::FYX, DimsOrder::CHW } }; - for (const auto& kp : customLayer->parameters()) { + for (const auto& kp : kernel.parameters()) { const auto& parameter = b2b[kp]; if (parameter.type == CustomParamType::Input) { @@ -512,7 +476,7 @@ void FrontEnd::parseCustom(const Model& model, const ie::CNNLayerPtr& layer, con stage->attrs().set("inputOrders", std::move(inputOrders)); stage->attrs().set("outputOrders", std::move(outputOrders)); - int buffer_size = customLayer->kernelBinary().length() + 1024; + int buffer_size = kernel.kernelBinary().length() + 1024; model->addTempBuffer( stage, DataDesc({buffer_size})); diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/dynamic_shape_resolver.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/dynamic_shape_resolver.cpp index 82b23c60445..cb95807a951 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/dynamic_shape_resolver.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/dynamic_shape_resolver.cpp @@ -7,48 +7,53 @@ namespace vpu { void FrontEnd::parseDSR(const Model& model, const ie::CNNLayerPtr& layer, const DataVector& inputs, const DataVector& outputs) const { - VPU_THROW_UNLESS(inputs.size() == 2, "Error while parsing {} with type {}, got {} inputs, while {} were expected", + VPU_THROW_UNLESS(inputs.size() == 2, "Error while parsing {} of type {}, got {} inputs, while {} were expected", layer->name, layer->type, inputs.size(), 2); - - VPU_THROW_UNLESS(outputs.size() == 1, "Error while parsing {} with type {}, got {} outputs, while {} were expected", - layer->name, layer->type, outputs.size(), 1); - const auto& data = inputs[0]; const auto& shape = inputs[1]; + VPU_THROW_UNLESS(outputs.size() == 1, "Parsing layer {} of type {} failed: got {} outputs, while {} were expected", + layer->name, layer->type, outputs.size(), 1); const auto& dataOutput = outputs[0]; + const auto dataProducerEdge = data->producerEdge(); + VPU_THROW_UNLESS(dataProducerEdge != nullptr, "Parsing layer {} of type {} failed: input with index {} (of name {}) must have a producer", + layer->name, layer->type, 0, data->name()); + VPU_THROW_UNLESS(shape->desc().numDims() == 1, - "Error while parsing {} with type {}, the number of dimensions for the second input {} should be equal to 1 " - "but got {} instead", - layer->name, layer->type, shape->name(), shape->desc().numDims()); + "Parsing layer {} of type {} failed: input with index {} (of name {}) must have rank equal to {}, actual is {}", + layer->name, layer->type, 0, shape->name(), 1, shape->desc().numDims()); VPU_THROW_UNLESS(shape->desc().totalDimSize() == data->desc().numDims(), - "Error while parsing {} with type {}, the total number of elements for the second input {} should be equal to " - "the number of dimensions for the first input {}, but got {} and {} respectively", - layer->name, layer->type, shape->name(), data->name(), shape->desc().totalDimSize(), data->desc().numDims()); + "Parsing layer {} of type {} failed: input with index {} (of name {}) must have the same total elements number as " + "input with index {} (of name {}), actual {} and {} respectively", + layer->name, layer->type, 0, shape->name(), 1, data->name(), shape->desc().totalDimSize(), data->desc().numDims()); - // Dynamic input shape is unsupported - VPU_THROW_UNLESS(data->producer() != nullptr, - "Parsing layer {} with type {} failed: DSR stages must have a producer, but actually it doesn't", - layer->name, layer->type); + const auto shapeProducerEdge = shape->producerEdge(); + VPU_THROW_UNLESS(shapeProducerEdge != nullptr, "Parsing layer {} of type {} failed: input with index {} (of name {}) must have a producer", + layer->name, layer->type, 1, shape->name()); - const auto dataOutputEdge = data->producerEdge(); - const auto shapeOutputEdge = shape->producerEdge(); + model->replaceStageOutput(dataProducerEdge, dataOutput); + if (const auto& dataToShapeEdge = data->parentDataToShapeEdge()) { + model->replaceDataToShapeChild(dataToShapeEdge, dataOutput); + } + model->removeUnusedData(data); if (dataOutput->usage() == DataUsage::Output) { // Create the second output with shape in case of dynamic output const auto& shapeOutput = model->addOutputData(dataOutput->name() + "@shape", shape->desc()); - model->replaceStageOutput(shapeOutputEdge, shapeOutput); + model->replaceStageOutput(shapeProducerEdge, shapeOutput); + model->connectDataWithShape(shapeOutput, dataOutput); + + for (const auto& dataToShapeEdge : shape->childDataToShapeEdges()) { + model->replaceDataToShapeParent(dataToShapeEdge, shapeOutput); + } model->removeUnusedData(shape); } else { model->connectDataWithShape(shape, dataOutput); } - - model->replaceStageOutput(dataOutputEdge, dataOutput); - model->removeUnusedData(data); } } // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/out_shape_of_reshape.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/out_shape_of_reshape.cpp new file mode 100644 index 00000000000..66d794caabf --- /dev/null +++ b/inference-engine/src/vpu/graph_transformer/src/stages/out_shape_of_reshape.cpp @@ -0,0 +1,103 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include + +#include +#include +#include +#include +#include + +namespace vpu { + +namespace { + +class OutShapeOfReshapeStage final : public StageNode { +private: + StagePtr cloneImpl() const override { + return std::make_shared(*this); + } + + void propagateDataOrderImpl(StageDataInfo& orderInfo) override { + } + + void getDataStridesRequirementsImpl(StageDataInfo& stridesInfo) override { + } + + void finalizeDataLayoutImpl() override { + } + + void getBatchSupportInfoImpl(StageDataInfo& batchInfo) override { + } + + void initialCheckImpl() const override { + assertInputsOutputsTypes(this, + {{DataType::S32}, {DataType::S32}}, + {{DataType::S32}}); + } + + void serializeParamsImpl(BlobSerializer& serializer) const override { + auto specialZero = attrs().get("specialZero"); + + serializer.append(static_cast(specialZero)); + } + + void serializeDataImpl(BlobSerializer& serializer) const override { + input(0)->serializeBuffer(serializer); + input(1)->serializeBuffer(serializer); + output(0)->serializeBuffer(serializer); + } +}; + +} // namespace + +void FrontEnd::parseOutShapeOfReshape( + const Model& model, + const ie::CNNLayerPtr& layer, + const DataVector& inputs, + const DataVector& outputs) const { + VPU_THROW_UNLESS(inputs.size() == 2, + "OutShapeOfReshape stage with name %s must have only 2 inputs, " + "actually provided %d", layer->name, inputs.size()); + VPU_THROW_UNLESS(outputs.size() == 1, + "OutShapeOfReshape stage with name %s must have only 1 output, " + "actually provided %d", layer->name, outputs.size()); + + auto inDataShape = inputs[0]; + auto outShapeDescriptor = inputs[1]; + auto outDataShape = outputs[0]; + + VPU_THROW_UNLESS(inDataShape->desc().numDims() == 1, + "OutShapeOfReshape stage with name %s must have 1D input data shape tensor, " + "actually provided %dD tensor", layer->name, inDataShape->desc().numDims()); + VPU_THROW_UNLESS(outShapeDescriptor->desc().numDims() == 1, + "OutShapeOfReshape stage with name %s must have 1D output shape descriptor " + "tensor, actually provided %dD tensor", + layer->name, outShapeDescriptor->desc().numDims()); + VPU_THROW_UNLESS(outDataShape->desc().numDims() == 1, + "OutShapeOfReshape stage with name %s must have 1D output data shape tensor, " + "actually provided %dD tensor", layer->name, outDataShape->desc().numDims()); + + VPU_THROW_UNLESS(outShapeDescriptor->desc().totalDimSize() == outDataShape->desc().totalDimSize(), + "OutShapeOfReshape stage with name %s must have output shape descriptor and " + "output data shape tensor with equal length, actually provided %d vs %d", + layer->name, outShapeDescriptor->desc().totalDimSize(), + outDataShape->desc().totalDimSize()); + + + auto outShapeOfReshapeStage = model->addNewStage( + layer->name, + StageType::OutShapeOfReshape, + layer, + inputs, + outputs); + + auto specialZero = layer->GetParamAsInt("special_zero", 0); + outShapeOfReshapeStage->attrs().set("specialZero", specialZero); +} + +} // namespace vpu diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/pooling.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/pooling.cpp index d4939884e19..2877522d859 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/pooling.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/pooling.cpp @@ -228,6 +228,10 @@ void parsePool2D(const Model & model, int outputWidth = output->desc().dim(Dim::W); int outputHeight = output->desc().dim(Dim::H); + // kernelStrideY doesn't matter when kernelSizeY==InputSizeY, change it to try HW in 1D case + if (kernelSizeY == inputHeight + padTop + padBottom) + kernelStrideY = kernelStrideX; + bool tryHW = canTryHW(poolType, inputWidth, inputHeight, diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/reduce.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/reduce.cpp index 6d211d3e212..b898b4e925a 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/reduce.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/reduce.cpp @@ -3,12 +3,14 @@ // #include +#include #include #include #include #include +#include #include namespace vpu { @@ -25,41 +27,33 @@ private: } void propagateDataOrderImpl(StageDataInfo& orderInfo) override { - auto input0 = inputEdge(0)->input(); - auto input1 = inputEdge(1)->input(); - auto output = outputEdge(0)->output(); - - auto in0Desc = input0->desc(); - auto in1Desc = input1->desc(); - auto outDesc = output->desc(); - - auto in0Order = DimsOrder::fromNumDims(in0Desc.numDims()); - auto in1Order = DimsOrder::fromNumDims(in1Desc.numDims()); - auto outOrder = DimsOrder::fromNumDims(outDesc.numDims()); - - orderInfo.setInput(inputEdge(0), in0Order); - orderInfo.setInput(inputEdge(1), in1Order); - orderInfo.setOutput(outputEdge(0), outOrder); + orderInfo.setInput(inputEdge(0), input(0)->desc().dimsOrder()); + orderInfo.setInput(inputEdge(1), input(1)->desc().dimsOrder()); + orderInfo.setOutput(outputEdge(0), output(0)->desc().dimsOrder()); } void getDataStridesRequirementsImpl(StageDataInfo& stridesInfo) override { } void finalizeDataLayoutImpl() override { - auto input0 = inputEdge(0)->input(); - auto input1 = inputEdge(1)->input(); - - auto in0Desc = input0->desc(); - auto in1Desc = input1->desc(); - - IE_ASSERT(input1->usage() == DataUsage::Const); + auto reductionAxes = input(1); + auto in0Desc = input(0)->desc(); + auto in1Desc = reductionAxes->desc(); + VPU_THROW_UNLESS(reductionAxes->usage() == DataUsage::Const, + "Stage {} of type {} expects input with index {} ({}) to be {}, but it is {}", + name(), type(), 1, reductionAxes->name(), DataUsage::Const, reductionAxes->usage()); size_t ndims = in0Desc.numDims(); - IE_ASSERT(in1Desc.numDims() == 1); + VPU_THROW_UNLESS(in1Desc.numDims() == 1, + "Stage {} of type {} expects input with index {} ({}) to have dimensions number is {}, but it is {}", + name(), type(), 1, reductionAxes->name(), 1, in1Desc.numDims()); size_t indicesSize = in1Desc.totalDimSize(); - IE_ASSERT(indicesSize <= ndims); + VPU_THROW_UNLESS(indicesSize <= ndims, + "Stage {} of type {} expects input with index {} ({}) to have total size not greater than dimensions ", + "number of input with index {} ({}), but it is {} > {}", + name(), type(), 1, reductionAxes->name(), 0, input(0)->name(), indicesSize, ndims); - const auto oldIndices = input1->content()->get(); + const auto oldIndices = reductionAxes->content()->get(); auto newIndicesBlob = ie::make_shared_blob(InferenceEngine::TensorDesc( ie::Precision::I32, @@ -69,28 +63,26 @@ private: auto newIndices = newIndicesBlob->buffer().as(); - const auto defDimsOrder = DimsOrder::fromNumDims(ndims); - const auto defPerm = defDimsOrder.toPermutation(); + const auto defPerm = DimsOrder::fromNumDims(ndims).toPermutation(); + const auto dimsOrder = in0Desc.dimsOrder(); for (size_t i = 0; i < indicesSize; ++i) { auto irIndex = oldIndices[i]; if (irIndex < 0) { // handle negative indices - irIndex = ndims - irIndex; + irIndex = ndims - std::abs(irIndex); } - IE_ASSERT(irIndex < ndims); + VPU_THROW_UNLESS(irIndex < ndims, + "Stage {} of type {} expects input with index {} ({}) include values less than ", + "dimensions number of input with index {} ({}), but it is {} >= {}", + name(), type(), 1, reductionAxes->name(), 0, input(0)->name(), irIndex, ndims); - const auto irRevIndex = ndims - 1 - irIndex; - - const auto irDim = defPerm[irRevIndex]; - - const auto vpuDimInd = in0Desc.dimsOrder().dimInd(irDim); - newIndices[i] = vpuDimInd; + const auto reducedDim = defPerm[ndims - 1 - irIndex]; + newIndices[i] = dimsOrder.dimInd(reducedDim); } - std::sort(newIndices, newIndices + indicesSize); auto newList = model()->duplicateData( - input1, + reductionAxes, "", DataDesc(), ieBlobContent(newIndicesBlob, DataType::S32)); @@ -106,7 +98,10 @@ private: } void initialCheckImpl() const override { - IE_ASSERT(input(0)->desc().type() == output(0)->desc().type()); + VPU_THROW_UNLESS(input(0)->desc().type() == output(0)->desc().type(), + "Stage {} of type {} expects that data types of input with index {} ({}) ", + "and output with index {} ({}) are the same, but it is {} and {}", + name(), type(), 0, input(0)->name(), 0, output(0)->name(), input(0)->desc().type(), output(0)->desc().type()); assertInputsOutputsTypes(this, {{DataType::FP16, DataType::S32}, {DataType::S32}}, {{DataType::FP16, DataType::S32}}); @@ -133,10 +128,15 @@ private: void FrontEnd::parseReduce(const Model& model, const ie::CNNLayerPtr& _layer, const DataVector& inputs, const DataVector& outputs) const { auto layer = std::dynamic_pointer_cast(_layer); - IE_ASSERT(layer != nullptr); - - IE_ASSERT(inputs.size() == 2); - IE_ASSERT(outputs.size() == 1); + VPU_THROW_UNLESS(layer != nullptr, + "Layer {} of type {} is nullptr", + layer->name, layer->type); + VPU_THROW_UNLESS(inputs.size() == 2, + "Layer {} of type {} expects {} inputs, but provided {}", + layer->name, layer->type, 2, inputs.size()); + VPU_THROW_UNLESS(outputs.size() == 1, + "Layer {} of type {} expects {} output, but provided {}", + layer->name, layer->type, 1, outputs.size()); auto stageType = StageType::None; if (layer->type == "ReduceAnd") { diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/topk.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/topk.cpp index 3314362424b..f8777d505ab 100644 --- a/inference-engine/src/vpu/graph_transformer/src/stages/topk.cpp +++ b/inference-engine/src/vpu/graph_transformer/src/stages/topk.cpp @@ -157,12 +157,8 @@ void FrontEnd::parseTopK(const Model& model, const ie::CNNLayerPtr& _layer, cons realOutputs = {outputValues}; } - const bool isArgMaxPossible = outputsMode != TopKOutputs::All && mode == TopKMode::Max - && ((sort == TopKSort::Value && outputsMode == TopKOutputs::ValueOnly) - || (sort == TopKSort::Index && outputsMode == TopKOutputs::IndexOnly)); - auto stage = model->addNewStage(layer->name, - isArgMaxPossible ? StageType::ArgMax : StageType::TopK, + StageType::TopK, layer, inputs, realOutputs); stage->attrs().set("axis", axis); diff --git a/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp b/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp index 7e44237f61e..146c93cc433 100644 --- a/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp +++ b/inference-engine/src/vpu/myriad_plugin/myriad_plugin.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #include "vpu/ngraph/transformations/dynamic_to_static_shape.hpp" #include "generic_ie.hpp" @@ -28,7 +29,6 @@ using namespace InferenceEngine::VPUConfigParams; using namespace vpu::MyriadPlugin; ExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl( - const ICore* /*core*/, const ICNNNetwork& network, const std::map& config) { VPU_PROFILE(LoadExeNetworkImpl); @@ -39,7 +39,8 @@ ExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl( auto clonedNetwork = cloneNetwork(network); if (auto function = clonedNetwork->getFunction()) { ngraph::op::GenericIE::DisableReshape noReshape(function); - vpu::DynamicToStaticShape().transform(*function); + ngraph::pass::CommonOptimizations().run_on_function(function); + vpu::DynamicToStaticShape().transform(function); } return std::make_shared(*clonedNetwork, _devicePool, parsedConfigCopy); diff --git a/inference-engine/src/vpu/myriad_plugin/myriad_plugin.h b/inference-engine/src/vpu/myriad_plugin/myriad_plugin.h index cbbd12f4aaf..7567a5fde69 100644 --- a/inference-engine/src/vpu/myriad_plugin/myriad_plugin.h +++ b/inference-engine/src/vpu/myriad_plugin/myriad_plugin.h @@ -28,7 +28,6 @@ public: void SetConfig(const std::map& config) override; ie::ExecutableNetworkInternal::Ptr LoadExeNetworkImpl( - const ie::ICore* core, const ie::ICNNNetwork& network, const std::map& config) override; diff --git a/inference-engine/tests/functional/inference_engine/CMakeLists.txt b/inference-engine/tests/functional/inference_engine/CMakeLists.txt index da353a06114..83ae2570dc4 100644 --- a/inference-engine/tests/functional/inference_engine/CMakeLists.txt +++ b/inference-engine/tests/functional/inference_engine/CMakeLists.txt @@ -13,14 +13,16 @@ addIeTargetTest( EXCLUDED_SOURCE_DIRS ${CMAKE_CURRENT_SOURCE_DIR}/extension_lib LINK_LIBRARIES + gmock funcTestUtils ngraphFunctions inference_engine_transformations ADD_CPPLINT - LABELS - INFERENCE_ENGINE DEPENDENCIES extension_tests + mock_engine + LABELS + IE ) include(CMakeParseArguments) @@ -86,6 +88,7 @@ function(ie_headers_compilation_with_custom_flags) set(content "#include <${header_file}>\n${content}") endforeach() set(source_file "${CMAKE_CURRENT_BINARY_DIR}/modern_flags_${IE_TEST_TEST_SUFFIX}.cpp") + file(REMOVE ${source_file}) file(GENERATE OUTPUT ${source_file} CONTENT ${content}) set(target_name ieFuncTestsCompilation${IE_TEST_TEST_SUFFIX}) diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/caslesseq_tests.cpp b/inference-engine/tests/functional/inference_engine/caseless_tests.cpp similarity index 90% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/caslesseq_tests.cpp rename to inference-engine/tests/functional/inference_engine/caseless_tests.cpp index 720a611592c..611304c1c4b 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/caslesseq_tests.cpp +++ b/inference-engine/tests/functional/inference_engine/caseless_tests.cpp @@ -10,17 +10,7 @@ using namespace std; using namespace InferenceEngine::details; -class CaselessTests : public ::testing::Test { - protected: - virtual void TearDown() { - } - - virtual void SetUp() { - } - - public: - -}; +using CaselessTests = ::testing::Test; TEST_F(CaselessTests, emptyAreEqual) { ASSERT_TRUE(InferenceEngine::details::equal("", "")); @@ -49,7 +39,6 @@ TEST_F(CaselessTests, canFindCaslessInMap) { } TEST_F(CaselessTests, canFindCaslessInUnordered) { - caseless_unordered_map storage = { {"Abc", 1}, {"bC", 2}, diff --git a/inference-engine/tests/functional/inference_engine/core_threading_tests.cpp b/inference-engine/tests/functional/inference_engine/core_threading_tests.cpp new file mode 100644 index 00000000000..9b786f9ee08 --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/core_threading_tests.cpp @@ -0,0 +1,149 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include
+#include +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +class CoreThreadingTests : public ::testing::Test { +public: + void SetUp() override { + } + + void runParallel(std::function func, + const unsigned int iterations = 100, + const unsigned int threadsNum = 8) { + std::vector threads(threadsNum); + + for (auto & thread : threads) { + thread = std::thread([&](){ + for (unsigned int i = 0; i < iterations; ++i) { + func(); + } + }); + } + + for (auto & thread : threads) { + if (thread.joinable()) + thread.join(); + } + } + + void safeAddExtension(InferenceEngine::Core & ie) { + try { + auto extension = InferenceEngine::make_so_pointer( + FileUtils::makeSharedLibraryName({}, + std::string("extension_tests") + IE_BUILD_POSTFIX)); + ie.AddExtension(extension); + } catch (const InferenceEngine::details::InferenceEngineException & ex) { + ASSERT_STR_CONTAINS(ex.what(), "name: experimental. Opset"); + } + } +}; + +// tested function: SetConfig +TEST_F(CoreThreadingTests, SetConfigPluginDoesNotExist) { + InferenceEngine::Core ie; + std::map localConfig = { + { CONFIG_KEY(PERF_COUNT), InferenceEngine::PluginConfigParams::YES } + }; + + runParallel([&] () { + ie.SetConfig(localConfig); + }, 10000); +} + +// tested function: RegisterPlugin +TEST_F(CoreThreadingTests, RegisterPlugin) { + InferenceEngine::Core ie; + std::atomic index{0}; + runParallel([&] () { + const std::string deviceName = std::to_string(index++); + ie.RegisterPlugin(std::string("mock_engine") + IE_BUILD_POSTFIX, deviceName); + ie.GetVersions(deviceName); + ie.UnregisterPlugin(deviceName); + }, 4000); +} + +// tested function: RegisterPlugins +TEST_F(CoreThreadingTests, RegisterPlugins) { + InferenceEngine::Core ie; + std::atomic index{0}; + + auto getPluginXml = [&] () -> std::tuple { + std::string indexStr = std::to_string(index++); + std::string pluginsXML = InferenceEngine::getIELibraryPath() + + FileUtils::FileSeparator + + "test_plugins" + indexStr + ".xml"; + std::ofstream file(pluginsXML); + + file << "::SharedLibraryPrefix(); + file << "mock_engine"; + file << IE_BUILD_POSTFIX; + file << FileUtils::DotSymbol::value; + file << FileUtils::FileTraits::SharedLibraryExt(); + file << "\" name=\""; + file << indexStr; + file << "\">"; + file.flush(); + file.close(); + + return std::tie(pluginsXML, indexStr); + }; + + runParallel([&] () { + std::string fileName, deviceName; + std:tie(fileName, deviceName) = getPluginXml(); + ie.RegisterPlugins(fileName); + ie.GetVersions(deviceName); + ASSERT_EQ(0, std::remove(fileName.c_str())); + }, 1000); +} + +// tested function: GetAvailableDevices, UnregisterPlugin +// TODO: some plugins initialization (e.g. GNA) failed during such stress-test scenario +TEST_F(CoreThreadingTests, DISABLED_GetAvailableDevices) { + InferenceEngine::Core ie; + runParallel([&] () { + std::vector devices = ie.GetAvailableDevices(); + + // unregister all the devices + for (auto && deviceName : devices) { + try { + ie.UnregisterPlugin(deviceName); + } catch (const InferenceEngine::details::InferenceEngineException & ex) { + // if several threads unload plugin at once, the first thread does this + // while all others will throw an exception that plugin is not registered + ASSERT_STR_CONTAINS(ex.what(), "name is not registered in the"); + } + } + }, 30); +} + +// tested function: ReadNetwork, AddExtension +TEST_F(CoreThreadingTests, ReadNetwork) { + InferenceEngine::Core ie; + auto model = FuncTestUtils::TestModel::convReluNormPoolFcModelFP32; + auto network = ie.ReadNetwork(model.model_xml_str, model.weights_blob); + + runParallel([&] () { + safeAddExtension(ie); + (void)ie.ReadNetwork(model.model_xml_str, model.weights_blob); + }, 100, 12); +} diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/data_test.cpp b/inference-engine/tests/functional/inference_engine/data_test.cpp similarity index 100% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/data_test.cpp rename to inference-engine/tests/functional/inference_engine/data_test.cpp diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/debug_tests.cpp b/inference-engine/tests/functional/inference_engine/debug_tests.cpp similarity index 77% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/debug_tests.cpp rename to inference-engine/tests/functional/inference_engine/debug_tests.cpp index afb9d32fce8..0a36c6803b4 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/debug_tests.cpp +++ b/inference-engine/tests/functional/inference_engine/debug_tests.cpp @@ -6,17 +6,7 @@ #include "debug.h" #include -class DebugTests : public ::testing::Test { -protected: - virtual void TearDown() { - } - - virtual void SetUp() { - } - -public: - -}; +using DebugTests = ::testing::Test; TEST_F(DebugTests, tolowerWorksWithEmptyString) { std::string str = ""; diff --git a/inference-engine/tests/functional/inference_engine/ie_extension_test.cpp b/inference-engine/tests/functional/inference_engine/ie_extension_test.cpp index e894913ce45..a72df362997 100644 --- a/inference-engine/tests/functional/inference_engine/ie_extension_test.cpp +++ b/inference-engine/tests/functional/inference_engine/ie_extension_test.cpp @@ -20,34 +20,8 @@ using namespace InferenceEngine; class ExtensionLibTests : public CommonTestUtils::TestsCommon { public: std::string getExtensionPath() { -#ifdef _WIN32 -#ifdef __MINGW32__ - const char pre[] = "lib"; -#else - const char pre[] = ""; -#endif -#ifdef NDEBUG - const char ext[] = ".dll"; -#else - const char ext[] = "d.dll"; -#endif - const char FileSeparator[] = "\\"; - -#else - const char FileSeparator[] = "/"; -#if defined __APPLE__ -#ifdef NDEBUG - const char ext[] = ".dylib"; -#else - const char ext[] = "d.dylib"; -#endif - const char pre[] = "lib"; -#else - const char pre[] = "lib"; - const char ext[] = ".so"; -#endif -#endif - return getIELibraryPath() + FileSeparator + pre + "extension_tests" + ext; + return FileUtils::makeSharedLibraryName({}, + std::string("extension_tests") + IE_BUILD_POSTFIX); } }; diff --git a/inference-engine/tests/functional/inference_engine/ngraph_reader/abs_tests.cpp b/inference-engine/tests/functional/inference_engine/ngraph_reader/abs_tests.cpp index f8d6143ee44..9567cab9636 100644 --- a/inference-engine/tests/functional/inference_engine/ngraph_reader/abs_tests.cpp +++ b/inference-engine/tests/functional/inference_engine/ngraph_reader/abs_tests.cpp @@ -4,6 +4,111 @@ #include #include "ngraph_reader_tests.hpp" +#include +#include +#include + +class FakeAbs : public ngraph::op::Op { +public: + static constexpr ngraph::NodeTypeInfo type_info{"Abs", 100500}; + const ngraph::NodeTypeInfo& get_type_info() const override { return type_info; } + + FakeAbs() = default; + FakeAbs(const ngraph::Output& arg): ngraph::op::Op({arg}) { + constructor_validate_and_infer_types(); + } + void validate_and_infer_types() override { + set_output_type(0, get_input_element_type(0), get_input_partial_shape(0)); + } + std::shared_ptr copy_with_new_args(const ngraph::NodeVector& new_args) const override { + return std::make_shared(new_args.at(0)); + } + bool visit_attributes(ngraph::AttributeVisitor& visitor) override { + return true; + } +}; +constexpr ngraph::NodeTypeInfo FakeAbs::type_info; + +class AbsFakeExtension: public InferenceEngine::IExtension { +public: + void GetVersion(const InferenceEngine::Version*& versionInfo) const noexcept override {} + void Release() noexcept override { delete this; } + void Unload() noexcept override {} + + std::map getOpSets() override{ + std::map opsets; + ngraph::OpSet opset; + opset.insert(); + opsets["experimental"] = opset; + return opsets; + } +}; + +TEST_F(NGraphReaderTests, ReadAbsFromCustomOpsetNetwork) { + std::string model = R"V0G0N( + + + + + + + 1 + 3 + 22 + 22 + + + + + + + 1 + 3 + 22 + 22 + + + + + 1 + 3 + 22 + 22 + + + + + + + 1 + 3 + 22 + 22 + + + + + + + + + +)V0G0N"; + + Core ie; + ie.AddExtension(std::make_shared()); + Blob::Ptr weights; + + auto network = ie.ReadNetwork(model, weights); + auto nGraph = network.getFunction(); + bool genericNodeExists = false; + const std::string type = "Abs"; + for (auto op : nGraph->get_ops()) { + if (type == op->get_type_info().name && 100500 == op->get_type_info().version) + genericNodeExists = true; + } + ASSERT_TRUE(genericNodeExists); +} TEST_F(NGraphReaderTests, ReadAbsNetwork) { std::string model = R"V0G0N( diff --git a/inference-engine/tests/functional/inference_engine/ngraph_reader/linear_ops_tests.cpp b/inference-engine/tests/functional/inference_engine/ngraph_reader/linear_ops_tests.cpp index 253501ebf6c..d17b60219fe 100644 --- a/inference-engine/tests/functional/inference_engine/ngraph_reader/linear_ops_tests.cpp +++ b/inference-engine/tests/functional/inference_engine/ngraph_reader/linear_ops_tests.cpp @@ -1757,7 +1757,7 @@ TEST_F(NGraphReaderTests, RemoveAdd2) { - + 1 diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/pre_allocator_test.cpp b/inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp similarity index 84% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/pre_allocator_test.cpp rename to inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp index 8f05fb8f588..464f9faf094 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/pre_allocator_test.cpp +++ b/inference-engine/tests/functional/inference_engine/pre_allocator_test.cpp @@ -25,14 +25,13 @@ class PreallocatorTests: public ::testing::Test { allocator = details::make_pre_allocator(&*mybuf.begin(), mybuf.size()); } std::shared_ptr allocator; - }; TEST_F(PreallocatorTests, canAccessPreAllocatedMemory) { - void * handle = allocator->alloc(3); - float * ptr = (float *)allocator->lock(handle); + void * handle = allocator->alloc(3); + float * ptr = reinterpret_cast(allocator->lock(handle)); - mybuf = {1.1f,2.2f,3.3f}; + mybuf = { 1.1f, 2.2f, 3.3f }; ASSERT_EQ(ptr, &*mybuf.begin()); ASSERT_EQ(ptr[0], 1.1f); @@ -48,5 +47,5 @@ TEST_F(PreallocatorTests, canNotAllocateMoreMemory) { TEST_F(PreallocatorTests, canNotLockWrongHandle) { void * handle = allocator->alloc(3); - EXPECT_EQ(nullptr, allocator->lock(1 + (int*)handle)); + EXPECT_EQ(nullptr, allocator->lock(1 + reinterpret_cast(handle))); } diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/preprocess_test.cpp b/inference-engine/tests/functional/inference_engine/preprocess_test.cpp similarity index 91% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/preprocess_test.cpp rename to inference-engine/tests/functional/inference_engine/preprocess_test.cpp index 0fcacdd4f7c..06c65c14883 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/preprocess_test.cpp +++ b/inference-engine/tests/functional/inference_engine/preprocess_test.cpp @@ -7,17 +7,7 @@ using namespace std; -class PreProcessTests : public ::testing::Test { -protected: - virtual void TearDown() { - } - - virtual void SetUp() { - } - -public: - -}; +using PreProcessTests = ::testing::Test; TEST_F(PreProcessTests, throwsOnSettingNullMeanImage) { InferenceEngine::PreProcessInfo info; diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/response_buffer_test.cpp b/inference-engine/tests/functional/inference_engine/response_buffer_test.cpp similarity index 94% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/response_buffer_test.cpp rename to inference-engine/tests/functional/inference_engine/response_buffer_test.cpp index 3bb427da139..6a3299c776e 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/response_buffer_test.cpp +++ b/inference-engine/tests/functional/inference_engine/response_buffer_test.cpp @@ -8,18 +8,7 @@ using namespace std; using namespace InferenceEngine; -class ResponseBufferTests: public ::testing::Test { -protected: - virtual void TearDown() { - } - - virtual void SetUp() { - } - -public: - -}; - +using ResponseBufferTests = ::testing::Test; TEST_F(ResponseBufferTests, canCreateResponseMessage) { ResponseDesc desc; diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/shared_object_loader_test.cpp b/inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp similarity index 81% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/shared_object_loader_test.cpp rename to inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp index 7c5de17d745..5818af7d8df 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/shared_object_loader_test.cpp +++ b/inference-engine/tests/functional/inference_engine/shared_object_loader_test.cpp @@ -2,10 +2,11 @@ // SPDX-License-Identifier: Apache-2.0 // +#include + #include -#include "tests_common.hpp" +#include #include "details/ie_so_loader.h" -#include "inference_engine.hpp" using namespace std; using namespace InferenceEngine; @@ -13,16 +14,21 @@ using namespace InferenceEngine::details; IE_SUPPRESS_DEPRECATED_START -class SharedObjectLoaderTests: public TestsCommon { +class SharedObjectLoaderTests: public ::testing::Test { protected: + std::string get_mock_engine_name() { + return FileUtils::makeSharedLibraryName(getIELibraryPath(), + std::string("mock_engine") + IE_BUILD_POSTFIX); + } + void loadDll(const string &libraryName) { sharedObjectLoader.reset(new details::SharedObjectLoader(libraryName.c_str())); } unique_ptr sharedObjectLoader; - + template std::function make_std_function(const std::string& functionName) { - std::function ptr (reinterpret_cast(sharedObjectLoader->get_symbol(functionName.c_str()))); + std::function ptr(reinterpret_cast(sharedObjectLoader->get_symbol(functionName.c_str()))); return ptr; } }; @@ -41,14 +47,14 @@ TEST_F(SharedObjectLoaderTests, loaderThrowsIfNoPlugin) { TEST_F(SharedObjectLoaderTests, canFindExistedMethod) { loadDll(get_mock_engine_name()); - + auto factory = make_std_function("CreatePluginEngine"); EXPECT_NE(nullptr, factory); } TEST_F(SharedObjectLoaderTests, throwIfMethodNofFoundInLibrary) { loadDll(get_mock_engine_name()); - + EXPECT_THROW(make_std_function("wrong_function"), InferenceEngine::details::InferenceEngineException); } diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/so_pointer_tests.cpp b/inference-engine/tests/functional/inference_engine/so_pointer_tests.cpp similarity index 75% rename from inference-engine/tests_deprecated/unit/inference_engine_tests/so_pointer_tests.cpp rename to inference-engine/tests/functional/inference_engine/so_pointer_tests.cpp index 9cb89354b01..4ad9517156b 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/so_pointer_tests.cpp +++ b/inference-engine/tests/functional/inference_engine/so_pointer_tests.cpp @@ -6,11 +6,15 @@ #include #include +#include +#include + #include -#include +#include #include
#include
+using namespace InferenceEngine; using namespace InferenceEngine::details; using namespace ::testing; using ::testing::InSequence; @@ -33,7 +37,7 @@ public: } }; -template +template class SoPointerHelper : public SOPointer { public: SoPointerHelper(std::shared_ptr&& loader, std::shared_ptr&& object) @@ -100,3 +104,19 @@ TEST_F(SoPointerTests, UnknownPluginExceptionStr) { ASSERT_STR_DOES_NOT_CONTAIN(e.what(), "from CWD:"); } } + +using SymbolLoaderTests = ::testing::Test; + +TEST_F(SymbolLoaderTests, throwCreateNullPtr) { + ASSERT_THROW(SymbolLoader(nullptr), InferenceEngineException); +} + +TEST_F(SymbolLoaderTests, instantiateSymbol) { + std::string name = FileUtils::makeSharedLibraryName(getIELibraryPath(), + std::string("mock_engine") + IE_BUILD_POSTFIX); + std::shared_ptr sharedLoader(new SharedObjectLoader(name.c_str())); + SymbolLoader loader(sharedLoader); + IE_SUPPRESS_DEPRECATED_START + ASSERT_NE(nullptr, loader.instantiateSymbol(SOCreatorTrait::name)); + IE_SUPPRESS_DEPRECATED_END +} diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp new file mode 100644 index 00000000000..f81076b32cf --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/convert_broadcast3_test.cpp @@ -0,0 +1,157 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; + +// Broadcast-3 is converted directly to Broadcast-1 for modes NUMPY, NONE and PDPD +TEST(TransformationTests, ConvertBroadcast3WithNumpyModeToBroadcast1) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{3, 1, 2}); + auto target_shape = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 2}); + auto broadcast = std::make_shared(input1, target_shape, ngraph::op::BroadcastType::NUMPY); + broadcast->set_friendly_name("broadcast"); + + f = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertBroadcast3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{3, 1, 2}); + auto target_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 2}); + auto broadcast = std::make_shared(input1, target_shape, ngraph::op::AutoBroadcastType::NUMPY); + broadcast->set_friendly_name("broadcast"); + + f_ref = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto broadcast_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + auto crop_node = broadcast_node->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(broadcast_node->get_friendly_name() == "broadcast") << "Transformation ConvertBroadcast3 should keep output names.\n"; +} + +TEST(TransformationTests, ConvertBroadcast3WithPDPDModeToBroadcast1) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{3, 1, 2}); + auto target_shape = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 2}); + auto broadcast = std::make_shared(input1, target_shape, ngraph::op::BroadcastType::PDPD); + broadcast->set_friendly_name("broadcast"); + + f = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertBroadcast3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{3, 1, 2}); + auto target_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 2}); + auto broadcast = std::make_shared(input1, target_shape, ngraph::op::AutoBroadcastType::PDPD); + broadcast->set_friendly_name("broadcast"); + + f_ref = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto broadcast_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + auto crop_node = broadcast_node->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(broadcast_node->get_friendly_name() == "broadcast") << "Transformation ConvertBroadcast3 should keep output names.\n"; +} + +TEST(TransformationTests, ConvertBroadcast3WithExplicitModeToBroadcast1) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{3, 5, 2}); + auto brodcast_axis = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{0, 1, 2}); + auto target_shape = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 2}); + auto broadcast = std::make_shared(input1, target_shape, brodcast_axis, ngraph::op::BroadcastType::EXPLICIT); + broadcast->set_friendly_name("broadcast"); + + f = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertBroadcast3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{3, 5, 2}); + auto brodcast_axis = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{0, 1, 2}); + auto target_shape = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 2}); + auto broadcast = std::make_shared(input1, target_shape, brodcast_axis, ngraph::op::AutoBroadcastType::EXPLICIT); + + f_ref = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto broadcast_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + auto crop_node = broadcast_node->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(broadcast_node->get_friendly_name() == "broadcast") << "Transformation ConvertBroadcast3 should keep output names.\n"; +} + +// Broadcast-3 with mode BIDIRECTIONAL is converted to Multiply with constant with 1s of the corresponding type +TEST(TransformationTests, ConvertBroadcast3WithBidirectionalModeToBroadcast1) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 1, 2}); + auto target_shape = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 1}); + auto broadcast = std::make_shared(input1, target_shape, ngraph::op::BroadcastType::BIDIRECTIONAL); + broadcast->set_friendly_name("broadcast"); + + f = std::make_shared(ngraph::NodeVector{broadcast}, ngraph::ParameterVector{input1}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertBroadcast3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 1, 2}); + auto target_shape = std::make_shared(ngraph::element::i64, ngraph::Shape{3}, std::vector{3, 5, 1}); + auto constant_one = std::make_shared(input->get_output_element_type(0), ngraph::Shape({1}), std::vector{1}); + auto broadcast_ones = std::make_shared(constant_one, target_shape, ngraph::op::AutoBroadcastType::NUMPY); + auto multiply = std::make_shared(input, broadcast_ones); + multiply->set_friendly_name("broadcast"); + + f_ref = std::make_shared(ngraph::NodeVector{multiply}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto result_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + auto crop_node = result_node->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(result_node->get_friendly_name() == "broadcast") << "Transformation ConvertBroadcast3 should keep output names.\n"; +} diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_nms3_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_nms3_test.cpp new file mode 100644 index 00000000000..1fc7528434e --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/convert_nms3_test.cpp @@ -0,0 +1,103 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; +using namespace ngraph; + +TEST(TransformationTests, ConvertNMS3I32Output) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto boxes = std::make_shared(element::f32, Shape{1, 1000, 4}); + auto scores = std::make_shared(element::f32, Shape{1, 1, 1000}); + auto max_output_boxes_per_class = opset3::Constant::create(element::i64, Shape{}, {10}); + auto iou_threshold = opset3::Constant::create(element::f32, Shape{}, {0.75}); + auto score_threshold = opset3::Constant::create(element::f32, Shape{}, {0.7}); + auto nms = std::make_shared(boxes, scores, max_output_boxes_per_class, + iou_threshold, score_threshold, opset3::NonMaxSuppression::BoxEncodingType::CORNER, true, element::i32); + nms->set_friendly_name("nms"); + + f = std::make_shared(NodeVector{nms}, ParameterVector{boxes, scores}); + + pass::InitNodeInfo().run_on_function(f); + pass::ConvertNMS3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto boxes = std::make_shared(element::f32, Shape{1, 1000, 4}); + auto scores = std::make_shared(element::f32, Shape{1, 1, 1000}); + auto max_output_boxes_per_class = opset3::Constant::create(element::i64, Shape{}, {10}); + auto iou_threshold = opset3::Constant::create(element::f32, Shape{}, {0.75}); + auto score_threshold = opset3::Constant::create(element::f32, Shape{}, {0.7}); + auto nms = std::make_shared(boxes, scores, max_output_boxes_per_class, + iou_threshold, score_threshold, opset1::NonMaxSuppression::BoxEncodingType::CORNER, true); + nms->set_friendly_name("nms"); + + f_ref = std::make_shared(NodeVector{nms}, ParameterVector{boxes, scores}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto nms_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(nms_node->get_friendly_name() == "nms") << "Transformation ConvertTopK3 should keep output names.\n"; +} + +TEST(TransformationTests, ConvertNMS3I64Output) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto boxes = std::make_shared(element::f32, Shape{1, 1000, 4}); + auto scores = std::make_shared(element::f32, Shape{1, 1, 1000}); + auto max_output_boxes_per_class = opset3::Constant::create(element::i64, Shape{}, {10}); + auto iou_threshold = opset3::Constant::create(element::f32, Shape{}, {0.75}); + auto score_threshold = opset3::Constant::create(element::f32, Shape{}, {0.7}); + auto nms = std::make_shared(boxes, scores, max_output_boxes_per_class, + iou_threshold, score_threshold, opset3::NonMaxSuppression::BoxEncodingType::CORNER, true, element::i64); + nms->set_friendly_name("nms"); + + f = std::make_shared(NodeVector{nms}, ParameterVector{boxes, scores}); + + pass::InitNodeInfo().run_on_function(f); + pass::ConvertNMS3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto boxes = std::make_shared(element::f32, Shape{1, 1000, 4}); + auto scores = std::make_shared(element::f32, Shape{1, 1, 1000}); + auto max_output_boxes_per_class = opset3::Constant::create(element::i64, Shape{}, {10}); + auto iou_threshold = opset3::Constant::create(element::f32, Shape{}, {0.75}); + auto score_threshold = opset3::Constant::create(element::f32, Shape{}, {0.7}); + auto nms = std::make_shared(boxes, scores, max_output_boxes_per_class, + iou_threshold, score_threshold, opset2::NonMaxSuppression::BoxEncodingType::CORNER, true); + auto convert = std::make_shared(nms, element::i64); + convert->set_friendly_name("nms"); + + f_ref = std::make_shared(NodeVector{convert}, ParameterVector{boxes, scores}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto nms_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(nms_node->get_friendly_name() == "nms") << "Transformation ConvertTopK3 should keep output names.\n"; +} \ No newline at end of file diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp new file mode 100644 index 00000000000..9b28daf0efe --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/convert_scatter_elements_to_scatter_test.cpp @@ -0,0 +1,131 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "common_test_utils/test_common.hpp" +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; + +std::shared_ptr get_initial_function(const ngraph::PartialShape & data_shape, + const ngraph::PartialShape & indexes_shape, + const ngraph::PartialShape & updates_shape, + const ngraph::PartialShape & broadcast_shape, + const int64_t & axis) { + auto data = std::make_shared(ngraph::element::f32, data_shape); + auto indexes = std::make_shared(ngraph::element::i64, indexes_shape); + auto updates = std::make_shared(ngraph::element::f32, updates_shape); + auto axis_const = ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {axis}); + + uint64_t broadcast_len = broadcast_shape.rank().get_length(); + auto broadcast_shape_param = std::make_shared(ngraph::element::i64, ngraph::Shape{broadcast_len}); + auto broadcast = std::make_shared(indexes, broadcast_shape_param); + + auto scatter = std::make_shared(data, broadcast, updates, axis_const); + + return std::make_shared(ngraph::NodeVector{scatter}, ngraph::ParameterVector{data, indexes, updates, broadcast_shape_param}); +} + +std::shared_ptr get_reference_function(const ngraph::PartialShape & data_shape, + const ngraph::PartialShape & indexes_shape, + const ngraph::PartialShape & updates_shape, + const int64_t & axis, + const ngraph::Shape & reshape_shape = {}, + const std::vector & squeeze_indices = {}) { + auto data = std::make_shared(ngraph::element::f32, data_shape); + auto indexes = std::make_shared(ngraph::element::i64, indexes_shape); + auto updates = std::make_shared(ngraph::element::f32, updates_shape); + auto axis_const = ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {axis}); + + ngraph::Output index_out = indexes->output(0); + if (!reshape_shape.empty()) { + index_out = std::make_shared(indexes, + ngraph::opset3::Constant::create(ngraph::element::i64, {reshape_shape.size()}, reshape_shape), false); + } + + if (!squeeze_indices.empty()) { + index_out = std::make_shared(indexes, + ngraph::opset3::Constant::create(ngraph::element::i64, {squeeze_indices.size()}, squeeze_indices)); + } + + auto scatter = std::make_shared(data, index_out, updates, axis_const); + + return std::make_shared(ngraph::NodeVector{scatter}, ngraph::ParameterVector{data, indexes, updates}); +} + +void test(std::shared_ptr f, std::shared_ptr f_ref) { + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertScatterElementsToScatter().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + ngraph::pass::ConstantFolding().run_on_function(f); + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} + +void test(std::shared_ptr f) { + test(f, f); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis0) { + test(get_initial_function({1000, 256, 7, 7}, {1000, 1, 1, 1}, {1000, 256, 7, 7}, {1000, 256, 7, 7}, 0), + get_reference_function({1000, 256, 7, 7}, {1000, 1, 1, 1}, {1000, 256, 7, 7}, 0, {1000})); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis1) { + test(get_initial_function({1000, 256, 7, 7}, {256, 1, 1}, {1000, 256, 7, 7}, {1000, 256, 7, 7}, 1), + get_reference_function({1000, 256, 7, 7}, {256, 1, 1}, {1000, 256, 7, 7}, 1, {256})); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestNoReshape) { + test(get_initial_function({1000, 256, 7, 7}, {1}, {1000, 1, 7, 7}, {1000, 1, 7, 7}, 1), + get_reference_function({1000, 256, 7, 7}, {1}, {1000, 1, 7, 7}, 1)); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestNoReshapeNegAxis) { + test(get_initial_function({1000, 256, 7, 7}, {1}, {1000, 1, 7, 7}, {1000, 1, 7, 7}, -3), + get_reference_function({1000, 256, 7, 7}, {1}, {1000, 1, 7, 7}, -3)); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestNegative) { + test(get_initial_function({1000, 256, 7, 7}, {1000, 256, 1, 1}, {1000, 256, 7, 7}, {1000, 256, 7, 7}, 0)); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis0Dyn) { + test(get_initial_function({DYN, 256, 7, 7}, {DYN, 1, 1, 1}, {DYN, 256, 7, 7}, {DYN, 256, 7, 7}, 0), + get_reference_function({DYN, 256, 7, 7}, {DYN, 1, 1, 1}, {DYN, 256, 7, 7}, 0, {}, {1, 2, 3})); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis1Dyn) { + test(get_initial_function({1000, DYN, 7, 7}, {DYN, 1, 1}, {1000, DYN, 7, 7}, {1000, DYN, 7, 7}, 1), + get_reference_function({1000, DYN, 7, 7}, {DYN, 1, 1}, {1000, DYN, 7, 7}, 1, {}, {1, 2})); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis1NoSqueezeDyn) { + test(get_initial_function({1000, DYN, 7, 7}, {DYN}, {1000, 256, 7, 7}, {1000, DYN, 7, 7}, 1), + get_reference_function({1000, DYN, 7, 7}, {DYN}, {1000, 256, 7, 7}, 1)); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis0Neg1Dyn) { + test(get_initial_function({DYN, 256, 7, 7}, {DYN, DYN, 1, 1}, {DYN, 256, 7, 7}, {DYN, 256, 7, 7}, 0)); +} + +TEST(TransformationTests, ConvertScatterElementsToScatterTestAxis0Neg2Dyn) { + test(get_initial_function({DYN, 256, 7, 7}, {DYN, 1, 2, 1}, {DYN, 256, 7, 7}, {DYN, 256, 7, 7}, 0)); +} \ No newline at end of file diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_shapeof3.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_shapeof3.cpp new file mode 100644 index 00000000000..5019643ed6d --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/convert_shapeof3.cpp @@ -0,0 +1,79 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include + +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; + +TEST(TransformationTests, ConvertShapeOf3WithI64) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 2, 3}); + auto shapeof = std::make_shared(input, ngraph::element::i64); + shapeof->set_friendly_name("shapeof"); + + f = std::make_shared(ngraph::NodeVector{shapeof}, ngraph::ParameterVector{input}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertShapeOf3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 2, 3}); + auto shapeof = std::make_shared(input); + shapeof->set_friendly_name("shapeof"); + + f_ref = std::make_shared(ngraph::NodeVector{shapeof}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto output_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(output_node->get_friendly_name() == "shapeof") << "Transformation ConvertShapeOf3 should keep output names.\n"; +} + +TEST(TransformationTests, ConvertShapeOf3WithI32) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 2, 3}); + auto shapeof = std::make_shared(input, ngraph::element::i32); + shapeof->set_friendly_name("shapeof"); + + f = std::make_shared(ngraph::NodeVector{shapeof}, ngraph::ParameterVector{input}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertShapeOf3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 2, 3}); + auto shapeof = std::make_shared(input); + auto convert = std::make_shared(shapeof, ngraph::element::i32); + convert->set_friendly_name("shapeof"); + + f_ref = std::make_shared(ngraph::NodeVector{convert}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto output_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(output_node->get_friendly_name() == "shapeof") << "Transformation ConvertShapeOf3 should keep output names.\n"; +} diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_topk3_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_topk3_test.cpp new file mode 100644 index 00000000000..e5ae2e159cc --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/convert_topk3_test.cpp @@ -0,0 +1,161 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; + +// check that the first output from the TopK-3 with I32 output indices is equal to the TopK-1 first output +TEST(TransformationTests, ConvertTopK3I32Output0) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i32); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f = std::make_shared(ngraph::OutputVector{topk->output(0)}, ngraph::ParameterVector{input}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertTopK3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i32); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f_ref = std::make_shared(ngraph::OutputVector{topk->output(0)}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto topk_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(topk_node->get_friendly_name() == "topk") << "Transformation ConvertTopK3 should keep output names.\n"; +} + +// check that the second output from the TopK-3 with I32 output indices is equal to the TopK-1 second output +TEST(TransformationTests, ConvertTopK3I32Output1) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i32); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f = std::make_shared(ngraph::OutputVector{topk->output(1)}, ngraph::ParameterVector{input}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertTopK3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i32); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f_ref = std::make_shared(ngraph::OutputVector{topk->output(1)}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto topk_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(topk_node->get_friendly_name() == "topk") << "Transformation ConvertTopK3 should keep output names.\n"; +} + +// check that the first output from the TopK-3 with I64 output indices is equal to the TopK-1 first output +TEST(TransformationTests, ConvertTopK3I64Output0) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i64); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f = std::make_shared(ngraph::OutputVector{topk->output(0)}, ngraph::ParameterVector{input}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertTopK3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i32); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f_ref = std::make_shared(ngraph::OutputVector{topk->output(0)}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto topk_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); + ASSERT_TRUE(topk_node->get_friendly_name() == "topk") << "Transformation ConvertTopK3 should keep output names.\n"; +} + +// check that the second output from the TopK-3 with I64 output indices is equal to the TopK-1 second output converted to I64 +TEST(TransformationTests, ConvertTopK3I64Output1) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i64); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f = std::make_shared(ngraph::OutputVector{topk->output(1)}, ngraph::ParameterVector{input}); + + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::ConvertTopK3().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + } + + { + auto input = std::make_shared(ngraph::element::f32, ngraph::Shape{15, 20, 3}); + auto k = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{}, {10}); + auto topk = std::make_shared(input, k, 1, "min", "value", ngraph::element::i32); + auto convert = std::make_shared(topk->output(1), topk->get_index_element_type()); + topk->set_friendly_name("topk"); + + // due to the 'compare_functions' limitation we will check only one output + f_ref = std::make_shared(ngraph::NodeVector{convert}, ngraph::ParameterVector{input}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; + + auto result_node_of_converted_f = f->get_output_op(0); + auto topk_node = result_node_of_converted_f->input(0).get_source_output().get_node_shared_ptr(); +} diff --git a/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.cpp b/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.cpp index 6d2c9dea522..42194e7a496 100644 --- a/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.cpp +++ b/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.cpp @@ -43,9 +43,9 @@ std::pair compare_functions(const std::shared_ptrinputs().size(); ++i) { - if (node1->input(i).get_shape() != node2->input(i).get_shape()) { + if (!node1->input(i).get_partial_shape().compatible(node2->input(i).get_partial_shape())) { std::ostringstream out("Different shape detected"); - out << node1->input(i).get_shape() << " and " << node2->input(i).get_shape(); + out << node1->input(i).get_partial_shape() << " and " << node2->input(i).get_partial_shape(); return {false, out.str()}; } diff --git a/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.hpp b/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.hpp index 5764088e6bb..01a9ee266cd 100644 --- a/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.hpp +++ b/inference-engine/tests/functional/inference_engine/transformations/ngraph_test_utils.hpp @@ -7,9 +7,12 @@ #include #include +#include #include "common_test_utils/test_common.hpp" +#define DYN ngraph::Dimension::dynamic() + using TransformationTests = CommonTestUtils::TestsCommon; std::pair compare_functions(const std::shared_ptr & f1, const std::shared_ptr & f2); diff --git a/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp new file mode 100644 index 00000000000..0d7446781c5 --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/optimize_strided_slice_test.cpp @@ -0,0 +1,274 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "common_test_utils/test_common.hpp" +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; + +TEST(TransformationTests, OptimizeSS_UselessDeletion_Negative1) { + std::shared_ptr f(nullptr); + { + auto data = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + auto begin = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + + std::vector begin_mask = {0, 0, 0, 0}; + std::vector end_mask = {1, 1, 1, 1}; // ignoring end -- slicing to the end + + auto ss = std::make_shared(data, begin, end, stride, begin_mask, end_mask); + + f = std::make_shared(ngraph::NodeVector{ss}, ngraph::ParameterVector{data}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + + auto res = compare_functions(f, f); + ASSERT_TRUE(res.first) << res.second; +} + +TEST(TransformationTests, OptimizeSS_UselessDeletion_Negative2) { + std::shared_ptr f(nullptr); + { + auto data = std::make_shared(ngraph::element::f32, ngraph::PartialShape::dynamic(4)); + auto relu = std::make_shared(data); + auto begin = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + + std::vector begin_mask = {0, 0, 0, 0}; + std::vector end_mask = {1, 1, 1, 1}; // ignoring end -- slicing to the end + + auto ss = std::make_shared(relu, begin, end, stride, begin_mask, end_mask); + + f = std::make_shared(ngraph::NodeVector{ss}, ngraph::ParameterVector{data}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + + auto res = compare_functions(f, f); + ASSERT_TRUE(res.first) << res.second; +} + +TEST(TransformationTests, OptimizeSS_UselessDeletion) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto data = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + auto relu = std::make_shared(data); + auto begin = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + + std::vector begin_mask = {0, 0, 0, 0}; + std::vector end_mask = {1, 1, 1, 1}; // ignoring end -- slicing to the end + + auto ss = std::make_shared(relu, begin, end, stride, begin_mask, end_mask); + + f = std::make_shared(ngraph::NodeVector{ss}, ngraph::ParameterVector{data}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + { + auto data = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + auto relu = std::make_shared(data); + f_ref = std::make_shared(ngraph::NodeVector{relu}, ngraph::ParameterVector{data}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} + +TEST(TransformationTests, OptimizeSS_Usefull_Test) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto data = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + auto begin = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + + std::vector begin_mask = {0, 0, 0, 0}; + std::vector end_mask = {0, 0, 0, 0}; + + auto ss = std::make_shared(data, begin, end, stride, begin_mask, end_mask); + + f = std::make_shared(ngraph::NodeVector{ss}, ngraph::ParameterVector{data}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + { + auto data = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + auto begin = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + + std::vector begin_mask = {0, 0, 0, 0}; + std::vector end_mask = {0, 0, 0, 0}; + + auto ss = std::make_shared(data, begin, end, stride, begin_mask, end_mask); + + f_ref = std::make_shared(ngraph::NodeVector{ss}, ngraph::ParameterVector{data}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} + +TEST(TransformationTests, OptimizeSS_Shared_Test) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto source = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + + auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask1 = {0, 0, 0, 0}; + std::vector end_mask1 = {0, 0, 0, 0}; + auto ss1 = std::make_shared(source, begin1, end1, stride1, begin_mask1, end_mask1); + + auto begin2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask2 = {0, 0, 0, 0}; + std::vector end_mask2 = {0, 0, 0, 0}; + auto ss2 = std::make_shared(source, begin2, end2, stride2, begin_mask2, end_mask2); + + auto concat = std::make_shared(ngraph::NodeVector{ss1, ss2}, 0); + + f = std::make_shared(ngraph::NodeVector{concat}, ngraph::ParameterVector{source}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + { + auto source = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + + auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask1 = {0, 0, 0, 0}; + std::vector end_mask1 = {0, 0, 0, 0}; + auto ss1 = std::make_shared(source, begin1, end1, stride1, begin_mask1, end_mask1); + + auto concat = std::make_shared(ngraph::NodeVector{ss1, ss1}, 0); + + f_ref = std::make_shared(ngraph::NodeVector{concat}, ngraph::ParameterVector{source}); + } + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} + +TEST(TransformationTests, OptimizeSS_NotShared_Test) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto source = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 6, 5, 5}); + + auto axis = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1}); + auto split = std::make_shared(source, axis, 2); + + auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask1 = {0, 0, 0, 0}; + std::vector end_mask1 = {0, 0, 0, 0}; + auto ss1 = std::make_shared(split->output(0), begin1, end1, stride1, begin_mask1, end_mask1); + + auto begin2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask2 = {0, 0, 0, 0}; + std::vector end_mask2 = {0, 0, 0, 0}; + auto ss2 = std::make_shared(split->output(1), begin2, end2, stride2, begin_mask2, end_mask2); + + auto concat = std::make_shared(ngraph::NodeVector{ss1, ss2}, 0); + + f = std::make_shared(ngraph::NodeVector{concat}, ngraph::ParameterVector{source}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + { + auto source = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 6, 5, 5}); + + auto axis = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1}); + auto split = std::make_shared(source, axis, 2); + + auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask1 = {0, 0, 0, 0}; + std::vector end_mask1 = {0, 0, 0, 0}; + auto ss1 = std::make_shared(split->output(0), begin1, end1, stride1, begin_mask1, end_mask1); + + auto begin2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {-1, -1, -1, -1}); + auto stride2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask2 = {0, 0, 0, 0}; + std::vector end_mask2 = {0, 0, 0, 0}; + auto ss2 = std::make_shared(split->output(1), begin2, end2, stride2, begin_mask2, end_mask2); + + auto concat = std::make_shared(ngraph::NodeVector{ss1, ss2}, 0); + + f_ref = std::make_shared(ngraph::NodeVector{concat}, ngraph::ParameterVector{source}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} + +TEST(TransformationTests, OptimizeSS_Groupped_Test) { + std::shared_ptr f(nullptr), f_ref(nullptr); + { + auto source = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + + auto begin1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 0, 0, 0}); + auto end1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {5, 3, 5, 5}); + auto stride1 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask1 = {0, 0, 0, 0}; + std::vector end_mask1 = {0, 0, 0, 0}; + auto ss1 = std::make_shared(source, begin1, end1, stride1, begin_mask1, end_mask1); + + auto begin2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {0, 3, 0, 0}); + auto end2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {5, 5, 5, 5}); + auto stride2 = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{4}, {1}); + std::vector begin_mask2 = {0, 0, 0, 0}; + std::vector end_mask2 = {0, 0, 0, 0}; + auto ss2 = std::make_shared(source, begin2, end2, stride2, begin_mask2, end_mask2); + + auto concat = std::make_shared(ngraph::NodeVector{ss1, ss2}, 1); + + f = std::make_shared(ngraph::NodeVector{concat}, ngraph::ParameterVector{source}); + ngraph::pass::StridedSliceOptimization().run_on_function(f); + ngraph::pass::ConstantFolding().run_on_function(f); + } + { + auto source = std::make_shared(ngraph::element::f32, ngraph::Shape{5, 5, 5, 5}); + + auto axis = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{}, {1}); + auto split_sizes = ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{2}, {3, 2}); + auto variadic_split = std::make_shared(source, axis, split_sizes); + + auto concat = std::make_shared(ngraph::OutputVector{variadic_split->output(0), variadic_split->output(1)}, 1); + + f_ref = std::make_shared(ngraph::NodeVector{concat}, ngraph::ParameterVector{source}); + } + + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} diff --git a/inference-engine/tests/functional/inference_engine/transformations/transpose_to_reshape_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/transpose_to_reshape_test.cpp new file mode 100644 index 00000000000..7028797c313 --- /dev/null +++ b/inference-engine/tests/functional/inference_engine/transformations/transpose_to_reshape_test.cpp @@ -0,0 +1,137 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "common_test_utils/test_common.hpp" +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "ngraph_test_utils.hpp" + +using namespace testing; + +using InputShape = ngraph::PartialShape; +using TransposeOrder = std::vector; + +struct ReferenceParams { + bool no_changes = false; + bool is_empty = false; + std::vector reshape_value; + + ReferenceParams() = default; + + explicit ReferenceParams(bool no_changes, bool is_empty) : no_changes(no_changes), is_empty(is_empty) {} + + explicit ReferenceParams(const std::vector & reshape_value): reshape_value(reshape_value) {} +}; + +class TransposeToReshapeTests: public CommonTestUtils::TestsCommon, + public testing::WithParamInterface > { +public: + std::shared_ptr f, f_ref; + + void SetUp() override { + const auto& input_shape = std::get<0>(GetParam()); + const auto& transpose_order = std::get<1>(GetParam()); + const auto& reference_params = std::get<2>(GetParam()); + + f = get_initial_function(input_shape, transpose_order); + f_ref = get_reference_function(input_shape, transpose_order, reference_params); + } + +private: + std::shared_ptr get_initial_function(const ngraph::PartialShape & input_shape, + const std::vector & transpose_order) { + auto data = std::make_shared(ngraph::element::f32, input_shape); + auto order_const = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{transpose_order.size()}, transpose_order); + auto transpose = std::make_shared(data, order_const); + + // WA to test cases with transpose elimination + auto relu = std::make_shared(transpose); + + return std::make_shared(ngraph::NodeVector{relu}, ngraph::ParameterVector{data}); + } + + std::shared_ptr get_reference_function(const ngraph::PartialShape & input_shape, + const std::vector & transpose_order, + const ReferenceParams & params) { + if (params.no_changes) { + return get_initial_function(input_shape, transpose_order); + } + + auto data = std::make_shared(ngraph::element::f32, input_shape); + + ngraph::Output reshape_dims, last(data); + if (!params.reshape_value.empty()) { + reshape_dims = ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{params.reshape_value.size()}, params.reshape_value); + } else { + auto shape_of = std::make_shared(data); + reshape_dims = std::make_shared(shape_of, + ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{transpose_order.size()}, transpose_order), + ngraph::opset3::Constant::create(ngraph::element::i64, ngraph::Shape{1}, {0})); + } + + if (!params.is_empty) { + last = std::make_shared(last, reshape_dims, true); + } + + last = std::make_shared(last); + + return std::make_shared(ngraph::NodeVector{last.get_node_shared_ptr()}, ngraph::ParameterVector{data}); + } +}; + +TEST_P(TransposeToReshapeTests, CompareFunctions) { + ngraph::pass::InitNodeInfo().run_on_function(f); + ngraph::pass::AlgebraicSimplification().run_on_function(f); + ASSERT_NO_THROW(check_rt_info(f)); + auto res = compare_functions(f, f_ref); + ASSERT_TRUE(res.first) << res.second; +} + +#define SAME_FUNCTION ReferenceParams(true, false) +#define EMPTY_FUNCTION ReferenceParams(false, true) +#define SHAPE_OF_GATHER ReferenceParams() + +INSTANTIATE_TEST_CASE_P(KeepTranspose, TransposeToReshapeTests, + testing::Values(std::make_tuple(InputShape{1, 3, 64, 64}, TransposeOrder{0, 1, 3, 2}, SAME_FUNCTION), + std::make_tuple(InputShape{1, 3, 1, 64}, TransposeOrder{2, 0, 3, 1}, SAME_FUNCTION), + std::make_tuple(InputShape{1, 3, 1, 3}, TransposeOrder{3, 0, 2, 1}, SAME_FUNCTION), + std::make_tuple(InputShape{DYN, 2, 64, 1}, TransposeOrder{1, 0, 3, 2}, SAME_FUNCTION), + std::make_tuple(InputShape{DYN, 3}, TransposeOrder{1, 0}, SAME_FUNCTION), + std::make_tuple(InputShape{DYN, DYN, 1}, TransposeOrder{2, 1, 0}, SAME_FUNCTION), + std::make_tuple(InputShape{DYN, DYN}, TransposeOrder{1, 0}, SAME_FUNCTION))); + +INSTANTIATE_TEST_CASE_P(EliminateTranspose, TransposeToReshapeTests, + testing::Values(std::make_tuple(InputShape{1, 3, 64, 64}, TransposeOrder{0, 1, 2, 3}, EMPTY_FUNCTION), + std::make_tuple(InputShape{1, 1, 1}, TransposeOrder{2, 0, 1}, EMPTY_FUNCTION), + std::make_tuple(InputShape{DYN, DYN}, TransposeOrder{0, 1}, EMPTY_FUNCTION))); + +INSTANTIATE_TEST_CASE_P(ReshapeWithConstant, TransposeToReshapeTests, + testing::Values(std::make_tuple(InputShape{1, 3, 64, 1}, TransposeOrder{0, 1, 3, 2}, ReferenceParams({1, 3, 1, 64})), + std::make_tuple(InputShape{1, 3, 1, 64}, TransposeOrder{1, 0, 3, 2}, ReferenceParams({3, 1, 64, 1})), + std::make_tuple(InputShape{DYN, DYN, 1}, TransposeOrder{0, 2, 1}, ReferenceParams({0, 1, -1})), + std::make_tuple(InputShape{1, 1, DYN}, TransposeOrder{2, 1, 0}, ReferenceParams({-1, 1, 1})), + std::make_tuple(InputShape{DYN, 1, 64, 1}, TransposeOrder{1, 0, 3, 2}, ReferenceParams({1, -1, 1, 64})))); + +INSTANTIATE_TEST_CASE_P(ReshapeWithGather, TransposeToReshapeTests, + testing::Values(std::make_tuple(InputShape{DYN, 1, DYN, 1}, TransposeOrder{1, 0, 3, 2}, SHAPE_OF_GATHER), + std::make_tuple(InputShape{1, DYN, DYN, DYN}, TransposeOrder{1, 2, 3, 0}, SHAPE_OF_GATHER))); + +#undef SAME_FUNCTION +#undef EMPTY_FUNCTION +#undef SHAPE_OF_GATHER diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/bf16_network_restoring.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/bf16_network_restoring.cpp index 3f02f6c41ef..3c352c8f07b 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/bf16_network_restoring.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/bf16_network_restoring.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class BF16NetworkRestore1 : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // + Power1(FP32) // | // + AvgPooling1(FP32) @@ -170,7 +170,7 @@ protected: return std::make_shared(ngraph::NodeVector{eltNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/bfloat16_helpers.hpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/bfloat16_helpers.hpp index 53532c54308..7bb2a554785 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/bfloat16_helpers.hpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/bfloat16_helpers.hpp @@ -130,7 +130,7 @@ typedef std::tuple< * * class ScaleshiftConv_x3_Eltwise : public BasicBF16Test { * protected: - * void SetUp()override { + * void SetUp() override { * fnPtr = std::make_shared(ngraph::NodeVector{convNode3}, ngraph::ParameterVector{input1}); // STAGE1: @@ -253,12 +253,11 @@ public: // BFloat16Helpers::getMaxAbsValue(lm1.as(), mout1->size()) << std::endl; // std::cout << "Max in fp32 network by output " << outputNameFP32 << ": " << // BFloat16Helpers::getMaxAbsValue(lm2.as(), mout2->size()) << std::endl; - FuncTestUtils::compareRawBuffers(lm1.as(), lm2.as(), mout1->size(), mout2->size(), + FuncTestUtils::CompareType::ABS, threshold); - // Stage2: verification of performance counters std::pair wrongLayer = BFloat16Helpers::matchPerfCountPrecisionVsExpected(req1.GetPerformanceCounts(), expectedPrecisions); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/concat_in_place.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/concat_in_place.cpp new file mode 100644 index 00000000000..5037e202fee --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/concat_in_place.cpp @@ -0,0 +1,160 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include + +#include "functional_test_utils/blob_utils.hpp" +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { +namespace { + static const int inputSize = 2, concatAxe = 1; + static std::vector paramVector = { + SizeVector({ 1, 1, inputSize, inputSize }), + SizeVector({ 1, 2, inputSize, inputSize }), + SizeVector({ 1, 3, inputSize, inputSize }), + SizeVector({ 1, 4, inputSize, inputSize }), + SizeVector({ 1, 5, inputSize, inputSize }), + SizeVector({ 1, 6, inputSize, inputSize }), + SizeVector({ 1, 7, inputSize, inputSize }), + SizeVector({ 1, 8, inputSize, inputSize }), + SizeVector({ 1, 9, inputSize, inputSize }), + SizeVector({ 1, 10, inputSize, inputSize })}; +} // namespace + +class Concat_in_place : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { + // scaleshift + // / \ + // Conv Conv + // \ / + // concat + // | + // relu + + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + // multiply + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + input1->set_friendly_name("Input_1"); + std::shared_ptr const1 = nullptr; + if (netPrecision == Precision::FP32) { + const1 = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + const1 = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto mulNode = std::make_shared(input1, const1); + + // add + std::shared_ptr const2 = nullptr; + if (netPrecision == Precision::FP32) { + const2 = opset1::Constant::create(ntype, Shape{1}, { 1.0f }); + } else { + const2 = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(1.0f)) }); + } + auto addNode = std::make_shared(mulNode, const2); + addNode->set_friendly_name("ADD_1"); + + // convolution + std::shared_ptr weightsNode = nullptr; + auto channelsCount = inputShapes[1]; + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } + + std::shared_ptr convNode1 = std::make_shared( + addNode, weightsNode, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode1->set_friendly_name("CONV_1"); + + std::shared_ptr convNode2 = std::make_shared( + addNode, weightsNode, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode2->set_friendly_name("CONV_2"); + + // Concat + ngraph::NodeVector concInputNodes = { convNode1, convNode2 }; + + auto concNode = std::make_shared(concInputNodes, concatAxe); + concNode->set_friendly_name("CONC_1_TEST"); + + // ReLU + auto reluNode = std::make_shared(concNode); + reluNode->set_friendly_name("RELU_1"); + + return std::make_shared(ngraph::NodeVector{reluNode}, ngraph::ParameterVector{input1}); + } + + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE1: + threshold = 10e-1; + // STAGE2: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["ADD_1"] = "FP32"; + expectedPrecisions["CONV_1"] = "BF16"; + expectedPrecisions["CONV_2"] = "BF16"; + expectedPrecisions["CONC_1_TEST"] = "FP32"; + expectedPrecisions["RELU_1"] = "FP32"; + } +}; + +TEST_P(Concat_in_place, CompareWithRefImpl) { + test(); +}; + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Concat_in_place, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::ValuesIn(paramVector), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Concat_in_place::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Concat_in_place, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::ValuesIn(paramVector), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Concat_in_place::getTestCaseName); +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_add.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_add.cpp new file mode 100644 index 00000000000..8f70ce84d02 --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_add.cpp @@ -0,0 +1,162 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class ConvAdd : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { +// Power (FP32) +// | +// Conv(BF16) +// | +// Eltwise (SUM)(BF16) +// | +// Conv (BF16) + + auto channelsCount = inputShapes[1]; + + // STAGE1: construction of the GRAPH + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + input1->set_friendly_name("Input_1"); + std::shared_ptr eltConst0 = nullptr, eltConst1 = nullptr; + if (netPrecision == Precision::FP32) { + eltConst0 = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + eltConst1 = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + eltConst0 = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + eltConst1 = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode0 = std::make_shared(input1, eltConst0); + addNode0->set_friendly_name("Add_0"); + + // convolution + std::shared_ptr weightsNode0 = nullptr, weightsNode1 = nullptr; + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode0 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode0 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } + + std::shared_ptr convNode0 = std::make_shared( + addNode0, weightsNode0, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode0->set_friendly_name("Convolution_0"); + + // eltwise, i.e. sum + auto eltSumNode = std::make_shared(convNode0, eltConst1); + eltSumNode->set_friendly_name("Elt_sum"); + + // convolution + std::shared_ptr convNode1 = std::make_shared( + eltSumNode, weightsNode1, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode1->set_friendly_name("Convolution_1"); + + return std::make_shared(convNode1, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + + // 256 channels + // threshold = 0.26f; // Max in fp32 network by output: 5.26852 + + // 3 channels + threshold = 0.2f; // Max in fp32 network by output: 4.90418 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["Convolution_0"] = "BF16"; + expectedPrecisions["Convolution_1"] = "BF16"; + expectedPrecisions["Elt_sum"] = "FP32"; + } +}; + +TEST_P(ConvAdd, CompareWithRefImpl) { + test(); +}; + +// CPU plug-in failure in that case + +//INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, ConvAdd, +// ::testing::Combine( +// ::testing::Values(Precision::FP32), +// ::testing::Values(Precision::FP32), +// ::testing::Values(SizeVector({1, 256, 38, 38})), +// ::testing::Values(SizeVector()), +// ::testing::Values(CommonTestUtils::DEVICE_CPU)), +// ConvAdd::getTestCaseName); +// +//INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, ConvAdd, +// ::testing::Combine( +// ::testing::Values(Precision::FP32), +// ::testing::Values(Precision::BF16), +// ::testing::Values(SizeVector({1, 256, 38, 38})), +// ::testing::Values(SizeVector()), +// ::testing::Values(CommonTestUtils::DEVICE_CPU)), +// ConvAdd::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, ConvAdd, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({1, 3, 38, 38})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + ConvAdd::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, ConvAdd, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::Values(SizeVector({1, 3, 38, 38})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + ConvAdd::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_conv.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_conv.cpp index a55c7e1378f..4db4d9f3771 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_conv.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_conv.cpp @@ -27,15 +27,16 @@ namespace LayerTestsDefinitions { class ConvConv : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // ScaleShift (FP32) // | // Conv (BF16) // | // Conv (BF16) + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; // multiply - auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ngraph::element::f32, ngraph::Shape{inputShapes}); auto const1 = opset1::Constant::create(ngraph::element::f32, Shape{1}, { 2.0f }); auto mulNode = std::make_shared(input1, const1); @@ -45,11 +46,22 @@ protected: addNode->set_friendly_name("ADD_1"); // convolution - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w - std::vector weightValues; - weightValues.resize(3 * 3 * 3 * 3); - BFloat16Helpers::fillInputsBySinValues(weightValues.data(), weightValues.size()); - auto weightsNode = std::make_shared(ngraph::element::f32, convFilterShape, weightValues); + std::shared_ptr weightsNode = nullptr; + + auto channelsCount = inputShapes[1]; + + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } std::shared_ptr convNode1 = std::make_shared( addNode, weightsNode, @@ -61,9 +73,9 @@ protected: convNode1->set_friendly_name("CONV_1"); // Convolution - ngraph::Shape convFilterShape2 = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape2 = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w std::vector weightValues2; - weightValues2.resize(3 * 3 * 3 * 3); + weightValues2.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2.data(), weightValues2.size()); auto weightsNode2 = std::make_shared(ngraph::element::f32, convFilterShape2, weightValues2); std::shared_ptr convNode2 = std::make_shared( @@ -77,13 +89,12 @@ protected: return std::make_shared(ngraph::NodeVector{convNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); // STAGE1: - // the maximum values in the latest tensor for this test is 24.4. It would be safe to set threshold eq to 0.1 - threshold = 0.3f; + threshold = 1.0f; // Max in fp32 network by output CONV_2: 49.3427 // STAGE2: // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in // performance counters diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_dwconv_relu.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_dwconv_relu.cpp index 1ab6f50ec0a..051c9bd843e 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_dwconv_relu.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_dwconv_relu.cpp @@ -19,7 +19,7 @@ namespace LayerTestsDefinitions { class ConvDWConvReLU : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // | // Conv (BF16) @@ -28,11 +28,10 @@ protected: // | // ReLU (Fused Info DW convolution) - // multiply ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -53,16 +52,18 @@ protected: addNode->set_friendly_name("ADD_1"); // convolution + auto channelsCount = inputShapes[1]; + std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -78,15 +79,15 @@ protected: // DW convolution std::shared_ptr weightsNode2 = nullptr; - ngraph::Shape convFilterShape2 = { 3, 1, 1, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape2 = { channelsCount, 1, 1, 3, 3 }; if (netPrecision == Precision::FP32) { std::vector weightValues2FP32; - weightValues2FP32.resize(3 * 1 * 1 * 3 * 3); + weightValues2FP32.resize(channelsCount * 1 * 1 * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2FP32.data(), weightValues2FP32.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2FP32); } else { std::vector weightValues2BF16; - weightValues2BF16.resize(3 * 1 * 1 * 3 * 3); + weightValues2BF16.resize(channelsCount * 1 * 1 * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2BF16.data(), weightValues2BF16.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2BF16.data()); } @@ -106,7 +107,7 @@ protected: return std::make_shared(reluNode2, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_eltwise_depthwise.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_eltwise_depthwise.cpp new file mode 100644 index 00000000000..dc9aec4f9c8 --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_eltwise_depthwise.cpp @@ -0,0 +1,256 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include + +#include "functional_test_utils/blob_utils.hpp" +#include "common_test_utils/common_utils.hpp" +#include "functional_test_utils/layer_test_utils.hpp" +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { +typedef std::tuple< Precision, SizeVector, string, size_t, CoordinateDiff, string> convEltwiseDepthwiseTestParamsSet; + +class ConvEltwiseDepthwise : + public testing::WithParamInterface, public LayerTestsUtils::LayerTestsCommon { +public: + std::shared_ptr fnPtr; + SizeVector inputShapes; + std::map expectedPrecisions; + float threshold = 3e-2; + Precision netPrecision; + size_t kernel; + CoordinateDiff pads; + string mkldnnPrimitive; + +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) { + // scaleshift (FP32) + // | + // Conv (BF16) + // | + // Relu (Eltwise Fused into Conv) + // | + // scaleshift (Depthwise Fused into Conv) + + element::Type ntype = (netPrecision == Precision::FP32) ? element::f32 : element::bf16; + size_t chCnt = inputShapes[1]; + + // multiply + auto input1 = std::make_shared(ntype, Shape{ inputShapes }); + input1->set_friendly_name("Input_1"); + std::shared_ptr const1 = nullptr; + if (netPrecision == Precision::FP32) { + const1 = opset1::Constant::create(ntype, Shape{ 1 }, { 2.0f }); + } else { + const1 = opset1::Constant::create(ntype, Shape{ 1 }, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto mulNode = std::make_shared(input1, const1); + + // add + std::shared_ptr const2 = nullptr; + if (netPrecision == Precision::FP32) { + const2 = opset1::Constant::create(ntype, Shape{ 1 }, { 1.0f }); + } else { + const2 = opset1::Constant::create(ntype, Shape{ 1 }, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(1.0f)) }); + } + auto addNode = std::make_shared(mulNode, const2); + addNode->set_friendly_name("SS_1"); + + // convolution + std::shared_ptr weightsNode = nullptr; + Shape convFilterShape = { chCnt, chCnt, kernel, kernel }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(chCnt * chCnt * kernel * kernel); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(chCnt * chCnt * kernel * kernel); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } + + std::shared_ptr convNode1 = std::make_shared( + addNode, weightsNode, Strides({ 1, 1 }), pads, pads, Strides({ 1, 1 }), op::PadType::EXPLICIT); + convNode1->set_friendly_name("CONV"); + + // Eltwise, i.e. Relu + auto reluNode = std::make_shared(convNode1); + reluNode->set_friendly_name("RELU"); + + // multiply + std::shared_ptr const3 = nullptr; + if (netPrecision == Precision::FP32) { + const3 = opset1::Constant::create(ntype, Shape{ 1, chCnt, 1, 1 }, { 3.0f }); + } else { + const3 = opset1::Constant::create(ntype, Shape{ 1, chCnt, 1, 1 }, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(3.0f)) }); + } + auto mulNode2 = std::make_shared(reluNode, const3); + + // add + std::shared_ptr const4 = nullptr; + if (netPrecision == Precision::FP32) { + const4 = opset1::Constant::create(ntype, Shape{ 1, chCnt, 1, 1 }, { 2.0f }); + } else { + const4 = opset1::Constant::create(ntype, Shape{ 1, chCnt, 1, 1 }, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode2 = std::make_shared(mulNode2, const4); + addNode2->set_friendly_name("SS_2"); + + return std::make_shared(NodeVector{ addNode2 }, ParameterVector{ input1 }); + } +public: + static string getTestCaseName(testing::TestParamInfo obj) { + Precision netPrecision; + SizeVector inputShapes; + string targetDevice; + size_t kernel; + CoordinateDiff pads; + string mkldnnPrimitive; + std::tie(netPrecision, inputShapes, targetDevice, kernel, pads, mkldnnPrimitive) = obj.param; + + std::ostringstream result; + result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_"; + result << "netPRC=" << netPrecision.name() << "_"; + result << "mkldnnPrimitive=" << mkldnnPrimitive << "_"; + result << "targetDevice=" << targetDevice; + return result.str(); + } + + void Run_test() { + if (!InferenceEngine::with_cpu_x86_bfloat16()) { + // on platforms which do not support bfloat16, we are disabling bf16 tests since there are no bf16 primitives, + // tests are useless on such platforms + return; + } + std::tie(netPrecision, inputShapes, targetDevice, kernel, pads, mkldnnPrimitive) = this->GetParam(); + InferenceEngine::CNNNetwork cnnNet(fnPtr); + + for (const auto& inputItem : cnnNet.getInputsInfo()) { + inputItem.second->setPrecision(Precision::FP32); + } + + string inputName = cnnNet.getInputsInfo().begin()->first; + string outputName = cnnNet.getOutputsInfo().begin()->first; + auto ie = InferenceEngine::Core(); + // BF16 inference + std::map options; + if (netPrecision == InferenceEngine::Precision::FP32) { + options[InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16] = InferenceEngine::PluginConfigParams::YES; + } else { + options[InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16] = InferenceEngine::PluginConfigParams::NO; + } + options[InferenceEngine::PluginConfigParams::KEY_PERF_COUNT] = InferenceEngine::PluginConfigParams::YES; + options[InferenceEngine::PluginConfigParams::KEY_DUMP_EXEC_GRAPH_AS_DOT] = "egraph_test"; + + auto exec_net1 = ie.LoadNetwork(cnnNet, targetDevice, options); + auto req1 = exec_net1.CreateInferRequest(); + + InferenceEngine::Blob::Ptr inBlob1 = req1.GetBlob(inputName); + BFloat16Helpers::fillInputsBySinValues(inBlob1); + + req1.Infer(); + auto outBlobBF16 = req1.GetBlob(outputName); + InferenceEngine::MemoryBlob::CPtr mout1 = InferenceEngine::as(outBlobBF16); + ASSERT_NE(mout1, nullptr); + auto lm1 = mout1->rmap(); + + // FP32 infrence + // if netPrecision is not eq to the FP32 - change network precision and recreate network + InferenceEngine::CNNNetwork cnnNetFP32(createGraph(InferenceEngine::Precision::FP32)); + string inputNameFP32 = cnnNetFP32.getInputsInfo().begin()->first; + string outputNameFP32 = cnnNetFP32.getOutputsInfo().begin()->first; + for (const auto& inputItem : cnnNetFP32.getInputsInfo()) { + inputItem.second->setPrecision(Precision::FP32); + } + auto exec_net2 = ie.LoadNetwork(cnnNetFP32, targetDevice, + { { InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16, InferenceEngine::PluginConfigParams::NO } }); + auto req2 = exec_net2.CreateInferRequest(); + + req2.SetBlob(inputNameFP32, inBlob1); + + req2.Infer(); + auto outBlobFP32 = req2.GetBlob(outputNameFP32); + InferenceEngine::MemoryBlob::CPtr mout2 = InferenceEngine::as(outBlobFP32); + ASSERT_NE(mout2, nullptr); + auto lm2 = mout2->rmap(); + + FuncTestUtils::compareRawBuffers(lm1.as(), lm2.as(), mout1->size(), mout2->size(), + FuncTestUtils::CompareType::ABS_AND_REL, + threshold, threshold); + + // Stage2: verification of performance counters + std::pair wrongLayer = + BFloat16Helpers::matchPerfCountPrecisionVsExpected(req1.GetPerformanceCounts(), expectedPrecisions); + if (wrongLayer.first != string("")) { + string layerInPerfCounts = wrongLayer.first + " " + wrongLayer.second; + string layerExpected = wrongLayer.first + " " + expectedPrecisions[wrongLayer.first]; + ASSERT_EQ(layerInPerfCounts, layerExpected); + } + fnPtr.reset(); + } + + void SetUp() override { + std::vector inputShape; + std::tie(netPrecision, inputShapes, targetDevice, kernel, pads, mkldnnPrimitive) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + expectedPrecisions["SS_1"] = "FP32"; + expectedPrecisions["CONV"] = mkldnnPrimitive; + expectedPrecisions["RELU"] = "ndef"; + expectedPrecisions["SS_2"] = "ndef"; + } +}; + +TEST_P(ConvEltwiseDepthwise, CompareWithRefImpl) { + Run_test(); +}; + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_1x1_depthwise_BF16, ConvEltwiseDepthwise, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({ 1, 5, 1, 1 })), + ::testing::Values(CommonTestUtils::DEVICE_CPU), + ::testing::Values(size_t(1)), + ::testing::Values(CoordinateDiff({ 0, 0 })), + ::testing::Values(string("jit_avx512_1x1_BF16"))), + ConvEltwiseDepthwise::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_gemm_depthwise_BF16, ConvEltwiseDepthwise, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({ 1, 3, 10, 10 })), + ::testing::Values(CommonTestUtils::DEVICE_CPU), + ::testing::Values(size_t(3)), + ::testing::Values(CoordinateDiff({ 1, 1 })), + ::testing::Values(string("jit_gemm_BF16"))), + ConvEltwiseDepthwise::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_conv_depthwise_BF16, ConvEltwiseDepthwise, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({ 1, 5, 10, 10 })), + ::testing::Values(CommonTestUtils::DEVICE_CPU), + ::testing::Values(size_t(3)), + ::testing::Values(CoordinateDiff({ 0, 0 })), + ::testing::Values(string("jit_avx512_BF16"))), + ConvEltwiseDepthwise::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_relu_pool_conv_relu_pool.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_relu_pool_conv_relu_pool.cpp index 8ec2cfd2e33..49d47e6218a 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_relu_pool_conv_relu_pool.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/conv_relu_pool_conv_relu_pool.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ConvReLUPoolConvReLUPool : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // Convolution1 (FP32) // | // ReLU1 (Fused) @@ -46,21 +46,23 @@ protected: // STAGE1: construction of the GRAPH ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); // convolution1 std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -90,15 +92,15 @@ protected: // convolution2 std::shared_ptr weightsNode2 = nullptr; - ngraph::Shape convFilterShape2 = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape2 = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValuesBF16.data()); } @@ -127,15 +129,15 @@ protected: // convolution3 std::shared_ptr weightsNode3 = nullptr; - ngraph::Shape convFilterShape3 = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape3 = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode3 = std::make_shared(ntype, convFilterShape3, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode3 = std::make_shared(ntype, convFilterShape3, weightValuesBF16.data()); } @@ -149,12 +151,9 @@ protected: ngraph::op::PadType::EXPLICIT); // pad type convNode3->set_friendly_name("Convolution_3"); - - - return std::make_shared(ngraph::NodeVector{convNode3}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/elt_max.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/elt_max.cpp new file mode 100644 index 00000000000..5c8e3924974 --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/elt_max.cpp @@ -0,0 +1,151 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class Elt_max : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { +// Power (FP32) +// | +// Conv(BF16) Const(FP32) +// | / +// Eltwise(MAX)(FP32) +// | +// Conv(BF16) + + // STAGE1: construction of the GRAPH + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + const int conv0OutputChannels = 1; + + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + input1->set_friendly_name("Input_1"); + std::shared_ptr powerConst = nullptr; + if (netPrecision == Precision::FP32) { + powerConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + powerConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto powerNode0 = std::make_shared(input1, powerConst); + powerNode0->set_friendly_name("Power_0"); + + // convolution + std::shared_ptr weightsNode0 = nullptr, weightsNode1 = nullptr; + ngraph::Shape convFilterShape0 = { conv0OutputChannels, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape1 = { 1, conv0OutputChannels, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32_0, weightValuesFP32_1; + weightValuesFP32_0.resize(conv0OutputChannels * channelsCount * 3 * 3); + weightValuesFP32_1.resize(1 * conv0OutputChannels * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32_0.data(), weightValuesFP32_0.size()); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32_1.data(), weightValuesFP32_1.size()); + weightsNode0 = std::make_shared(ntype, convFilterShape0, weightValuesFP32_0); + weightsNode1 = std::make_shared(ntype, convFilterShape1, weightValuesFP32_1); + } else { + std::vector weightValuesBF16_0, weightValuesBF16_1; + weightValuesBF16_0.resize(conv0OutputChannels * channelsCount * 3 * 3); + weightValuesBF16_1.resize(1 * conv0OutputChannels * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16_0.data(), weightValuesBF16_0.size()); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16_1.data(), weightValuesBF16_1.size()); + weightsNode0 = std::make_shared(ntype, convFilterShape0, weightValuesBF16_0.data()); + weightsNode1 = std::make_shared(ntype, convFilterShape1, weightValuesBF16_1.data()); + } + + std::shared_ptr convNode0 = std::make_shared( + powerNode0, weightsNode0, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode0->set_friendly_name("Convolution_0"); + + // Eltwise, i.e. Max + std::shared_ptr maxConst = nullptr; + auto batchSize = inputShapes[0]; + auto heightSize = inputShapes[2]; + auto widthSize = inputShapes[3]; + if (netPrecision == Precision::FP32) { + maxConst = opset1::Constant::create(ntype, Shape{batchSize, conv0OutputChannels, heightSize, widthSize}, { 2.0f }); + } else { + maxConst = opset1::Constant::create(ntype, Shape{batchSize, conv0OutputChannels, heightSize, widthSize}, + { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + maxConst->set_friendly_name("Max_const"); + auto eltMaxNode = std::make_shared(convNode0, maxConst); + eltMaxNode->set_friendly_name("Elt_max"); + + std::shared_ptr convNode1 = std::make_shared( + eltMaxNode, weightsNode1, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode1->set_friendly_name("Convolution_1"); + + return std::make_shared(convNode1, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + threshold = 0.2f; // Max in fp32 network by output: 20.0761 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["Convolution_0"] = "BF16"; + expectedPrecisions["Convolution_1"] = "BF16"; + expectedPrecisions["Elt_max"] = "FP32"; + } +}; + +TEST_P(Elt_max, CompareWithRefImpl) { + test(); +}; + + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Elt_max, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({1, 3, 40, 40})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Elt_max::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Elt_max, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::Values(SizeVector({1, 3, 40, 40})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Elt_max::getTestCaseName); +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/elt_x3.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/elt_x3.cpp new file mode 100644 index 00000000000..987c53cde26 --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/elt_x3.cpp @@ -0,0 +1,229 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class Elt_x3 : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { +// Power (FP32) +// / | \ +// Conv(BF16) Conv(BF16) Conv(BF16) +// / | / +// ---------------------------------------------- +// Eltwise(MAX)(FP32) Eltwise(Mul) (FP32) +// | | +// Conv(BF16) Conv(BF16) +// \ / +// Eltwise (SUM)(BF16) +// | +// Conv (BF16) + + auto channelsCount = inputShapes[1]; + + // STAGE1: construction of the GRAPH + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + input1->set_friendly_name("Input_1"); + std::shared_ptr addConst = nullptr; + if (netPrecision == Precision::FP32) { + addConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + addConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode0 = std::make_shared(input1, addConst); + addNode0->set_friendly_name("Add_0"); + + // convolution + std::shared_ptr weightsNode0_1 = nullptr, weightsNode0_2 = nullptr, + weightsNode0_3 = nullptr, weightsNode1 = nullptr, + weightsNode2 = nullptr, weightsNode3 = nullptr; + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode0_1 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode0_2 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode0_3 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode2 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode3 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode0_1 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode0_2 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode0_3 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode2 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode3 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } + + std::shared_ptr convNode0_1 = std::make_shared( + addNode0, weightsNode0_1, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode0_1->set_friendly_name("Convolution_0_1"); + + std::shared_ptr convNode0_2 = std::make_shared( + addNode0, weightsNode0_2, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode0_2->set_friendly_name("Convolution_0_2"); + + std::shared_ptr convNode0_3 = std::make_shared( + addNode0, weightsNode0_3, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode0_3->set_friendly_name("Convolution_0_3"); + + // Eltwise, i.e. Mul + auto eltMulNode = std::make_shared(convNode0_1, convNode0_2); + eltMulNode->set_friendly_name("Elt_mul"); + + // Eltwise, i.e. Max + std::shared_ptr maxConst = nullptr; + if (netPrecision == Precision::FP32) { + maxConst = opset1::Constant::create(ntype, Shape{inputShapes}, { 2.0f }); + } else { + maxConst = opset1::Constant::create(ntype, Shape{inputShapes}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto eltMaxNode = std::make_shared(convNode0_3, maxConst); + eltMaxNode->set_friendly_name("Elt_max"); + + // convolution + std::shared_ptr convNode1 = std::make_shared( + eltMulNode, weightsNode1, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode1->set_friendly_name("Convolution_1"); + + std::shared_ptr convNode2 = std::make_shared( + eltMaxNode, weightsNode2, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode2->set_friendly_name("Convolution_2"); + + // eltwise, i.e. sum + auto eltSumNode = std::make_shared(convNode1, convNode2); + eltSumNode->set_friendly_name("Elt_sum"); + + // convolution + std::shared_ptr convNode3 = std::make_shared( + eltSumNode, weightsNode3, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode3->set_friendly_name("Convolution_3"); + + return std::make_shared(convNode3, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + + // 256 channels, 38 x 38 size + // threshold = 0.6f; // Max in fp32 network by output: 12.0983 + + // 3 channels, 4 x 4 size + threshold = 20.6f; // Max in fp32 network by output: 879.077 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["Convolution_1"] = "BF16"; + expectedPrecisions["Convolution_2"] = "BF16"; + expectedPrecisions["Convolution_3"] = "BF16"; + expectedPrecisions["Elt_max"] = "FP32"; + expectedPrecisions["Elt_mul"] = "FP32"; + expectedPrecisions["Elt_sum"] = "ndef"; + } +}; + +TEST_P(Elt_x3, CompareWithRefImpl) { + test(); +}; + +// CPU plug-in failure in that case + +//INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Elt_x3, +// ::testing::Combine( +// ::testing::Values(Precision::FP32), +// ::testing::Values(Precision::FP32), +// ::testing::Values(SizeVector({1, 256, 38, 38})), +// ::testing::Values(SizeVector()), +// ::testing::Values(CommonTestUtils::DEVICE_CPU)), +// Elt_x3::getTestCaseName); +// +//INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Elt_x3, +// ::testing::Combine( +// ::testing::Values(Precision::FP32), +// ::testing::Values(Precision::BF16), +// ::testing::Values(SizeVector({1, 256, 38, 38})), +// ::testing::Values(SizeVector()), +// ::testing::Values(CommonTestUtils::DEVICE_CPU)), +// Elt_x3::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Elt_x3, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({1, 3, 4, 4})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Elt_x3::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Elt_x3, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::Values(SizeVector({1, 3, 4, 4})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Elt_x3::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/faster_100_5_1_1_conv.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/faster_100_5_1_1_conv.cpp index efc9c6d2105..ca97b105a3c 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/faster_100_5_1_1_conv.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/faster_100_5_1_1_conv.cpp @@ -28,16 +28,18 @@ namespace LayerTestsDefinitions { class Faster100_5_1_1_Conv : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // Power (FP32) // | // Convolution (BF16) // STAGE1: constructin og the GRAPH + auto channelsCount = inputShapes[1]; + // multiply ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{10, 5, 1, 1}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -59,10 +61,10 @@ protected: // problematic convolution: 100x5x1x1 std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 5, 5, 1, 1 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 1, 1 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValues; - weightValues.resize(5 * 5 * 1 * 1, 0.f); + weightValues.resize(channelsCount * channelsCount * 1 * 1, 0.f); weightValues[0] = 1.0f; weightValues[7] = 1.0f; weightValues[11] = 1.0f; @@ -71,7 +73,7 @@ protected: weightsNode = std::make_shared(ngraph::element::f32, convFilterShape, weightValues); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(5 * 5 * 1 * 1, BFloat16Helpers::reducePrecisionBitwiseS(0.0f)); + weightValuesBF16.resize(channelsCount * channelsCount * 1 * 1, BFloat16Helpers::reducePrecisionBitwiseS(0.0f)); weightValuesBF16[0] = BFloat16Helpers::reducePrecisionBitwiseS(1.0f); weightValuesBF16[7] = BFloat16Helpers::reducePrecisionBitwiseS(1.0f); weightValuesBF16[11] = BFloat16Helpers::reducePrecisionBitwiseS(1.0f); @@ -96,7 +98,7 @@ protected: return std::make_shared(ngraph::NodeVector{reluNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/gather_multiply.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/gather_multiply.cpp new file mode 100644 index 00000000000..596a018b7f1 --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/gather_multiply.cpp @@ -0,0 +1,130 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class Gather_multiply : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { +// Add (FP32) +// | +// FC (BF16) +// / +// ------------------------------------------- +// Gather(BF16) Const +// \ / +// Mul(FP32) + + // STAGE1: construction of the GRAPH + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto inputSize = inputShapes[1]; + + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + + input1->set_friendly_name("Input_1"); + std::shared_ptr addConst = nullptr; + if (netPrecision == Precision::FP32) { + addConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + addConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode0 = std::make_shared(input1, addConst); + addNode0->set_friendly_name("Add_1"); + + // matmul + std::shared_ptr matmulConst0 = nullptr; + if (netPrecision == Precision::FP32) { + matmulConst0 = opset1::Constant::create(ntype, Shape{inputSize, inputSize}, { 2.0f }); + } else { + matmulConst0 = opset1::Constant::create(ntype, Shape{inputSize, inputSize}, + { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto matmulNode = std::make_shared(addNode0, matmulConst0); + matmulNode->set_friendly_name("Matmul_0"); + + // gather + auto axesConst = opset1::Constant::create(ngraph::element::i64, Shape{1}, { 1 }); + std::vector gatherArray; + for (size_t i = 0; i < inputSize; i++) { + gatherArray.push_back(i); + } + auto indexesConst = opset1::Constant::create(ngraph::element::i64, Shape{inputSize}, gatherArray); + auto gatherNode = std::make_shared(matmulNode, indexesConst, axesConst); + gatherNode->set_friendly_name("Gather_1"); + + // multiply + std::shared_ptr mulConst = nullptr; + if (netPrecision == Precision::FP32) { + mulConst = opset1::Constant::create(ntype, Shape{inputShapes}, { 2.0f }); + } else { + mulConst = opset1::Constant::create(ntype, Shape{inputShapes}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto mulNode = std::make_shared(gatherNode, mulConst); + mulNode->set_friendly_name("Mul_1"); + + return std::make_shared(mulNode, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + threshold = 0.1f; // Max in fp32 network by output: 21.7285 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + + expectedPrecisions["Matmul_0"] = "BF16"; + expectedPrecisions["Mul_1"] = "FP32"; + } +}; + +TEST_P(Gather_multiply, CompareWithRefImpl) { + test(); +}; + + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Gather_multiply, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({1, 4})), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Gather_multiply::getTestCaseName); +// CPU plug-in failure in that case + +//INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Gather_multiply, +// ::testing::Combine( +// ::testing::Values(Precision::FP32), +// ::testing::Values(Precision::BF16), +// ::testing::Values(SizeVector({1, 4})), +// ::testing::Values(SizeVector()), +// ::testing::Values(CommonTestUtils::DEVICE_CPU)), +// Gather_multiply::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/gather_x2_add_mul_relu_concat_matmul.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/gather_x2_add_mul_relu_concat_matmul.cpp new file mode 100644 index 00000000000..6c8c714e30d --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/gather_x2_add_mul_relu_concat_matmul.cpp @@ -0,0 +1,156 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class Gather_x2_add_mul_relu_concat_matmul : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { +// Add (FP32) +// | +// FullyConnected (BF16) +// / | \ +// ------------------------------------------- +// Gather(FP32) Gather(FP32) Add (FP32) +// \ / / +// Mul(FP32) ReLU(FP32) +// \ / +// Concat(FP32) Const +// \ / +// Matmul(BF16) + + // STAGE1: construction of the GRAPH + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + auto inputSize = inputShapes[1]; + + input1->set_friendly_name("Input_1"); + std::shared_ptr addConst = nullptr; + if (netPrecision == Precision::FP32) { + addConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + addConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode0 = std::make_shared(input1, addConst); + addNode0->set_friendly_name("Add_1"); + + // matmul + std::shared_ptr matmulConst0 = nullptr; + if (netPrecision == Precision::FP32) { + matmulConst0 = opset1::Constant::create(ntype, Shape{inputSize, inputSize}, { 2.0f }); + } else { + matmulConst0 = opset1::Constant::create(ntype, Shape{inputSize, inputSize}, + { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto matmulNode = std::make_shared(addNode0, matmulConst0); + matmulNode->set_friendly_name("Matmul_0"); + + // gather + std::vector gatherArray; + for (size_t i = 0; i < inputSize; i++) { + gatherArray.push_back(i); + } + auto axesConst = opset1::Constant::create(ngraph::element::i64, Shape{1}, { 1 }); + auto indexesConst = opset1::Constant::create(ngraph::element::i64, Shape{inputSize}, gatherArray); + auto gatherNode1 = std::make_shared(matmulNode, indexesConst, axesConst); + gatherNode1->set_friendly_name("Gather_1"); + + auto gatherNode2 = std::make_shared(matmulNode, indexesConst, axesConst); + gatherNode2->set_friendly_name("Gather_2"); + + // multiply + auto mulNode = std::make_shared(gatherNode1, gatherNode2); + mulNode->set_friendly_name("Mul_1"); + + // add + auto addNode1 = std::make_shared(matmulNode, addConst); + addNode0->set_friendly_name("Add_1"); + + // ReLU + auto reluNode = std::make_shared(addNode1); + reluNode->set_friendly_name("Relu_1"); + + // Concat + ngraph::NodeVector concInputNodes = {mulNode, reluNode}; + auto concNode = std::make_shared(concInputNodes, 1); + concNode->set_friendly_name("Conc_1"); + + // matmul + std::shared_ptr matmulConst1 = nullptr; + if (netPrecision == Precision::FP32) { + matmulConst1 = opset1::Constant::create(ntype, Shape{inputSize * 2, inputSize * 2}, { 2.0f }); + } else { + matmulConst1 = opset1::Constant::create(ntype, Shape{inputSize * 2, inputSize * 2}, + { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto matmulNode1 = std::make_shared(concNode, matmulConst1); + matmulNode1->set_friendly_name("Matmul_1"); + + return std::make_shared(matmulNode1, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + threshold = 170.02f; // Max in fp32 network by output: 3887.11 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["Matmul_0"] = "BF16"; + expectedPrecisions["Mul_1"] = "FP32"; + expectedPrecisions["Add_1"] = "FP32"; + expectedPrecisions["Relu_1"] = "FP32"; + expectedPrecisions["Conc_1"] = "FP32"; + expectedPrecisions["Matmul_1"] = "BF16"; + } +}; + +TEST_P(Gather_x2_add_mul_relu_concat_matmul, CompareWithRefImpl) { + test(); +}; + + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Gather_x2_add_mul_relu_concat_matmul, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({ 2048, 64 })), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Gather_x2_add_mul_relu_concat_matmul::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Gather_x2_add_mul_relu_concat_matmul, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::Values(SizeVector({ 2048, 64 })), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Gather_x2_add_mul_relu_concat_matmul::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/interpolation.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/interpolation.cpp new file mode 100644 index 00000000000..75047fe4988 --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/interpolation.cpp @@ -0,0 +1,146 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class Interpolation : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { + // Convolution (BF16) + // | + // Interpolation (In the case of mode = "linear") (FP32) + // | + // Convolution (BF16) + + // STAGE1: construction of the GRAPH + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + input1->set_friendly_name("Input_1"); + std::shared_ptr addConst = nullptr; + if (netPrecision == Precision::FP32) { + addConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + addConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode = std::make_shared(input1, addConst); + addNode->set_friendly_name("Add_1"); + + // convolution + std::shared_ptr weightsNode1 = nullptr, weightsNode2 = nullptr; + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode2 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode2 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } + + std::shared_ptr convNode1 = std::make_shared( + addNode, weightsNode1, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode1->set_friendly_name("Convolution_1"); + + // interpolation + auto heightSize = static_cast(inputShapes[2]); + auto weigthSize = static_cast(inputShapes[3]); + std::vector outShape = {2 * heightSize, 2 * weigthSize}; + + auto interpolShape = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, outShape); + ngraph::op::InterpolateAttrs attrs; + attrs.pads_begin.push_back(0); + attrs.pads_end.push_back(0); + attrs.axes = ngraph::AxisSet{2, 3}; + attrs.align_corners = false; + attrs.mode = "linear"; + attrs.antialias = false; + auto interpolNode = std::make_shared( + convNode1, + interpolShape, attrs); + interpolNode->set_friendly_name("Interp"); + + std::shared_ptr convNode2 = std::make_shared( + interpolNode, weightsNode2, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode2->set_friendly_name("Convolution_2"); + return std::make_shared(convNode2, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + threshold = 0.02f; // Max in fp32 network by output: 2.531 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["Convolution_1"] = "BF16"; + expectedPrecisions["Interp"] = "FP32"; + expectedPrecisions["Convolution_2"] = "BF16"; + } +}; + +TEST_P(Interpolation, CompareWithRefImpl) { + test(); +}; + + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Interpolation, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({ 1, 1, 2, 2 })), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Interpolation::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Interpolation, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::Values(SizeVector({ 1, 1, 2, 2 })), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Interpolation::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/mobilenet_ssd_with_branching.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/mobilenet_ssd_with_branching.cpp index e8eafcbead6..e2498189a32 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/mobilenet_ssd_with_branching.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/mobilenet_ssd_with_branching.cpp @@ -19,7 +19,7 @@ namespace LayerTestsDefinitions { class MobileNet_ssd_with_branching : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift // | // Conv1 (FP32) @@ -35,8 +35,10 @@ protected: // Concat ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -58,15 +60,15 @@ protected: // Conv1 std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -96,15 +98,15 @@ protected: // DW convolution std::shared_ptr weightsNode2 = nullptr; - ngraph::Shape convFilterShape2 = { 3, 1, 1, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape2 = { channelsCount, 1, 1, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValues2FP32; - weightValues2FP32.resize(3 * 1 * 1 * 3 * 3); + weightValues2FP32.resize(channelsCount * 1 * 1 * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2FP32.data(), weightValues2FP32.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2FP32); } else { std::vector weightValues2BF16; - weightValues2BF16.resize(3 * 1 * 1 * 3 * 3); + weightValues2BF16.resize(channelsCount * 1 * 1 * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2BF16.data(), weightValues2BF16.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2BF16.data()); } @@ -138,7 +140,7 @@ protected: return std::make_shared(concNode, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/resample.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/resample.cpp new file mode 100644 index 00000000000..2c803050fbd --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/resample.cpp @@ -0,0 +1,146 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "bfloat16_helpers.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include "common_test_utils/common_utils.hpp" + +#include "ngraph/opsets/opset1.hpp" + +using namespace std; +using namespace ngraph; +using namespace InferenceEngine; + +namespace LayerTestsDefinitions { + +class Resample : public BasicBF16Test { +protected: + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { + // Convolution (BF16) + // | + // Interpolation (Resample in the case of mode = "nearest") (FP32) + // | + // Convolution (BF16) + + // STAGE1: construction of the GRAPH + + ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + // add + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + auto channelsCount = inputShapes[1]; + input1->set_friendly_name("Input_1"); + std::shared_ptr addConst = nullptr; + if (netPrecision == Precision::FP32) { + addConst = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); + } else { + addConst = opset1::Constant::create(ntype, Shape{1}, { bfloat16::from_bits(BFloat16Helpers::reducePrecisionBitwiseS(2.0f)) }); + } + auto addNode = std::make_shared(input1, addConst); + addNode->set_friendly_name("Add_1"); + + // convolution + std::shared_ptr weightsNode1 = nullptr, weightsNode2 = nullptr; + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w + if (netPrecision == Precision::FP32) { + std::vector weightValuesFP32; + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + weightsNode2 = std::make_shared(ntype, convFilterShape, weightValuesFP32); + } else { + std::vector weightValuesBF16; + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); + BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); + weightsNode1 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + weightsNode2 = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); + } + + std::shared_ptr convNode1 = std::make_shared( + addNode, weightsNode1, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode1->set_friendly_name("Convolution_1"); + + // interpolation + auto heightSize = static_cast(inputShapes[2]); + auto weigthSize = static_cast(inputShapes[3]); + std::vector outShape = {2 * heightSize, 2 * weigthSize}; + + auto interpolShape = std::make_shared(ngraph::element::i64, ngraph::Shape{2}, outShape); + ngraph::op::InterpolateAttrs attrs; + attrs.pads_begin.push_back(0); + attrs.pads_end.push_back(0); + attrs.axes = ngraph::AxisSet{2, 3}; + attrs.align_corners = false; + attrs.mode = "nearest"; + attrs.antialias = false; + auto interpolNode = std::make_shared( + convNode1, + interpolShape, attrs); + interpolNode->set_friendly_name("Interp"); + + std::shared_ptr convNode2 = std::make_shared( + interpolNode, weightsNode2, + ngraph::Strides({ 1, 1 }), // strides + ngraph::CoordinateDiff({ 1, 1 }), // pad begin + ngraph::CoordinateDiff({ 1, 1 }), // pad end + ngraph::Strides({ 1, 1 }), // dilation + ngraph::op::PadType::EXPLICIT); // pad type + convNode2->set_friendly_name("Convolution_2"); + return std::make_shared(convNode2, ngraph::ParameterVector{input1}); + } + void SetUp() override { + std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); + fnPtr = createGraph(netPrecision); + + // STAGE2: set up safe threshold <= 5% from maximum value of output tensor + threshold = 0.02f; // Max in fp32 network by output: 2.35926 + + // STAGE3: + // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in + // performance counters + expectedPrecisions["Convolution_1"] = "BF16"; + expectedPrecisions["Interp"] = "FP32"; + expectedPrecisions["Convolution_2"] = "BF16"; + } +}; + +TEST_P(Resample, CompareWithRefImpl) { + test(); +}; + + +INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Resample, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::FP32), + ::testing::Values(SizeVector({ 1, 1, 2, 2 })), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Resample::getTestCaseName); + +INSTANTIATE_TEST_CASE_P(BF16_bfloat16_NoReshape, Resample, + ::testing::Combine( + ::testing::Values(Precision::FP32), + ::testing::Values(Precision::BF16), + ::testing::Values(SizeVector({ 1, 1, 2, 2 })), + ::testing::Values(SizeVector()), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + Resample::getTestCaseName); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_conv.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_conv.cpp index e165f867c7f..fb1308fbdd3 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_conv.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_conv.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConvEltwiseConv : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) Conv (FP32) // \ / // Eltwise (Fused into Conv) @@ -37,7 +37,9 @@ protected: ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); + auto channelsCount = inputShapes[1]; + input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -59,15 +61,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -87,15 +89,15 @@ protected: // Convolution std::shared_ptr weightsNode2 = nullptr; - ngraph::Shape convFilterShape2 = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape2 = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValues2; - weightValues2.resize(3 * 3 * 3 * 3); + weightValues2.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2.data(), weightValues2.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2); } else { std::vector weightValues2BF16; - weightValues2BF16.resize(3 * 3 * 3 * 3); + weightValues2BF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2BF16.data(), weightValues2BF16.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2BF16.data()); } @@ -112,12 +114,12 @@ protected: return std::make_shared(ngraph::NodeVector{convNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); // STAGE1: - threshold = 0.2f; // max value in the latest tensor for FP32 network is 37.77 + threshold = 1.0f; // max value in the latest tensor for FP32 network is 37.77 // STAGE2: // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in // performance counters diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_relu_conv.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_relu_conv.cpp index ba7c17b29ec..2ac468e81df 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_relu_conv.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_relu_conv.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConvEltwiseReluConv : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) Conv (FP32_ // \ / // Eltwise (Fused into conv) @@ -37,8 +37,10 @@ protected: // Conv (BF16) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -60,15 +62,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -92,15 +94,15 @@ protected: // Convolution std::shared_ptr weightsNode2 = nullptr; - ngraph::Shape convFilterShape2 = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape2 = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValues2; - weightValues2.resize(3 * 3 * 3 * 3); + weightValues2.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2.data(), weightValues2.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2); } else { std::vector weightValues2BF16; - weightValues2BF16.resize(3 * 3 * 3 * 3); + weightValues2BF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValues2BF16.data(), weightValues2BF16.size()); weightsNode2 = std::make_shared(ntype, convFilterShape2, weightValues2BF16.data()); } @@ -116,12 +118,12 @@ protected: return std::make_shared(ngraph::NodeVector{convNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); // STAGE1: - threshold = 9e-2; + threshold = 1.0f; // Max in fp32 network by output CONV_2: 30.1374 // STAGE2: // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in // performance counters diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_scaleshift.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_scaleshift.cpp index 41f81cbb252..e0fa90445c3 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_scaleshift.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_eltwise_scaleshift.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConvEltwiseScaleshift : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // | // Conv (BF16) @@ -37,8 +37,10 @@ protected: // scaleshift (FP32) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -60,15 +62,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -110,7 +112,7 @@ protected: return std::make_shared(ngraph::NodeVector{addNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_elu_conv.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_elu_conv.cpp index fc63492e2d7..110ef28a8ae 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_elu_conv.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_elu_conv.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConvEluConv : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // | // Conv (BF16) @@ -37,8 +37,10 @@ protected: // Conv (BF16) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -60,15 +62,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -98,7 +100,7 @@ protected: return std::make_shared(ngraph::NodeVector{convNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); @@ -109,7 +111,6 @@ protected: // performance counters expectedPrecisions["ADD_1"] = "FP32"; expectedPrecisions["CONV_1"] = "BF16"; - expectedPrecisions["ELU_1"] = "FP32"; expectedPrecisions["CONV_2"] = "BF16"; } }; diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_relu.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_relu.cpp index 6e5df70f31e..e6a62a84861 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_relu.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_relu.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConvRelu : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // | // Conv (BF16) @@ -35,8 +35,10 @@ protected: // relu (Fused into convolution) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -58,15 +60,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -86,7 +88,7 @@ protected: return std::make_shared(ngraph::NodeVector{reluNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_concat_relu.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_concat_relu.cpp index 11dcb1e841a..5597b97f27a 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_concat_relu.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_concat_relu.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConv_x2_ConcatRelu : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift // / \ // Conv Conv @@ -37,8 +37,10 @@ protected: // relu ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -60,15 +62,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -93,9 +95,7 @@ protected: // Concat ngraph::NodeVector concInputNodes = { convNode1, convNode2 }; - - // test is to be failed, if axis == 1 - TODO - auto concNode = std::make_shared(concInputNodes, 2); + auto concNode = std::make_shared(concInputNodes, 1); concNode->set_friendly_name("CONC_1"); // ReLU @@ -105,7 +105,7 @@ protected: return std::make_shared(ngraph::NodeVector{reluNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_eltwise.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_eltwise.cpp index 82340d26d23..d4f1fda6899 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_eltwise.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_eltwise.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConv_x2_Eltwise : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // / \ // Conv1 (BF16) Conv1 (BF16) @@ -35,8 +35,10 @@ protected: // eltwise (Fused into Conv1) produce FP32 output ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -58,15 +60,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -95,7 +97,7 @@ protected: return std::make_shared(ngraph::NodeVector{eltNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed1_eltwise.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed1_eltwise.cpp index 86bcc5212c2..d3b72634e2b 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed1_eltwise.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed1_eltwise.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConv_x2_mixed1_Eltwise : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // | | // Conv1(BF16) Conv2(FP32) @@ -35,8 +35,10 @@ protected: // eltwise(Fused into Conv1) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -58,15 +60,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -95,7 +97,7 @@ protected: return std::make_shared(ngraph::NodeVector{eltNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed2_eltwise.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed2_eltwise.cpp index 1e483bf20db..f76c90874bb 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed2_eltwise.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x2_mixed2_eltwise.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConv_x2_mixed2_Eltwise : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // | | // Conv1 (FP32) Conv2 (Bf16) @@ -35,19 +35,21 @@ protected: // eltwise (Fused into Conv1) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto channelsCount = inputShapes[1]; + + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -98,7 +100,7 @@ protected: return std::make_shared(ngraph::NodeVector{eltNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x3_eltwise.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x3_eltwise.cpp index 361dc01796c..1a4fb8110c2 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x3_eltwise.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_conv_x3_eltwise.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class ScaleshiftConv_x3_Eltwise : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // scaleshift (FP32) // // / \ @@ -43,8 +43,11 @@ protected: // Conv3 (BF16) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + const int outChannels = 16; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -66,15 +69,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 16, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { outChannels, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(16 * 3 * 3 * 3); + weightValuesFP32.resize(outChannels * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(16 * 3 * 3 * 3); + weightValuesBF16.resize(outChannels * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -103,15 +106,15 @@ protected: // Convolution std::shared_ptr weightsNode3 = nullptr; - ngraph::Shape convFilterShape3 = { 16, 16, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape3 = { outChannels, outChannels, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(16 * 16 * 3 * 3); + weightValuesFP32.resize(outChannels * outChannels * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode3 = std::make_shared(ntype, convFilterShape3, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(16 * 16 * 3 * 3); + weightValuesBF16.resize(outChannels * outChannels * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode3 = std::make_shared(ntype, convFilterShape3, weightValuesBF16.data()); } @@ -128,12 +131,12 @@ protected: return std::make_shared(ngraph::NodeVector{convNode3}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); // STAGE1: - threshold = 1.0f; // max value in the latest tensor for FP32 network is 93.3 + threshold = 2.0f; // max value in the latest tensor for FP32 network is 93.3 // STAGE2: // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x2_conv_x2_eltwise.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x2_conv_x2_eltwise.cpp index 3daece18ba1..f90d4f4f84d 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x2_conv_x2_eltwise.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x2_conv_x2_eltwise.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class Scaleshift_x2_Conv_x2_Eltwise : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // // scaleshift (FP32) scaleshift (FP32) // \ / \ @@ -37,8 +37,10 @@ protected: // | ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -83,15 +85,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -116,7 +118,7 @@ protected: return std::make_shared(ngraph::NodeVector{eltNode, convNode2}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x3_conv_eltwise_relu.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x3_conv_eltwise_relu.cpp index 3c22d1890b9..2b0de268f02 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x3_conv_eltwise_relu.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/scaleshift_x3_conv_eltwise_relu.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class Scaleshift_x3_ConvEltwiseRelu : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // // scaleshift (FP32) // | @@ -42,9 +42,10 @@ protected: // scaleshift (FP32) ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { const1 = opset1::Constant::create(ntype, Shape{1}, { 2.0f }); @@ -65,15 +66,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 3, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { channelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(3 * 3 * 3 * 3); + weightValuesFP32.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(3 * 3 * 3 * 3); + weightValuesBF16.resize(channelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -136,7 +137,7 @@ protected: return std::make_shared(ngraph::NodeVector{addNode3}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); @@ -155,9 +156,9 @@ protected: } }; - TEST_P(Scaleshift_x3_ConvEltwiseRelu, CompareWithRefImpl) { - test(); - }; +TEST_P(Scaleshift_x3_ConvEltwiseRelu, CompareWithRefImpl) { + test(); +}; INSTANTIATE_TEST_CASE_P(FP32_bfloat16_NoReshape, Scaleshift_x3_ConvEltwiseRelu, ::testing::Combine( diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/tail_fp32_optimization.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/tail_fp32_optimization.cpp index 3a99203e252..4ed11f72ee7 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/tail_fp32_optimization.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/tail_fp32_optimization.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class PoolingAfterConv : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // Scaleshift (FP32) // | // Convolution (BF16) @@ -40,8 +40,11 @@ protected: // STAGE1: construction of the GRAPH ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + const int outChannels = 16; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -63,15 +66,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 16, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { outChannels, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(16 * 3 * 3 * 3); + weightValuesFP32.resize(outChannels * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(16 * 3 * 3 * 3); + weightValuesBF16.resize(outChannels * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -100,7 +103,7 @@ protected: return std::make_shared(ngraph::NodeVector{avgpoolNode}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); diff --git a/inference-engine/tests/functional/plugin/cpu/bfloat16/topk_inputs_i32.cpp b/inference-engine/tests/functional/plugin/cpu/bfloat16/topk_inputs_i32.cpp index ef704c07853..29ae8e0cb2c 100644 --- a/inference-engine/tests/functional/plugin/cpu/bfloat16/topk_inputs_i32.cpp +++ b/inference-engine/tests/functional/plugin/cpu/bfloat16/topk_inputs_i32.cpp @@ -27,7 +27,7 @@ namespace LayerTestsDefinitions { class TopKInputsI32 : public BasicBF16Test { protected: - std::shared_ptr createGraph(InferenceEngine::Precision netPrecision)override { + std::shared_ptr createGraph(InferenceEngine::Precision netPrecision) override { // Power (FP32) // | // Convolution1 (BF16) Const (I32) @@ -41,8 +41,11 @@ protected: // STAGE1: construction of the GRAPH ngraph::element::Type ntype = (netPrecision == Precision::FP32) ? ngraph::element::f32 : ngraph::element::bf16; + auto channelsCount = inputShapes[1]; + const int intermediateChannelsCount = 16; + // multiply - auto input1 = std::make_shared(ntype, ngraph::Shape{1, 3, 40, 40}); + auto input1 = std::make_shared(ntype, ngraph::Shape{inputShapes}); input1->set_friendly_name("Input_1"); std::shared_ptr const1 = nullptr; if (netPrecision == Precision::FP32) { @@ -64,15 +67,15 @@ protected: // convolution std::shared_ptr weightsNode = nullptr; - ngraph::Shape convFilterShape = { 16, 3, 3, 3 }; // out channel, /input channels, kernel h, kernel w + ngraph::Shape convFilterShape = { intermediateChannelsCount, channelsCount, 3, 3 }; // out channel, /input channels, kernel h, kernel w if (netPrecision == Precision::FP32) { std::vector weightValuesFP32; - weightValuesFP32.resize(16 * 3 * 3 * 3); + weightValuesFP32.resize(intermediateChannelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesFP32.data(), weightValuesFP32.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesFP32); } else { std::vector weightValuesBF16; - weightValuesBF16.resize(16 * 3 * 3 * 3); + weightValuesBF16.resize(intermediateChannelsCount * channelsCount * 3 * 3); BFloat16Helpers::fillInputsBySinValues(weightValuesBF16.data(), weightValuesBF16.size()); weightsNode = std::make_shared(ntype, convFilterShape, weightValuesBF16.data()); } @@ -123,11 +126,11 @@ protected: return std::make_shared(ngraph::NodeVector{convNode2, goe1}, ngraph::ParameterVector{input1}); } - void SetUp()override { + void SetUp() override { std::tie(inputPrecision, netPrecision, inputShapes, newInputShapes, targetDevice) = this->GetParam(); fnPtr = createGraph(netPrecision); - threshold = 0.14f; // max value in the latest tensor for FP32 network is 22.6 + threshold = 0.5f; // max value in the latest tensor for FP32 network is 22.6 // STAGE2: // filling of expected precision of layer execution defined by precisoin of input tensor to the primitive and reflected in diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/core_threading_tests.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/core_threading_tests.cpp new file mode 100644 index 00000000000..70f2f259cda --- /dev/null +++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/core_threading_tests.cpp @@ -0,0 +1,22 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +namespace { + +Params params[] = { + std::tuple { "CPU", { { CONFIG_KEY(PERF_COUNT), CONFIG_VALUE(YES) } } }, + std::tuple { "HETERO", { { "TARGET_FALLBACK", "CPU" } } }, + std::tuple { "MULTI", { { MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , "CPU" } } } +}; + +} // namespace + +INSTANTIATE_TEST_CASE_P(CPU, CoreThreadingTests, testing::ValuesIn(params)); + +INSTANTIATE_TEST_CASE_P(CPU, CoreThreadingTestsWithIterations, + testing::Combine(testing::ValuesIn(params), + testing::Values(4), + testing::Values(50))); diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp index 1968599966e..6a619838a76 100644 --- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp +++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp @@ -58,8 +58,7 @@ INSTANTIATE_TEST_CASE_P(MaxPool_ExplicitPad_FloorRpunding, PoolingLayerTest, const auto maxPool_ExplicitPad_CeilRounding_Params = ::testing::Combine( ::testing::Values(ngraph::helpers::PoolingTypes::MAX), ::testing::ValuesIn(kernels), - // TODO: Non 1 strides fails in ngraph reference implementation with error "The end corner is out of bounds at axis 3" thrown in the test body. - ::testing::Values(std::vector({1, 1})), + ::testing::ValuesIn(strides), ::testing::ValuesIn(padBegins), ::testing::ValuesIn(padEnds), ::testing::Values(ngraph::op::RoundingType::CEIL), @@ -82,10 +81,9 @@ const auto avgPoolExplicitPadCeilRoundingParams = ::testing::Combine( ::testing::Values(ngraph::helpers::PoolingTypes::AVG), ::testing::ValuesIn(kernels), // TODO: Non 1 strides fails in ngraph reference implementation with error "The end corner is out of bounds at axis 3" thrown in the test body. - ::testing::Values(std::vector({1, 1})), - // TODO: Non zero pads excluded because of accuracy mismatch - ::testing::Values(std::vector({0, 0})), - ::testing::Values(std::vector({0, 0})), + ::testing::ValuesIn(strides), + ::testing::ValuesIn(std::vector>({{0, 0}, {1, 1}, {0, 1}})), + ::testing::ValuesIn(std::vector>({{0, 0}, {1, 1}, {0, 1}})), ::testing::Values(ngraph::op::RoundingType::CEIL), ::testing::Values(ngraph::op::PadType::EXPLICIT), ::testing::Values(true, false) @@ -98,14 +96,27 @@ INSTANTIATE_TEST_CASE_P(AvgPool_ExplicitPad_CeilRounding, PoolingLayerTest, ::testing::Values(std::vector({1, 3, 30, 30})), ::testing::Values(CommonTestUtils::DEVICE_CPU)), PoolingLayerTest::getTestCaseName); + +std::vector psParams({poolSpecificParams(ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {0, 0}, {0, 0}, + ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false), + poolSpecificParams(ngraph::helpers::PoolingTypes::AVG, {7, 7}, {1, 1}, {0, 0}, {1, 1}, + ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false)}); + +INSTANTIATE_TEST_CASE_P(AvgPool_ExplicitPad_CeilRounding_corner, PoolingLayerTest, + ::testing::Combine( + ::testing::ValuesIn(psParams), + ::testing::ValuesIn(netPrecisions), + ::testing::Values(std::vector({1, 1024, 6, 6})), + ::testing::Values(CommonTestUtils::DEVICE_CPU)), + PoolingLayerTest::getTestCaseName); + /* +========== Explicit Pad Floor Rounding ========== */ const auto avgPoolExplicitPadFloorRoundingParams = ::testing::Combine( ::testing::Values(ngraph::helpers::PoolingTypes::AVG), ::testing::ValuesIn(kernels), ::testing::ValuesIn(strides), - // TODO: Non zero pads excluded because of accuracy mismatch - ::testing::Values(std::vector({0, 0})), - ::testing::Values(std::vector({0, 0})), + ::testing::ValuesIn(std::vector>({{0, 0}, {1, 1}})), + ::testing::ValuesIn(std::vector>({{0, 0}, {1, 1}})), ::testing::Values(ngraph::op::RoundingType::FLOOR), ::testing::Values(ngraph::op::PadType::EXPLICIT), ::testing::Values(true, false) @@ -144,4 +155,5 @@ INSTANTIATE_TEST_CASE_P(MAX_and_AVGPool_ValidPad, PoolingLayerTest, -} // namespace \ No newline at end of file +} // namespace + diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp index 8db78c156dc..536f0bb224a 100644 --- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp +++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp @@ -9,5 +9,7 @@ std::vector disabledTestPatterns() { return { + // TODO: Issue 26264 + R"(.*(MaxPool|AvgPool).*S\(1\.2\).*Rounding=CEIL.*)" }; } \ No newline at end of file diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/convolution.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/convolution.cpp index 42198d319c8..05d1c8cc1b9 100644 --- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/convolution.cpp +++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/convolution.cpp @@ -78,7 +78,7 @@ const std::vector> strides3d = {{1, 1, 1}, const std::vector> dilations3d = {{1, 1, 1}, {1, 2, 1}}; -const auto conv3DParams_FP32 = ::testing::Combine( +const auto conv3DParams = ::testing::Combine( ::testing::ValuesIn(kernels3d), ::testing::ValuesIn(strides3d), ::testing::ValuesIn(paddings3d), @@ -88,30 +88,11 @@ const auto conv3DParams_FP32 = ::testing::Combine( ::testing::Values(ngraph::op::PadType::EXPLICIT) ); -const auto conv3DParams_FP16 = ::testing::Combine( - ::testing::Values(std::vector({3, 3, 3})), - ::testing::ValuesIn(strides3d), - ::testing::ValuesIn(paddings3d), - ::testing::ValuesIn(paddings3d), - ::testing::ValuesIn(dilations3d), - ::testing::Values(5), - ::testing::Values(ngraph::op::PadType::EXPLICIT) -); - -INSTANTIATE_TEST_CASE_P(Convolution3D_FP32, ConvolutionLayerTest, +INSTANTIATE_TEST_CASE_P(Convolution3D, ConvolutionLayerTest, ::testing::Combine( - conv3DParams_FP32, - ::testing::Values(InferenceEngine::Precision::FP32), + conv3DParams, + ::testing::ValuesIn(netPrecisions), ::testing::Values(std::vector({1, 3, 10, 10, 10})), ::testing::Values(CommonTestUtils::DEVICE_GPU)), ConvolutionLayerTest::getTestCaseName); - -INSTANTIATE_TEST_CASE_P(Convolution3D_FP16, ConvolutionLayerTest, - ::testing::Combine( - conv3DParams_FP16, - ::testing::Values(InferenceEngine::Precision::FP16), - ::testing::Values(std::vector({1, 3, 10, 10, 10})), - ::testing::Values(CommonTestUtils::DEVICE_GPU)), - ConvolutionLayerTest::getTestCaseName); - } // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/operations/out_shape_of_reshape.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/operations/out_shape_of_reshape.cpp new file mode 100644 index 00000000000..d1b0525f1b7 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/operations/out_shape_of_reshape.cpp @@ -0,0 +1,175 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/operations/out_shape_of_reshape.hpp" + +#include + +#include +#include + +#include
+ +#include + +namespace { + +using TensorShape = ngraph::PartialShape; +using TensorType = ngraph::element::Type; + +using TestParams = std::tuple< + TensorShape, + TensorType, + TensorShape, + TensorType>; + +class OutShapeOfReshapeTests + : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& inDataShapeTensorShape = std::get<0>(parameters); + const auto& inTensorShapeTensorType = std::get<1>(parameters); + const auto& outShapeDescriptorTensorShape = std::get<2>(parameters); + const auto& outShapeDescriptorTensorType = std::get<3>(parameters); + + m_inDataShapeParam = std::make_shared( + inTensorShapeTensorType, inDataShapeTensorShape); + m_outShapeDescriptorParam = std::make_shared( + outShapeDescriptorTensorType, outShapeDescriptorTensorShape); + } + +protected: + std::shared_ptr m_inDataShapeParam; + std::shared_ptr m_outShapeDescriptorParam; +}; + +std::vector tensorShapes { + TensorShape{1}, + TensorShape{3}, + TensorShape{4}, +}; + +std::set allNGraphTypes() { + return { + ngraph::element::dynamic, + ngraph::element::boolean, + ngraph::element::bf16, + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::f64, + ngraph::element::i8, + ngraph::element::i16, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u1, + ngraph::element::u8, + ngraph::element::u16, + ngraph::element::u32, + ngraph::element::u64 + }; +} + +std::set allNGraphIntegralNumberTypes() { + return { + ngraph::element::i8, + ngraph::element::i16, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u1, + ngraph::element::u8, + ngraph::element::u16, + ngraph::element::u32, + ngraph::element::u64 + }; +} + +// +// Positive tests +// + +TEST_P(OutShapeOfReshapeTests, CanValidateAndInferTypes) { + std::shared_ptr op; + ASSERT_NO_THROW(op = std::make_shared( + m_inDataShapeParam, m_outShapeDescriptorParam, true)); + ASSERT_NO_THROW(std::make_shared( + ngraph::OutputVector{op->output(0)}, + ngraph::ParameterVector{m_inDataShapeParam, m_outShapeDescriptorParam})); +} + +INSTANTIATE_TEST_CASE_P(NGraph, OutShapeOfReshapeTests, testing::Combine( + testing::ValuesIn(tensorShapes), + testing::ValuesIn(allNGraphIntegralNumberTypes()), + testing::ValuesIn(tensorShapes), + testing::ValuesIn(allNGraphIntegralNumberTypes())) +); + +// +// Negative tests +// + +std::set allNGraphNotIntegralTypes() { + auto notIntegralTypes = std::set{}; + const auto& allTypes = allNGraphTypes(); + const auto& allIntegralTypes = allNGraphIntegralNumberTypes(); + std::set_difference(allTypes.cbegin(), allTypes.cend(), allIntegralTypes.cbegin(), allIntegralTypes.cend(), + std::inserter(notIntegralTypes, notIntegralTypes.begin())); + return notIntegralTypes; +} + +using OutShapeOfReshapeTestsNegativeDataType = OutShapeOfReshapeTests; +TEST_P(OutShapeOfReshapeTestsNegativeDataType, ThrowsOnInvalidDataType) { + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared( + m_inDataShapeParam, m_outShapeDescriptorParam, true), + ngraph::NodeValidationFailure); +} +INSTANTIATE_TEST_CASE_P(InvalidInDataShapeTensorType, OutShapeOfReshapeTestsNegativeDataType, + testing::Combine( + testing::Values(TensorShape{4}), + testing::ValuesIn(allNGraphNotIntegralTypes()), + testing::Values(TensorShape{3}), + testing::Values(ngraph::element::i64)) +); + +INSTANTIATE_TEST_CASE_P(InvalidOutShapeDescriptorTensorType, OutShapeOfReshapeTestsNegativeDataType, + testing::Combine( + testing::Values(TensorShape{4}), + testing::Values(ngraph::element::i64), + testing::Values(TensorShape{3}), + testing::ValuesIn(allNGraphNotIntegralTypes())) +); + +std::vector invalidTensorShapes { + TensorShape{}, + TensorShape{4, 8}, + TensorShape{ngraph::Dimension::dynamic()}, +}; + +using OutShapeOfReshapeTestsNegativeDataShape = OutShapeOfReshapeTests; +TEST_P(OutShapeOfReshapeTestsNegativeDataShape, ThrowsOnInvalidDataShape) { + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared( + m_inDataShapeParam, m_outShapeDescriptorParam, true), + ngraph::NodeValidationFailure); +} + +INSTANTIATE_TEST_CASE_P(InvalidInDataShapeTensorShape, OutShapeOfReshapeTestsNegativeDataShape, + testing::Combine( + testing::ValuesIn(invalidTensorShapes), + testing::Values(ngraph::element::i64), + testing::ValuesIn(tensorShapes), + testing::Values(ngraph::element::i64)) +); + +INSTANTIATE_TEST_CASE_P(InvalidOutShapeDescriptorTensorShape, OutShapeOfReshapeTestsNegativeDataShape, + testing::Combine( + testing::ValuesIn(tensorShapes), + testing::Values(ngraph::element::i64), + testing::ValuesIn(invalidTensorShapes), + testing::Values(ngraph::element::i64)) +); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_broadcast.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_broadcast.cpp new file mode 100644 index 00000000000..f1e7dd760ce --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_broadcast.cpp @@ -0,0 +1,202 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/operations/static_shape_broadcast.hpp" + +#include + +#include +#include + +#include
+ +#include + +namespace { + +using TensorType = ngraph::element::Type; +using TensorShape = ngraph::PartialShape; +using AxesMapping = std::vector; + +struct BroadcastNumpyShapes { + TensorShape srcShape; + TensorShape targetShape; +}; + +struct BroadcastExplicitShapes { + TensorShape srcShape; + TensorShape targetShape; + AxesMapping axesMapping; +}; + +using BroadcastNumpyTestParams = std::tuple; +using BroadcastExplicitTestParams = std::tuple; + +class StaticShapeBroadcastNumpyTests + : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& tensorType = std::get<0>(parameters); + const auto& tensorShape = std::get<1>(parameters).srcShape; + const auto& targetShape = std::get<1>(parameters).targetShape; + + m_tensor = std::make_shared(tensorType, tensorShape); + m_tensorWithTargetShape = std::make_shared(tensorType, targetShape); + } +protected: + std::shared_ptr m_tensor; + std::shared_ptr m_tensorWithTargetShape; +}; + +class StaticShapeBroadcastExplicitTests + : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& tensorType = std::get<0>(parameters); + const auto& tensorShape = std::get<1>(parameters).srcShape; + const auto& targetShape = std::get<1>(parameters).targetShape; + const auto& axesMapping = std::get<1>(parameters).axesMapping; + + m_tensor = std::make_shared(tensorType, tensorShape); + m_tensorWithTargetShape = std::make_shared(tensorType, targetShape); + m_axesMapping = std::make_shared( + ngraph::element::u64, ngraph::Shape{axesMapping.size()}, axesMapping); + } +protected: + std::shared_ptr m_tensor; + std::shared_ptr m_tensorWithTargetShape; + std::shared_ptr m_axesMapping; +}; + +std::vector testNumpyStaticShapes { + BroadcastNumpyShapes{TensorShape{1, 100}, TensorShape{4, 100}}, + BroadcastNumpyShapes{TensorShape{1, 100}, TensorShape{2, 4, 100}}, + BroadcastNumpyShapes{TensorShape{16, 1, 1}, TensorShape{2, 16, 50, 50}}, +}; + +std::vector testExplicitStaticShapes { + BroadcastExplicitShapes{TensorShape{16}, TensorShape{1, 16, 50, 50}, AxesMapping{1}}, + BroadcastExplicitShapes{TensorShape{50, 50}, TensorShape{1, 50, 50, 16}, AxesMapping{1, 2}}, +}; + +std::vector testNGraphNumericTypes { + ngraph::element::dynamic, + ngraph::element::bf16, + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::f64, + ngraph::element::i8, + ngraph::element::i16, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u1, + ngraph::element::u8, + ngraph::element::u16, + ngraph::element::u32, + ngraph::element::u64, +}; + +// +// Positive tests +// + +TEST_P(StaticShapeBroadcastNumpyTests, CanValidateAndInferTypes) { + const auto shapeOf = std::make_shared(m_tensorWithTargetShape); + std::shared_ptr op; + ASSERT_NO_THROW(op = std::make_shared( + m_tensor, shapeOf)); + ASSERT_NO_THROW(std::make_shared( + ngraph::OutputVector{op->output(0)}, + ngraph::ParameterVector{m_tensor, m_tensorWithTargetShape})); + ASSERT_EQ(m_tensorWithTargetShape->get_shape(), op->output(0).get_shape()); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeBroadcastNumpyTests, testing::Combine( + testing::ValuesIn(testNGraphNumericTypes), + testing::ValuesIn(testNumpyStaticShapes)) +); + +TEST_P(StaticShapeBroadcastExplicitTests, CanValidateAndInferTypes) { + const auto shapeOf = std::make_shared(m_tensorWithTargetShape); + std::shared_ptr op; + ASSERT_NO_THROW(op = std::make_shared( + m_tensor, shapeOf, m_axesMapping)); + ASSERT_NO_THROW(std::make_shared( + ngraph::OutputVector{op->output(0)}, + ngraph::ParameterVector{m_tensor, m_tensorWithTargetShape})); + ASSERT_EQ(m_tensorWithTargetShape->get_shape(), op->output(0).get_shape()); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeBroadcastExplicitTests, testing::Combine( + testing::ValuesIn(testNGraphNumericTypes), + testing::ValuesIn(testExplicitStaticShapes)) +); + +// +// Negative tests +// + +using StaticShapeBroadcastNumpyTestsNegativeNumInputs = StaticShapeBroadcastNumpyTests; +TEST_P(StaticShapeBroadcastNumpyTestsNegativeNumInputs, ThrowsOnInvalidNumInputs) { + const auto shapeOf = std::make_shared(m_tensorWithTargetShape); + const auto axesMapping = std::make_shared( + ngraph::element::u64, ngraph::Shape{1}, 0); + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared( + m_tensor, shapeOf, axesMapping, ngraph::op::BroadcastType::NUMPY), + ngraph::NodeValidationFailure); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeBroadcastNumpyTestsNegativeNumInputs, testing::Combine( + testing::Values(ngraph::element::f16), + testing::Values(testNumpyStaticShapes[0])) +); + +using StaticShapeBroadcastExplicitTestsNegativeNumInputs = StaticShapeBroadcastExplicitTests; +TEST_P(StaticShapeBroadcastExplicitTestsNegativeNumInputs, ThrowsOnInvalidNumInputs) { + const auto shapeOf = std::make_shared(m_tensorWithTargetShape); + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared( + m_tensor, shapeOf, ngraph::op::BroadcastType::EXPLICIT), + ngraph::NodeValidationFailure); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeBroadcastExplicitTestsNegativeNumInputs, testing::Combine( + testing::Values(ngraph::element::f16), + testing::Values(testExplicitStaticShapes[0])) +); + +using StaticShapeBroadcastTestsNegativeMode = StaticShapeBroadcastNumpyTests; +TEST_P(StaticShapeBroadcastTestsNegativeMode, ThrowsOnInvalidMode) { + const auto shapeOf = std::make_shared(m_tensorWithTargetShape); + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared( + m_tensor, shapeOf, ngraph::op::BroadcastType::BIDIRECTIONAL), + ngraph::NodeValidationFailure); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeBroadcastTestsNegativeMode, testing::Combine( + testing::Values(ngraph::element::f16), + testing::Values(testNumpyStaticShapes[0])) +); + +using StaticShapeBroadcastTestsNegativeEvaluate = StaticShapeBroadcastNumpyTests; +TEST_P(StaticShapeBroadcastTestsNegativeEvaluate, ThrowsOnInvalidMode) { + const auto targetShape = std::make_shared( + ngraph::element::u64, ngraph::Shape{4}); + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared( + m_tensor, targetShape), ngraph::NodeValidationFailure); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeBroadcastTestsNegativeEvaluate, testing::Combine( + testing::Values(ngraph::element::f16), + testing::Values(testNumpyStaticShapes[0])) +); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_nonzero.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_nonzero.cpp index e1e856f8771..c6e9b289efa 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_nonzero.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/operations/static_shape_nonzero.cpp @@ -18,19 +18,27 @@ namespace { using TensorType = ngraph::element::Type; using TensorShape = ngraph::PartialShape; +typedef std::tuple< + TensorType, // input type + TensorShape, // input shape + TensorType // output type +> staticShapeNonZeroTestParams; + class StaticShapeNonZeroTests : public CommonTestUtils::TestsCommon, - public testing::WithParamInterface> { + public testing::WithParamInterface { public: void SetUp() override { const auto& parameters = GetParam(); const auto& tensorType = std::get<0>(parameters); const auto& tensorShape = std::get<1>(parameters); + m_outputType = std::get<2>(parameters); m_param = std::make_shared(tensorType, tensorShape); } protected: std::shared_ptr m_param; + ngraph::element::Type m_outputType; }; std::vector testStaticShapes { @@ -63,13 +71,19 @@ std::vector testNGraphNumericTypes { ngraph::element::u64, }; +std::vector outputTypes { + ngraph::element::i32, + ngraph::element::i64, +}; + + // // Positive tests // TEST_P(StaticShapeNonZeroTests, CanValidateAndInferTypes) { std::shared_ptr op; - ASSERT_NO_THROW(op = std::make_shared(m_param)); + ASSERT_NO_THROW(op = std::make_shared(m_param, m_outputType)); ASSERT_NO_THROW(std::make_shared( ngraph::OutputVector{op->output(0), op->output(1)}, ngraph::ParameterVector{m_param})); @@ -77,35 +91,51 @@ TEST_P(StaticShapeNonZeroTests, CanValidateAndInferTypes) { INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeNonZeroTests, testing::Combine( testing::ValuesIn(testNGraphNumericTypes), - testing::ValuesIn(testStaticShapes)) + testing::ValuesIn(testStaticShapes), + testing::ValuesIn(outputTypes)) ); // // Negative tests // -using StaticShapeNonZeroTestsNegativeDataType = StaticShapeNonZeroTests; -TEST_P(StaticShapeNonZeroTestsNegativeDataType, ThrowsOnInvalidDataType) { +using StaticShapeNonZeroTestsNegativeInputDataType = StaticShapeNonZeroTests; +TEST_P(StaticShapeNonZeroTestsNegativeInputDataType, ThrowsOnInvalidInputType) { std::shared_ptr op; - ASSERT_THROW(op = std::make_shared(m_param), + ASSERT_THROW(op = std::make_shared(m_param, m_outputType), ngraph::NodeValidationFailure); } -INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeNonZeroTestsNegativeDataType, testing::Combine( +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeNonZeroTestsNegativeInputDataType, testing::Combine( testing::Values(ngraph::element::boolean), - testing::ValuesIn(testStaticShapes)) + testing::ValuesIn(testStaticShapes), + testing::ValuesIn(outputTypes)) ); using StaticShapeNonZeroTestsNegativeDataShape = StaticShapeNonZeroTests; TEST_P(StaticShapeNonZeroTestsNegativeDataShape, ThrowsOnInvalidDataShape) { std::shared_ptr op; - ASSERT_THROW(op = std::make_shared(m_param), + ASSERT_THROW(op = std::make_shared(m_param, m_outputType), ngraph::NodeValidationFailure); } INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeNonZeroTestsNegativeDataShape, testing::Combine( testing::ValuesIn(testNGraphNumericTypes), - testing::ValuesIn(testDynamicShapes)) + testing::ValuesIn(testDynamicShapes), + testing::ValuesIn(outputTypes)) +); + +using StaticShapeNonZeroTestsNegativeOutputDataType = StaticShapeNonZeroTests; +TEST_P(StaticShapeNonZeroTestsNegativeOutputDataType, ThrowsOnInvalidOutputType) { + std::shared_ptr op; + ASSERT_THROW(op = std::make_shared(m_param, m_outputType), + ngraph::NodeValidationFailure); +} + +INSTANTIATE_TEST_CASE_P(NGraph, StaticShapeNonZeroTestsNegativeOutputDataType, testing::Combine( + testing::ValuesIn(testNGraphNumericTypes), + testing::ValuesIn(testStaticShapes), + testing::Values(ngraph::element::boolean)) ); } // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_binary_elementwise.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_binary_elementwise.cpp index 668d71d6c72..f85deef8040 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_binary_elementwise.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_binary_elementwise.cpp @@ -62,7 +62,7 @@ protected: eltwise->set_output_type(0, eltwise->get_input_element_type(0), ngraph::PartialShape::dynamic(eltwise->get_output_partial_shape(0).rank())); const auto transformations = vpu::Transformations{{eltwiseType, vpu::dynamicToStaticShapeBinaryEltwise}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } @@ -200,7 +200,7 @@ protected: eltwise->set_output_type(0, eltwise->get_input_element_type(0), ngraph::PartialShape::dynamic(eltwise->get_output_partial_shape(0).rank())); const auto transformations = vpu::Transformations{{eltwiseType, vpu::dynamicToStaticShapeBinaryEltwise}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_broadcast.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_broadcast.cpp new file mode 100644 index 00000000000..06e691314aa --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_broadcast.cpp @@ -0,0 +1,134 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_broadcast.hpp" +#include "vpu/ngraph/transformations/dynamic_to_static_shape.hpp" +#include "vpu/ngraph/operations/static_shape_broadcast.hpp" +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" + +#include +#include +#include + +#include +#include + +#include +#include +#include +#include + +namespace { + +using TensorType = ngraph::element::Type; +using TensorShape = ngraph::PartialShape; +using AxesMapping = std::vector; + +struct BroadcastExplicitShapes { + TensorShape srcShape; + TensorShape targetShape; + AxesMapping axesMapping; +}; +using BroadcastExplicitTestParams = std::tuple; + +class DynamicToStaticShapeBroadcastTests + : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& tensorType = std::get<0>(parameters); + const auto& tensorShape = std::get<1>(parameters).srcShape; + const auto& targetShape = std::get<1>(parameters).targetShape; + const auto& axesMapping = std::get<1>(parameters).axesMapping; + + ngraph::helpers::CompareFunctions( + *transform(tensorType, tensorShape, targetShape, axesMapping), + *reference(tensorType, tensorShape, targetShape, axesMapping)); + } + +protected: + std::shared_ptr transform( + const TensorType& tensorType, + const TensorShape& tensorShape, + const TensorShape& targetShape, + const AxesMapping& axesMapping) const { + const auto tensorParam = std::make_shared( + tensorType, tensorShape); + const auto tensorWithTargetShapeParam = std::make_shared( + tensorType, targetShape); + + const auto shapeOfNode = std::make_shared(tensorWithTargetShapeParam); + shapeOfNode->set_is_foldable(false); + + const auto axesMappingConstant = std::make_shared( + ngraph::element::u64, ngraph::Shape{axesMapping.size()}, axesMapping); + + const auto broadcast = std::make_shared( + tensorParam, shapeOfNode, axesMappingConstant); + + auto function = std::make_shared( + ngraph::NodeVector{broadcast}, + ngraph::ParameterVector{tensorParam, tensorWithTargetShapeParam}, + "Actual"); + + // We need to set broadcast output shape to make its rank static. + // In opset3::Broadcast implementation with Explicit mode output shape gets + // static rank only in cases when the second input is Concat + std::vector broadcastOutShape( + shapeOfNode->get_output_shape(0)[0], ngraph::Dimension::dynamic()); + broadcast->set_output_type(0, tensorParam->get_output_element_type(0), + ngraph::PartialShape(broadcastOutShape)); + function->get_result()->set_output_type(0, tensorParam->get_output_element_type(0), + targetShape); + + const auto transformations = vpu::Transformations{{ + ngraph::opset3::Broadcast::type_info, vpu::dynamicToStaticShapeBroadcast}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const TensorType& tensorType, + const TensorShape& tensorShape, + const TensorShape& targetShape, + const AxesMapping& axesMapping) const { + const auto tensorParam = std::make_shared( + tensorType, tensorShape); + const auto tensorWithTargetShapeParam = std::make_shared( + tensorType, targetShape); + const auto shapeOf = std::make_shared(tensorWithTargetShapeParam); + + const auto axesMappingConstant = std::make_shared( + ngraph::element::u64, ngraph::Shape{axesMapping.size()}, axesMapping); + + const auto staticShapeBroadcast = std::make_shared( + tensorParam, shapeOf, axesMappingConstant); + + const auto dsrOut = std::make_shared( + staticShapeBroadcast, shapeOf); + return std::make_shared( + ngraph::NodeVector{dsrOut}, + ngraph::ParameterVector{tensorParam, tensorWithTargetShapeParam}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeBroadcastTests, compareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeBroadcastTests, testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + BroadcastExplicitShapes{TensorShape{16}, TensorShape{1, 16, 50, 50}, AxesMapping{1}}, + BroadcastExplicitShapes{TensorShape{50, 50}, TensorShape{1, 50, 50, 16}, AxesMapping{1, 2}}) + +)); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_clamp.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_clamp.cpp index 576ee66cf29..08c34f37ceb 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_clamp.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_clamp.cpp @@ -48,7 +48,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{ngraph::opset3::Clamp::type_info, vpu::dynamicToStaticUnaryElementwise}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_concat.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_concat.cpp new file mode 100644 index 00000000000..f2bd533cfdc --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_concat.cpp @@ -0,0 +1,141 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace { + +using DataType = ngraph::element::Type; +using DataShape = ngraph::Shape; +using DataShapes = std::vector; + +struct ConcatParam { + DataShapes dataShapes; + int axis; +}; +using ConcatTestParam = std::tuple; + +class DynamicToStaticShapeConcatTests + : public CommonTestUtils::TestsCommon, public testing::WithParamInterface { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& dataType = std::get<0>(parameters); + const auto& concatParam = std::get<1>(parameters); + const auto& dataShapes = concatParam.dataShapes; + const auto& axis = concatParam.axis; + + ngraph::helpers::CompareFunctions( + *transform(dataType, dataShapes, axis), + *reference(dataType, dataShapes, axis)); + } + +protected: + std::shared_ptr createDSRWithParams( + const DataShape& dataShape, + const ngraph::element::Type& dataType, + ngraph::ParameterVector& params) const { + const auto param = std::make_shared( + dataType, dataShape); + const auto shape = std::make_shared( + ngraph::element::i64, ngraph::Shape{dataShape.size()}); + params.push_back(param); + params.push_back(shape); + return std::make_shared(param, shape); + } + + std::shared_ptr transform( + const ngraph::element::Type& dataType, + const DataShapes& dataShapes, + const int axis) const { + ngraph::NodeVector dsrVector; + ngraph::ParameterVector params; + for (const auto& dataShape : dataShapes) { + dsrVector.push_back(createDSRWithParams(dataShape, dataType, params)); + } + + const auto concat = std::make_shared(dsrVector, axis); + const auto function = std::make_shared( + ngraph::NodeVector{concat}, params, "Actual"); + concat->set_output_type(0, dsrVector[0]->get_input_element_type(0), + ngraph::PartialShape::dynamic(concat->get_output_partial_shape(0).rank())); + + const auto transformations = vpu::Transformations{ + {ngraph::opset3::Concat::type_info, vpu::dynamicToStaticShapeConcat}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const ngraph::element::Type& dataType, + const DataShapes& dataShapes, + const int axis) const { + ngraph::NodeVector dsrVector; + ngraph::ParameterVector params; + + dsrVector.push_back(createDSRWithParams(dataShapes.front(), dataType, params)); + + auto accumulatedShape = params.back()->output(0); + for (size_t inputIdx = 1; inputIdx < dataShapes.size(); ++inputIdx) { + dsrVector.push_back(createDSRWithParams( + dataShapes.at(inputIdx), dataType, params)); + const auto shapeAccumulatorOp = std::make_shared( + accumulatedShape, params.back()); + accumulatedShape = shapeAccumulatorOp->output(0); + } + + const size_t rank = dataShapes.front().size(); + std::vector dividerValues(rank, dataShapes.size()); + dividerValues[axis < 0 ? axis + rank : axis] = 1; + const auto divider = std::make_shared( + ngraph::element::i64, ngraph::Shape{rank}, dividerValues); + const auto divide = std::make_shared(accumulatedShape, divider); + + const auto concat = std::make_shared(dsrVector, axis); + const auto outDsr = std::make_shared(concat, divide); + return std::make_shared( + ngraph::NodeVector{outDsr}, params, "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeConcatTests, CompareFunctions) { +} + +std::vector dataTypes = { + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8, +}; + +std::vector concatParams = { + {DataShapes{DataShape{128}, DataShape{256}, DataShape{512}, DataShape{1024}}, 0}, + {DataShapes{DataShape{1, 1000}, DataShape{2, 1000}, DataShape{4, 1000}, DataShape{8, 1000}}, 0}, + {DataShapes{DataShape{128, 100}, DataShape{128, 200}, DataShape{128, 400}, DataShape{128, 800}}, 1}, + {DataShapes{DataShape{3, 64, 128}, DataShape{4, 64, 128}, DataShape{5, 64, 128}}, 0}, + {DataShapes{DataShape{3, 64, 128}, DataShape{3, 64, 256}, DataShape{3, 64, 512}}, 2}, + {DataShapes{DataShape{3, 64, 128}, DataShape{3, 64, 256}, DataShape{3, 64, 512}}, -1}, +}; + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeConcatTests, testing::Combine( + testing::ValuesIn(dataTypes), + testing::ValuesIn(concatParams))); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_convert.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_convert.cpp index c222af04333..f0863d341da 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_convert.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_convert.cpp @@ -48,7 +48,7 @@ protected: convert->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{ngraph::opset3::Convert::type_info, vpu::dynamicToStaticUnaryElementwise}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_gather.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_gather.cpp new file mode 100644 index 00000000000..cbefc4bc824 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_gather.cpp @@ -0,0 +1,320 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace { + +using DataType = ngraph::element::Type_t; +using DataDims = ngraph::Shape; + +struct GatherTestCase { + ngraph::Shape data_shape, index_shape; + int64_t axis, first_split_point, second_split_point; +}; + +const auto combinations = testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + GatherTestCase{{6}, {15, 4, 20, 28}, 0, 0, 0}, + GatherTestCase{{6, 12, 10, 24}, {6}, 0, 0, 1}, + GatherTestCase{{6, 12}, {15, 4, 20, 28}, 1, 1, 2}, + GatherTestCase{{6, 12, 10, 24}, {15, 4, 20, 28}, 3, 3, 4}, + GatherTestCase{{6, 12, 10, 24}, {15, 4, 20, 28}, -1, 3, 4}, + GatherTestCase{{6, 12, 10, 24}, {15, 4, 20, 28}, -4, 0, 1})); + + +class DynamicToStaticShapeGatherDataDSR : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& gather_setup = std::get<2>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, idx_type, gather_setup), + *reference(data_type, idx_type, gather_setup)); + } + +protected: + std::shared_ptr transform( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const GatherTestCase& gather_setup) const { + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.data_shape.size()}); + + const auto dsr = std::make_shared(data, dims); + const auto node = std::make_shared(dsr, indices, axis); + + auto outputShape = node->get_output_partial_shape(0); + const auto function = std::make_shared( + ngraph::NodeVector{node}, + ngraph::ParameterVector{data, dims, indices}, + "Actual"); + node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic( + gather_setup.data_shape.size() + gather_setup.index_shape.size() - 1)); + + const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeGather}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const GatherTestCase& gather_setup) const { + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.data_shape.size()}); + + const auto dsr = std::make_shared(data, dims); + const auto node = std::make_shared(dsr, indices, axis); + + const auto indices_shape = ngraph::opset3::Constant::create(dims->get_element_type(), {gather_setup.index_shape.size()}, gather_setup.index_shape); + ngraph::OutputVector output_dims; + if (gather_setup.first_split_point) { + std::vector idxs(gather_setup.first_split_point); + std::iota(idxs.begin(), idxs.end(), 0); + output_dims.push_back( + std::make_shared( + dims, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + if (!gather_setup.index_shape.empty()) + output_dims.push_back(indices_shape); + if (gather_setup.first_split_point + 1 < gather_setup.data_shape.size()) { + std::vector idxs(gather_setup.data_shape.size() - gather_setup.second_split_point); + std::iota(idxs.begin(), idxs.end(), gather_setup.second_split_point); + output_dims.push_back( + std::make_shared( + dims, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + const auto output_shape = std::make_shared(output_dims, 0); + const auto dsr1 = std::make_shared(node, output_shape); + return std::make_shared( + ngraph::NodeVector{dsr1}, + ngraph::ParameterVector{data, dims, indices}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeGatherDataDSR, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeGatherDataDSR, combinations); + +class DynamicToStaticShapeGatherIdxDSR : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& gather_setup = std::get<2>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, idx_type, gather_setup), + *reference(data_type, idx_type, gather_setup)); + } + +protected: + std::shared_ptr transform( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const GatherTestCase& gather_setup) const { + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.index_shape.size()}); + + const auto dsr = std::make_shared(indices, dims); + const auto node = std::make_shared(data, dsr, axis); + + auto outputShape = node->get_output_partial_shape(0); + const auto function = std::make_shared( + ngraph::NodeVector{node}, + ngraph::ParameterVector{data, dims, indices}, + "Actual"); + node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic( + gather_setup.data_shape.size() + gather_setup.index_shape.size() - 1)); + + const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeGather}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const GatherTestCase& gather_setup) const { + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.index_shape.size()}); + + const auto dsr = std::make_shared(indices, dims); + const auto node = std::make_shared(data, dsr, axis); + + const auto data_shape = ngraph::opset3::Constant::create(dims->get_element_type(), {gather_setup.data_shape.size()}, gather_setup.data_shape); + + ngraph::OutputVector output_dims; + if (gather_setup.first_split_point) { + std::vector idxs(gather_setup.first_split_point); + std::iota(idxs.begin(), idxs.end(), 0); + output_dims.push_back( + std::make_shared( + data_shape, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + if (!gather_setup.index_shape.empty()) + output_dims.push_back(dims); + if (gather_setup.first_split_point + 1 < gather_setup.data_shape.size()) { + std::vector idxs(gather_setup.data_shape.size() - gather_setup.second_split_point); + std::iota(idxs.begin(), idxs.end(), gather_setup.second_split_point); + output_dims.push_back( + std::make_shared( + data_shape, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + const auto output_shape = std::make_shared(output_dims, 0); + const auto dsr1 = std::make_shared(node, output_shape); + return std::make_shared( + ngraph::NodeVector{dsr1}, + ngraph::ParameterVector{data, dims, indices}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeGatherIdxDSR, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeGatherIdxDSR, combinations); + +class DynamicToStaticShapeGather : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& gather_setup = std::get<2>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, idx_type, gather_setup), + *reference(data_type, idx_type, gather_setup)); + } + +protected: + std::shared_ptr transform( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const GatherTestCase& gather_setup) const { + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto data_dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.data_shape.size()}); + const auto indices_dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.index_shape.size()}); + + const auto data_dsr = std::make_shared(data, data_dims); + const auto indices_dsr = std::make_shared(indices, indices_dims); + const auto node = std::make_shared(data_dsr, indices_dsr, axis); + + auto outputShape = node->get_output_partial_shape(0); + const auto function = std::make_shared( + ngraph::NodeVector{node}, + ngraph::ParameterVector{data, data_dims, indices, indices_dims}, + "Actual"); + node->set_output_type(0, node->get_input_element_type(0), ngraph::PartialShape::dynamic( + gather_setup.data_shape.size() + gather_setup.index_shape.size() - 1)); + + const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeGather}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const GatherTestCase& gather_setup) const { + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto data_dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.data_shape.size()}); + const auto indices_dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.index_shape.size()}); + + const auto data_dsr = std::make_shared(data, data_dims); + const auto indices_dsr = std::make_shared(indices, indices_dims); + const auto node = std::make_shared(data_dsr, indices_dsr, axis); + + ngraph::OutputVector output_dims; + if (gather_setup.first_split_point) { + std::vector idxs(gather_setup.first_split_point); + std::iota(idxs.begin(), idxs.end(), 0); + output_dims.push_back( + std::make_shared( + data_dims, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + if (!gather_setup.index_shape.empty()) + output_dims.push_back(indices_dims); + if (gather_setup.first_split_point + 1 < gather_setup.data_shape.size()) { + std::vector idxs(gather_setup.data_shape.size() - gather_setup.second_split_point); + std::iota(idxs.begin(), idxs.end(), gather_setup.second_split_point); + output_dims.push_back( + std::make_shared( + data_dims, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + const auto output_shape = std::make_shared(output_dims, 0); + const auto dsr1 = std::make_shared(node, output_shape); + return std::make_shared( + ngraph::NodeVector{dsr1}, + ngraph::ParameterVector{data, data_dims, indices, indices_dims}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeGather, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeGather, combinations); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_non_max_suppression.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_non_max_suppression.cpp index 06e7440a6ae..fbe2a968694 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_non_max_suppression.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_non_max_suppression.cpp @@ -65,7 +65,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticNonMaxSuppression}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp index 07486aa4a1d..cddf3bd5f79 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_nonzero.cpp @@ -24,31 +24,39 @@ namespace { using TensorType = ngraph::element::Type_t; using TensorShape = ngraph::Shape; -class DynamicToStaticShapeNonZeroTests : public CommonTestUtils::TestsCommon, public testing::WithParamInterface> { +typedef std::tuple< + TensorType, // input type + TensorShape, // input shape + TensorType // output type +> dynamicToStaticShapeNonZeroTestParams; + +class DynamicToStaticShapeNonZeroTests : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface { public: void prepareFunctions() { const auto& parameters = GetParam(); - const auto& tensorType = std::get<0>(parameters); - const auto& tensorShape = std::get<1>(parameters); + const auto& inputType = std::get<0>(parameters); + const auto& inputShape = std::get<1>(parameters); + const auto& resultType = std::get<2>(parameters); // Create a function with only op::NonZero // And then run conversion pass { - const auto input = std::make_shared(tensorType, tensorShape); + const auto input = std::make_shared(inputType, inputShape); - const auto nonZero = std::make_shared(input); + const auto nonZero = std::make_shared(input, resultType); nonZero->set_friendly_name(s_FriendlyName); actual = std::make_shared(ngraph::NodeVector{nonZero}, ngraph::ParameterVector{input}); const auto transformation = vpu::Transformations{{ngraph::opset3::NonZero::type_info, vpu::dynamicToStaticShapeNonZero}}; - vpu::DynamicToStaticShape(transformation).transform(*actual); + vpu::DynamicToStaticShape(transformation).transform(actual); } // Create a reference function { - const auto input = std::make_shared(tensorType, tensorShape); + const auto input = std::make_shared(inputType, inputShape); - const auto staticShapeNonZero = std::make_shared(input); + const auto staticShapeNonZero = std::make_shared(input, resultType); staticShapeNonZero->set_friendly_name(std::string(s_FriendlyName) + "/static_shape"); const auto dynamicShapeResolver = std::make_shared( staticShapeNonZero->output(0), staticShapeNonZero->output(1)); @@ -98,7 +106,10 @@ INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeNonZeroTests, testing::Combi TensorShape{1000}, TensorShape{4, 1000}, TensorShape{3, 128, 256}, - TensorShape{2, 3, 128, 256}) + TensorShape{2, 3, 128, 256}), + testing::Values( + ngraph::element::i32, + ngraph::element::i64) )); } // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_reshape.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_reshape.cpp new file mode 100644 index 00000000000..ee14731f367 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_reshape.cpp @@ -0,0 +1,113 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_reshape.hpp" +#include "vpu/ngraph/operations/out_shape_of_reshape.hpp" +#include "vpu/ngraph/operations/dynamic_shape_resolver.hpp" + +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include + +namespace { + +using DataType = ngraph::element::Type; +using DataShape = ngraph::Shape; +using TestParams = std::tuple; + +class DynamicToStaticShapeReshapeTests + : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& inDataShape = std::get<0>(parameters); + const auto& inDataType = std::get<1>(parameters); + + ngraph::helpers::CompareFunctions( + *transform(inDataType, inDataShape), + *reference(inDataType, inDataShape)); + } + +protected: + std::shared_ptr transform( + const ngraph::element::Type& inDataType, + const ngraph::Shape& inDataShape) const { + const auto inDataParam = std::make_shared( + inDataType, inDataShape); + const auto inDataDimsParam = std::make_shared( + ngraph::element::i64, ngraph::Shape{inDataShape.size()}); + const auto outShapeDescriptorParam = std::make_shared( + ngraph::element::i64, ngraph::Shape{inDataShape.size()}, inDataShape); + + const auto dsr = std::make_shared( + inDataParam, inDataDimsParam); + const auto reshape = std::make_shared( + dsr, outShapeDescriptorParam, true); + + auto function = std::make_shared( + ngraph::NodeVector{reshape}, + ngraph::ParameterVector{inDataParam, inDataDimsParam}, + "Actual"); + reshape->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic( + outShapeDescriptorParam->get_output_partial_shape(0).rank())); + + const auto transformations = vpu::Transformations{{ + ngraph::op::v1::Reshape::type_info, vpu::dynamicToStaticShapeReshape}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const ngraph::element::Type& inDataType, + const ngraph::Shape& inDataShape) const { + const auto inDataParam = std::make_shared( + inDataType, inDataShape); + const auto inDataDimsParam = std::make_shared( + ngraph::element::i64, ngraph::Shape{inDataShape.size()}); + const auto outShapeDescriptorParam = std::make_shared( + ngraph::element::i64, ngraph::Shape{inDataShape.size()}, inDataShape); + + const auto dsr0 = std::make_shared( + inDataParam, inDataDimsParam); + const auto reshape = std::make_shared( + dsr0, outShapeDescriptorParam, true); + + const auto outShapeOfReshape = std::make_shared( + inDataDimsParam, outShapeDescriptorParam, true); + const auto dsr1 = std::make_shared( + reshape, outShapeOfReshape); + return std::make_shared( + ngraph::NodeVector{dsr1}, + ngraph::ParameterVector{inDataParam, inDataDimsParam}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeReshapeTests, compareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeReshapeTests, testing::Combine( + testing::Values( + DataShape{4, 1000}, + DataShape{3, 128, 256}, + DataShape{2, 3, 128, 256}), + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8) +)); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_roialign.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_roialign.cpp index af4cec8c188..cdbc929e85f 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_roialign.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_roialign.cpp @@ -62,7 +62,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeROIAlign}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } @@ -150,7 +150,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeROIAlign}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } @@ -241,7 +241,7 @@ protected: node->set_output_type(0, data_dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeROIAlign}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_scatter.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_scatter.cpp index 81c12977c45..b05f17c2926 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_scatter.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_scatter.cpp @@ -61,7 +61,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{scatter_setup.scatter_type_info, vpu::dynamicToStaticUnaryElementwise}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_shapeof.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_shapeof.cpp new file mode 100644 index 00000000000..4c28ef72aa2 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_shapeof.cpp @@ -0,0 +1,213 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/transformations/dynamic_to_static_shape_shapeof.hpp" + +#include +#include + +#include +#include +#include + +#include +#include + + +namespace { + +using TensorType = ngraph::element::Type_t; +using TensorShape = ngraph::Shape; + +class DynamicToStaticShapeShapeOfRemoveDSR : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& data_shape = std::get<1>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, data_shape), + *reference(data_type, data_shape)); + } + +protected: + std::shared_ptr transform( + const TensorType& dataType, + const TensorShape& dataShape) const { + const auto data = std::make_shared(dataType, dataShape); + const auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{dataShape.size()}); + + const auto dsr = std::make_shared(data, shape); + const auto shapeOf = std::make_shared(dsr->output(0)); + + const auto function = std::make_shared( + ngraph::NodeVector{shapeOf}, + ngraph::ParameterVector{data, shape}, + "Actual"); + + vpu::DynamicToStaticShapeShapeOf().run_on_function(function); + return function; + } + + std::shared_ptr reference( + const TensorType& dataType, + const TensorShape& dataShape) const { + const auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{dataShape.size()}); + + return std::make_shared( + ngraph::NodeVector{shape}, + ngraph::ParameterVector{shape}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeShapeOfRemoveDSR, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeShapeOfRemoveDSR, testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + TensorShape{1000}, + TensorShape{4, 1000}, + TensorShape{3, 128, 256}, + TensorShape{2, 3, 128, 256}) +)); + +class DynamicToStaticShapeShapeOfWithOutRemoveDSR : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& data_shape = std::get<1>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, data_shape), + *reference(data_type, data_shape)); + } + +protected: + std::shared_ptr transform( + const TensorType& dataType, + const TensorShape& dataShape) const { + const auto data = std::make_shared(dataType, dataShape); + const auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{dataShape.size()}); + + const auto dsr = std::make_shared(data, shape); + const auto shapeOf = std::make_shared(dsr->output(0)); + const auto shapeOfOutputRelu = std::make_shared(shapeOf->output(0)); + + const auto function = std::make_shared( + ngraph::NodeVector{shapeOfOutputRelu}, + ngraph::ParameterVector{data, shape}, + "Actual"); + + vpu::DynamicToStaticShapeShapeOf().run_on_function(function); + return function; + } + + std::shared_ptr reference( + const TensorType& dataType, + const TensorShape& dataShape) const { + const auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{dataShape.size()}); + + const auto shapeRelu = std::make_shared(shape); + + return std::make_shared( + ngraph::NodeVector{shapeRelu}, + ngraph::ParameterVector{shape}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeShapeOfWithOutRemoveDSR, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeShapeOfWithOutRemoveDSR, testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + TensorShape{1000}, + TensorShape{4, 1000}, + TensorShape{3, 128, 256}, + TensorShape{2, 3, 128, 256}) +)); + +class DynamicToStaticShapeShapeOfKeepDSR : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& data_shape = std::get<1>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, data_shape), + *reference(data_type, data_shape)); + } + +protected: + std::shared_ptr transform( + const TensorType& dataType, + const TensorShape& dataShape) const { + const auto data = std::make_shared(dataType, dataShape); + const auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{dataShape.size()}); + + const auto dsr = std::make_shared(data, shape); + const auto shapeOf = std::make_shared(dsr->output(0)); + const auto dsrOutputRelu = std::make_shared(dsr->output(0)); + const auto shapeOfOutputRelu = std::make_shared(shapeOf->output(0)); + + const auto function = std::make_shared( + ngraph::NodeVector{dsrOutputRelu}, + ngraph::ParameterVector{data, shape}, + "Actual"); + + vpu::DynamicToStaticShapeShapeOf().run_on_function(function); + return function; + } + + std::shared_ptr reference( + const TensorType& dataType, + const TensorShape& dataShape) const { + const auto data = std::make_shared(dataType, dataShape); + const auto shape = std::make_shared(ngraph::element::i64, ngraph::Shape{dataShape.size()}); + + const auto dsr = std::make_shared(data, shape); + const auto shapeRelu = std::make_shared(shape); + const auto dsrOutputRelu = std::make_shared(dsr->output(0)); + + return std::make_shared( + ngraph::NodeVector{dsrOutputRelu}, + ngraph::ParameterVector{data, shape}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeShapeOfKeepDSR, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeShapeOfKeepDSR, testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + TensorShape{1000}, + TensorShape{4, 1000}, + TensorShape{3, 128, 256}, + TensorShape{2, 3, 128, 256}) +)); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_squeeze.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_squeeze.cpp index ecf3d197623..c5169cf8985 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_squeeze.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_squeeze.cpp @@ -65,7 +65,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(node->get_output_partial_shape(0).rank())); const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeSqueeze}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_transpose.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_transpose.cpp index aa501d41e53..e5c9efb92f9 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_transpose.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_transpose.cpp @@ -73,7 +73,7 @@ protected: transpose->set_output_type(0, dsr->get_input_element_type(0), makeDynamicShape(transposition->get_output_partial_shape(0))); const auto transformations = vpu::Transformations{{ngraph::opset3::Transpose::type_info, vpu::dynamicToStaticShapeTranspose}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unary_elementwise.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unary_elementwise.cpp index 6263fc79756..295b16dbd75 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unary_elementwise.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unary_elementwise.cpp @@ -50,7 +50,7 @@ protected: node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(outputShape.rank())); const auto transformations = vpu::Transformations{{type_info, vpu::dynamicToStaticUnaryElementwise}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unsqueeze.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unsqueeze.cpp index 530c3876724..b43e541d36d 100644 --- a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unsqueeze.cpp +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_unsqueeze.cpp @@ -67,7 +67,7 @@ protected: ngraph::PartialShape::dynamic(node->get_output_partial_shape(0).rank() + unsqueeze_axes.size())); const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeUnsqueeze}}; - vpu::DynamicToStaticShape(transformations).transform(*function); + vpu::DynamicToStaticShape(transformations).transform(function); return function; } diff --git a/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_variadic_split.cpp b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_variadic_split.cpp new file mode 100644 index 00000000000..9a6ed08affa --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/ngraph/transformations/dynamic_to_static_shape_variadic_split.cpp @@ -0,0 +1,151 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace { + +using DataType = ngraph::element::Type_t; +using DataDims = ngraph::Shape; + +struct VariadicSplitTestCase { + ngraph::Shape data_shape; + std::vector split_lengths; + int64_t axis, first_split_point, second_split_point; +}; + +const auto combinations = testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + ngraph::element::i32, + ngraph::element::i64), + testing::Values( + VariadicSplitTestCase{{6}, {2, 1, 2, 1}, 0, 0, 0}, + VariadicSplitTestCase{{6, 12, 10, 24}, {1, 1, 3, 1}, 0, 0, 1}, + VariadicSplitTestCase{{6, 12}, {7, 2, 1, 2}, 1, 1, 2}, + VariadicSplitTestCase{{6, 12, 10, 24}, {10, 14}, 3, 3, 4}, + VariadicSplitTestCase{{6, 12, 10, 24}, {14, 10}, -1, 3, 4}, + VariadicSplitTestCase{{6, 12, 10, 24}, {6}, -4, 0, 1})); + + +class DynamicToStaticShapeVeriadicSplit : public CommonTestUtils::TestsCommon, + public testing::WithParamInterface> { +public: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& variadic_split_setup = std::get<2>(parameters); + + ngraph::helpers::CompareFunctions(*transform(data_type, idx_type, variadic_split_setup), + *reference(data_type, idx_type, variadic_split_setup)); + } + +protected: + std::shared_ptr transform( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const VariadicSplitTestCase& variadic_split_setup) const { + const auto data = std::make_shared(data_type, variadic_split_setup.data_shape); + const auto axis = ngraph::opset3::Constant::create(idx_type, {}, std::vector{variadic_split_setup.axis}); + const auto split_lengths = ngraph::opset3::Constant::create(idx_type, + {variadic_split_setup.split_lengths.size()}, std::vector{variadic_split_setup.split_lengths}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{variadic_split_setup.data_shape.size()}); + + const auto dsr = std::make_shared(data, dims); + const auto node = std::make_shared(dsr, axis, split_lengths); + + const auto tests_wa = std::make_shared(node->outputs(), variadic_split_setup.axis); + + auto outputShape = node->get_output_partial_shape(0); + const auto function = std::make_shared( + ngraph::NodeVector{tests_wa}, + ngraph::ParameterVector{data, dims}, + "Actual"); + node->set_output_type(0, dsr->get_input_element_type(0), ngraph::PartialShape::dynamic(variadic_split_setup.data_shape.size())); + + const auto transformations = vpu::Transformations{{node->type_info, vpu::dynamicToStaticShapeVariadicSplit}}; + vpu::DynamicToStaticShape(transformations).transform(function); + return function; + } + + std::shared_ptr reference( + const ngraph::element::Type_t& data_type, + const ngraph::element::Type_t& idx_type, + const VariadicSplitTestCase& variadic_split_setup) const { + const auto data = std::make_shared(data_type, variadic_split_setup.data_shape); + const auto axis = ngraph::opset3::Constant::create(idx_type, {}, std::vector{variadic_split_setup.axis}); + const auto split_lengths = ngraph::opset3::Constant::create(idx_type, + {variadic_split_setup.split_lengths.size()}, std::vector{variadic_split_setup.split_lengths}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{variadic_split_setup.data_shape.size()}); + + const auto dsr = std::make_shared(data, dims); + const auto node = std::make_shared(dsr, axis, split_lengths); + + ngraph::OutputVector first_shape_part, second_shape_part; + if (variadic_split_setup.first_split_point) { + std::vector idxs(variadic_split_setup.first_split_point); + std::iota(idxs.begin(), idxs.end(), 0); + first_shape_part.push_back( + std::make_shared( + dims, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + if (variadic_split_setup.first_split_point + 1 < variadic_split_setup.data_shape.size()) { + std::vector idxs(variadic_split_setup.data_shape.size() - variadic_split_setup.second_split_point); + std::iota(idxs.begin(), idxs.end(), variadic_split_setup.second_split_point); + second_shape_part.push_back( + std::make_shared( + dims, + ngraph::opset3::Constant::create(ngraph::element::i64, {idxs.size()}, idxs), + ngraph::opset3::Constant::create(ngraph::element::i64, {1}, {0}))); + } + ngraph::NodeVector results; + for (auto i = 0; i < variadic_split_setup.split_lengths.size(); ++i) { + const auto dim = ngraph::opset3::Constant::create(dims->get_element_type(), {1}, {variadic_split_setup.split_lengths[i]}); + if (!first_shape_part.empty() || !second_shape_part.empty()) { + ngraph::OutputVector output_dims{dim}; + output_dims.insert(output_dims.begin(), first_shape_part.begin(), first_shape_part.end()); + output_dims.insert(output_dims.end(), second_shape_part.begin(), second_shape_part.end()); + const auto output_shape = std::make_shared(output_dims, 0); + results.push_back(std::make_shared(node->output(i), output_shape)); + } else { + results.push_back(std::make_shared(node->output(i), dim)); + } + } + const auto tests_wa = std::make_shared(results, variadic_split_setup.axis); + + return std::make_shared( + tests_wa, + ngraph::ParameterVector{data, dims}, + "Expected"); + } +}; + +TEST_P(DynamicToStaticShapeVeriadicSplit, CompareFunctions) { +} + +INSTANTIATE_TEST_CASE_P(NGraph, DynamicToStaticShapeVeriadicSplit, combinations); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/core_threading_tests.cpp b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/core_threading_tests.cpp new file mode 100644 index 00000000000..fd694f51fde --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/core_threading_tests.cpp @@ -0,0 +1,22 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +namespace { + +Params params[] = { + std::tuple { "MYRIAD", { { CONFIG_KEY(PERF_COUNT), CONFIG_VALUE(YES) } } }, + std::tuple { "HETERO", { { "TARGET_FALLBACK", "MYRIAD" } } }, + std::tuple { "MULTI", { { MULTI_CONFIG_KEY(DEVICE_PRIORITIES) , "MYRIAD" } } } +}; + +} // namespace + +INSTANTIATE_TEST_CASE_P(MYRIAD, CoreThreadingTests, testing::ValuesIn(params)); + +INSTANTIATE_TEST_CASE_P(DISABLED_MYRIAD, CoreThreadingTestsWithIterations, + testing::Combine(testing::ValuesIn(params), + testing::Values(2), + testing::Values(2))); diff --git a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/concat.cpp b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/concat.cpp index 123636327e1..a47f9c194f2 100644 --- a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/concat.cpp +++ b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/concat.cpp @@ -10,8 +10,7 @@ using namespace LayerTestsDefinitions; namespace { -// TODO: All concat on axis 0 always fails by accuracy -std::vector axes = {1, 2, 3}; +std::vector axes = {0, 1, 2, 3}; std::vector>> inShapes = { {{10, 10, 10, 10}, {10, 10, 10, 10}}, {{10, 10, 10, 10}, {10, 10, 10, 10}, {10, 10, 10, 10}}, @@ -21,21 +20,11 @@ std::vector>> inShapes = { std::vector netPrecisions = {InferenceEngine::Precision::FP16}; -INSTANTIATE_TEST_CASE_P(Axis_1_and_3, ConcatLayerTest, +INSTANTIATE_TEST_CASE_P(Concat_Basic, ConcatLayerTest, ::testing::Combine( - ::testing::Values(1, 3), + ::testing::ValuesIn(axes), ::testing::ValuesIn(inShapes), ::testing::ValuesIn(netPrecisions), ::testing::Values(CommonTestUtils::DEVICE_MYRIAD)), ConcatLayerTest::getTestCaseName); - - -// TODO: concat on axis 2 fails by accuracy with input precision different from FP16 -INSTANTIATE_TEST_CASE_P(Axis_2, ConcatLayerTest, - ::testing::Combine( - ::testing::Values(2), - ::testing::ValuesIn(inShapes), - ::testing::ValuesIn(netPrecisions), - ::testing::Values(CommonTestUtils::DEVICE_MYRIAD)), - ConcatLayerTest::getTestCaseName); -} // namespace \ No newline at end of file +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/proposal.cpp b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/proposal.cpp new file mode 100644 index 00000000000..afe76b61805 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/single_layer_tests/proposal.cpp @@ -0,0 +1,50 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "single_layer_tests/proposal.hpp" +#include "common_test_utils/test_constants.hpp" + +using namespace ngraph::helpers; +using namespace LayerTestsDefinitions; + +namespace { + +/* ============= Proposal ============= */ +const std::vector base_size_ = {16}; +const std::vector pre_nms_topn_ = {100}; +const std::vector post_nms_topn_ = {100}; +const std::vector nms_thresh_ = {0.7f}; +const std::vector min_size_ = {1}; +const std::vector ratio_ = {{1.0f, 2.0f}}; +const std::vector scale_ = {{1.2f, 1.5f}}; +const std::vector clip_before_nms_ = {false}; +const std::vector clip_after_nms_ = {false}; + +// empty string corresponds to Caffe framework +// Myriad plugin does not take this parameter; uses "" by default +const std::vector framework_ = {""}; + +const auto proposalParams = ::testing::Combine( + ::testing::ValuesIn(base_size_), + ::testing::ValuesIn(pre_nms_topn_), + ::testing::ValuesIn(post_nms_topn_), + ::testing::ValuesIn(nms_thresh_), + ::testing::ValuesIn(min_size_), + ::testing::ValuesIn(ratio_), + ::testing::ValuesIn(scale_), + ::testing::ValuesIn(clip_before_nms_), + ::testing::ValuesIn(clip_after_nms_), + ::testing::ValuesIn(framework_) +); + +INSTANTIATE_TEST_CASE_P(Proposal_tests, ProposalLayerTest, + ::testing::Combine( + proposalParams, + ::testing::Values(CommonTestUtils::DEVICE_MYRIAD)), + ProposalLayerTest::getTestCaseName +); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp index 9042d5a5732..85c824fb348 100644 --- a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp +++ b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp @@ -9,6 +9,9 @@ std::vector disabledTestPatterns() { return { + // Issue 26268 + ".*ConcatLayerTest.*axis=0.*", + // Not supported activation types ".*ActivationLayerTest\\.CompareWithRefs/Tanh.*netPRC=FP32.*", ".*ActivationLayerTest\\.CompareWithRefs/Exp.*netPRC=FP32.*", ".*ActivationLayerTest\\.CompareWithRefs/Log.*netPRC=FP32.*", diff --git a/inference-engine/tests/functional/plugin/myriad/single_layer_tests/static_shape_nonzero.cpp b/inference-engine/tests/functional/plugin/myriad/single_layer_tests/static_shape_nonzero.cpp new file mode 100644 index 00000000000..faf379bb4e2 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/single_layer_tests/static_shape_nonzero.cpp @@ -0,0 +1,119 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "vpu/ngraph/operations/static_shape_nonzero.hpp" + +#include "vpu/private_plugin_config.hpp" + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +typedef std::tuple< + InferenceEngine::SizeVector, // Input shape + InferenceEngine::Precision, // Input precision + LayerTestsUtils::TargetDevice // Device name +> staticShapeNonZeroLayerTestParams; + +namespace LayerTestsDefinitions { + +class StaticShapeNonZeroLayerTest : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +public: + static std::string getTestCaseName(testing::TestParamInfo obj) { + InferenceEngine::SizeVector inputShape; + InferenceEngine::Precision inputPrecision; + std::string targetDevice; + std::tie(inputShape, inputPrecision, targetDevice) = obj.param; + + std::ostringstream result; + result << "IS=" << CommonTestUtils::vec2str(inputShape) << "_"; + result << "inPrc=" << inputPrecision.name() << "_"; + result << "targetDevice=" << targetDevice; + return result.str(); + } + +protected: + void SetUp() override { + SetRefMode(LayerTestsUtils::RefMode::INTERPRETER); + configuration[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); + configuration[VPU_CONFIG_KEY(DISABLE_REORDER)] = CONFIG_VALUE(YES); + + InferenceEngine::SizeVector inputShape; + std::tie(inputShape, inPrc, targetDevice) = this->GetParam(); + auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(inPrc); + + const auto input = std::make_shared(ngPrc, ngraph::Shape(inputShape)); + const auto staticShapeNonZero = std::make_shared(input, ngraph::element::i32); + ngraph::ResultVector results{std::make_shared(staticShapeNonZero->output(0)), + std::make_shared(staticShapeNonZero->output(1))}; + function = std::make_shared(results, ngraph::ParameterVector{input}); + outPrc = InferenceEngine::Precision::I32; + } + + InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo &info) const override { + return FuncTestUtils::createAndFillBlobFloat(info.getTensorDesc(), 4, -2, 2); + } + + void Compare(const std::vector>& expectedOutput, const std::vector& actualOutputs) override { + const auto expectedIndicesPtr = reinterpret_cast(expectedOutput[0].data()); + const auto expectedDimsPtr = reinterpret_cast(expectedOutput[1].data()); + + const auto actualIndices = actualOutputs[0]; + const auto actualDims = actualOutputs[1]; + + const auto actualIndicesPtr = InferenceEngine::as(actualIndices)->rmap().as(); + const auto actualDimsPtr = InferenceEngine::as(actualDims)->rmap().as(); + + ASSERT_EQ(expectedDimsPtr[0], actualDimsPtr[0]); + ASSERT_EQ(expectedDimsPtr[1], actualDimsPtr[1]); + + const auto totalDimsSize = actualIndices->getTensorDesc().getDims()[1]; + + for (int axis = 0; axis < actualDimsPtr[1]; ++axis) { + for (int i = 0; i < actualDimsPtr[0]; ++i) { + const auto idx = i + axis * totalDimsSize; + ASSERT_EQ(expectedIndicesPtr[idx], actualIndicesPtr[idx]); + } + } + } +}; + +TEST_P(StaticShapeNonZeroLayerTest, accuracy) { + Run(); +} + +std::vector inputDims = { + { 7 }, + { 1000 }, + { 3, 5 }, + { 65, 33 }, + { 33, 65 }, + { 1, 1000 }, + { 223, 217, 21 }, + { 3, 4, 5, 1 }, + { 3, 4, 1, 5, 1 } +}; + +std::vector inputPrecisions = { + InferenceEngine::Precision::U8, + InferenceEngine::Precision::FP32, + InferenceEngine::Precision::I32, +}; + +INSTANTIATE_TEST_CASE_P(accuracy, StaticShapeNonZeroLayerTest, + ::testing::Combine( + ::testing::ValuesIn(inputDims), + ::testing::ValuesIn(inputPrecisions), + ::testing::Values(CommonTestUtils::DEVICE_MYRIAD))); + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_concat.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_concat.cpp new file mode 100644 index 00000000000..3f5995e9f90 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_concat.cpp @@ -0,0 +1,81 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include + +namespace { + +using DataType = ngraph::element::Type; +using DataShape = ngraph::Shape; +using DataShapes = std::vector; + +struct ConcatParam { + DataShapes dataShapes; + int axis; +}; +using ConcatTestParam = std::tuple; + +class DSR_Concat + : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& dataType = std::get<0>(parameters); + const auto& concatParam = std::get<1>(parameters); + targetDevice = std::get<2>(GetParam()); + + const auto& dataShapes = concatParam.dataShapes; + const auto& axis = concatParam.axis; + + ngraph::NodeVector dsrVector; + ngraph::ParameterVector params; + for (const auto& dataShape : dataShapes) { + const auto param = std::make_shared( + dataType, dataShape); + const auto shape = std::make_shared( + ngraph::element::i64, ngraph::Shape{dataShape.size()}); + dsrVector.emplace_back(std::make_shared( + param, shape)); + params.push_back(param); + params.push_back(shape); + } + + const auto concat = std::make_shared(dsrVector, axis); + const auto result = std::make_shared(concat); + + function = std::make_shared( + ngraph::NodeVector{result}, params, "DSR-Concat"); + } +}; + +TEST_P(DSR_Concat, CompareWithReference) { + Run(); +} + +std::vector dataTypes = { + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8, +}; + +std::vector concatParams = { + {DataShapes{DataShape{128}, DataShape{256}, DataShape{512}, DataShape{1024}}, 0}, + {DataShapes{DataShape{1, 1000}, DataShape{2, 1000}, DataShape{4, 1000}, DataShape{8, 1000}}, 0}, + {DataShapes{DataShape{128, 100}, DataShape{128, 200}, DataShape{128, 400}, DataShape{128, 800}}, 1}, + {DataShapes{DataShape{3, 64, 128}, DataShape{4, 64, 128}, DataShape{5, 64, 128}}, 0}, + {DataShapes{DataShape{3, 64, 128}, DataShape{3, 64, 256}, DataShape{3, 64, 512}}, 2}, +}; + +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicConcat, DSR_Concat, ::testing::Combine( + ::testing::ValuesIn(dataTypes), + ::testing::ValuesIn(concatParams), + ::testing::Values(CommonTestUtils::DEVICE_MYRIAD))); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_gather.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_gather.cpp new file mode 100644 index 00000000000..94fc83926fc --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_gather.cpp @@ -0,0 +1,143 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include + +namespace { + +using DataType = ngraph::element::Type_t; +using DataDims = ngraph::Shape; + + +struct GatherTestCase { + ngraph::Shape data_shape, index_shape; + int64_t axis, first_split_point, second_split_point; +}; + +const auto combinations = testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + GatherTestCase{{6}, {15, 4, 20, 28}, 0, 0, 0}, + GatherTestCase{{6, 12, 10, 24}, {6}, 0, 0, 1}, + GatherTestCase{{6, 12}, {15, 4, 20, 28}, 1, 1, 2}, + GatherTestCase{{6, 12, 10, 24}, {15, 4, 20, 28}, 3, 3, 4}, + GatherTestCase{{6, 12, 10, 24}, {15, 4, 20, 28}, -1, 3, 4}, + GatherTestCase{{6, 12, 10, 24}, {15, 4, 20, 28}, -4, 0, 1}), + testing::Values(CommonTestUtils::DEVICE_MYRIAD)); + + +using Parameters = std::tuple< + DataType, + DataType, + GatherTestCase, + LayerTestsUtils::TargetDevice +>; + +class DSR_GatherData : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& gather_setup = std::get<2>(parameters); + targetDevice = std::get<3>(parameters); + + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.data_shape.size()}); + + const auto dsr = std::make_shared(data, dims); + const auto node = std::make_shared(dsr, indices, axis); + + const auto result = std::make_shared(node); + function = std::make_shared(ngraph::ResultVector{result}, + ngraph::ParameterVector{data, indices, dims}, "DSR-GatherData"); + } +}; + +TEST_P(DSR_GatherData, CompareWithReference) { + Run(); +} + +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicGatherData, DSR_GatherData, combinations); + +class DSR_GatherIdx : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& gather_setup = std::get<2>(parameters); + targetDevice = std::get<3>(parameters); + + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.index_shape.size()}); + + const auto dsr = std::make_shared(indices, dims); + const auto node = std::make_shared(data, dsr, axis); + + const auto result = std::make_shared(node); + function = std::make_shared(ngraph::ResultVector{result}, + ngraph::ParameterVector{data, indices, dims}, "DSR-GatherIdx"); + } +}; + +TEST_P(DSR_GatherIdx, CompareWithReference) { + Run(); +} + +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicGatherIdx, DSR_GatherIdx, combinations); + +class DSR_Gather : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& gather_setup = std::get<2>(parameters); + targetDevice = std::get<3>(parameters); + + const auto data = std::make_shared(data_type, gather_setup.data_shape); + const auto indices = std::make_shared(idx_type, gather_setup.index_shape); + const auto axis = ngraph::opset3::Constant::create(ngraph::element::i32, {1}, std::vector{gather_setup.axis}); + + const auto data_dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.data_shape.size()}); + const auto indices_dims = std::make_shared(ngraph::element::i64, ngraph::Shape{gather_setup.index_shape.size()}); + + const auto data_dsr = std::make_shared(data, data_dims); + const auto indices_dsr = std::make_shared(indices, indices_dims); + const auto node = std::make_shared(data_dsr, indices_dsr, axis); + + const auto result = std::make_shared(node); + function = std::make_shared(ngraph::ResultVector{result}, + ngraph::ParameterVector{data, indices, data_dims, indices_dims}, "DSR-Gather"); + } +}; + +TEST_P(DSR_Gather, CompareWithReference) { + Run(); +} + +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicGatherIdx, DSR_Gather, combinations); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_reshape.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_reshape.cpp new file mode 100644 index 00000000000..58413e1811c --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_reshape.cpp @@ -0,0 +1,71 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include + +namespace { + +using DataType = ngraph::element::Type_t; +using DataShape = ngraph::Shape; +using ShapeDescriptor = std::vector; +using ReshapeTestParams = std::tuple; + +using Parameters = std::tuple< + DataType, + ReshapeTestParams, + LayerTestsUtils::TargetDevice +>; + +class DSR_Reshape : public testing::WithParamInterface, public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& inDataType = std::get<0>(GetParam()); + const auto& reshapeTestParams = std::get<1>(GetParam()); + targetDevice = std::get<2>(GetParam()); + + const auto& inDataShape = std::get<0>(reshapeTestParams); + const auto& specialZero = std::get<1>(reshapeTestParams); + const auto& outShapeDescriptor = std::get<2>(reshapeTestParams); + + const auto inDataParam = std::make_shared( + inDataType, inDataShape); + const auto inDataShapeParam = std::make_shared( + ngraph::element::i32, ngraph::Shape{inDataShape.size()}); + const auto dsr = std::make_shared( + inDataParam, inDataShapeParam); + + const auto outShapeDescriptorConstNode = std::make_shared( + ngraph::element::i64, ngraph::Shape{outShapeDescriptor.size()}, outShapeDescriptor); + const auto reshape = std::make_shared( + dsr, outShapeDescriptorConstNode, specialZero); + + const auto result = std::make_shared(reshape); + function = std::make_shared( + ngraph::ResultVector{result}, + ngraph::ParameterVector{inDataParam, inDataShapeParam}, + "DSR-Reshape"); + } +}; + +TEST_P(DSR_Reshape, CompareWithReference) { + Run(); +} + +std::vector reshapeTestParams = { + std::make_tuple(DataShape{1, 5, 5, 24}, true, ShapeDescriptor{0, -1, 4}), + std::make_tuple(DataShape{1, 5, 5, 0}, false, ShapeDescriptor{0, 4}), + std::make_tuple(DataShape{1, 3, 128, 256}, true, ShapeDescriptor{0, 0, 64, 512}), +}; + +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicReshape, DSR_Reshape, + ::testing::Combine( + ::testing::Values(ngraph::element::f16, ngraph::element::f32, ngraph::element::i32), + ::testing::ValuesIn(reshapeTestParams), + ::testing::Values(CommonTestUtils::DEVICE_MYRIAD))); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_variadic_split.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_variadic_split.cpp new file mode 100644 index 00000000000..ec868b96daa --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_variadic_split.cpp @@ -0,0 +1,81 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include + +namespace { + +using DataType = ngraph::element::Type_t; +using DataDims = ngraph::Shape; + + +struct VariadicSplitTestCase { + ngraph::Shape data_shape; + std::vector split_lengths; + int64_t axis, first_split_point, second_split_point; +}; + +const auto combinations = testing::Combine( + testing::Values( + ngraph::element::f16, + ngraph::element::f32, + ngraph::element::i32, + ngraph::element::i64, + ngraph::element::u8), + testing::Values( + ngraph::element::i32, + ngraph::element::i64), + testing::Values( + VariadicSplitTestCase{{6}, {2, 1, 2, 1}, 0, 0, 0}, + VariadicSplitTestCase{{6, 12, 10, 24}, {1, 1, 3, 1}, 0, 0, 1}, + VariadicSplitTestCase{{6, 12}, {7, 2, 1, 2}, 1, 1, 2}, + VariadicSplitTestCase{{6, 12, 10, 24}, {10, 14}, 3, 3, 4}, + VariadicSplitTestCase{{6, 12, 10, 24}, {14, 10}, -1, 3, 4}, + VariadicSplitTestCase{{6, 12, 10, 24}, {6}, -4, 0, 1}), + testing::Values(CommonTestUtils::DEVICE_MYRIAD)); + + +using Parameters = std::tuple< + DataType, + DataType, + VariadicSplitTestCase, + LayerTestsUtils::TargetDevice +>; + +class DSR_VariadicSplit : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& data_type = std::get<0>(parameters); + const auto& idx_type = std::get<1>(parameters); + const auto& variadic_split_setup = std::get<2>(parameters); + targetDevice = std::get<3>(parameters); + + const auto data = std::make_shared(data_type, variadic_split_setup.data_shape); + const auto axis = ngraph::opset3::Constant::create(idx_type, {}, std::vector{variadic_split_setup.axis}); + const auto split_lengths = ngraph::opset3::Constant::create(idx_type, + {variadic_split_setup.split_lengths.size()}, std::vector{variadic_split_setup.split_lengths}); + + const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{variadic_split_setup.data_shape.size()}); + + const auto dsr = std::make_shared(data, dims); + const auto node = std::make_shared(dsr, axis, split_lengths); + + const auto tests_wa = std::make_shared(node->outputs(), variadic_split_setup.axis); + const auto result = std::make_shared(tests_wa); + function = std::make_shared(ngraph::ResultVector{result}, + ngraph::ParameterVector{data, dims}, "DSR-VariadicSplit"); + } +}; + +TEST_P(DSR_VariadicSplit, CompareWithReference) { + Run(); +} + +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicGatherData, DSR_VariadicSplit, combinations); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp new file mode 100644 index 00000000000..b2c5c61d8a9 --- /dev/null +++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/nonzero_broadcast.cpp @@ -0,0 +1,62 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include +#include + +namespace { + +using TensorType = ngraph::element::Type; +using TensorShape = ngraph::PartialShape; + +using BroadcastExplicitTestParams = std::tuple< + TensorType, TensorShape, LayerTestsUtils::TargetDevice>; + +class NonZero_Broadcast : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +protected: + void SetUp() override { + const auto& parameters = GetParam(); + const auto& tensorType = std::get<0>(parameters); + const auto& tensorShape = std::get<1>(parameters); + targetDevice = std::get<2>(GetParam()); + + const auto tensorParam = std::make_shared( + tensorType, tensorShape); + const auto nonZero = std::make_shared(tensorParam); + const auto shapeOfNonZero = std::make_shared(nonZero); + + const auto broadcastConstant = std::make_shared( + tensorType, ngraph::Shape{1}, 1); + + const auto axesMappingConstant = std::make_shared( + ngraph::element::u64, ngraph::Shape{1}, 0); + + const auto broadcast = std::make_shared( + broadcastConstant, shapeOfNonZero, axesMappingConstant); + + const auto result = std::make_shared(broadcast); + + function = std::make_shared( + ngraph::ResultVector{result}, + ngraph::ParameterVector{tensorParam}, + "NonZero-Broadcast"); + } +}; + +TEST_P(NonZero_Broadcast, CompareWithReference) { + Run(); +} +// Blocked by #-30913, #-30915 +INSTANTIATE_TEST_CASE_P(DISABLED_DynamicBroadcast, NonZero_Broadcast, ::testing::Combine( + ::testing::Values(ngraph::element::f16, ngraph::element::f32, ngraph::element::i32), + ::testing::Values( + TensorShape{1000}, + TensorShape{4, 1000}, + TensorShape{3, 128, 256}), + ::testing::Values(CommonTestUtils::DEVICE_MYRIAD))); + +} // namespace diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_transpose.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/nonzero_transpose.cpp similarity index 71% rename from inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_transpose.cpp rename to inference-engine/tests/functional/plugin/myriad/subgraph_tests/nonzero_transpose.cpp index 8198d7add5b..775724a9463 100644 --- a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/dsr_transpose.cpp +++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/nonzero_transpose.cpp @@ -5,6 +5,7 @@ #include #include #include +#include namespace { @@ -17,7 +18,7 @@ using Parameters = std::tuple< LayerTestsUtils::TargetDevice >; -class DSR_Transpose : public testing::WithParamInterface, public LayerTestsUtils::LayerTestsCommon { +class NonZero_Transpose : public testing::WithParamInterface, public LayerTestsUtils::LayerTestsCommon { protected: void SetUp() override { const auto& parameters = GetParam(); @@ -26,25 +27,29 @@ protected: targetDevice = std::get<2>(GetParam()); const auto data = std::make_shared(dataType, dataDims); - const auto dims = std::make_shared(ngraph::element::i64, ngraph::Shape{dataDims.size()}); - const auto dsr = std::make_shared(data, dims); + const auto nonZero = std::make_shared(data); auto permutation = std::vector(dataDims.size()); std::iota(permutation.begin(), permutation.end(), 0); std::shuffle(permutation.begin(), permutation.end(), std::mt19937()); const auto transposition = std::make_shared(ngraph::element::i64, ngraph::Shape{dataDims.size()}, permutation); - const auto transpose = std::make_shared(dsr, transposition); + const auto transpose = std::make_shared(nonZero, transposition); const auto result = std::make_shared(transpose); - function = std::make_shared(ngraph::ResultVector{result}, ngraph::ParameterVector{data, dims}, "DSR-Transpose"); + function = std::make_shared(ngraph::ResultVector{result}, ngraph::ParameterVector{data}, "NonZero-Transpose"); } }; -TEST_P(DSR_Transpose, CompareWithReference) { - Run(); +TEST_P(NonZero_Transpose, CompareWithReference) { + SKIP_IF_CURRENT_TEST_IS_DISABLED() + + configuration.emplace(VPU_MYRIAD_CONFIG_KEY(PLATFORM), VPU_MYRIAD_CONFIG_VALUE(2480)); + ConfigurePlugin(); + + ASSERT_NO_THROW(LoadNetwork()); } -INSTANTIATE_TEST_CASE_P(DISABLED_DynamicTranspose, DSR_Transpose, +INSTANTIATE_TEST_CASE_P(DynamicTranspose, NonZero_Transpose, ::testing::Combine( ::testing::Values(ngraph::element::f16, ngraph::element::f32, ngraph::element::i32), ::testing::Values(ngraph::Shape{1, 800}), diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/core_threading_tests.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/core_threading_tests.hpp new file mode 100644 index 00000000000..0af6860f659 --- /dev/null +++ b/inference-engine/tests/functional/plugin/shared/include/behavior/core_threading_tests.hpp @@ -0,0 +1,198 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include
+#include +#include +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +using Device = std::string; +using Config = std::map; +using Params = std::tuple; + +class CoreThreadingTestsBase { +public: + static void runParallel(std::function func, + const unsigned int iterations = 100, + const unsigned int threadsNum = 8) { + std::vector threads(threadsNum); + + for (auto & thread : threads) { + thread = std::thread([&](){ + for (unsigned int i = 0; i < iterations; ++i) { + func(); + } + }); + } + + for (auto & thread : threads) { + if (thread.joinable()) + thread.join(); + } + } + + void safePluginUnregister(InferenceEngine::Core & ie) { + try { + ie.UnregisterPlugin(deviceName); + } catch (const InferenceEngine::details::InferenceEngineException & ex) { + // if several threads unload plugin at once, the first thread does this + // while all others will throw an exception that plugin is not registered + ASSERT_STR_CONTAINS(ex.what(), "name is not registered in the"); + } + } + + void safeAddExtension(InferenceEngine::Core & ie) { + try { + auto extension = InferenceEngine::make_so_pointer( + FileUtils::makeSharedLibraryName({}, "extension_tests")); + ie.AddExtension(extension); + } catch (const InferenceEngine::details::InferenceEngineException & ex) { + ASSERT_STR_CONTAINS(ex.what(), "name: experimental"); + } + } + + Device deviceName; + Config config; +}; + +// +// Common threading plugin tests +// + +class CoreThreadingTests : public CoreThreadingTestsBase, + public ::testing::TestWithParam { +public: + void SetUp() override { + std::tie(deviceName, config) = GetParam(); + } +}; + +// tested function: GetVersions, UnregisterPlugin +TEST_P(CoreThreadingTests, smoke_GetVersions) { + InferenceEngine::Core ie; + + runParallel([&] () { + auto versions = ie.GetVersions(deviceName); + ASSERT_LE(1u, versions.size()); + safePluginUnregister(ie); + }); +} + +// tested function: SetConfig for already created plugins +TEST_P(CoreThreadingTests, smoke_SetConfigPluginExists) { + InferenceEngine::Core ie; + + ie.SetConfig(config); + auto versions = ie.GetVersions(deviceName); + + runParallel([&] () { + ie.SetConfig(config); + }, 10000); +} + +// tested function: GetConfig, UnregisterPlugin +TEST_P(CoreThreadingTests, smoke_GetConfig) { + InferenceEngine::Core ie; + std::string configKey = config.begin()->first; + + ie.SetConfig(config); + runParallel([&] () { + ie.GetConfig(deviceName, configKey); + safePluginUnregister(ie); + }); +} + +// tested function: GetMetric, UnregisterPlugin +TEST_P(CoreThreadingTests, smoke_GetMetric) { + InferenceEngine::Core ie; + runParallel([&] () { + ie.GetMetric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)); + safePluginUnregister(ie); + }); +} + +// tested function: QueryNetwork +TEST_P(CoreThreadingTests, smoke_QueryNetwork) { + InferenceEngine::Core ie; + auto model = FuncTestUtils::TestModel::convReluNormPoolFcModelFP32; + auto network = ie.ReadNetwork(model.model_xml_str, model.weights_blob); + + ie.SetConfig(config, deviceName); + InferenceEngine::QueryNetworkResult refResult = ie.QueryNetwork(network, deviceName); + + runParallel([&] () { + const auto result = ie.QueryNetwork(network, deviceName); + safePluginUnregister(ie); + + // compare QueryNetworkResult with reference + for (auto && r : refResult.supportedLayersMap) { + ASSERT_NE(result.supportedLayersMap.end(), result.supportedLayersMap.find(r.first)); + } + for (auto && r : result.supportedLayersMap) { + ASSERT_NE(refResult.supportedLayersMap.end(), refResult.supportedLayersMap.find(r.first)); + } + }, 3000); +} + +// +// Parametrized tests with numfer of parallel threads, iterations +// + +using Threads = unsigned int; +using Iterations = unsigned int; + +class CoreThreadingTestsWithIterations : public ::testing::TestWithParam >, + public CoreThreadingTestsBase { +public: + void SetUp() override { + std::tie(deviceName, config) = std::get<0>(GetParam()); + numThreads = std::get<1>(GetParam()); + numIterations = std::get<2>(GetParam()); + } + + unsigned int numIterations; + unsigned int numThreads; +}; + +// tested function: LoadNetwork, AddExtension +TEST_P(CoreThreadingTestsWithIterations, smoke_LoadNetwork) { + InferenceEngine::Core ie; + std::atomic counter{0u}; + + const FuncTestUtils::TestModel::TestModel models[] = { + FuncTestUtils::TestModel::convReluNormPoolFcModelFP32, + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16 + }; + std::vector networks; + for (auto & model : models) { + networks.emplace_back(ie.ReadNetwork(model.model_xml_str, model.weights_blob)); + } + + // TODO: uncomment after fixing *-31414 + // networks.emplace_back(InferenceEngine::CNNNetwork(ngraph::builder::subgraph::make2InputSubtract())); + // networks.emplace_back(InferenceEngine::CNNNetwork(ngraph::builder::subgraph::makeMultiSingleConv())); + // networks.emplace_back(InferenceEngine::CNNNetwork(ngraph::builder::subgraph::makeSingleConv())); + // networks.emplace_back(InferenceEngine::CNNNetwork(ngraph::builder::subgraph::makeSplitConvConcat())); + // networks.emplace_back(InferenceEngine::CNNNetwork(ngraph::builder::subgraph::makeSplitMultiConvConcat())); + + ie.SetConfig(config, deviceName); + runParallel([&] () { + auto value = counter++; + (void)ie.LoadNetwork(networks[(counter++) % networks.size()], deviceName); + }, numIterations, numThreads); +} diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/concat.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/concat.hpp index 53f0b61a6d3..ecd7c57c34c 100644 --- a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/concat.hpp +++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/concat.hpp @@ -16,6 +16,7 @@ namespace LayerTestsDefinitions { using concatParamsTuple = typename std::tuple< + //TODO: according to specification axis have to be int, negative values are allowed size_t, // Concat axis std::vector>, // Input shapes InferenceEngine::Precision, // Network precision diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/proposal.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/proposal.hpp new file mode 100644 index 00000000000..c8a9dc40ea9 --- /dev/null +++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/proposal.hpp @@ -0,0 +1,66 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include +#include +#include + +#include "functional_test_utils/layer_test_utils.hpp" +#include "ngraph_functions/builders.hpp" +#include "ngraph_functions/utils/ngraph_helpers.hpp" + +namespace LayerTestsDefinitions { + +namespace proposalTypes { + +typedef size_t base_size_type; +typedef size_t pre_nms_topn_type; +typedef size_t post_nms_topn_type; +typedef float nms_thresh_type; +typedef size_t min_size_type; +typedef std::vector ratio_type; +typedef std::vector scale_type; +typedef bool clip_before_nms_type; +typedef bool clip_after_nms_type; +typedef bool normalize_type; +typedef size_t feat_stride_type; +typedef float box_size_scale_type; +typedef float box_coordinate_scale_type; +typedef std::string framework_type; + +}; // namespace proposalTypes + +using namespace proposalTypes; + +typedef std::tuple< + base_size_type, + pre_nms_topn_type, + post_nms_topn_type, + nms_thresh_type, + min_size_type, + ratio_type, + scale_type, + clip_before_nms_type, + clip_after_nms_type, + framework_type> proposalSpecificParams; +typedef std::tuple< + proposalSpecificParams, + std::string> proposalLayerTestParamsSet; + +class ProposalLayerTest + : public testing::WithParamInterface, + public LayerTestsUtils::LayerTestsCommon { +public: + static std::string getTestCaseName(testing::TestParamInfo obj); + InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo &info) const override; + +protected: + void SetUp() override; + void Validate() override; +}; + +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/activation.cpp b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/activation.cpp index 1adf2b8fdc9..b7add9d286e 100644 --- a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/activation.cpp +++ b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/activation.cpp @@ -101,12 +101,15 @@ TEST_P(ActivationLayerTest, CompareWithRefs) { auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision); convertFuncToF32(fnPtr, netPrecision); auto refOutData = ngraph::helpers::inferFnWithInterp(fnPtr, inRawData); - auto thr = FuncTestUtils::GetComparisonThreshold(netPrecision); + float thr1, thr2; + FuncTestUtils::GetComparisonThreshold(netPrecision, thr1, thr2); + size_t outElementsCount = std::accumulate(begin(fnPtr->get_output_shape(0)), end(fnPtr->get_output_shape(0)), 1, std::multiplies()); - FuncTestUtils::compareRawBuffers(outBlob->cbuffer().as(), *refOutData[0], outElementsCount, - outElementsCount, - thr); + FuncTestUtils::compareRawBuffers(outBlob->cbuffer().as(), *refOutData[0], + outElementsCount, outElementsCount, + FuncTestUtils::CompareType::ABS_AND_REL, + thr1, thr2); fnPtr.reset(); if (targetDevice.find(CommonTestUtils::DEVICE_GPU) != std::string::npos) { PluginCache::get().reset(); diff --git a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/concat.cpp b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/concat.cpp index 251ec74cac0..24d1d6ba32e 100644 --- a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/concat.cpp +++ b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/concat.cpp @@ -21,7 +21,7 @@ namespace LayerTestsDefinitions { std::string ConcatLayerTest::getTestCaseName(const testing::TestParamInfo &obj) { - size_t axis; + int axis; std::vector> inputShapes; InferenceEngine::Precision netPrecision; std::string targetName; @@ -35,7 +35,7 @@ std::string ConcatLayerTest::getTestCaseName(const testing::TestParamInfo> inputShape; InferenceEngine::Precision netPrecision; std::tie(axis, inputShape, netPrecision, targetDevice) = this->GetParam(); diff --git a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/proposal.cpp b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/proposal.cpp new file mode 100644 index 00000000000..d5cc953ff8a --- /dev/null +++ b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/proposal.cpp @@ -0,0 +1,154 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include +#include +#include +#include +#include + +#include "ie_core.hpp" + +#include "common_test_utils/common_utils.hpp" +#include "functional_test_utils/blob_utils.hpp" +#include "functional_test_utils/plugin_cache.hpp" +#include "functional_test_utils/layer_test_utils.hpp" + +#include "single_layer_tests/proposal.hpp" + +namespace LayerTestsDefinitions { + +const normalize_type normalize = true; +const feat_stride_type feat_stride = 1; +const box_size_scale_type box_size_scale = 2.0f; +const box_coordinate_scale_type box_coordinate_scale = 2.0f; + +std::string ProposalLayerTest::getTestCaseName(testing::TestParamInfo obj) { + proposalSpecificParams proposalParams; + + std::string targetDevice; + std::tie(proposalParams, targetDevice) = obj.param; + + base_size_type base_size; + pre_nms_topn_type pre_nms_topn; + post_nms_topn_type post_nms_topn; + nms_thresh_type nms_thresh; + min_size_type min_size; + ratio_type ratio; + scale_type scale; + clip_before_nms_type clip_before_nms; + clip_after_nms_type clip_after_nms; + framework_type framework; + std::tie(base_size, pre_nms_topn, + post_nms_topn, + nms_thresh, + min_size, + ratio, + scale, + clip_before_nms, + clip_after_nms, + framework) = proposalParams; + + std::ostringstream result; + result << "base_size=" << base_size << "_"; + result << "pre_nms_topn=" << pre_nms_topn << "_"; + result << "post_nms_topn=" << post_nms_topn << "_"; + result << "nms_thresh=" << nms_thresh << "_"; + result << "feat_stride=" << feat_stride << "_"; + result << "min_size=" << min_size << "_"; + result << "ratio = " << CommonTestUtils::vec2str(ratio) << "_"; + result << "scale = " << CommonTestUtils::vec2str(scale) << "_"; + result << "clip_before_nms=" << clip_before_nms << "_"; + result << "clip_after_nms=" << clip_after_nms << "_"; + result << "normalize=" << normalize << "_"; + result << "box_size_scale=" << box_size_scale << "_"; + result << "box_coordinate_scale=" << box_coordinate_scale << "_"; + result << "framework=" << framework << "_"; + result << "targetDevice=" << targetDevice; + + return result.str(); +} + +void ProposalLayerTest::SetUp() { + proposalSpecificParams proposalParams; + + std::tie(proposalParams, targetDevice) = this->GetParam(); + base_size_type base_size; + pre_nms_topn_type pre_nms_topn; + post_nms_topn_type post_nms_topn; + nms_thresh_type nms_thresh; + min_size_type min_size; + ratio_type ratio; + scale_type scale; + clip_before_nms_type clip_before_nms; + clip_after_nms_type clip_after_nms; + framework_type framework; + + std::tie(base_size, pre_nms_topn, + post_nms_topn, + nms_thresh, + min_size, + ratio, + scale, + clip_before_nms, + clip_after_nms, + framework) = proposalParams; + + size_t bottom_w = base_size; + size_t bottom_h = base_size; + size_t num_anchors = ratio.size() * scale.size(); + + std::vector scoresShape = {1, 2 * num_anchors, bottom_h, bottom_w}; + std::vector boxesShape = {1, 4 * num_anchors, bottom_h, bottom_w}; + std::vector imageInfoShape = {3}; + + auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(InferenceEngine::Precision::FP16); + auto params = ngraph::builder::makeParams(ngPrc, {{"scores", scoresShape}, {"boxes", boxesShape}, {"image_info", imageInfoShape}}); + auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes(params)); + + auto proposal = std::dynamic_pointer_cast( + ngraph::builder::makeProposal(paramOuts[0], paramOuts[1], paramOuts[2], ngPrc, + base_size, + pre_nms_topn, + post_nms_topn, + nms_thresh, + feat_stride, + min_size, + ratio, + scale, + clip_before_nms, + clip_after_nms, + normalize, + box_size_scale, + box_coordinate_scale, + framework)); + + ngraph::ResultVector results{std::make_shared(proposal)}; + function = std::make_shared(results, params, "proposal"); +} + +InferenceEngine::Blob::Ptr ProposalLayerTest::GenerateInput(const InferenceEngine::InputInfo &info) const { + InferenceEngine::Blob::Ptr blobPtr; + + const std::string name = info.name(); + if (name == "scores") { + blobPtr = FuncTestUtils::createAndFillBlobFloat(info.getTensorDesc(), 1, 0, 1000, 8234231); + } else if (name == "boxes") { + blobPtr = FuncTestUtils::createAndFillBlobFloatNormalDistribution(info.getTensorDesc(), 0.0f, 0.2f, 7235346); + } else if (name == "image_info") { + const float image_info[] = {225.0f, 225.0f, 1.0f}; + blobPtr = FuncTestUtils::createAndFillBlobWithFloatArray(info.getTensorDesc(), image_info, 3); + } + + return blobPtr; +} + +// TODO: for validation, reference version is required (#28373) +void ProposalLayerTest::Validate() {} + +TEST_P(ProposalLayerTest, CompareWithRefs) { + Run(); +} +} // namespace LayerTestsDefinitions diff --git a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/select.cpp b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/select.cpp index 17a1909f04d..2bd874e8cb5 100644 --- a/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/select.cpp +++ b/inference-engine/tests/functional/plugin/shared/src/single_layer_tests/select.cpp @@ -87,10 +87,15 @@ namespace LayerTestsDefinitions { std::vector convRefOutData(outElementsCount); for (size_t i = 0; i < outElementsCount; i++) convRefOutData[i] = static_cast(refOutData[i]); - FuncTestUtils::compareRawBuffers(outBlob->cbuffer().as(), convRefOutData.data(), outElementsCount, outElementsCount); + FuncTestUtils::compareRawBuffers(outBlob->cbuffer().as(), convRefOutData.data(), + outElementsCount, outElementsCount, FuncTestUtils::CompareType::ABS_AND_REL); } else { - auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32); - FuncTestUtils::compareRawBuffers(outBlob->cbuffer().as(), refOutData.data(), outElementsCount, outElementsCount, thr); + float thr1, thr2; + FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32, thr1, thr2); + FuncTestUtils::compareRawBuffers(outBlob->cbuffer().as(), refOutData.data(), + outElementsCount, outElementsCount, + FuncTestUtils::CompareType::ABS_AND_REL, + thr1, thr2); } layer.fnPtr.reset(); diff --git a/inference-engine/tests/ie_test_utils/common_test_utils/data_utils.hpp b/inference-engine/tests/ie_test_utils/common_test_utils/data_utils.hpp index b40b2d76794..031919ce994 100644 --- a/inference-engine/tests/ie_test_utils/common_test_utils/data_utils.hpp +++ b/inference-engine/tests/ie_test_utils/common_test_utils/data_utils.hpp @@ -92,9 +92,10 @@ void inline fill_data_random(InferenceEngine::Blob::Ptr &blob, const uint32_t r } template -void inline fill_data_random_float(InferenceEngine::Blob::Ptr &blob, const uint32_t range, int32_t start_from, const int32_t k) { +void inline fill_data_random_float(InferenceEngine::Blob::Ptr &blob, const uint32_t range, int32_t start_from, const int32_t k, + const int seed = 1) { using dataType = typename InferenceEngine::PrecisionTrait::value_type; - std::default_random_engine random(1); + std::default_random_engine random(seed); // 1/k is the resolution of the floating point numbers std::uniform_int_distribution distribution(k * start_from, k * (start_from + range)); @@ -110,6 +111,41 @@ void inline fill_data_random_float(InferenceEngine::Blob::Ptr &blob, const uint3 } } +template +void inline fill_data_normal_random_float(InferenceEngine::Blob::Ptr &blob, + const float mean, + const float stddev, + const int seed = 1) { + using dataType = typename InferenceEngine::PrecisionTrait::value_type; + std::default_random_engine random(seed); + std::normal_distribution<> normal_d{mean, stddev}; + + auto *rawBlobDataPtr = blob->buffer().as(); + for (size_t i = 0; i < blob->size(); i++) { + auto value = static_cast(normal_d(random)); + if (typeid(dataType) == typeid(typename InferenceEngine::PrecisionTrait::value_type)) { + rawBlobDataPtr[i] = ngraph::float16(value).to_bits(); + } else { + rawBlobDataPtr[i] = value; + } + } +} + +template +void inline fill_data_float_array(InferenceEngine::Blob::Ptr &blob, const float values[], const size_t size) { + using dataType = typename InferenceEngine::PrecisionTrait::value_type; + + auto *rawBlobDataPtr = blob->buffer().as(); + for (size_t i = 0; i < std::min(size, blob->size()); i++) { + auto value = values[i]; + if (typeid(dataType) == typeid(typename InferenceEngine::PrecisionTrait::value_type)) { + rawBlobDataPtr[i] = ngraph::float16(value).to_bits(); + } else { + rawBlobDataPtr[i] = value; + } + } +} + template<> void inline fill_data_random(InferenceEngine::Blob::Ptr &blob, const uint32_t range, int32_t start_from, const int32_t k) { fill_data_random_float(blob, range, start_from, k); diff --git a/inference-engine/tests_deprecated/helpers/test_assertions.hpp b/inference-engine/tests/ie_test_utils/common_test_utils/test_assertions.hpp similarity index 68% rename from inference-engine/tests_deprecated/helpers/test_assertions.hpp rename to inference-engine/tests/ie_test_utils/common_test_utils/test_assertions.hpp index f984535b0c0..b74ac0aaef0 100644 --- a/inference-engine/tests_deprecated/helpers/test_assertions.hpp +++ b/inference-engine/tests/ie_test_utils/common_test_utils/test_assertions.hpp @@ -4,33 +4,59 @@ #include +#include + +#include #include +#include +#include +#include + +namespace { + +bool strContains(const std::string & str, const std::string & substr) { + return str.find(substr) != std::string::npos; +} + +bool strDoesnotContain(const std::string & str, const std::string & substr) { + (void)strDoesnotContain; // to overcome unused warning + return !strContains(str, substr); +} + +} // namespace + +#define ASSERT_STR_CONTAINS(str, substr) \ + ASSERT_PRED2(&strContains, str, substr) + +#define ASSERT_STR_DOES_NOT_CONTAIN(str, substr) \ + ASSERT_PRED2(&strDoesnotContain, str, substr) + +#define EXPECT_STR_CONTAINS(str, substr) \ + EXPECT_PRED2(&strContains, str, substr) #define ASSERT_BLOB_EQ(lhs, rhs) \ -compare_blob(lhs,rhs) + compare_blob(lhs, rhs) #define ASSERT_DIMS_EQ(lhs, rhs) \ -compare_dims(lhs,rhs) + compare_dims(lhs, rhs) #define ASSERT_DATA_EQ(lhs, rhs) \ -compare_data(lhs,rhs) + compare_data(lhs, rhs) #define ASSERT_PREPROCESS_CHANNEL_EQ(lhs, rhs) \ -compare_preprocess(lhs,rhs) + compare_preprocess(lhs, rhs) #define ASSERT_PREPROCESS_INFO_EQ(lhs, rhs) \ -compare_preprocess_info(lhs,rhs) + compare_preprocess_info(lhs, rhs) #define ASSERT_OUTPUTS_INFO_EQ(lhs, rhs) \ -compare_outputs_info(lhs,rhs) + compare_outputs_info(lhs, rhs) #define ASSERT_INPUTS_INFO_EQ(lhs, rhs) \ -compare_inputs_info(lhs,rhs) + compare_inputs_info(lhs, rhs) #define ASSERT_STRINGEQ(lhs, rhs) \ -compare_cpp_strings(lhs,rhs) - - + compare_cpp_strings(lhs, rhs) inline void compare_blob(InferenceEngine::Blob::Ptr lhs, InferenceEngine::Blob::Ptr rhs) { ASSERT_EQ(lhs.get(), rhs.get()); @@ -39,7 +65,7 @@ inline void compare_blob(InferenceEngine::Blob::Ptr lhs, InferenceEngine::Blob:: inline void compare_dims(const InferenceEngine::SizeVector & lhs, const InferenceEngine::SizeVector & rhs) { ASSERT_EQ(lhs.size(), rhs.size()); - for(int i=0;ifirst.c_str(), j->first.c_str()); ASSERT_DIMS_EQ(i->second->getTensorDesc().getDims(), j->second->getTensorDesc().getDims()); ASSERT_PREPROCESS_INFO_EQ(i->second->getPreProcess(), j->second->getPreProcess()); diff --git a/inference-engine/tests/ie_test_utils/functional_test_utils/blob_utils.hpp b/inference-engine/tests/ie_test_utils/functional_test_utils/blob_utils.hpp index 75b94501c34..76ffcd9ec5f 100644 --- a/inference-engine/tests/ie_test_utils/functional_test_utils/blob_utils.hpp +++ b/inference-engine/tests/ie_test_utils/functional_test_utils/blob_utils.hpp @@ -17,10 +17,32 @@ #include "common_test_utils/test_constants.hpp" namespace FuncTestUtils { -template -void inline compareRawBuffers(const dType *res, const dType *ref, - size_t resSize, size_t refSize, - float max_diff = 0.01, bool printData = false) { + +enum CompareType{ + ABS, + REL, + ABS_AND_REL // if absolute and relative differences are too high, an exception is thrown +}; +/** + * @brief Checks values of two blobs according to given algorithm and thresholds. + * In ABS and REL cases thr1 corresponds to the single threshold, + * In ABS_AND_REL case thr1 and thr2 mean absolute and relative threshold + * + * @tparam dType Type of blob data + * @param res Pointer to considered blob + * @param ref Pointer to reference blob + * @param resSize Size of considered blob + * @param refSize Size of reference blob + * @param compareType Defines an algorithm of comparision + * @param thr1 First threshold of difference + * @param thr2 Second threshold of difference + * @param printData A flag if data printing is demanded + */ + template +static void inline compareRawBuffers(const dType *res, const dType *ref, + size_t resSize, size_t refSize, + CompareType compareType, float thr1 = 0.01, float thr2 = 0.01, + bool printData = false) { if (printData) { std::cout << "Reference results: " << std::endl; for (size_t i = 0; i < refSize; i++) { @@ -34,43 +56,144 @@ void inline compareRawBuffers(const dType *res, const dType *ref, std::cout << std::endl; } - for (size_t i = 0; i < refSize; i++) { - float absDiff = std::abs(res[i] - ref[i]); - if (absDiff > max_diff) { - float relDiff = absDiff / std::max(res[i], ref[i]); - ASSERT_LT(relDiff, max_diff) << "Relative comparison of values ref: " << ref[i] << " and res: " - << res[i] << " , index in blobs: " << i << " failed!"; - } + switch (compareType) { + case CompareType::ABS: + for (size_t i = 0; i < refSize; i++) { + float absDiff = std::abs(res[i] - ref[i]); + ASSERT_LT(absDiff, thr1) << "Relative comparison of values ref: " << ref[i] << " and res: " + << res[i] << " , index in blobs: " << i << " failed!"; + } + break; + case CompareType::REL: + for (size_t i = 0; i < refSize; i++) { + float absDiff = std::abs(res[i] - ref[i]); + float relDiff = absDiff / std::max(res[i], ref[i]); + ASSERT_LT(relDiff, thr2) << "Relative comparison of values ref: " << ref[i] << " and res: " + << res[i] << " , index in blobs: " << i << " failed!"; + } + break; + case CompareType::ABS_AND_REL: + for (size_t i = 0; i < refSize; i++) { + float absDiff = std::abs(res[i] - ref[i]); + if (absDiff > thr1) { + float relDiff = absDiff / std::max(res[i], ref[i]); + ASSERT_LT(relDiff, thr2) << "Comparison of values ref: " << ref[i] << " and res: " + << res[i] << " , index in blobs: " << i << " failed!"; + } + } + break; } } - +/** + * @brief Checks absolute and relative difference of blob values according to given threshold. + * + * @tparam dType Type of blob data + * @param res Pointer to considered blob + * @param ref Pointer to reference blob + * @param resSize Size of considered blob + * @param refSize Size of reference blob + * @param thr Threshold of difference, absolute and relative simultaneously + * @param printData Flag if data printing is demanded + */ template -void inline compareRawBuffers(const std::vector res, const std::vector ref, +static void inline compareRawBuffers(const dType *res, const dType *ref, + size_t resSize, size_t refSize, + float thr = 0.01, + bool printData = false) { + compareRawBuffers(res, ref, resSize, refSize, CompareType::ABS_AND_REL, thr, thr, printData); +} +/** + * @brief Checks values of two blobs according to given algorithm and thresholds. + * In ABS and REL cases thr1 corresponds to the single threshold, + * In ABS_AND_REL case thr1 and thr2 mean absolute and relative threshold + * + * @tparam dType Type of blob data + * @param res Vector of considered blob values + * @param ref Vector of reference blob values + * @param resSize Size of considered blob + * @param refSize Size of reference blob + * @param compareType Defines an algorithm of comparision + * @param thr1 First threshold of difference + * @param thr2 Second threshold of difference + * @param printData A flag if data printing is demanded + */ +template +static void inline compareRawBuffers(const std::vector res, const std::vector ref, const std::vector &resSizes, const std::vector &refSizes, - float max_diff = 0.01, bool printData = false) { + CompareType compareType, + float thr1 = 0.01, float thr2 = 0.01, bool printData = false) { ASSERT_TRUE(res.size() == ref.size()) << "Reference and Results vector have to be same length"; ASSERT_TRUE(res.size() == resSizes.size()) << "Results vector and elements count vector have to be same length"; ASSERT_TRUE(ref.size() == refSizes.size()) << "Reference vector and elements count vector have to be same length"; for (size_t i = 0; i < res.size(); i++) { if (printData) std::cout << "BEGIN CHECK BUFFER [" << i << "]" << std::endl; - compareRawBuffers(res[i], ref[i], resSizes[i], refSizes[i], max_diff, printData); + compareRawBuffers(res[i], ref[i], resSizes[i], refSizes[i], compareType, thr1, thr2, printData); if (printData) std::cout << "END CHECK BUFFER [" << i << "]" << std::endl; } } - +/** + * @brief Checks absolute and relative difference of blob values according to given threshold. + * + * @tparam dType Type of blob data + * @param res Vector of considered blob values + * @param ref Vector of reference blob values + * @param resSize Size of considered blob + * @param refSize Size of reference blob + * @param thr Threshold of difference, absolute and relative simultaneously + * @param printData A flag if data printing is demanded + */ template -void inline compareRawBuffers(const std::vector res, const std::vector> ref, +static void inline compareRawBuffers(const std::vector res, const std::vector ref, + const std::vector &resSizes, const std::vector &refSizes, + float thr = 0.01, bool printData = false) { + compareRawBuffers(res, ref, resSizes, refSizes, CompareType::ABS_AND_REL, thr, thr, printData); +} +/** + * @brief Checks values of two blobs according to given algorithm and thresholds. + * In ABS and REL cases thr1 corresponds to the single threshold, + * In ABS_AND_REL case thr1 and thr2 mean absolute and relative threshold + * + * @tparam dType Type of blob data + * @param res Vector of considered blob values + * @param ref Vector of reference blob values + * @param resSize Size of considered blob + * @param refSize Size of reference blob + * @param compareType Defines an algorithm of comparision + * @param thr1 First threshold of difference + * @param thr2 Second threshold of difference + * @param printData A flag if data printing is demanded + */ +template +static void inline compareRawBuffers(const std::vector res, const std::vector> ref, const std::vector &resSizes, const std::vector &refSizes, - float max_diff = 0.01, bool printData = false) { + CompareType compareType, + float thr1 = 0.01, float thr2 = 0.01, bool printData = false) { ASSERT_TRUE(res.size() == ref.size()) << "Reference and Results vector have to be same length"; ASSERT_TRUE(res.size() == resSizes.size()) << "Results vector and elements count vector have to be same length"; ASSERT_TRUE(ref.size() == refSizes.size()) << "Reference vector and elements count vector have to be same length"; for (size_t i = 0; i < res.size(); i++) { if (printData) std::cout << "BEGIN CHECK BUFFER [" << i << "]" << std::endl; - compareRawBuffers(res[i], *ref[i], resSizes[i], refSizes[i], max_diff, printData); + compareRawBuffers(res[i], *ref[i], resSizes[i], refSizes[i], compareType, thr1, thr2, printData); if (printData) std::cout << "END CHECK BUFFER [" << i << "]" << std::endl; } } +/** + * @brief Checks absolute and relative difference of blob values according to given threshold. + * + * @tparam dType Type of blob data + * @param res Vector of considered blob values + * @param ref Vector of reference blob values + * @param resSize Size of considered blob + * @param refSize Size of reference blob + * @param thr Threshold of difference, absolute and relative simultaneously + * @param printData A flag if data printing is demanded + */ +template +static void inline compareRawBuffers(const std::vector res, const std::vector> ref, + const std::vector &resSizes, const std::vector &refSizes, + float thr = 0.01, bool printData = false) { + compareRawBuffers(res, ref, resSizes, refSizes, CompareType::ABS_AND_REL, thr, thr, printData); +} template void inline @@ -154,21 +277,30 @@ compareBlobs(const InferenceEngine::Blob::Ptr &res, const InferenceEngine::Blob: } } -float inline GetComparisonThreshold(InferenceEngine::Precision prc) { +void inline GetComparisonThreshold(InferenceEngine::Precision prc, float &absoluteThreshold, float &relativeThreshold) { switch (prc) { case InferenceEngine::Precision::FP32: - return 1e-4; + absoluteThreshold = relativeThreshold = 1e-4; + break; case InferenceEngine::Precision::FP16: - return 1e-2; + absoluteThreshold = relativeThreshold = 1e-2; + break; case InferenceEngine::Precision::I16: case InferenceEngine::Precision::I8: case InferenceEngine::Precision::U8: - return 1; + absoluteThreshold = relativeThreshold = 1; + break; default: THROW_IE_EXCEPTION << "Unhandled precision " << prc << " passed to the GetComparisonThreshold()"; } } +float inline GetComparisonThreshold(InferenceEngine::Precision prc) { + float res; + GetComparisonThreshold(prc, res, res); + return res; +} + // Copy from net_pass.h template void inline convertArrayPrecision(typename InferenceEngine::PrecisionTrait::value_type *dst, @@ -238,6 +370,82 @@ InferenceEngine::Blob::Ptr inline copyBlobWithCast(const InferenceEngine::Blob:: return newBlob; } +InferenceEngine::Blob::Ptr inline createAndFillBlobFloatNormalDistribution(const InferenceEngine::TensorDesc &td, + const float mean, + const float stddev, + const int32_t seed = 1) { + InferenceEngine::Blob::Ptr blob = make_blob_with_precision(td); + blob->allocate(); + switch (td.getPrecision()) { +#define CASE(X) case X: CommonTestUtils::fill_data_normal_random_float(blob, mean, stddev, seed); break; + CASE(InferenceEngine::Precision::FP32) + CASE(InferenceEngine::Precision::FP16) + CASE(InferenceEngine::Precision::U8) + CASE(InferenceEngine::Precision::U16) + CASE(InferenceEngine::Precision::I8) + CASE(InferenceEngine::Precision::I16) + CASE(InferenceEngine::Precision::I64) + CASE(InferenceEngine::Precision::BIN) + CASE(InferenceEngine::Precision::I32) + CASE(InferenceEngine::Precision::BOOL) +#undef CASE + default: + THROW_IE_EXCEPTION << "Wrong precision specified: " << td.getPrecision().name(); + } + return blob; +} + +InferenceEngine::Blob::Ptr inline createAndFillBlobFloat(const InferenceEngine::TensorDesc &td, + const uint32_t range = 10, + const int32_t start_from = 0, + const int32_t resolution = 1, + const int32_t seed = 1) { + InferenceEngine::Blob::Ptr blob = make_blob_with_precision(td); + + blob->allocate(); + switch (td.getPrecision()) { +#define CASE(X) case X: CommonTestUtils::fill_data_random_float(blob, range, start_from, resolution, seed); break; + CASE(InferenceEngine::Precision::FP32) + CASE(InferenceEngine::Precision::FP16) + CASE(InferenceEngine::Precision::U8) + CASE(InferenceEngine::Precision::U16) + CASE(InferenceEngine::Precision::I8) + CASE(InferenceEngine::Precision::I16) + CASE(InferenceEngine::Precision::I64) + CASE(InferenceEngine::Precision::BIN) + CASE(InferenceEngine::Precision::I32) + CASE(InferenceEngine::Precision::BOOL) +#undef CASE + default: + THROW_IE_EXCEPTION << "Wrong precision specified: " << td.getPrecision().name(); + } + return blob; +} + +InferenceEngine::Blob::Ptr inline createAndFillBlobWithFloatArray(const InferenceEngine::TensorDesc &td, + const float values[], + const int size) { + InferenceEngine::Blob::Ptr blob = make_blob_with_precision(td); + blob->allocate(); + switch (td.getPrecision()) { +#define CASE(X) case X: CommonTestUtils::fill_data_float_array(blob, values, size); break; + CASE(InferenceEngine::Precision::FP32) + CASE(InferenceEngine::Precision::FP16) + CASE(InferenceEngine::Precision::U8) + CASE(InferenceEngine::Precision::U16) + CASE(InferenceEngine::Precision::I8) + CASE(InferenceEngine::Precision::I16) + CASE(InferenceEngine::Precision::I64) + CASE(InferenceEngine::Precision::BIN) + CASE(InferenceEngine::Precision::I32) + CASE(InferenceEngine::Precision::BOOL) +#undef CASE + default: + THROW_IE_EXCEPTION << "Wrong precision specified: " << td.getPrecision().name(); + } + return blob; +} + InferenceEngine::Blob::Ptr inline createAndFillBlob(const InferenceEngine::TensorDesc &td, const uint32_t range = 10, const int32_t start_from = 0, diff --git a/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp b/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp index e0a68732ad9..27f8a9abee2 100644 --- a/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp +++ b/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.cpp @@ -84,6 +84,9 @@ void LayerTestsCommon::LoadNetwork() { cnnNetwork = InferenceEngine::CNNNetwork{function}; ConfigureNetwork(); executableNetwork = core->LoadNetwork(cnnNetwork, targetDevice); +} + +void LayerTestsCommon::Infer() { inferRequest = executableNetwork.CreateInferRequest(); for (const auto &input : cnnNetwork.getInputsInfo()) { @@ -93,9 +96,6 @@ void LayerTestsCommon::LoadNetwork() { inferRequest.SetBlob(info->name(), blob); inputs.push_back(blob); } -} - -void LayerTestsCommon::Infer() { inferRequest.Infer(); } @@ -108,6 +108,14 @@ std::vector LayerTestsCommon::GetOutputs() { return outputs; } +void LayerTestsCommon::Compare(const std::vector>& expectedOutputs, const std::vector& actualOutputs) { + for (std::size_t outputIndex = 0; outputIndex < expectedOutputs.size(); ++outputIndex) { + const auto &expected = expectedOutputs[outputIndex]; + const auto &actual = actualOutputs[outputIndex]; + Compare(expected, actual); + } +} + void LayerTestsCommon::Validate() { // nGraph interpreter does not support f16 // IE converts f16 to f32 @@ -142,11 +150,7 @@ void LayerTestsCommon::Validate() { IE_ASSERT(actualOutputs.size() == expectedOutputs.size()) << "nGraph interpreter has " << expectedOutputs.size() << " outputs, while IE " << actualOutputs.size(); - for (std::size_t outputIndex = 0; outputIndex < expectedOutputs.size(); ++outputIndex) { - const auto &expected = expectedOutputs[outputIndex]; - const auto &actual = actualOutputs[outputIndex]; - Compare(expected, actual); - } + Compare(expectedOutputs, actualOutputs); } void LayerTestsCommon::SetRefMode(RefMode mode) { diff --git a/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp b/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp index 60cacea25a0..6e317b760ad 100644 --- a/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp +++ b/inference-engine/tests/ie_test_utils/functional_test_utils/layer_test_utils.hpp @@ -137,9 +137,11 @@ public: // Run ngraph Interpreter backend to calculate references auto refOutData = ngraph::helpers::inferFnWithInterp(fnPtr, inRawData); // Compare IE infer results vs ngraph Interpreter reference results - auto thr = FuncTestUtils::GetComparisonThreshold(netPrecision); - FuncTestUtils::compareRawBuffers(outBlobsRawData, refOutData, outElementsCount, outElementsCount, thr); - + float thr1, thr2; + FuncTestUtils::GetComparisonThreshold(netPrecision, thr1, thr2); + FuncTestUtils::compareRawBuffers(outBlobsRawData, refOutData, outElementsCount, outElementsCount, + FuncTestUtils::CompareType::ABS_AND_REL, + thr1, thr2); // Deallocate ngraph::Function pointer fnPtr.reset(); if (targetDevice.find(CommonTestUtils::DEVICE_GPU) != std::string::npos) { @@ -229,6 +231,8 @@ public: virtual void Run(); + virtual void Compare(const std::vector>& expectedOutputs, const std::vector& actualOutputs); + virtual void Compare(const std::vector &expected, const InferenceEngine::Blob::Ptr &actual); virtual void SetRefMode(RefMode mode); @@ -262,6 +266,10 @@ protected: return refMode; } + void ConfigurePlugin() const; + + void LoadNetwork(); + TargetDevice targetDevice; std::shared_ptr function; std::map configuration; @@ -272,19 +280,15 @@ protected: InferenceEngine::Precision outPrc = InferenceEngine::Precision::UNSPECIFIED; InferenceEngine::ExecutableNetwork executableNetwork; + virtual void Validate(); + private: - void ConfigurePlugin() const; - void ConfigureNetwork() const; - void LoadNetwork(); - void Infer(); std::vector GetOutputs(); - void Validate(); - InferenceEngine::Core *core = nullptr; InferenceEngine::CNNNetwork cnnNetwork; InferenceEngine::InferRequest inferRequest; diff --git a/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/impl/mock_inference_plugin_internal.hpp b/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/impl/mock_inference_plugin_internal.hpp index fc8605aae4e..c68604a3781 100644 --- a/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/impl/mock_inference_plugin_internal.hpp +++ b/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/impl/mock_inference_plugin_internal.hpp @@ -18,8 +18,8 @@ class MockInferencePluginInternal2 : public InferenceEngine::InferencePluginInternal { public: - MOCK_METHOD3(LoadExeNetworkImpl, std::shared_ptr( - const InferenceEngine::ICore *, const InferenceEngine::ICNNNetwork &, const std::map &)); + MOCK_METHOD2(LoadExeNetworkImpl, std::shared_ptr( + const InferenceEngine::ICNNNetwork &, const std::map &)); MOCK_METHOD3(LoadNetwork, void( InferenceEngine::IExecutableNetwork::Ptr &, const InferenceEngine::ICNNNetwork &, @@ -30,8 +30,8 @@ public: class MockInferencePluginInternal : public InferenceEngine::InferencePluginInternal { public: - MOCK_METHOD3(LoadExeNetworkImpl, std::shared_ptr( - const InferenceEngine::ICore *, const InferenceEngine::ICNNNetwork &, const std::map &)); + MOCK_METHOD2(LoadExeNetworkImpl, std::shared_ptr( + const InferenceEngine::ICNNNetwork &, const std::map &)); MOCK_METHOD1(AddExtension, void(InferenceEngine::IExtensionPtr ext_ptr)); MOCK_METHOD1(SetConfig, void(const std::map &)); @@ -47,8 +47,8 @@ public: class MockInferencePluginInternal3 : public InferenceEngine::InferencePluginInternal { public: - MOCK_METHOD3(LoadExeNetworkImpl, std::shared_ptr( - const InferenceEngine::ICore *, const InferenceEngine::ICNNNetwork &, const std::map &)); + MOCK_METHOD2(LoadExeNetworkImpl, std::shared_ptr( + const InferenceEngine::ICNNNetwork &, const std::map &)); MOCK_METHOD1(AddExtension, void(InferenceEngine::IExtensionPtr ext_ptr)); MOCK_METHOD1(SetConfig, void(const std::map &)); }; diff --git a/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/mock_plugin_impl.hpp b/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/mock_plugin_impl.hpp index 5fac6e97f9f..996dc641a21 100644 --- a/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/mock_plugin_impl.hpp +++ b/inference-engine/tests/ie_test_utils/unit_test_utils/mocks/cpp_interfaces/mock_plugin_impl.hpp @@ -36,7 +36,7 @@ class MockPluginImpl { InferenceEngine::ExecutableNetwork ImportNetwork(const std::istream&, const std::map &) {return {};} MOCK_QUALIFIED_METHOD0(GetName, const noexcept, std::string(void)); MOCK_QUALIFIED_METHOD1(SetName, noexcept, void(const std::string &)); - MOCK_QUALIFIED_METHOD0(GetCore, const noexcept, const InferenceEngine::ICore*(void)); + MOCK_QUALIFIED_METHOD0(GetCore, const noexcept, InferenceEngine::ICore*(void)); MOCK_QUALIFIED_METHOD1(SetCore, noexcept, void(InferenceEngine::ICore*)); MOCK_CONST_METHOD2(GetConfig, InferenceEngine::Parameter(const std::string& name, diff --git a/inference-engine/tests/ngraph_functions/include/ngraph_functions/builders.hpp b/inference-engine/tests/ngraph_functions/include/ngraph_functions/builders.hpp index c4a92d62dcc..a7e246210c8 100644 --- a/inference-engine/tests/ngraph_functions/include/ngraph_functions/builders.hpp +++ b/inference-engine/tests/ngraph_functions/include/ngraph_functions/builders.hpp @@ -17,6 +17,7 @@ namespace ngraph { namespace builder { ngraph::ParameterVector makeParams(const element::Type &type, const std::vector> &shapes); +ngraph::ParameterVector makeParams(const element::Type &type, const std::vector>> &inputs); std::shared_ptr makeConstant(const element::Type &type, const std::vector &shape, const std::vector &data, bool random = false); @@ -92,5 +93,24 @@ std::shared_ptr makeSqueeze(const ngraph::Output &in, std::shared_ptr makeUnsqueeze(const ngraph::Output &in, const element::Type &type, const std::vector &squeeze_indices); + +std::shared_ptr makeProposal(const ngraph::Output &class_probs, + const ngraph::Output &class_logits, + const ngraph::Output &image_shape, + const element::Type &type, + size_t base_size, + size_t pre_nms_topn, + size_t post_nms_topn, + float nms_thresh, + size_t feat_stride, + size_t min_size, + const std::vector &ratio, + const std::vector &scale, + bool clip_before_nms, + bool clip_after_nms, + bool normalize, + float box_size_scale, + float box_coordinate_scale, + std::string framework); } // namespace builder } // namespace ngraph diff --git a/inference-engine/tests/ngraph_functions/src/params_vector.cpp b/inference-engine/tests/ngraph_functions/src/params_vector.cpp index 0ed3a35aaae..ba7ef1e75b8 100644 --- a/inference-engine/tests/ngraph_functions/src/params_vector.cpp +++ b/inference-engine/tests/ngraph_functions/src/params_vector.cpp @@ -15,7 +15,22 @@ ngraph::ParameterVector makeParams(const element::Type &type, const std::vector< auto paramNode = std::make_shared(type, ngraph::Shape(shape)); outs.push_back(paramNode); } + return outs; } + +ngraph::ParameterVector makeParams(const element::Type &type, const std::vector>> &inputs) { + ngraph::ParameterVector outs; + for (const auto &input : inputs) { + const auto &name = input.first; + const auto &shape = input.second; + auto paramNode = std::make_shared(type, ngraph::Shape(shape)); + paramNode->set_friendly_name(name); + outs.push_back(paramNode); + } + + return outs; +} + } // namespace builder } // namespace ngraph \ No newline at end of file diff --git a/inference-engine/tests/ngraph_functions/src/proposal.cpp b/inference-engine/tests/ngraph_functions/src/proposal.cpp new file mode 100644 index 00000000000..cbcbfdaeee2 --- /dev/null +++ b/inference-engine/tests/ngraph_functions/src/proposal.cpp @@ -0,0 +1,52 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// +// + +#include +#include + +#include "ngraph_functions/builders.hpp" + +namespace ngraph { +namespace builder { + +std::shared_ptr makeProposal(const ngraph::Output &class_probs, + const ngraph::Output &class_logits, + const ngraph::Output &image_shape, + const element::Type &type, + size_t base_size, + size_t pre_nms_topn, + size_t post_nms_topn, + float nms_thresh, + size_t feat_stride, + size_t min_size, + const std::vector &ratio, + const std::vector &scale, + bool clip_before_nms, + bool clip_after_nms, + bool normalize, + float box_size_scale, + float box_coordinate_scale, + std::string framework) { + ngraph::op::ProposalAttrs attrs; + attrs.base_size = base_size; + attrs.pre_nms_topn = pre_nms_topn; + attrs.post_nms_topn = post_nms_topn; + attrs.nms_thresh = nms_thresh; + attrs.feat_stride = feat_stride; + attrs.min_size = min_size; + attrs.ratio = ratio; + attrs.scale = scale; + attrs.clip_before_nms = clip_before_nms; + attrs.clip_after_nms = clip_after_nms; + attrs.normalize = normalize; + attrs.box_size_scale = box_size_scale; + attrs.box_coordinate_scale = box_coordinate_scale; + attrs.framework = framework; + + return std::make_shared(class_probs, class_logits, image_shape, attrs); +} + +} // namespace builder +} // namespace ngraph diff --git a/inference-engine/tests/ngraph_functions/src/utils/ngraph_helpers.cpp b/inference-engine/tests/ngraph_functions/src/utils/ngraph_helpers.cpp index be02b12e6e7..673075821fc 100644 --- a/inference-engine/tests/ngraph_functions/src/utils/ngraph_helpers.cpp +++ b/inference-engine/tests/ngraph_functions/src/utils/ngraph_helpers.cpp @@ -60,10 +60,9 @@ std::vector> interpreterFunction(const std::shared_ptr auto outputTensors = std::vector>{}; const auto &results = function->get_results(); - std::transform(results.cbegin(), results.cend(), std::back_inserter(outputTensors), - [&backend](const std::shared_ptr &result) { - return backend->create_tensor(result->get_element_type(), result->get_shape()); - }); + for (size_t i = 0; i ()); + } auto handle = backend->compile(function); handle->call_with_validate(outputTensors, inputTensors); diff --git a/inference-engine/tests/unit/engines/vpu/split_large_kernel_conv_tests.cpp b/inference-engine/tests/unit/engines/vpu/split_large_kernel_conv_tests.cpp new file mode 100644 index 00000000000..99ffa78714e --- /dev/null +++ b/inference-engine/tests/unit/engines/vpu/split_large_kernel_conv_tests.cpp @@ -0,0 +1,105 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "graph_transformer_tests.hpp" +#include +#include + +using namespace vpu; + +class VPU_SplitLargeKernelConvTest : public GraphTransformerTest { + protected: + PassSet pipeline; + Model model; + + public: + void InitConvStage( + int inputX = 8960, + int inputY = 1, + bool isOutput4D = true) { + + int kernelx = 16; + int kernely = 1; + int kernelStrideX = 1; + int kernelStrideY = 1; + int dilationX = 1; + int dilationY = 1; + int padx_begin = 7; + int pady_begin = 0; + int padx_end = 8; + int pady_end = 0; + model = CreateModel(); + + auto input = model->addInputData( + "Input", + DataDesc(DataType::FP16, DimsOrder::NCHW, {inputX, inputY, 8, 1})); + model->attrs().set("numInputs", 1); + + Data output; + if (isOutput4D) { + output = model->addOutputData( + "Output", + DataDesc(DataType::FP16, + DimsOrder::NCHW, + {(inputX + padx_begin + padx_end - kernelx) / kernelStrideX + 1, + (inputY + pady_begin + pady_end - kernely) / kernelStrideY + 1, 8, 1})); + } else { + output = model->addOutputData( + "Output", + DataDesc(DataType::FP16, + DimsOrder::CHW, + {(inputX + padx_begin + padx_end - kernelx) / kernelStrideX + 1, + (inputY + pady_begin + pady_end - kernely) / kernelStrideY + 1, 8})); + } + + auto conv = std::make_shared(ie::LayerParams{"conv", "Convolution", ie::Precision::FP16}); + conv->_kernel_x = kernelx; + conv->_kernel_y = kernely; + conv->_stride_x = kernelStrideX; + conv->_stride_y = kernelStrideY; + conv->_dilation_x = dilationX; + conv->_dilation_x = dilationY; + + conv->_padding.insert(0, padx_begin); + conv->_padding.insert(1, pady_begin); + conv->_pads_end.insert(0, padx_end); + conv->_pads_end.insert(1, pady_end); + conv->_auto_pad = "same_upper"; + + conv->_weights = ie::make_shared_blob({ ie::Precision::FP16, {static_cast(kernelx * kernely * 8 * 8)}, ie::Layout::C }); + conv->_weights->allocate(); + + frontEnd->parseConvolution(model, conv, {input}, {output}); + + pipeline.addPass(passManager->dumpModel("initial")); + + pipeline.addPass(passManager->hwPadding()); + pipeline.addPass(passManager->dumpModel("hwPadding")); + + // if large kernel conv converted to conv that can be ran on HW, then hwConvTiling will work - if not will got an exception + pipeline.addPass(passManager->splitLargeKernelConv()); + pipeline.addPass(passManager->dumpModel("splitLargeKernelConv")); + + pipeline.addPass(passManager->hwConvTiling()); + pipeline.addPass(passManager->dumpModel("hwConvTiling")); + + pipeline.addPass(passManager->adjustDataLayout()); + pipeline.addPass(passManager->dumpModel("adjustDataLayout")); + + pipeline.addPass(passManager->processSpecialStages()); + pipeline.addPass(passManager->dumpModel("processSpecialStages")); + + pipeline.addPass(passManager->adjustDataLocation()); + pipeline.addPass(passManager->dumpModel("adjustDataLocation")); + + pipeline.addPass(passManager->finalCheck()); + } +}; + +TEST_F(VPU_SplitLargeKernelConvTest, splitLargeKernelConvIfKernelSizeIs1x16) { + InitCompileEnv(); + InitConvStage(); + + ASSERT_NO_THROW(pipeline.run(model)); +} \ No newline at end of file diff --git a/inference-engine/tests/unit/inference_engine/CMakeLists.txt b/inference-engine/tests/unit/inference_engine/CMakeLists.txt index 55e52b59262..629a2cecfbb 100644 --- a/inference-engine/tests/unit/inference_engine/CMakeLists.txt +++ b/inference-engine/tests/unit/inference_engine/CMakeLists.txt @@ -11,5 +11,5 @@ addIeTargetTest( unitTestUtils ADD_CPPLINT LABELS - INFERENCE_ENGINE + IE ) \ No newline at end of file diff --git a/inference-engine/tests/unit/vpu/frontend_tests/dsr_parsing_tests.cpp b/inference-engine/tests/unit/vpu/frontend_tests/dsr_parsing_tests.cpp index 8c9de8b0552..b4fbbb55b30 100644 --- a/inference-engine/tests/unit/vpu/frontend_tests/dsr_parsing_tests.cpp +++ b/inference-engine/tests/unit/vpu/frontend_tests/dsr_parsing_tests.cpp @@ -24,6 +24,21 @@ protected: _testModel = CreateTestModel(); } + void checkShapeConnection(const Data& parent, const Data& child) { + ASSERT_NE(child->parentDataToShapeEdge(), nullptr); + ASSERT_EQ(child->childDataToShapeEdges().size(), 0); + const auto& parentDataToShapeEdge = child->parentDataToShapeEdge(); + ASSERT_EQ(parentDataToShapeEdge->parent(), parent); + ASSERT_EQ(parentDataToShapeEdge->child(), child); + + ASSERT_EQ(parent->parentDataToShapeEdge(), nullptr); + + const auto& childDataToShapeEdges = parent->childDataToShapeEdges(); + + const auto& it = std::find(childDataToShapeEdges.begin(), childDataToShapeEdges.end(), parentDataToShapeEdge); + ASSERT_NE(it, childDataToShapeEdges.end()); + } + ie::CNNLayerPtr createDSRLayer() { return std::make_shared(ie::LayerParams{"DSR", "DynamicShapeResolver", ie::Precision::I32}); } @@ -97,25 +112,34 @@ TEST_F(DSRParsingTests, DSRParserDoesntAssertOnCorrectIO) { {inputStage->output(0), inputStage->output(1)}, _testModel.getOutputs())); } -class DSRParsingFromNgraphTests : public DSRParsingTests { -protected: - void checkShapeConnection(const Data& parent, const Data& child) { - ASSERT_NE(child->parentDataToShapeEdge(), nullptr); - ASSERT_EQ(child->childDataToShapeEdges().size(), 0); - const auto& parentDataToShapeEdge = child->parentDataToShapeEdge(); - ASSERT_EQ(parentDataToShapeEdge->parent(), parent); - ASSERT_EQ(parentDataToShapeEdge->child(), child); +TEST_F(DSRParsingTests, DSRParserPreservesConnectionsOnOutputDSR) { + _testModel.createInputs({_dataDesc}); + _testModel.createOutputs({_dataDesc}); - ASSERT_EQ(parent->parentDataToShapeEdge(), nullptr); + const auto& model = _testModel.getBaseModel(); - const auto& childDataToShapeEdges = parent->childDataToShapeEdges(); - ASSERT_EQ(childDataToShapeEdges.size(), 1); + const auto& inputStage = _testModel.addStage({InputInfo::fromNetwork(0)}, + {OutputInfo::intermediate(_dataDesc), OutputInfo::intermediate(_correstShapeDesc)}); - ASSERT_EQ(childDataToShapeEdges.front(), parentDataToShapeEdge); - } -}; + model->connectDataWithShape(inputStage->output(1), inputStage->output(0)); -TEST_F(DSRParsingFromNgraphTests, DSRParserCreatesTwoOutputsOnOutputDSR) { + checkShapeConnection(inputStage->output(1), inputStage->output(0)); + + const auto& outputStage = _testModel.addStage({InputInfo::fromPrevStage(0)}, + {OutputInfo::intermediate(_dataDesc)}); + + const auto& dsrLayer = createDSRLayer(); + + ASSERT_NO_THROW(frontEnd->parseDSR(_testModel.getBaseModel(), dsrLayer, + {outputStage->output(0), inputStage->output(1)}, _testModel.getOutputs())); + + checkShapeConnection(inputStage->output(1), inputStage->output(0)); + checkShapeConnection(inputStage->output(1), outputStage->output(0)); +} + +typedef DSRParsingTests DSRParsingFromNgraphTests; + +TEST_F(DSRParsingFromNgraphTests, DSRParserCreatesAndConnectsTwoOutputsOnOutputDSR) { const auto& inPrecision = ::ngraph::element::Type(::ngraph::element::Type_t::i32); const auto& tensor = std::make_shared(inPrecision, ngraph::Shape{1, 800}); @@ -139,6 +163,15 @@ TEST_F(DSRParsingFromNgraphTests, DSRParserCreatesTwoOutputsOnOutputDSR) { } } ASSERT_EQ(numOutputs, 2); + + const auto& it = std::find_if(model->getStages().begin(), model->getStages().end(), [](const Stage& stage) { + return stage->type() == StageType::NonZero; + }); + + ASSERT_NE(it, model->getStages().end()); + const auto& nonZeroStage = *it; + + checkShapeConnection(nonZeroStage->output(1), nonZeroStage->output(0)); } TEST_F(DSRParsingFromNgraphTests, DSRWithSingleProducerCreatesConnectionBetweenDataAndShape) { @@ -159,16 +192,12 @@ TEST_F(DSRParsingFromNgraphTests, DSRWithSingleProducerCreatesConnectionBetweenD ModelPtr model; ASSERT_NO_THROW(model = frontEnd->buildInitialModel(cnnNet)); - Stage nonZeroStage = nullptr; + const auto& it = std::find_if(model->getStages().begin(), model->getStages().end(), [](const Stage& stage) { + return stage->type() == StageType::NonZero; + }); - for (const auto& stage : model->getStages()) { - if (stage->type() != StageType::NonZero) { - continue; - } - nonZeroStage = stage; - } - - ASSERT_NE(nonZeroStage, nullptr); + ASSERT_NE(it, model->getStages().end()); + const auto& nonZeroStage = *it; checkShapeConnection(nonZeroStage->output(1), nonZeroStage->output(0)); } @@ -193,16 +222,12 @@ TEST_F(DSRParsingFromNgraphTests, DSRWithTwoProducersCreatesConnectionBetweenDat ModelPtr model; ASSERT_NO_THROW(model = frontEnd->buildInitialModel(cnnNet)); - Stage nonZeroStage = nullptr; + const auto& it = std::find_if(model->getStages().begin(), model->getStages().end(), [](const Stage& stage) { + return stage->type() == StageType::NonZero; + }); - for (const auto& stage : model->getStages()) { - if (stage->type() != StageType::NonZero) { - continue; - } - nonZeroStage = stage; - } - - ASSERT_NE(nonZeroStage, nullptr); + ASSERT_NE(it, model->getStages().end()); + const auto& nonZeroStage = *it; const auto& stageReluData = nonZeroStage->output(0)->singleConsumer(); const auto& stageReluShape = nonZeroStage->output(1)->singleConsumer(); diff --git a/inference-engine/tests/unit/vpu/middleend_tests/edges_tests/stage_dependency_edge.cpp b/inference-engine/tests/unit/vpu/middleend_tests/edges_tests/stage_dependency_edge.cpp index e41b52f9226..80b6bd17179 100644 --- a/inference-engine/tests/unit/vpu/middleend_tests/edges_tests/stage_dependency_edge.cpp +++ b/inference-engine/tests/unit/vpu/middleend_tests/edges_tests/stage_dependency_edge.cpp @@ -24,7 +24,7 @@ protected: PassSet::Ptr _middleEnd = nullptr; }; -TEST_F(StageDependencyEdgeProcessingTests, AddStageDependencyAssertsOnOutputData) { +TEST_F(StageDependencyEdgeProcessingTests, AddStageDependencyDoesNotAssertOnOutputData) { // // -> [Data] -> (Stage) -> [Output] // [Input] -> (Stage) | @@ -43,7 +43,7 @@ TEST_F(StageDependencyEdgeProcessingTests, AddStageDependencyAssertsOnOutputData auto model = _testModel.getBaseModel(); - ASSERT_ANY_THROW(model->addStageDependency(dependentStage, dependencyProducer->output(0))); + ASSERT_NO_THROW(model->addStageDependency(dependentStage, dependencyProducer->output(0))); } TEST_F(StageDependencyEdgeProcessingTests, NetWithTwoStagesHasCorrectExecOrder) { diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp index 8a8876d0ec6..4c8ff9a2f59 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp @@ -4,7 +4,7 @@ #include "holders_tests.hpp" -INSTANTIATE_TEST_CASE_P(ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { +INSTANTIATE_TEST_CASE_P(smoke_ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { // 0 - plugin // 1 - executable_network // 2 - infer_request diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp index d72defce5bc..3cf345d3755 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp @@ -6,9 +6,9 @@ #include "behavior_test_plugins.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp index 01cde8fc897..718d09932dd 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp @@ -6,13 +6,13 @@ #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, ValuesIn(supportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp index f41607dc29e..9979c087f36 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp @@ -7,7 +7,7 @@ // Disabled due to a bug on CentOS that leads to segmentation fault of application on exit // when perf counters are enabled -//INSTANTIATE_TEST_CASE_P( +//INSTANTIATE_TEST_CASE_P(smoke_ // BehaviorTest, // BehaviorPluginTestExecGraphInfo, // ValuesIn(supportedValues), diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp index 220d66d636b..d6312a9cb89 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_infer_request.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp index 919fccd2a3f..1f78f6d8bc3 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_callback.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp index 9e5800d7600..52338059f8d 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_config.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), getConfigTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp index 01971669903..d71e2d5d0d0 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_input.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp index 9719941190a..1ebe6221d8b 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_output.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp index d35e5b83e60..ad2113ffe08 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp @@ -10,7 +10,7 @@ memory_test_params memory_test_cases[] = { // FIXME //#if (defined INSTANTIATE_TESTS) -//INSTANTIATE_TEST_CASE_P(BehaviorTest, MemoryLayerTest, +//INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, MemoryLayerTest, // ::testing::ValuesIn(memory_test_cases), // getTestName); //#endif diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp index 93367391b18..ce4f8653524 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp @@ -14,8 +14,8 @@ layout_test_params conv_test_cases[] = { layout_test_params("GPU", "FP32", Layout::NCHW, power_params({ { 1, 3, 16, 16 } }, 1, 2, 2)), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadPower, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadPower, ::testing::ValuesIn(power_test_cases), getTestName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadConv, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadConv, ::testing::ValuesIn(conv_test_cases), getTestName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_perf_counters.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_perf_counters.cpp index dc309f4b819..1ac533a2576 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_perf_counters.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_perf_counters.cpp @@ -7,7 +7,7 @@ // Disabled due to a bug on CentOS that leads to segmentation fault of application on exit // when perf counters are enabled -//INSTANTIATE_TEST_CASE_P( +//INSTANTIATE_TEST_CASE_P(smoke_ // BehaviorTest, // BehaviorPluginTestPerfCounters, // ValuesIn(supportedValues), diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp index 75009b9575c..14d5d0339fc 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp @@ -5,7 +5,7 @@ #include "behavior_test_plugin_set_preprocess.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestPreProcess, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp index 3aa7335218c..51b6f824c86 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp @@ -5,8 +5,8 @@ #include "behavior_test_plugin_unsupported.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp index 1915219e239..a9857f629ed 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_version.hpp" #include "cldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/cldnn_test_data.hpp b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/cldnn_test_data.hpp index 1b1ec48c3d3..81df192fdda 100644 --- a/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/cldnn_test_data.hpp +++ b/inference-engine/tests_deprecated/behavior/cldnn/shared_tests_instances/plugin_tests/cldnn_test_data.hpp @@ -14,19 +14,34 @@ FuncTestUtils::TestModel::convReluNormPoolFcModelFP32.model_xml_str, \ FuncTestUtils::TestModel::convReluNormPoolFcModelFP32.weights_blob, \ Precision::FP32) +// for multi-device we are testing the fp16 (as it is supported by all device combos we are considering for testing +// e.g. GPU and VPU, for CPU the network is automatically (internally) converted to fp32. +const std::map multi_device_conf = {{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}}; +#define BEH_MULTI BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.weights_blob, \ + Precision::FP32, \ + multi_device_conf) // all parameters are unsupported - reversed #define BEH_US_ALL_CLDNN BehTestParams("GPU", \ FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.model_xml_str, \ FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.weights_blob, \ Precision::Q78) +#define BEH_US_ALL_MULTI BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.weights_blob, \ + Precision::Q78, \ + multi_device_conf) const BehTestParams supportedValues[] = { BEH_CLDNN, + BEH_MULTI, }; const BehTestParams requestsSupportedValues[] = { BEH_CLDNN, + BEH_MULTI, }; const BehTestParams allInputSupportedValues[] = { @@ -36,19 +51,34 @@ const BehTestParams allInputSupportedValues[] = { BEH_CLDNN.withIn(Precision::FP16).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}}), BEH_CLDNN.withIn(Precision::I16).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}}), BEH_CLDNN.withIn(Precision::I32).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}}), + BEH_MULTI, BEH_MULTI.withIn(Precision::FP16), BEH_MULTI.withIn(Precision::U8), BEH_MULTI.withIn(Precision::I16), + BEH_MULTI.withIn(Precision::I32), + BEH_MULTI.withIn(Precision::U8).withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}}), + BEH_MULTI.withIn(Precision::FP16).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}, + {MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}}), + BEH_MULTI.withIn(Precision::I16).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}, + {MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}}), + BEH_MULTI.withIn(Precision::I32).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}, + {MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}}), }; const BehTestParams allOutputSupportedValues[] = { BEH_CLDNN, BEH_CLDNN.withOut(Precision::FP16), BEH_CLDNN.withIn(Precision::FP16).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}}), + BEH_MULTI, BEH_MULTI.withOut(Precision::FP16), + BEH_MULTI.withIn(Precision::FP16).withConfig({{KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}, + {MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}}), }; const BehTestParams typeUnSupportedValues[] = { BEH_CLDNN.withIn(Precision::Q78), BEH_CLDNN.withIn(Precision::I8), + BEH_MULTI.withIn(Precision::Q78), BEH_MULTI.withIn(Precision::I8), }; const BehTestParams allUnSupportedValues[] = { BEH_US_ALL_CLDNN, + BEH_US_ALL_MULTI, }; const std::vector withCorrectConfValues = { @@ -63,6 +93,23 @@ const std::vector withCorrectConfValues = { BEH_CLDNN.withConfig({{KEY_TUNING_MODE, TUNING_CREATE}, {KEY_TUNING_FILE, "tfile"}}), BEH_CLDNN.withConfig({{KEY_DEVICE_ID, "0"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_GPU_THROUGHPUT_STREAMS, GPU_THROUGHPUT_AUTO}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_GPU_THROUGHPUT_STREAMS, "2"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_PERF_COUNT, NO}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_DUMP_KERNELS, NO}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_DUMP_KERNELS, YES}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_TUNING_MODE, TUNING_DISABLED}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_TUNING_MODE, TUNING_CREATE}, + {KEY_TUNING_FILE, "tfile"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_DEVICE_ID, "0"}}), }; const BehTestParams withIncorrectConfValues[] = { @@ -75,6 +122,18 @@ const BehTestParams withIncorrectConfValues[] = { // FIXME: [IE clDNN] The plugin doesn't throw GENERAL_ERROR if use non-exist tuning file. CVS-8593 //BEH_CLDNN.withConfig({ { KEY_TUNING_MODE, TUNING_USE_EXISTING }, // { KEY_TUNING_FILE, "unknown_file" } }), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_GPU_THROUGHPUT_STREAMS, "OFF"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_PERF_COUNT, "ON"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_CONFIG_FILE, "unknown_file"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_DUMP_KERNELS, "ON"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_TUNING_MODE, "TUNING_UNKNOWN_MODE"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "GPU"}, + {KEY_DEVICE_ID, "DEVICE_UNKNOWN"}}) }; const std::vector withCorrectConfValuesNetworkOnly = { @@ -83,4 +142,5 @@ const std::vector withCorrectConfValuesNetworkOnly = { const BehTestParams withIncorrectConfKeys[] = { BEH_CLDNN.withIncorrectConfigItem(), + BEH_MULTI.withIncorrectConfigItem(), }; diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/cpp_wrappers/holders_tests.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/cpp_wrappers/holders_tests.cpp index 7b7ed39cf26..75e76e2f854 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/cpp_wrappers/holders_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/cpp_wrappers/holders_tests.cpp @@ -4,7 +4,7 @@ #include "holders_tests.hpp" -INSTANTIATE_TEST_CASE_P(ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { +INSTANTIATE_TEST_CASE_P(smoke_ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { // 0 - plugin // 1 - executable_network // 2 - infer_request diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp index ce4219d51cc..5e8a3f7110a 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp @@ -6,9 +6,9 @@ #include "behavior_test_plugins.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp index fe72b8e2749..bff01fb2f8f 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp @@ -5,13 +5,13 @@ #include "behavior_test_plugin_config.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, ValuesIn(withIncorrectConfKeys), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp index 93411409ece..c8099abab34 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp @@ -6,7 +6,7 @@ #include "gna_test_data.hpp" INSTANTIATE_TEST_CASE_P( - BehaviorTest, + smoke_BehaviorTest, BehaviorPluginTestExecGraphInfo, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp index 3a2a25ceb35..9918dc508fc 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_infer_request.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp index 906805e5df4..17c4deb6eaa 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp @@ -6,5 +6,5 @@ #include "gna_test_data.hpp" // TODO: support InferRequestCallback in GNAPlugin -//INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), +//INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), // getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp index 6eb64e9001a..67ae245bbe2 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp @@ -5,11 +5,11 @@ #include "behavior_test_plugin_infer_request_config.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfig, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfig, ValuesIn(withCorrectConfValues), getConfigTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), getConfigTestCaseName); bool CheckGnaHw() { @@ -38,6 +38,6 @@ TEST_P(BehaviorPluginTestInferRequestWithGnaHw, CanInferOrFailWithGnaHw) { } } -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestWithGnaHw, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestWithGnaHw, ValuesIn(withGnaHwConfValue), getConfigTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp index 235c647481e..2c9c0d1ede8 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_input.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp index bb123323940..d9c222058d5 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_output.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp index bbda3162b89..d7dca53f937 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp @@ -10,7 +10,7 @@ conv_test_params deconv_test_cases[] = { conv_test_params(CommonTestUtils::DEVICE_GNA, conv_case) }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, DeconvolutionLayerTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, DeconvolutionLayerTest, ::testing::ValuesIn(deconv_test_cases), getTestName); @@ -19,17 +19,17 @@ pool_test_params roi_pool_test_cases[] = { }; // TODO: fix this -//INSTANTIATE_TEST_CASE_P(BehaviorTest, PoolingLayerTest, +//INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, PoolingLayerTest, // ::testing::Values(pool_test_params("GNAPlugin", "FP32", pool_case)), // getTestName); // -//INSTANTIATE_TEST_CASE_P(BehaviorTest, ReLULayerTest, +//INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, ReLULayerTest, // ::testing::Values(activ_test_params("GNAPlugin", "FP32", activation_case)), // getTestName); // FIXME //#if (defined INSTANTIATE_TESTS) -//INSTANTIATE_TEST_CASE_P(BehaviorTest, MemoryLayerTest, +//INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, MemoryLayerTest, // ::testing::ValuesIn(memory_test_cases), // getTestName); //#endif diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp index 385fc84ae5d..7e621e71d12 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp @@ -11,5 +11,5 @@ layout_test_params activ_test_cases[] = { layout_test_params(CommonTestUtils::DEVICE_GNA, "FP32", Layout::NCHW, power_params({ { 1, 3, 16, 16 } }, 2, 2, 2)), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadActiv, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadActiv, ::testing::ValuesIn(activ_test_cases), getTestName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp index 6220d6974da..56b4c501bce 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp @@ -5,10 +5,10 @@ #include "behavior_test_plugin_unsupported.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), getTestCaseName); - INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestBatchUnsupported, ValuesIn(batchUnSupportedValues), + INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestBatchUnsupported, ValuesIn(batchUnSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp index 7b3c76db38e..2900cffb31e 100644 --- a/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp +++ b/inference-engine/tests_deprecated/behavior/gna/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_version.hpp" #include "gna_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp index 4a48d06f408..4d4dc98a194 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/cpp_wrappers/holders_tests.cpp @@ -4,7 +4,7 @@ #include "holders_tests.hpp" -INSTANTIATE_TEST_CASE_P(ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { +INSTANTIATE_TEST_CASE_P(smoke_ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { // 0 - plugin // 1 - executable_network // 2 - infer_request diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp index 3ee37d6660a..471137bea84 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp @@ -6,9 +6,9 @@ #include "behavior_test_plugins.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp index 487dc47a146..bdabdb8563b 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp @@ -5,17 +5,17 @@ #include "behavior_test_plugin_config.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginCorrectConfigTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginCorrectConfigTest, ValuesIn(BehTestParams::concat(withCorrectConfValues, withCorrectConfValuesPluginOnly)), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, ValuesIn(withIncorrectConfKeys), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp index ec20b0cbe47..af7aa87d7a2 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp @@ -6,7 +6,7 @@ #include "mkldnn_test_data.hpp" INSTANTIATE_TEST_CASE_P( - BehaviorTest, + smoke_BehaviorTest, BehaviorPluginTestExecGraphInfo, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp index 41bd3e38c18..f74a84c4bb1 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_infer_request.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp index ee7164699b3..fe0f54b336a 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_callback.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp index d1d7c863c47..408d020f192 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp @@ -5,10 +5,10 @@ #include "behavior_test_plugin_infer_request_config.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfig, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfig, ValuesIn(BehTestParams::concat(withCorrectConfValues, withCorrectConfValuesNetworkOnly)), getConfigTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), getConfigTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp index 088844ff64e..12f48efdd7a 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_input.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp index 7bc7022eb0c..575a8653417 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_output.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp index 5e47e3aa6da..2bd8e2d9df0 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp @@ -8,7 +8,7 @@ pool_test_params roi_pool_test_cases[] = { pool_test_params(CommonTestUtils::DEVICE_CPU, "FP32", pool_case), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, ROIPoolingLayerTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, ROIPoolingLayerTest, ::testing::ValuesIn(roi_pool_test_cases), getTestName); @@ -16,11 +16,11 @@ activ_test_params activ_test_cases[] = { activ_test_params(CommonTestUtils::DEVICE_CPU, "FP32", activation_case), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, ActivationLayerTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, ActivationLayerTest, ::testing::ValuesIn(activ_test_cases), getTestName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, ReLULayerTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, ReLULayerTest, ::testing::Values(activ_test_params("CPU", "FP32", activation_case)), getTestName); @@ -28,6 +28,6 @@ norm_test_params norm_test_cases[] = { norm_test_params(CommonTestUtils::DEVICE_CPU, "FP32", norm_case), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, NormalizeLayerTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, NormalizeLayerTest, ::testing::ValuesIn(norm_test_cases), getTestName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp index 49750d115fd..efb38cf232a 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp @@ -21,9 +21,9 @@ layout_test_params conv_neg_test_cases[] = { layout_test_params(CommonTestUtils::DEVICE_CPU, "FP32", Layout::CHW, power_params({ { 3, 32, 16 } }, 2, 2, 2)), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadPower, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadPower, ::testing::ValuesIn(power_test_cases), getTestName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadConv, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadConv, ::testing::ValuesIn(conv_test_cases), getTestName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanNotLoadConv, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanNotLoadConv, ::testing::ValuesIn(conv_neg_test_cases), getTestName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp index 266f1b04175..17369e12bf3 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp @@ -5,7 +5,7 @@ #include "behavior_test_plugin_set_preprocess.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestPreProcess, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp index aadb3dc3cdc..9ca1d5d601c 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp @@ -5,8 +5,8 @@ #include "behavior_test_plugin_unsupported.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp index 8d05905f446..3811e22f627 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_version.hpp" #include "mkldnn_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/mkldnn_test_data.hpp b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/mkldnn_test_data.hpp index 7f7f36ec9ed..71539e2ea1b 100644 --- a/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/mkldnn_test_data.hpp +++ b/inference-engine/tests_deprecated/behavior/mkldnn/shared_tests_instances/plugin_tests/mkldnn_test_data.hpp @@ -17,21 +17,39 @@ FuncTestUtils::TestModel::convReluNormPoolFcModelFP32.model_xml_str, \ FuncTestUtils::TestModel::convReluNormPoolFcModelFP32.weights_blob, \ Precision::FP32) +// for multi-device we are testing the fp16 (as it is supported by all device combos we are considering for testing +// e.g. GPU and VPU, for CPU the network is automatically (internally) converted to fp32. +// Yet the input precision FP16 is not supported by the CPU yet +const std::map multi_device_conf = {{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}}; +#define BEH_MULTI BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.weights_blob, \ + Precision::FP32, \ + multi_device_conf) // all parameters are unsupported - reversed #define BEH_US_ALL_MKLDNN BehTestParams("CPU", \ FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.model_xml_str, \ FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.weights_blob, \ Precision::Q78) +#define BEH_US_ALL_MULTI BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.weights_blob, \ + Precision::Q78, \ + multi_device_conf) const BehTestParams supportedValues[] = { BEH_MKLDNN, + BEH_MULTI, }; const BehTestParams requestsSupportedValues[] = { BEH_MKLDNN, // the following adds additional test the MKLDNNGraphlessInferRequest (explicitly created for streams) BEH_MKLDNN.withConfig({{KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), + BEH_MKLDNN.withConfig({{CONFIG_KEY(CPU_THROUGHPUT_STREAMS),"0"}, + {CONFIG_KEY(CPU_THREADS_NUM), "1"}}), + BEH_MULTI, }; const BehTestParams allInputSupportedValues[] = { @@ -50,21 +68,36 @@ const BehTestParams allInputSupportedValues[] = { BEH_MKLDNN_FP16.withIn(Precision::U8).withConfig({{KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), BEH_MKLDNN_FP16.withIn(Precision::U16).withConfig({{KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), BEH_MKLDNN_FP16.withIn(Precision::I16).withConfig({{KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), + BEH_MULTI, + BEH_MULTI.withIn(Precision::U8), + BEH_MULTI.withIn(Precision::U16), + BEH_MULTI.withIn(Precision::I16), + BEH_MULTI.withIn(Precision::U8).withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), + BEH_MULTI.withIn(Precision::U16).withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), + BEH_MULTI.withIn(Precision::I16).withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), }; const BehTestParams allOutputSupportedValues[] = { BEH_MKLDNN, // the following withConfig test checks the MKLDNNGraphlessInferRequest (explicitly created for streams) BEH_MKLDNN.withConfig({{KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), + BEH_MULTI.withOut(Precision::FP32), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), }; const BehTestParams typeUnSupportedValues[] = { BEH_MKLDNN.withIn(Precision::Q78), BEH_MKLDNN_FP16, + BEH_MULTI.withIn(Precision::Q78), }; const BehTestParams allUnSupportedValues[] = { BEH_US_ALL_MKLDNN, + BEH_US_ALL_MULTI, }; const std::vector withCorrectConfValues = { @@ -74,20 +107,40 @@ const std::vector withCorrectConfValues = { BEH_MKLDNN.withConfig({{KEY_CPU_BIND_THREAD, NO}}), BEH_MKLDNN.withConfig({{KEY_CPU_BIND_THREAD, YES}}), BEH_MKLDNN.withConfig({{KEY_DYN_BATCH_LIMIT, "10"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_NUMA}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, CPU_THROUGHPUT_AUTO}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, "8"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_BIND_THREAD, NO}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_BIND_THREAD, YES}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_DYN_BATCH_LIMIT, "10"}}), }; const BehTestParams withIncorrectConfValues[] = { BEH_MKLDNN.withConfig({{KEY_CPU_THROUGHPUT_STREAMS, "OFF"}}), BEH_MKLDNN.withConfig({{KEY_CPU_BIND_THREAD, "OFF"}}), BEH_MKLDNN.withConfig({{KEY_DYN_BATCH_LIMIT, "NAN"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_THROUGHPUT_STREAMS, "OFF"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_CPU_BIND_THREAD, "OFF"}}), + BEH_MULTI.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}, + {KEY_DYN_BATCH_LIMIT, "NAN"}}), }; const std::vector withCorrectConfValuesPluginOnly; const std::vector withCorrectConfValuesNetworkOnly = { BEH_MKLDNN.withConfig({}), + BEH_MULTI }; const BehTestParams withIncorrectConfKeys[] = { BEH_MKLDNN.withIncorrectConfigItem(), + BEH_MULTI.withIncorrectConfigItem(), }; diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt b/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt index c570cb96907..942efa46e29 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt +++ b/inference-engine/tests_deprecated/behavior/shared_tests/CMakeLists.txt @@ -11,7 +11,7 @@ file(GLOB_RECURSE SHARED_TESTS_SRC ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp) add_library(${TARGET_NAME} STATIC ${SHARED_TESTS_SRC}) -add_dependencies(${TARGET_NAME} inference_engine_preproc) +add_dependencies(${TARGET_NAME} MultiDevicePlugin inference_engine_preproc) target_include_directories(${TARGET_NAME} PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/plugin_tests") diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin.h b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin.h index c08d9ee1f9a..a1e593e3014 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin.h +++ b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin.h @@ -14,7 +14,7 @@ #include #include #include -#include +#include #include #include diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_config.hpp b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_config.hpp index 4edc6b5096d..7b43063ac71 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_config.hpp +++ b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_config.hpp @@ -193,7 +193,7 @@ TEST_P(BehaviorPluginCorrectConfigTestInferRequestAPI, reusableCPUStreamsExecuto // for multi-device the number of Executors is not known (defined by the devices configuration) } else { ASSERT_EQ(0u, ExecutorManager::getInstance()->getExecutorsNumber()); - ASSERT_GE(1u, ExecutorManager::getInstance()->getIdleCPUStreamsExecutorsNumber()); + ASSERT_GE(2u, ExecutorManager::getInstance()->getIdleCPUStreamsExecutorsNumber()); } } if (GetParam().device == CommonTestUtils::DEVICE_CPU) { diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request.hpp b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request.hpp index 38d520ea89d..b0fea6154d5 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request.hpp +++ b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request.hpp @@ -13,8 +13,11 @@ using namespace InferenceEngine::details; namespace { std::string getTestCaseName(testing::TestParamInfo obj) { - return obj.param.device + "_" + obj.param.input_blob_precision.name() - + (obj.param.config.size() ? "_" + obj.param.config.begin()->second : ""); + std::string config; + for (auto&& cfg : obj.param.config) { + config += "_" + cfg.first + "_" + cfg.second; + } + return obj.param.device + "_" + obj.param.input_blob_precision.name() + config; } } @@ -500,6 +503,15 @@ TEST_P(BehaviorPluginTestInferRequest, canWaitWithotStartAsync) { TEST_P(BehaviorPluginTestInferRequest, returnDeviceBusyOnSetBlobAfterAsyncInfer) { SKIP_IF_CURRENT_TEST_IS_DISABLED() + auto&& config = GetParam().config; + auto itConfig = config.find(CONFIG_KEY(CPU_THROUGHPUT_STREAMS)); + if (itConfig != config.end()) { + if (itConfig->second != "CPU_THROUGHPUT_AUTO") { + if (std::stoi(itConfig->second) == 0) { + GTEST_SKIP() << "Not applicable with disabled streams"; + } + } + } TestEnv::Ptr testEnv; ASSERT_NO_FATAL_FAILURE(_createAndCheckInferRequest(GetParam(), testEnv)); Blob::Ptr input; diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request_callback.hpp b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request_callback.hpp index 08c98f7063c..75565eafddd 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request_callback.hpp +++ b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_infer_request_callback.hpp @@ -13,8 +13,11 @@ using namespace InferenceEngine::details; namespace { std::string getTestCaseName(testing::TestParamInfo obj) { - return obj.param.device + "_" + obj.param.input_blob_precision.name() - + (obj.param.config.size() ? "_" + obj.param.config.begin()->second : ""); + std::string config; + for (auto&& cfg : obj.param.config) { + config += "_" + cfg.first + "_" + cfg.second; + } + return obj.param.device + "_" + obj.param.input_blob_precision.name() + config; } } diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_set_preprocess.hpp b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_set_preprocess.hpp index 8b17573a1b5..0881e51dd57 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_set_preprocess.hpp +++ b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugin_set_preprocess.hpp @@ -3,7 +3,6 @@ // #include "behavior_test_plugin.h" -#include using namespace std; using namespace ::testing; diff --git a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugins.hpp b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugins.hpp index 99c225ef11a..f94a71a725b 100644 --- a/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugins.hpp +++ b/inference-engine/tests_deprecated/behavior/shared_tests/plugin_tests/behavior_test_plugins.hpp @@ -68,7 +68,7 @@ IE_SUPPRESS_DEPRECATED_END class BehaviorPluginTestInput : public BehaviorPluginTest { }; class BehaviorPluginTestOutput : public BehaviorPluginTest { }; -TEST_F(BehaviorPluginTest, AllocateNullBlob) { +TEST_F(BehaviorPluginTest, smoke_llocateNullBlob) { TensorDesc tdesc = TensorDesc(Precision::FP32, NCHW); InferenceEngine::TBlob blob(tdesc); ASSERT_NO_THROW(blob.allocate()); diff --git a/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt b/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt index 99c6d89691e..217a2aa9e39 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt +++ b/inference-engine/tests_deprecated/behavior/vpu/CMakeLists.txt @@ -66,8 +66,8 @@ function(enable_vpu TARGET_NAME FLAG_NAME PLUGIN_NAME) target_link_libraries(${TARGET_NAME} PRIVATE mvnc) endif() - add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME}) + add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME}) + set_property(TEST ${TARGET_NAME} PROPERTY LABELS VPU MYRIAD) add_dependencies(${TARGET_NAME} ${DEPENDENCIES}) endfunction(enable_vpu) diff --git a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/aot_behavior_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/aot_behavior_tests.cpp index 66f79723549..40eb1684d49 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/aot_behavior_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/aot_behavior_tests.cpp @@ -204,6 +204,6 @@ const BehTestParams vpuValues[] = { BEH_MYRIAD, }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, AOTBehaviorTests, ValuesIn(vpuValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, AOTBehaviorTests, ValuesIn(vpuValues), getTestCaseName); #endif diff --git a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_boot_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_boot_tests.cpp index 8525def9fba..20142b85c4f 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_boot_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_boot_tests.cpp @@ -112,4 +112,4 @@ const BehTestParams vpuValues[] = { BEH_MYRIAD, }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, MYRIADBoot, ValuesIn(vpuValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, MYRIADBoot, ValuesIn(vpuValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_get_metric_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_get_metric_tests.cpp index 890fb3bd91e..6555a67709e 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_get_metric_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_get_metric_tests.cpp @@ -26,7 +26,7 @@ protected: } }; -TEST_F(VPUGetMetric, GetThermalStatsFromNetwork) { +TEST_F(VPUGetMetric, smoke_GetThermalStatsFromNetwork) { const auto exe_network = loadNetworkOnDevice("MYRIAD"); auto result = Parameter{}; @@ -36,7 +36,7 @@ TEST_F(VPUGetMetric, GetThermalStatsFromNetwork) { ASSERT_GT(result.as(), 0); } -TEST_F(VPUGetMetric, GetThermalStatsFromPlugin) { +TEST_F(VPUGetMetric, smoke_GetThermalStatsFromPlugin) { std::vector availableDevices; ASSERT_NO_THROW(availableDevices = getAvailableDevices()); ASSERT_TRUE(!availableDevices.empty()); @@ -53,7 +53,7 @@ TEST_F(VPUGetMetric, GetThermalStatsFromPlugin) { } } -TEST_F(VPUGetMetric, ThermalStatsFromPluginWithIncorrectID) { +TEST_F(VPUGetMetric, smoke_ThermalStatsFromPluginWithIncorrectID) { std::vector availableDevices; ASSERT_NO_THROW(availableDevices = getAvailableDevices()); ASSERT_TRUE(!availableDevices.empty()); @@ -70,7 +70,7 @@ TEST_F(VPUGetMetric, ThermalStatsFromPluginWithIncorrectID) { ASSERT_TRUE(result.empty()); } -TEST_F(VPUGetMetric, ThermalStatsFromPluginWithoutLoadedNetwork) { +TEST_F(VPUGetMetric, smoke_ThermalStatsFromPluginWithoutLoadedNetwork) { std::vector availableDevices; ASSERT_NO_THROW(availableDevices = getAvailableDevices()); ASSERT_TRUE(!availableDevices.empty()); @@ -83,7 +83,7 @@ TEST_F(VPUGetMetric, ThermalStatsFromPluginWithoutLoadedNetwork) { ASSERT_TRUE(result.empty()); } -TEST_F(VPUGetMetric, MyriadGetAvailableDevices) { +TEST_F(VPUGetMetric, smoke_MyriadGetAvailableDevices) { std::vector availableDevices; ASSERT_NO_THROW(availableDevices = getAvailableDevices()); ASSERT_TRUE(!availableDevices.empty()); diff --git a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_load_network_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_load_network_tests.cpp index eb4baabc986..fa470686981 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_load_network_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_load_network_tests.cpp @@ -8,12 +8,12 @@ #include "behavior_test_plugin.h" #include "helpers/myriad_load_network_case.hpp" -TEST_F(MyriadLoadNetworkTestCase, ReloadPlugin) { +TEST_F(MyriadLoadNetworkTestCase, smoke_ReloadPlugin) { ASSERT_NO_THROW(LoadNetwork()); ASSERT_NO_THROW(LoadNetwork()); } -TEST_F(MyriadLoadNetworkTestCase, SimpleLoading) { +TEST_F(MyriadLoadNetworkTestCase, smoke_SimpleLoading) { auto devices = getDevicesList(); ASSERT_TRUE(devices.size()); @@ -28,7 +28,7 @@ TEST_F(MyriadLoadNetworkTestCase, SimpleLoading) { ASSERT_TRUE(!IsDeviceAvailable(device_to_load)); } -TEST_F(MyriadLoadNetworkTestCase, LoadingAtTheSameDevice) { +TEST_F(MyriadLoadNetworkTestCase, smoke_LoadingAtTheSameDevice) { auto devices = getDevicesList(); ASSERT_TRUE(devices.size()); @@ -46,7 +46,7 @@ TEST_F(MyriadLoadNetworkTestCase, LoadingAtTheSameDevice) { ie->LoadNetwork(cnnNetwork, "MYRIAD", config)); } -TEST_F(MyriadLoadNetworkTestCase, ThrowsExeptionWhenNameIsInvalid) { +TEST_F(MyriadLoadNetworkTestCase, smoke_ThrowsExeptionWhenNameIsInvalid) { auto device_to_load = "SomeVeryBadName"; std::map config = { {KEY_DEVICE_ID, device_to_load}, @@ -56,7 +56,7 @@ TEST_F(MyriadLoadNetworkTestCase, ThrowsExeptionWhenNameIsInvalid) { ie->LoadNetwork(cnnNetwork, "MYRIAD", config)); } -TEST_F(MyriadLoadNetworkTestCase, ThrowsExeptionWhenPlatformConflictWithProtocol) { +TEST_F(MyriadLoadNetworkTestCase, smoke_ThrowsExeptionWhenPlatformConflictWithProtocol) { std::string wrong_platform; auto devices = getDevicesList(); ASSERT_TRUE(devices.size()); diff --git a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_protocol_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_protocol_tests.cpp index 76e201a1220..d66b56ed36a 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_protocol_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_protocol_tests.cpp @@ -26,7 +26,7 @@ TEST_P(MyriadProtocolTests, CanInferenceWithProtocol) { ASSERT_EQ(statusCode, StatusCode::OK) << resp.msg; } -INSTANTIATE_TEST_CASE_P(VPUConfigProtocolTests, +INSTANTIATE_TEST_CASE_P(smoke_VPUConfigProtocolTests, MyriadProtocolTests, ::testing::ValuesIn(myriadProtocols), MyriadProtocolTests::getTestCaseName); \ No newline at end of file diff --git a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_watchdog_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_watchdog_tests.cpp index a20cb244b16..eec34b12bbf 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_watchdog_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/myriad_tests/vpu_watchdog_tests.cpp @@ -235,4 +235,4 @@ const BehTestParams vpuValues[] = { BEH_MYRIAD, }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, MYRIADWatchdog, ValuesIn(vpuValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, MYRIADWatchdog, ValuesIn(vpuValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/cpp_wrappers/holders_tests.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/cpp_wrappers/holders_tests.cpp index eaa3e591b27..cd57d39fadf 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/cpp_wrappers/holders_tests.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/cpp_wrappers/holders_tests.cpp @@ -4,7 +4,7 @@ #include "holders_tests.hpp" -INSTANTIATE_TEST_CASE_P(ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { +INSTANTIATE_TEST_CASE_P(smoke_ReleaseOrderTests, CPP_HoldersTests, testing::Combine(testing::ValuesIn(std::vector> { // 0 - plugin // 1 - executable_network // 2 - infer_request diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp index 4ff0124ecf9..44b689cec12 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin.cpp @@ -6,9 +6,9 @@ #include "behavior_test_plugins.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTest, ValuesIn(supportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInput, ValuesIn(allInputSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp index b33e8e855a0..f70454633ca 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_config.cpp @@ -6,7 +6,7 @@ #include "vpu_test_data.hpp" INSTANTIATE_TEST_CASE_P( - BehaviorTest, BehaviorPluginCorrectConfigTest, + smoke_BehaviorTest, BehaviorPluginCorrectConfigTest, ValuesIn( BehTestParams::concat( BehTestParams::concat(deviceSpecificConfigurations, deviceAgnosticConfigurations), @@ -16,13 +16,13 @@ INSTANTIATE_TEST_CASE_P( getTestCaseName ); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTest, ValuesIn(withIncorrectConfValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginIncorrectConfigTestInferRequestAPI, ValuesIn(withIncorrectConfKeys), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginCorrectConfigTestInferRequestAPI, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp index ab60df5fedd..f0eb203add9 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_exec_graph_info.cpp @@ -7,7 +7,7 @@ // TODO: currently this tests are not applicable to myriadPlugin #if 0 -INSTANTIATE_TEST_CASE_P( +INSTANTIATE_TEST_CASE_P(smoke_ BehaviorTest, BehaviorPluginTestExecGraphInfo, ValuesIn(supportedValues), diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp index d05de675d0e..0721ad56ca8 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_infer_request.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequest, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp index 24daac13c3a..14db00a1912 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_callback.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_callback.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestCallback, ValuesIn(requestsSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp index 7b9db6b8754..437662c1592 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_config.cpp @@ -5,9 +5,9 @@ #include "behavior_test_plugin_infer_request_config.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfig, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfig, ValuesIn(BehTestParams::concat(deviceAgnosticConfigurations, withCorrectConfValuesNetworkOnly)), getConfigTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestConfigExclusiveAsync, ValuesIn(supportedValues), getConfigTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp index cdde2469ae8..e8633b7b08f 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_input.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_input.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestInput, ValuesIn(allInputSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp index fb6812220bf..c7e891db1b2 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_infer_request_output.cpp @@ -5,5 +5,5 @@ #include "behavior_test_plugin_infer_request_output.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestInferRequestOutput, ValuesIn(allOutputSupportedValues), getOutputTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp index 86c076e1e18..845c8858f40 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layers.cpp @@ -8,7 +8,7 @@ pool_test_params roi_pool_test_cases[] = { pool_test_params(CommonTestUtils::DEVICE_MYRIAD, "FP16", pool_case), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, ROIPoolingLayerTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, ROIPoolingLayerTest, ::testing::ValuesIn(roi_pool_test_cases), getTestName); @@ -18,7 +18,7 @@ memory_test_params memory_test_cases[] = { // FIXME //#if (defined INSTANTIATE_TESTS) -//INSTANTIATE_TEST_CASE_P(BehaviorTest, MemoryLayerTest, +//INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, MemoryLayerTest, // ::testing::ValuesIn(memory_test_cases), // getTestName); //#endif diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp index c26895af4a0..623fe83445b 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_layout.cpp @@ -10,7 +10,7 @@ layout_test_params power_test_cases[] = { layout_test_params(CommonTestUtils::DEVICE_MYRIAD, "FP16", Layout::CHW, power_params({ { 3, 32, 16 } }, 2, 2, 2)), layout_test_params(CommonTestUtils::DEVICE_MYRIAD, "FP16", Layout::NCHW, power_params({ { 1, 3, 16, 16 } }, 2, 2, 2)), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadPower, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadPower, ::testing::ValuesIn(power_test_cases), getTestName); layout_test_params conv_neg_test_cases[] = { @@ -18,12 +18,12 @@ layout_test_params conv_neg_test_cases[] = { layout_test_params(CommonTestUtils::DEVICE_MYRIAD, "FP16", Layout::NC, power_params({ { 1, 3 } }, 2, 2, 2)), }; -INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanNotLoadConv, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanNotLoadConv, ::testing::ValuesIn(conv_neg_test_cases), getTestName); layout_test_params conv_test_cases[] = { layout_test_params(CommonTestUtils::DEVICE_MYRIAD, "FP16", Layout::CHW, power_params({ { 3, 32, 16 } }, 2, 2, 2)), layout_test_params(CommonTestUtils::DEVICE_MYRIAD, "FP16", Layout::NCHW, power_params({ { 1, 3, 16, 16 } }, 2, 2, 2)), }; - INSTANTIATE_TEST_CASE_P(BehaviorTest, LayoutTestCanLoadConv, + INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, LayoutTestCanLoadConv, ::testing::ValuesIn(conv_test_cases), getTestName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp index 24a12342ebf..8fa9bbbd09f 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_set_preprocess.cpp @@ -5,7 +5,7 @@ #include "behavior_test_plugin_set_preprocess.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestPreProcess, ValuesIn(supportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp index abe5a37eda3..c3a37c68e97 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_unsupported.cpp @@ -5,10 +5,10 @@ #include "behavior_test_plugin_unsupported.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestAllUnsupported, ValuesIn(allUnSupportedValues), getTestCaseName); -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestTypeUnsupported, ValuesIn(typeUnSupportedValues), getTestCaseName); - INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestBatchUnsupported, ValuesIn(batchUnSupportedValues), + INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestBatchUnsupported, ValuesIn(batchUnSupportedValues), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp index db9b742fdcd..aae56e3feb6 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/behavior_test_plugin_version.cpp @@ -5,4 +5,4 @@ #include "behavior_test_plugin_version.hpp" #include "vpu_test_data.hpp" -INSTANTIATE_TEST_CASE_P(BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); +INSTANTIATE_TEST_CASE_P(smoke_BehaviorTest, BehaviorPluginTestVersion, ValuesIn(add_element_into_array(supportedValues, BEH_HETERO)), getTestCaseName); diff --git a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/vpu_test_data.hpp b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/vpu_test_data.hpp index 39ae92458ca..9858ad0858a 100644 --- a/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/vpu_test_data.hpp +++ b/inference-engine/tests_deprecated/behavior/vpu/shared_tests_instances/plugin_tests/vpu_test_data.hpp @@ -13,42 +13,67 @@ FuncTestUtils::TestModel::convReluNormPoolFcModelFP32.model_xml_str, \ FuncTestUtils::TestModel::convReluNormPoolFcModelFP32.weights_blob, \ Precision::FP32) +// for multi-device we are testing the fp16 (as it is supported by all device combos we are considering for testing +// e.g. GPU and VPU, for CPU the network is automatically (internally) converted to fp32. +#define BEH_MULTI(device) BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.weights_blob, \ + Precision::FP32, \ + {{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, #device}}) +#define BEH_MULTI_CONFIG BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelFP16.weights_blob, \ + Precision::FP32) // all parameters are unsupported - reversed #define BEH_US_ALL_MYRIAD BehTestParams("MYRIAD", \ FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.model_xml_str, \ FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.weights_blob, \ Precision::Q78) +#define BEH_US_ALL_MULTI(device) BehTestParams("MULTI", \ + FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.model_xml_str, \ + FuncTestUtils::TestModel::convReluNormPoolFcModelQ78.weights_blob, \ + Precision::Q78, \ + {{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, #device}}) const BehTestParams supportedValues[] = { BEH_MYRIAD, + BEH_MULTI(MYRIAD), }; const BehTestParams requestsSupportedValues[] = { BEH_MYRIAD, + BEH_MULTI(MYRIAD), }; const BehTestParams allInputSupportedValues[] = { BEH_MYRIAD, BEH_MYRIAD.withIn(Precision::U8), BEH_MYRIAD.withIn(Precision::FP16), + BEH_MULTI(MYRIAD), BEH_MULTI(MYRIAD).withIn(Precision::U8), BEH_MULTI(MYRIAD).withIn(Precision::FP16), // I16 not supported yet - // (ISSUE-7979) [IE myriad] The plugin should support I16 format for Input + // (CVS-7979) [IE myriad] The plugin should support I16 format for Input //BEH_MYRIAD.withIn(Precision::I16), }; const BehTestParams allOutputSupportedValues[] = { BEH_MYRIAD, BEH_MYRIAD.withOut(Precision::FP16), + BEH_MULTI(MYRIAD), BEH_MULTI(MYRIAD).withOut(Precision::FP16), }; const BehTestParams typeUnSupportedValues[] = { BEH_MYRIAD.withIn(Precision::Q78), BEH_MYRIAD.withIn(Precision::U16), BEH_MYRIAD.withIn(Precision::I8), BEH_MYRIAD.withIn(Precision::I16), BEH_MYRIAD.withIn(Precision::I32), + BEH_MULTI(MYRIAD).withIn(Precision::Q78), BEH_MULTI(MYRIAD).withIn(Precision::U16), + BEH_MULTI(MYRIAD).withIn(Precision::I8), + BEH_MULTI(MYRIAD).withIn(Precision::I16), BEH_MULTI(MYRIAD).withIn(Precision::I32), }; const BehTestParams batchUnSupportedValues[] = { BEH_MYRIAD.withBatchSize(0), + BEH_MULTI(MYRIAD).withBatchSize(0), }; const BehTestParams allUnSupportedValues[] = { BEH_US_ALL_MYRIAD, + BEH_US_ALL_MULTI(MYRIAD), }; const std::vector deviceSpecificConfigurations = { @@ -78,6 +103,15 @@ const std::vector deviceAgnosticConfigurations = { BEH_MYRIAD.withConfig({{VPU_CONFIG_KEY(PRINT_RECEIVE_TENSOR_TIME), CONFIG_VALUE(YES)}}), BEH_MYRIAD.withConfig({{VPU_CONFIG_KEY(PRINT_RECEIVE_TENSOR_TIME), CONFIG_VALUE(NO)}}), + + BEH_MULTI_CONFIG.withConfig({ + {MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {CONFIG_KEY(LOG_LEVEL), CONFIG_VALUE(LOG_DEBUG)} + }), + BEH_MULTI_CONFIG.withConfig({ + {MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {VPU_CONFIG_KEY(HW_STAGES_OPTIMIZATION), CONFIG_VALUE(YES)} + }), }; const std::vector withCorrectConfValuesPluginOnly = { @@ -107,8 +141,22 @@ const BehTestParams withIncorrectConfValues[] = { BEH_MYRIAD.withConfig({{VPU_CONFIG_KEY(PRINT_RECEIVE_TENSOR_TIME), "ON"}}), BEH_MYRIAD.withConfig({{VPU_CONFIG_KEY(PRINT_RECEIVE_TENSOR_TIME), "OFF"}}), + + BEH_MULTI_CONFIG.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {VPU_CONFIG_KEY(HW_STAGES_OPTIMIZATION),"ON"}}), + BEH_MULTI_CONFIG.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {CONFIG_KEY(LOG_LEVEL), "VERBOSE"}}), + BEH_MULTI_CONFIG.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {VPU_CONFIG_KEY(IGNORE_IR_STATISTIC), "ON"}}), + BEH_MULTI_CONFIG.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {VPU_MYRIAD_CONFIG_KEY(PLATFORM), "-1"}}), + BEH_MULTI_CONFIG.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {VPU_MYRIAD_CONFIG_KEY(PLATFORM), "0"}}), + BEH_MULTI_CONFIG.withConfig({{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "MYRIAD"}, + {VPU_MYRIAD_CONFIG_KEY(PLATFORM), "1"}}), }; const BehTestParams withIncorrectConfKeys[] = { BEH_MYRIAD.withIncorrectConfigItem(), + BEH_MULTI(MYRIAD).withIncorrectConfigItem(), }; diff --git a/inference-engine/tests_deprecated/fluid_preproc/CMakeLists.txt b/inference-engine/tests_deprecated/fluid_preproc/CMakeLists.txt index c7f9416ad75..840d5f80958 100644 --- a/inference-engine/tests_deprecated/fluid_preproc/CMakeLists.txt +++ b/inference-engine/tests_deprecated/fluid_preproc/CMakeLists.txt @@ -29,3 +29,6 @@ if(GAPI_TEST_PERF) else() target_compile_definitions(${TARGET} PRIVATE -DPERF_TEST=0) endif() + +add_test(NAME ${TARGET} COMMAND ${TARGET}) +set_property(TEST ${TARGET} PROPERTY LABELS IE PREPROC) diff --git a/inference-engine/tests_deprecated/functional/cldnn/CMakeLists.txt b/inference-engine/tests_deprecated/functional/cldnn/CMakeLists.txt index 88d6e04aace..76302d48e98 100644 --- a/inference-engine/tests_deprecated/functional/cldnn/CMakeLists.txt +++ b/inference-engine/tests_deprecated/functional/cldnn/CMakeLists.txt @@ -23,7 +23,6 @@ list(APPEND TEST_SRC ${CLDNN_TEST_SOURCES}) list(APPEND CLDNN_LIBS IESharedTests inference_engine_lp_transformations - inference_engine_ir_readers ${CLDNN__IOCL_ICD_LIBPATH}) # try to find VA libraries @@ -69,5 +68,5 @@ target_link_libraries(${TARGET_NAME} PRIVATE ${CLDNN_LIBS}) add_dependencies(${TARGET_NAME} ${DEPENDENCIES}) -add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME}) +add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME}) +set_property(TEST ${TARGET_NAME} PROPERTY LABELS GPU) diff --git a/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt b/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt index 6528fe5cd66..35a90e87554 100644 --- a/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt +++ b/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt @@ -50,10 +50,8 @@ target_include_directories(${TARGET_NAME} add_dependencies(${TARGET_NAME} ${DEPENDENCIES}) -add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME}) - -set_target_properties(${TARGET_NAME} PROPERTIES COMPILE_PDB_NAME ${TARGET_NAME}) +add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME}) +set_property(TEST ${TARGET_NAME} PROPERTY LABELS GNA) if(GNA_LIBRARY_VERSION STREQUAL "GNA1") target_compile_definitions(${TARGET_NAME} PRIVATE GNA1_LIB) diff --git a/inference-engine/tests_deprecated/functional/gna/shared_tests_instance/ie_class/ie_class.cpp b/inference-engine/tests_deprecated/functional/gna/shared_tests_instance/ie_class/ie_class.cpp index b984f0e4ab1..a83653b43d9 100644 --- a/inference-engine/tests_deprecated/functional/gna/shared_tests_instance/ie_class/ie_class.cpp +++ b/inference-engine/tests_deprecated/functional/gna/shared_tests_instance/ie_class/ie_class.cpp @@ -89,11 +89,10 @@ INSTANTIATE_TEST_CASE_P( INSTANTIATE_TEST_CASE_P( DISABLED_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_NETWORK_NAME, ::testing::Values("GNA", "MULTI:GNA", "HETERO:GNA")); -// -// TODO: this metric is not supported by the plugin + INSTANTIATE_TEST_CASE_P( - DISABLED_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_OPTIMAL_NUMBER_OF_INFER_REQUESTS, - ::testing::Values("GNA", "MULTI:GNA", "HETERO:GNA")); + IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_OPTIMAL_NUMBER_OF_INFER_REQUESTS, + ::testing::Values("GNA"/*, "MULTI:GNA", "HETERO:GNA" */)); INSTANTIATE_TEST_CASE_P( IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_ThrowsUnsupported, diff --git a/inference-engine/tests_deprecated/functional/mkldnn/CMakeLists.txt b/inference-engine/tests_deprecated/functional/mkldnn/CMakeLists.txt index 63a1f585c90..8947a8350f3 100644 --- a/inference-engine/tests_deprecated/functional/mkldnn/CMakeLists.txt +++ b/inference-engine/tests_deprecated/functional/mkldnn/CMakeLists.txt @@ -30,7 +30,6 @@ file(GLOB MKL_DNN_TEST_SOURCES list(APPEND MKL_DNN_LIBS IESharedTests inference_engine_lp_transformations - inference_engine_ir_readers ${Boost_REGEX_LIBRARY}) list(APPEND TEST_SRC ${MKL_DNN_TEST_SOURCES}) @@ -63,5 +62,5 @@ target_link_libraries(${TARGET_NAME} PRIVATE ${LIBRARIES}) add_dependencies(${TARGET_NAME} ${DEPENDENCIES}) -add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME}) +add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME}) +set_property(TEST ${TARGET_NAME} PROPERTY LABELS CPU) diff --git a/inference-engine/tests_deprecated/functional/mkldnn/config_param_test/config_param_test.cpp b/inference-engine/tests_deprecated/functional/mkldnn/config_param_test/config_param_test.cpp index ae9ea61364b..2db8c491288 100644 --- a/inference-engine/tests_deprecated/functional/mkldnn/config_param_test/config_param_test.cpp +++ b/inference-engine/tests_deprecated/functional/mkldnn/config_param_test/config_param_test.cpp @@ -40,12 +40,13 @@ TEST_F(smoke_PropertyTest, onSplitConvConcat) { inferRequest1.Infer(); float* outRawDataWithConfig = inferRequest1.GetBlob(net.getOutputsInfo().begin()->first)->cbuffer().as(); - auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32); + float thr1, thr2; + FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32, thr1, thr2); size_t outElementsCount = std::accumulate(begin(fnPtr->get_output_shape(0)), end(fnPtr->get_output_shape(0)), 1, std::multiplies()); - FuncTestUtils::compareRawBuffers(outRawData, outRawDataWithConfig, outElementsCount, - outElementsCount, - thr); + FuncTestUtils::compareRawBuffers(outRawData, outRawDataWithConfig, outElementsCount, outElementsCount, + FuncTestUtils::CompareType::ABS_AND_REL, + thr1, thr2); } diff --git a/inference-engine/tests_deprecated/functional/mkldnn/extensions_tests/extensions_test.cpp b/inference-engine/tests_deprecated/functional/mkldnn/extensions_tests/extensions_test.cpp index 4023b5ca708..a8b437d2c63 100644 --- a/inference-engine/tests_deprecated/functional/mkldnn/extensions_tests/extensions_test.cpp +++ b/inference-engine/tests_deprecated/functional/mkldnn/extensions_tests/extensions_test.cpp @@ -6,7 +6,6 @@ #include #include #include -#include #include #include #include @@ -386,36 +385,3 @@ TEST_F(smoke_ExtensionTest, MKLDNN_no_share_new_extension_between_engines) { std::shared_ptr ext(new NewTestExtension()); checkNotSharedExtensions(ext, "CPU"); } - -TEST_F(smoke_ExtensionTest, MULTI_delete_extension) { - try { - InferenceEngine::Core ie; - ie.GetVersions("MULTI"); - } catch (...) { - GTEST_SKIP(); - } - std::shared_ptr ext(new TestExtension()); - checkExtensionRemoved({"MultiDevice", ext, {{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}}}); -} - -TEST_F(smoke_ExtensionTest, MULTI_no_delete_extension_from_another_engine) { - try { - InferenceEngine::Core ie; - ie.GetVersions("MULTI"); - } catch (...) { - GTEST_SKIP(); - } - std::shared_ptr ext(new TestExtension()); - checkExtensionNotRemovedFromAnotherEngineObject({"MultiDevice", ext, {{MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES, "CPU"}}}); -} - -TEST_F(smoke_ExtensionTest, MULTI_no_share_extension_between_engines) { - try { - InferenceEngine::Core ie; - ie.GetVersions("MULTI"); - } catch (...) { - GTEST_SKIP(); - } - std::shared_ptr ext(new TestExtension()); - checkNotSharedExtensions(ext, "MULTI:CPU"); -} diff --git a/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/ie_class/ie_class.cpp b/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/ie_class/ie_class.cpp index e4c55c02dfd..09cb58e5c97 100644 --- a/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/ie_class/ie_class.cpp +++ b/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/ie_class/ie_class.cpp @@ -28,11 +28,11 @@ INSTANTIATE_TEST_CASE_P( INSTANTIATE_TEST_CASE_P( smoke_IEClassGetMetricTest, IEClassGetMetricTest_SUPPORTED_CONFIG_KEYS, - ::testing::Values("CPU", "HETERO")); + ::testing::Values("CPU", "MULTI", "HETERO")); INSTANTIATE_TEST_CASE_P( smoke_IEClassGetMetricTest, IEClassGetMetricTest_SUPPORTED_METRICS, - ::testing::Values("CPU", "HETERO")); + ::testing::Values("CPU", "MULTI", "HETERO")); INSTANTIATE_TEST_CASE_P( smoke_IEClassGetMetricTest, IEClassGetMetricTest_AVAILABLE_DEVICES, @@ -40,7 +40,7 @@ INSTANTIATE_TEST_CASE_P( INSTANTIATE_TEST_CASE_P( smoke_IEClassGetMetricTest, IEClassGetMetricTest_FULL_DEVICE_NAME, - ::testing::Values("CPU", "HETERO")); + ::testing::Values("CPU", "MULTI", "HETERO")); INSTANTIATE_TEST_CASE_P( smoke_IEClassGetMetricTest, IEClassGetMetricTest_OPTIMIZATION_CAPABILITIES, @@ -56,11 +56,11 @@ INSTANTIATE_TEST_CASE_P( INSTANTIATE_TEST_CASE_P( smoke_IEClassGetMetricTest, IEClassGetMetricTest_ThrowUnsupported, - ::testing::Values("CPU", "HETERO")); + ::testing::Values("CPU", "MULTI", "HETERO")); INSTANTIATE_TEST_CASE_P( smoke_IEClassGetConfigTest, IEClassGetConfigTest_ThrowUnsupported, - ::testing::Values("CPU", "HETERO")); + ::testing::Values("CPU", "MULTI", "HETERO")); INSTANTIATE_TEST_CASE_P( smoke_IEClassGetAvailableDevices, IEClassGetAvailableDevices, @@ -80,23 +80,23 @@ INSTANTIATE_TEST_CASE_P( INSTANTIATE_TEST_CASE_P( smoke_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS, - ::testing::Values("CPU", "HETERO:CPU")); + ::testing::Values("CPU", "MULTI:CPU", "HETERO:CPU")); INSTANTIATE_TEST_CASE_P( smoke_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_SUPPORTED_METRICS, - ::testing::Values("CPU", "HETERO:CPU")); + ::testing::Values("CPU", "MULTI:CPU", "HETERO:CPU")); INSTANTIATE_TEST_CASE_P( smoke_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_NETWORK_NAME, - ::testing::Values("CPU", "HETERO:CPU")); + ::testing::Values("CPU", "MULTI:CPU", "HETERO:CPU")); INSTANTIATE_TEST_CASE_P( smoke_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_OPTIMAL_NUMBER_OF_INFER_REQUESTS, - ::testing::Values("CPU", "HETERO:CPU")); + ::testing::Values("CPU", "MULTI:CPU", "HETERO:CPU")); INSTANTIATE_TEST_CASE_P( smoke_IEClassExecutableNetworkGetMetricTest, IEClassExecutableNetworkGetMetricTest_ThrowsUnsupported, - ::testing::Values("CPU", "HETERO:CPU")); + ::testing::Values("CPU", "MULTI:CPU", "HETERO:CPU")); // // Executable Network GetConfig / SetConfig diff --git a/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/network_tests/network_test.cpp b/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/network_tests/network_test.cpp index c525fcbbe15..6930beb4039 100644 --- a/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/network_tests/network_test.cpp +++ b/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/network_tests/network_test.cpp @@ -121,14 +121,14 @@ INSTANTIATE_TEST_CASE_P( smoke_Inception, ModelTransformationsTest, ::testing::Values( - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, false, false, createParam(), {}, 3ul), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, false, true, createParamI8I8(), {}, 0, false), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, false, true, createParamU8I8(), {}, 0), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, false, true, createParamU8U8(), {}, 0), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, false, true, createParamCpu().setQuantizedTensorAlignmentOnActivations(LayerTransformation::QuantizedTensorAlignment::UpdateLevel)), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, false, true, createParamCpu().setQuantizedTensorAlignmentOnActivations(LayerTransformation::QuantizedTensorAlignment::UpdateIntervals)), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 1ul, true, false, createParam()), - TransformationsParams("MKLDNN", getModelParams("inception_v3_tf"), 2ul, true, false, createParam()) + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, false, false, createParam(), {}, 3ul), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, false, true, createParamI8I8(), {}, 0, false), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, false, true, createParamU8I8(), {}, 0), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, false, true, createParamU8U8(), {}, 0), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, false, true, createParamCpu().setQuantizedTensorAlignmentOnActivations(LayerTransformation::QuantizedTensorAlignment::UpdateLevel)), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, false, true, createParamCpu().setQuantizedTensorAlignmentOnActivations(LayerTransformation::QuantizedTensorAlignment::UpdateIntervals)), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 1ul, true, false, createParam()), + TransformationsParams("CPU", getModelParams("inception_v3_tf"), 2ul, true, false, createParam()) ), TransformationsParams::getLowPrecisionTransformerSingleLayerTestName); @@ -136,14 +136,14 @@ INSTANTIATE_TEST_CASE_P( smoke_MobileNet, ModelTransformationsTest, ::testing::Values( - TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false), + TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false), // TODO: eshoguli: fix this issue -// TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamI8I8()), -// TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamU8I8()), -// TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamU8U8(), {}, 2), -// TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamCpu(), { "464/Pool", "465/Pool" }), - TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, true), - TransformationsParams("MKLDNN", getModelParams("mobilenet_v2_tf_depthwise"), 2ul, true) +// TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamI8I8()), +// TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamU8I8()), +// TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamU8U8(), {}, 2), +// TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, false, true, createParamCpu(), { "464/Pool", "465/Pool" }), + TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 1ul, true), + TransformationsParams("CPU", getModelParams("mobilenet_v2_tf_depthwise"), 2ul, true) ), TransformationsParams::getLowPrecisionTransformerSingleLayerTestName); @@ -151,28 +151,28 @@ INSTANTIATE_TEST_CASE_P( smoke_ResNet, ModelTransformationsTest, ::testing::Values( - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 1ul, false), - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 1ul, false, true, createParamI8I8(), { + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 1ul, false), + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 1ul, false, true, createParamI8I8(), { // TODO: remove when eltwise validation was added "resnet_v1_50/block1/unit_2/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars", "resnet_v1_50/block2/unit_3/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars" }), - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 1ul, false, true, createParamU8I8(), { + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 1ul, false, true, createParamU8I8(), { // // TODO: remove when eltwise validation was added "resnet_v1_50/block1/unit_2/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars", "resnet_v1_50/block2/unit_3/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars" }), - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 1ul, false, true, createParamU8U8(), { + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 1ul, false, true, createParamU8U8(), { // TODO: remove when eltwise validation was added "resnet_v1_50/block1/unit_2/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars", "resnet_v1_50/block2/unit_3/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars" }), - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 1ul, false, true, createParamCpu(), { + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 1ul, false, true, createParamCpu(), { // TODO: remove when eltwise validation was added "resnet_v1_50/block1/unit_2/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars", "resnet_v1_50/block2/unit_3/bottleneck_v1/act_quant/FakeQuantWithMinMaxVars" }), - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 1ul, true), - TransformationsParams("MKLDNN", getModelParams("resnet_50_tf"), 2ul, true) + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 1ul, true), + TransformationsParams("CPU", getModelParams("resnet_50_tf"), 2ul, true) ), TransformationsParams::getLowPrecisionTransformerSingleLayerTestName); diff --git a/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/transformations/low_precision_transformer_single_layer_tests.cpp b/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/transformations/low_precision_transformer_single_layer_tests.cpp index b2ea9beae8d..1e8adfb62b3 100644 --- a/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/transformations/low_precision_transformer_single_layer_tests.cpp +++ b/inference-engine/tests_deprecated/functional/mkldnn/shared_tests_instance/transformations/low_precision_transformer_single_layer_tests.cpp @@ -18,6 +18,36 @@ INSTANTIATE_TEST_CASE_P( smoke_SingleLayerTransformationsTestFP32, SingleLayerTransformationsTest, ::testing::Values( + SingleLayerTransformationsTestParams( + "CPU", + PowerTestModel::Ptr(new PowerTestModel(1.f, 1.f, 0)), + { { 1, 3, 299, 299 } }, + { { 1, 3, 299, 299 } }), + + SingleLayerTransformationsTestParams( + "CPU", + PowerTestModel::Ptr(new PowerTestModel(1.f, 2.89f, 64)), + { { 1, 3, 299, 299 } }, + { { 1, 3, 299, 299 } }), + + SingleLayerTransformationsTestParams( + "CPU", + PowerTestModel::Ptr(new PowerTestModel(1.f, -32.f, 0)), + { { 1, 3, 299, 299 } }, + { { 1, 3, 299, 299 } }), + + SingleLayerTransformationsTestParams( + "CPU", + PowerTestModel::Ptr(new PowerTestModel(1.f, 1.f, -64.f)), + { { 1, 3, 299, 299 } }, + { { 1, 3, 299, 299 } }), + + SingleLayerTransformationsTestParams( + "CPU", + PowerTestModel::Ptr(new PowerTestModel(3.5f, 1.f, 0)), + { { 1, 3, 299, 299 } }, + { { 1, 3, 299, 299 } }), + SingleLayerTransformationsTestParams( "CPU", SingleLayerTestModel::Ptr(new ResampleTestModel()), diff --git a/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt b/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt index 20ea1863752..0cb368c8e34 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt +++ b/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt @@ -11,7 +11,6 @@ list(APPEND SHARED_LIBRARIES ${NGRAPH_LIBRARIES} ie_tests ngraphFunctions - inference_engine_ir_readers ) file(GLOB SHARED_TESTS_SRC @@ -26,7 +25,7 @@ file(GLOB SHARED_TESTS_SRC ) add_library(${TARGET_NAME} STATIC ${SHARED_TESTS_SRC}) -add_dependencies(${TARGET_NAME} inference_engine_preproc mock_engine) +add_dependencies(${TARGET_NAME} inference_engine_preproc MultiDevicePlugin mock_engine) if(ENABLE_MKL_DNN) add_dependencies(${TARGET_NAME} MKLDNNPlugin) diff --git a/inference-engine/tests_deprecated/functional/shared_tests/common_single_layer_tests/single_layer_tests.hpp b/inference-engine/tests_deprecated/functional/shared_tests/common_single_layer_tests/single_layer_tests.hpp index df5db8f5a5b..6e0ab598b9b 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/common_single_layer_tests/single_layer_tests.hpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/common_single_layer_tests/single_layer_tests.hpp @@ -401,7 +401,7 @@ protected: } template - static details::CNNNetworkImplPtr + static InferenceEngine::CNNNetwork buildSingleLayerNetwork(const std::string &layerType, const CommonTestUtils::InOutShapes &inOutShapes, std::map *params, @@ -410,8 +410,7 @@ protected: size_t weightsSize = 0, size_t biasesSize = 0, const TBlob::Ptr &weights = nullptr) { - auto *parser = new details::FormatParser(Version); - return buildSingleLayerNetworkCommon(parser, layerType, inOutShapes, params, layerDataName, precision, + return buildSingleLayerNetworkCommon(layerType, inOutShapes, params, layerDataName, precision, weightsSize, biasesSize, weights); } @@ -441,10 +440,9 @@ TEST_P(CommonSingleLayerTest, inferAfterReshape) { auto weights = createWeights(elementSize, weightByteSize, biasByteSize); - auto networkImplPtr = buildSingleLayerNetwork<3>(layerHelper->getType(), initialShapes, ¶ms, "data", - pluginParams.precision, weightByteSize, biasByteSize, weights); + auto network = buildSingleLayerNetwork<3>(layerHelper->getType(), initialShapes, ¶ms, "data", + pluginParams.precision, weightByteSize, biasByteSize, weights); - CNNNetwork network(networkImplPtr); std::tie(inputName, inputData) = (*network.getInputsInfo().begin()); inputData->setPrecision(pluginParams.precision); inputData->setLayout(pluginParams.layout); diff --git a/inference-engine/tests_deprecated/functional/shared_tests/ie_class/ie_class.hpp b/inference-engine/tests_deprecated/functional/shared_tests/ie_class/ie_class.hpp index c19573fa6ad..8191938c382 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/ie_class/ie_class.hpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/ie_class/ie_class.hpp @@ -35,6 +35,15 @@ using namespace InferenceEngine; using namespace InferenceEngine::details; using namespace InferenceEngine::PluginConfigParams; +#define CHECK_MULTI() do { \ + try { \ + Core ie; \ + ie.GetVersions("MULTI"); \ + } catch (...) { \ + GTEST_SKIP(); \ + } \ + } while(false)\ + class IEClassBasicTest : public TestsCommon { public: void SetUp() override { @@ -188,7 +197,9 @@ TEST_P(IEClassBasicTestP, smoke_registerPluginsXMLUnicodePath) { GTEST_COUT << "Core created " << testIndex << std::endl; ASSERT_NO_THROW(ie.RegisterPlugins(wStringtoMBCSstringChar(pluginsXmlW))); CommonTestUtils::removeFile(pluginsXmlW); - +#if defined __linux__ && !defined(__APPLE__) + ASSERT_NO_THROW(ie.GetVersions("mock")); // from pluginXML +#endif ASSERT_NO_THROW(ie.GetVersions(deviceName)); GTEST_COUT << "Plugin created " << testIndex << std::endl; @@ -344,8 +355,8 @@ TEST_F(IEClassBasicTest, smoke_ImportNetworkHeteroThrows) { } TEST_F(IEClassBasicTest, smoke_ImportNetworkMultiThrows) { - Core ie; - + CHECK_MULTI(); + InferenceEngine::Core ie; ASSERT_THROW(ie.ImportNetwork("model", "MULTI"), InferenceEngineException); } @@ -465,6 +476,7 @@ TEST_P(IEClassNetworkTestP, QueryNetworkHeteroActualNoThrow) { } TEST_P(IEClassNetworkTestP, QueryNetworkMultiThrows) { + CHECK_MULTI(); Core ie; ASSERT_THROW(ie.QueryNetwork(actualNetwork, "MULTI"), InferenceEngineException); } @@ -1206,8 +1218,9 @@ TEST_P(IEClassLoadNetworkTest, LoadNetworkHETEROAndDeviceIDThrows) { // TEST_P(IEClassLoadNetworkTest, LoadNetworkHETEROwithMULTINoThrow) { - Core ie; + CHECK_MULTI(); + Core ie; if (supportsDeviceID(ie, deviceName) && supportsAvaliableDevices(ie, deviceName)) { std::string devices; auto availableDevices = ie.GetMetric(deviceName, METRIC_KEY(AVAILABLE_DEVICES)).as>(); @@ -1227,6 +1240,7 @@ TEST_P(IEClassLoadNetworkTest, LoadNetworkHETEROwithMULTINoThrow) { } TEST_P(IEClassLoadNetworkTest, LoadNetworkMULTIwithHETERONoThrow) { + CHECK_MULTI(); Core ie; if (supportsDeviceID(ie, deviceName) && supportsAvaliableDevices(ie, deviceName)) { @@ -1251,6 +1265,7 @@ TEST_P(IEClassLoadNetworkTest, LoadNetworkMULTIwithHETERONoThrow) { // TEST_P(IEClassLoadNetworkTest, QueryNetworkHETEROwithMULTINoThrowv7) { + CHECK_MULTI(); Core ie; if (supportsDeviceID(ie, deviceName) && supportsAvaliableDevices(ie, deviceName)) { @@ -1279,6 +1294,7 @@ TEST_P(IEClassLoadNetworkTest, QueryNetworkHETEROwithMULTINoThrowv7) { } TEST_P(IEClassLoadNetworkTest, QueryNetworkMULTIwithHETERONoThrowv7) { + CHECK_MULTI(); Core ie; if (supportsDeviceID(ie, deviceName) && supportsAvaliableDevices(ie, deviceName)) { @@ -1307,6 +1323,7 @@ TEST_P(IEClassLoadNetworkTest, QueryNetworkMULTIwithHETERONoThrowv7) { } TEST_P(IEClassLoadNetworkTest, QueryNetworkHETEROwithMULTINoThrowv10) { + CHECK_MULTI(); Core ie; if (supportsDeviceID(ie, deviceName) && supportsAvaliableDevices(ie, deviceName)) { @@ -1335,6 +1352,7 @@ TEST_P(IEClassLoadNetworkTest, QueryNetworkHETEROwithMULTINoThrowv10) { } TEST_P(IEClassLoadNetworkTest, DISABLED_QueryNetworkMULTIwithHETERONoThrowv10) { + CHECK_MULTI(); Core ie; if (supportsDeviceID(ie, deviceName) && supportsAvaliableDevices(ie, deviceName)) { @@ -1393,8 +1411,9 @@ TEST_P(IEClassLoadNetworkTest, DISABLED_QueryNetworkMULTIwithHETERONoThrowv10) { using IEClassLoadNetworkAfterCoreRecreateTest = IEClassLoadNetworkTest; TEST_P(IEClassLoadNetworkAfterCoreRecreateTest, LoadAfterRecreateCoresAndPlugins) { + CHECK_MULTI(); + Core ie; { - Core ie; auto versions = ie.GetVersions("MULTI:" + deviceName + ",CPU"); ASSERT_EQ(3, versions.size()); } diff --git a/inference-engine/tests_deprecated/functional/shared_tests/inference_engine_regression_tests/common_dyn_batch_regression.hpp b/inference-engine/tests_deprecated/functional/shared_tests/inference_engine_regression_tests/common_dyn_batch_regression.hpp index 3e4dac95850..5255bc0744f 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/inference_engine_regression_tests/common_dyn_batch_regression.hpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/inference_engine_regression_tests/common_dyn_batch_regression.hpp @@ -69,10 +69,14 @@ TEST_P(TestNoRegressionDynBatchFP32, dynBatch) { auto refOutData = ngraph::helpers::inferFnWithInterp(fnPtr, {blob->cbuffer().as()}); - auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32); + float thr1, thr2; + FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP32, thr1, thr2); + std::vector inShapeLimited{size_t(bsz), 4, 20, 20}; size_t outElementsCount = std::accumulate(begin(inShapeLimited), end(inShapeLimited), 1, std::multiplies()); - FuncTestUtils::compareRawBuffers(outRawData, *refOutData[0], outElementsCount, outElementsCount, thr); + FuncTestUtils::compareRawBuffers(outRawData, *refOutData[0], outElementsCount, outElementsCount, + FuncTestUtils::CompareType::ABS_AND_REL, + thr1, thr2); if (GetParam().deviceName.find(CommonTestUtils::DEVICE_GPU) != std::string::npos) { PluginCache::get().reset(); } diff --git a/inference-engine/tests_deprecated/functional/shared_tests/network_tests/network_i8.hpp b/inference-engine/tests_deprecated/functional/shared_tests/network_tests/network_i8.hpp index d6a3b160864..af91077932d 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/network_tests/network_i8.hpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/network_tests/network_i8.hpp @@ -51,7 +51,7 @@ inline void checkLayerOuputPrecision(const ICNNNetwork& network, const std::stri } struct network_params { - std::string pluginName; + std::string deviceName; std::string modelFile; std::string imageName; std::string statFile; @@ -87,20 +87,6 @@ struct network_params { result += statFile; return result; } - - std::string plugin() { return pluginName + "Plugin"; } - - std::string deviceName() { - if (pluginName == "MultiDevice") { - return "MULTI:CPU"; - } - if (pluginName == "MKLDNN") { - return "CPU"; - } - - return ""; - } - }; static LayerTransformation::Params createParam() { @@ -254,7 +240,7 @@ public: const size_t classesCanBeChangedIndex = 9999, const bool compareRawValues = true, const std::unordered_set& removedLayers = {}) : - pluginName(""), + deviceName(""), modelParams(ModelParams("", "", "", {})), batchSize(1ul), transformationsInPluginEnabled(transformationsInPluginEnabled), @@ -266,7 +252,7 @@ public: removedLayers(removedLayers) {} TransformationsParams( - const std::string pluginName, + const std::string deviceName, const ModelParams modelParams, const size_t batchSize, const bool transformationsInPluginEnabled = true, @@ -278,7 +264,7 @@ public: const std::unordered_set& removedLayers = {}, const std::vector>> inputs = {}, const std::vector>> transformations = {}) : - pluginName(pluginName), + deviceName(deviceName), modelParams(modelParams), batchSize(batchSize), transformationsInPluginEnabled(transformationsInPluginEnabled), @@ -289,7 +275,7 @@ public: compareRawValues(compareRawValues), removedLayers(removedLayers) {} - const std::string pluginName; + const std::string deviceName; const ModelParams modelParams; const size_t batchSize; @@ -436,7 +422,7 @@ protected: // transformationsParams.transformationsInPluginEnabled ? PluginConfigParams::YES : PluginConfigParams::NO); usedNetwork = cloneNet(network); - ExecutableNetwork exeNetwork = ie.LoadNetwork(network, p.deviceName(), config); + ExecutableNetwork exeNetwork = ie.LoadNetwork(network, p.deviceName, config); InferRequest inferRequest = exeNetwork.CreateInferRequest(); if (inputs.empty()) { Blob::Ptr src = readInput(p.image(), batch_size); @@ -541,7 +527,7 @@ protected: } network_params p{ - "MKLDNN", + "CPU", transformationsParam.modelParams.irFilePath, transformationsParam.modelParams.dataFilePath, "", diff --git a/inference-engine/tests_deprecated/functional/shared_tests/single_layer_tests/ti_tests.hpp b/inference-engine/tests_deprecated/functional/shared_tests/single_layer_tests/ti_tests.hpp index b939b48c3b3..f7ee40f39cf 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/single_layer_tests/ti_tests.hpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/single_layer_tests/ti_tests.hpp @@ -293,21 +293,24 @@ class TITest2Base: public PlgTest { protected: virtual void RunTITest(const std::map & config = {}) { - try { ti_test_params p = param(); std::string model = getModel(p); auto weights = make_shared_blob(TensorDesc {Precision::U8, {p.precision.size() * p.tensorSize}, C}); weights->allocate(); - auto weights_size = details::product(std::begin(weights->getTensorDesc().getDims()), std::end(weights->getTensorDesc().getDims())); + if (p.precision == Precision::FP32) { - std::vector weights_vector(weights_size, 1.0f); - ie_memcpy(weights->buffer().as(), sizeof(float), &weights_vector[0], weights_vector.size() * sizeof(float)); - } else { + std::vector weights_vector(p.tensorSize, 1.0f); + ie_memcpy(weights->buffer().as(), p.tensorSize * sizeof(float), + &weights_vector[0], p.tensorSize * sizeof(float)); + } else if (p.precision == Precision::FP16) { // FP16 case - std::vector weights_vector(weights_size, PrecisionUtils::f32tof16(1.0f)); - ie_memcpy(weights->buffer().as(), sizeof(ie_fp16), &weights_vector[0], weights_vector.size() * sizeof(ie_fp16)); + std::vector weights_vector(p.tensorSize, PrecisionUtils::f32tof16(1.0f)); + ie_memcpy(weights->buffer().as(), p.tensorSize * sizeof(ie_fp16), + &weights_vector[0], p.tensorSize * sizeof(ie_fp16)); + } else { + ASSERT_TRUE(false); } Core ie; diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_and_scaleshift_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_and_scaleshift_test.cpp index e7a83e3f8a7..8f4d6edb77b 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_and_scaleshift_test.cpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_and_scaleshift_test.cpp @@ -14,8 +14,9 @@ std::string FakeQuantizeAndScaleShiftTestModel::getModel(SingleLayerTransformati std::map fake_quantize_params = { {"levels", "256"} }; + std::map power_params = { - {"power", "1"}, {"scale", "1"}, {"shift", "0"} + {"power", "2"}, {"scale", "1"}, {"shift", "0"} }; std::vector> edges = { diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_reshape_pooling_test_model_without_constants_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_reshape_pooling_test_model_without_constants_test.cpp index 4cf0f3692a8..eda64fda143 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_reshape_pooling_test_model_without_constants_test.cpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/fake_quantize_reshape_pooling_test_model_without_constants_test.cpp @@ -35,7 +35,7 @@ std::string FakeQuantizeReshapePoolingTestModelWithoutConstants::getModel(Single std::vector biasesConvolutionConstDims = { conv.out_c }; std::map const_params = {}; std::map fakeQuantizeParams = {{ "levels", "256" }}; - std::map power_params = {{"power", "1"}, {"scale", "1"}, {"shift", "0"}}; + std::map power_params = {{"power", "2"}, {"scale", "1"}, {"shift", "0"}}; std::map poolingParams = { {"kernel", "7,1"}, { "pool-method", "avg" }, { "strides", "1,1" } }; std::vector> edges = { diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/low_precision_transformer_single_layer_tests.hpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/low_precision_transformer_single_layer_tests.hpp index 2464694a0ca..18014f33f00 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/low_precision_transformer_single_layer_tests.hpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/low_precision_transformer_single_layer_tests.hpp @@ -852,6 +852,20 @@ public: std::string getModel(SingleLayerTransformationsTestParams& p) const override; }; +class PowerTestModel : public SingleLayerTestModel { +public: + PowerTestModel(const float& power, const float& scale, const float& shift) : power(power), scale(scale), shift(shift) {} + void resetTransformation(CNNNetwork& network) const override; + std::string getName() const override; + bool transform(CNNNetwork& network, LayerTransformation::Params& params) const override; + std::string getModel(SingleLayerTransformationsTestParams& p) const override; + +private: + const float power; + const float scale; + const float shift; +}; + class ConvolutionAndQuantizeOnWeightsWithMultiOutputIntervalsTestModel : public SingleLayerTestModel { public: std::string getModel(SingleLayerTransformationsTestParams& p) const override; diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/power_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/power_test.cpp new file mode 100644 index 00000000000..92a7217ba3a --- /dev/null +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/power_test.cpp @@ -0,0 +1,78 @@ +// Copyright (C) 2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "low_precision_transformer_single_layer_tests.hpp" + +std::string PowerTestModel::getModel(SingleLayerTransformationsTestParams& p) const { + size_t type_size = sizeof(InferenceEngine::PrecisionTrait::value_type); + if (p._network_precision == "FP16") + type_size = sizeof(InferenceEngine::PrecisionTrait::value_type); + + CommonTestUtils::pool_common_params pooling = { {1, 1}, {1, 1}, {0, 0}, {0, 0}, "valid", false, true }; + std::vector poolOutShape(p.inputDimensions[0].size()); + CommonTestUtils::getPoolOutShape(p.inputDimensions[0], pooling, poolOutShape); + + std::map power_params = {{"power", std::to_string(power)}, {"scale", std::to_string(scale)}, {"shift", std::to_string(shift)}}; + std::map const_params = {}; + std::map fake_quantize_params = {{"levels", "256"}}; + + std::vector> edges = { + {"0,0", "1,1"}, {"1,2", "6,7"}, // power + {"2,3", "6,8"}, {"3,4", "6,9"}, {"4,5", "6,10"}, {"5,6", "6,11"}, // const + {"6,12", "7,13"}, {"7,14", "8,15"} // pool, power + }; + + return CommonTestUtils::DefaultNetBuilder::buildNetworkWithOneInput( + "Conv_ScaleShift_transformations", p.inputDimensions[0], p._network_precision) + .addLayer("Power", p._network_precision, &power_params, {{p.inputDimensions[0]}, {p.inputDimensions[0]}}) + .addLayer("Const", p._network_precision, &const_params, {{}, {{1}}}, type_size, 0) + .addLayer("Const", p._network_precision, &const_params, {{}, {{1}}}, type_size, 0) + .addLayer("Const", p._network_precision, &const_params, {{}, {{1}}}, type_size, 0) + .addLayer("Const", p._network_precision, &const_params, {{}, {{1}}}, type_size, 0) + .addLayer("FakeQuantize", p._network_precision, &fake_quantize_params, {{p.inputDimensions[0], {1}, {1}, {1}, {1}}, {{p.inputDimensions[0]}}}) + .poolingLayer(p._network_precision, {{p.inputDimensions[0]}, {poolOutShape}}, pooling) + .addLayer("Power", p._network_precision, &power_params, {{poolOutShape}, {poolOutShape}}) + .finish(&edges); +} + +void PowerTestModel::resetTransformation(CNNNetwork& network) const { + fillData(getLayer(network, "Const2"), 63.5, "custom"); + fillData(getLayer(network, "Const3"), 127.0, "custom"); + fillData(getLayer(network, "Const4"), 63.5, "custom"); + fillData(getLayer(network, "Const5"), 127.0, "custom"); +} + +std::string PowerTestModel::getName() const { + return std::string("PowerTestModel") + + (power == 1.f ? std::string("") : "_power!=1") + + (scale == 1.f ? "" : "_scale=" + std::to_string(scale)) + + (shift == 0 ? "" : "_shift!=" + std::to_string(shift)); +} + +bool PowerTestModel::transform(CNNNetwork& network, LayerTransformation::Params& params) const { + LowPrecisionTransformer transformer(LowPrecisionTransformer::getAllTransformations(params)); + transformer.transform(network); + + const Precision precision = params.updatePrecisions ? Precision(Precision::U8) : network.getPrecision(); + + CNNLayerPtr fakeQuantize = getLayer(network, "FakeQuantize6"); + if (fakeQuantize->outData[0]->getPrecision() != precision) { + THROW_IE_EXCEPTION << fakeQuantize->name << " precision " << precision << " is not correct"; + } + + CNNLayerPtr pooling = getLayer(network, "Pooling7"); + if (pooling->outData[0]->getPrecision() != precision) { + THROW_IE_EXCEPTION << pooling->name << " precision " << precision << " is not correct"; + } + + CNNLayerPtr powerLayer = getLayer(network, "Power8"); + + const bool deleteLayer = params.quantizeOutputs && power == 1.f && powerLayer != nullptr && powerLayer->type == "Power"; + + if (deleteLayer) { + THROW_IE_EXCEPTION << "Power layer is present after transformation"; + } + + return true; +} diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_and_fake_quantize_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_and_fake_quantize_test.cpp index a0d8baf5981..bed3fb8e83c 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_and_fake_quantize_test.cpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_and_fake_quantize_test.cpp @@ -29,7 +29,7 @@ std::string ScaleShiftAndFakeQuantizeTestModel::getModel(SingleLayerTransformati std::map const_params = {}; std::map fake_quantize_params = {{"levels", "256"}}; - std::map power_params = {{"power", "1"}, {"scale", "1"}, {"shift", "0"}}; + std::map power_params = {{"power", "2"}, {"scale", "1"}, {"shift", "0"}}; std::vector> edges = { {"0,0", "1,1"}, {"1,2", "6,7"}, // Power diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_concat_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_concat_test.cpp index 0a2f19ac5ed..2d668a2650e 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_concat_test.cpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_concat_test.cpp @@ -27,7 +27,7 @@ std::string ScaleShiftToConvolutionAfterConcatTestModel::getModel(SingleLayerTra {"axis", "1"} }; std::map power_params = { - {"power", "1"}, {"scale", "1"}, {"shift", "0"} + {"power", "2"}, {"scale", "1"}, {"shift", "0"} }; std::vector> edges = { diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_fakequantize_ignore_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_fakequantize_ignore_test.cpp index 053e5a403aa..97c94faff00 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_fakequantize_ignore_test.cpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/scaleshift_to_conv_after_fakequantize_ignore_test.cpp @@ -42,7 +42,7 @@ std::string ScaleShiftToConvolutionAfterFakeQuantizeIgnoreTestModel::getModel(Si std::map const_params = {}; std::map fake_quantize_params = { {"levels", "256"} }; - std::map power_params = { {"power", "1"}, {"scale", "1"}, {"shift", "0"} }; + std::map power_params = { {"power", "2"}, {"scale", "1"}, {"shift", "0"} }; std::vector> edges = { {"0,0", "1,1"}, // Input -> Power {"1,2", "6,7"}, // Power -> FakeQuantize diff --git a/inference-engine/tests_deprecated/functional/shared_tests/transformations/single_layer_transformations_test.cpp b/inference-engine/tests_deprecated/functional/shared_tests/transformations/single_layer_transformations_test.cpp index c234284b121..50b9e1fd735 100644 --- a/inference-engine/tests_deprecated/functional/shared_tests/transformations/single_layer_transformations_test.cpp +++ b/inference-engine/tests_deprecated/functional/shared_tests/transformations/single_layer_transformations_test.cpp @@ -304,9 +304,14 @@ void SingleLayerTransformationsTest::SetUp() { const float threshold = p.model->getThreshold(p.device_name, net_precision, param); const float zeroThreshold = p.model->getZeroThreshold(); - // const float threshold = net_precision == Precision::FP16 ? 0.0005f : 0.0003f; + + const auto outName = transformedOutput.find(name); + if (outName == transformedOutput.end()) { + THROW_IE_EXCEPTION << "Original output name " + name + " doesn't exist in transformed model"; + } + relative_compare( - CNNNetworkHelper::getFloatData(transformedOutput.find(name)->second).get(), + CNNNetworkHelper::getFloatData(outName->second).get(), CNNNetworkHelper::getFloatData(originalOutput.second).get(), outSize, threshold, diff --git a/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt b/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt index 8e6ada389b5..e8fe9b1bceb 100644 --- a/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt +++ b/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt @@ -41,7 +41,6 @@ addIeTarget( ${CMAKE_CURRENT_SOURCE_DIR}/common/regression/helpers LINK_LIBRARIES IESharedTests - inference_engine_ir_readers vpu_graph_transformer vpu_custom_kernels DEFINES @@ -69,7 +68,6 @@ addIeTargetTest( LINK_LIBRARIES IESharedTests vpu_custom_kernels - inference_engine_ir_readers LINK_LIBRARIES_WHOLE_ARCHIVE myriadTestData VPUCommonTests diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.cpp index 49fa67994dc..28996ae24c8 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.cpp @@ -4,7 +4,11 @@ #include "myriad_layers_CTCDecoder_test.hpp" -INSTANTIATE_TEST_CASE_P(myriad, myriadCTCDecoderLayerTests_nightly, - ::testing::Combine( - ::testing::Values(true, false), - ::testing::ValuesIn(s_DimsConfig))); +INSTANTIATE_TEST_CASE_P( + accuracy, myriadCTCDecoderLayerTests_smoke, + ::testing::Combine( + ::testing::Values({{1, 88, 1, 71}}), + ::testing::Values(true, false), + ::testing::Values(IRVersion::v7, IRVersion::v10), + ::testing::ValuesIn(s_CustomConfig) +)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.hpp index d047eee2418..69a4d79d47a 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_CTCDecoder_test.hpp @@ -9,17 +9,11 @@ using namespace InferenceEngine; #define ERROR_BOUND 0.2f -typedef struct { - SizeVector src_dims; - SizeVector seq_ind_dims; - SizeVector dst_dims; - std::string custom_config; -} dims_config; +PRETTY_PARAM(CustomConfig, std::string); +PRETTY_PARAM(HwOptimization, bool); -PRETTY_PARAM(hwAcceleration, std::string); -PRETTY_PARAM(dimsConfig, dims_config); - -typedef myriadLayerTestBaseWithParam> myriadCTCDecoderLayerTests_nightly; +typedef myriadLayerTestBaseWithParam> + myriadCTCDecoderLayerTests_smoke; void refCTCDecoder(const Blob::Ptr src, const Blob::Ptr seq_ind, Blob::Ptr dst) { ie_fp16 *src_data = static_cast(src->buffer()); @@ -89,46 +83,40 @@ void refCTCDecoder(const Blob::Ptr src, const Blob::Ptr seq_ind, Blob::Ptr dst) } } -TEST_P(myriadCTCDecoderLayerTests_nightly, CTCGreedyDecoder) { +TEST_P(myriadCTCDecoderLayerTests_smoke, CTCGreedyDecoder) { + const tensor_test_params dims = std::get<0>(GetParam()); + const bool hwOptimization = std::get<1>(GetParam()); + _irVersion = std::get<2>(GetParam()); + const std::string customConfig = std::get<3>(GetParam()); - bool HWConfigValue = std::get<0>(GetParam()); - dims_config dimsConfig = std::get<1>(GetParam()); + if (!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; + } - if(!dimsConfig.custom_config.empty() && !CheckMyriadX()) { - GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; - } + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; - _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = dimsConfig.custom_config; - - IN_OUT_desc inputTensors; - IN_OUT_desc outputTensors; - - inputTensors.resize(2); - outputTensors.resize(1); - - inputTensors[0] = dimsConfig.src_dims; - inputTensors[1] = dimsConfig.seq_ind_dims; - outputTensors[0] = dimsConfig.dst_dims; + const auto inputTensors = IN_OUT_desc{{dims.c, dims.h, dims.w}, {dims.h, dims.c}}; + const auto outputTensors = IN_OUT_desc{{1, 1, dims.h, dims.c}}; SetInputTensors(inputTensors); SetOutputTensors(outputTensors); - ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("CTCGreedyDecoder"), NetworkInitParams().useHWOpt(HWConfigValue))); + std::map params; + params["ctc_merge_repeated"] = "1"; - auto iter = _inputMap.begin(); - auto first_input = iter->first; - ++iter; - auto second_input = iter->first; + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("CTCGreedyDecoder").params(params), + NetworkInitParams() + .useHWOpt(hwOptimization) + .layoutPreference(vpu::LayoutPreference::ChannelMajor) + .lockLayout(true))); - Blob::Ptr data; - auto dataBlob = _inputMap[first_input]; + auto dataBlob = _inputMap.begin()->second; + auto seqIndBlob = std::next(_inputMap.begin())->second; - auto seqIndBlob = _inputMap[second_input]; - uint16_t *blobRawSeqFp16 = seqIndBlob->buffer().as(); - size_t count = seqIndBlob->size(); - blobRawSeqFp16[0] = PrecisionUtils::f32tof16(0.0); - for (size_t indx = 1; indx < count; ++indx) { - blobRawSeqFp16[indx] = PrecisionUtils::f32tof16(1.0); + auto seqIndFp16 = seqIndBlob->buffer().as(); + seqIndFp16[0] = PrecisionUtils::f32tof16(0.0); + for (size_t i = 1; i < seqIndBlob->size(); ++i) { + seqIndFp16[i] = PrecisionUtils::f32tof16(1.0); } std::string inputTensorBinary = TestDataHelpers::get_data_path() + "/vpu/InputGreedyDecoderMyriadCHW.bin"; @@ -136,19 +124,14 @@ TEST_P(myriadCTCDecoderLayerTests_nightly, CTCGreedyDecoder) { ASSERT_TRUE(Infer()); - auto outputBlob = _outputMap.begin()->second; - - _refBlob = make_shared_blob(TensorDesc(Precision::FP16, outputBlob->getTensorDesc().getDims(), ANY)); - _refBlob->allocate(); - refCTCDecoder(dataBlob, seqIndBlob, _refBlob); - CompareCommonAbsolute(outputBlob, _refBlob, 0.0); + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, 0.0); } -static std::vector s_DimsConfig = { - {{88, 1, 71}, {88, 1}, {1, 88, 1, 1}, ""}, +static std::vector s_CustomConfig = { + {""}, #ifdef VPU_HAS_CUSTOM_KERNELS - {{88, 1, 71}, {88, 1}, {1, 88, 1, 1}, getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml"}, + getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" #endif }; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.cpp index 9739f14a768..a30d8b1d215 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.cpp @@ -5,7 +5,7 @@ #include "myriad_layers_batch_normalization_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsBatchNormalization_nightly, + accuracy, myriadLayersTestsBatchNormalization_smoke, ::testing::Values( bn_test_params{{1, 1, 16, 8}, 0.001f}, bn_test_params{{1, 4, 8, 16}, 0.00001f}, diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.hpp index 8028d608e63..20d451b1cfc 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_batch_normalization_test.hpp @@ -58,11 +58,11 @@ void ref_batch_normalization(const InferenceEngine::Blob::Ptr src, } } -class myriadLayersTestsBatchNormalization_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsBatchNormalization_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersTestsBatchNormalization_nightly, TestsBatchNorm) +TEST_P(myriadLayersTestsBatchNormalization_smoke, TestsBatchNorm) { bn_test_params p = ::testing::WithParamInterface::GetParam(); size_t sz_weights = p.in.c; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.cpp index f8521f8bbe8..eb89b5c1805 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.cpp @@ -4,6 +4,6 @@ #include "myriad_layers_bias_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsBias_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsBias_smoke, ::testing::ValuesIn(s_biasDims) ); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.hpp index 61abb8820bb..c35dd523eb5 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_bias_test.hpp @@ -88,11 +88,11 @@ void ref_bias(const InferenceEngine::Blob::Ptr src1, } } -class myriadLayersTestsBias_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsBias_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersTestsBias_nightly, TestsBias) { +TEST_P(myriadLayersTestsBias_smoke, TestsBias) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto input_dim = GetParam(); InferenceEngine::SizeVector input_dim1; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_blob_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_blob_test.cpp index f663e73171f..de0614d8a5f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_blob_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_blob_test.cpp @@ -10,7 +10,7 @@ using namespace InferenceEngine; using namespace ::testing; -typedef myriadLayerTestBaseWithParam myriadBlobTests_nightly; +typedef myriadLayerTestBaseWithParam myriadBlobTests_smoke; std::vector readBinFile(std::string filename) { @@ -33,7 +33,7 @@ std::vector readBinFile(std::string filename) return vec; } -TEST_P(myriadBlobTests_nightly, CanGetSameBlobsOnSameIR) { +TEST_P(myriadBlobTests_smoke, CanGetSameBlobsOnSameIR) { std::string HWConfigValue = GetParam(); auto fnPtr = ngraph::builder::subgraph::makeSplitConvConcat(); @@ -64,20 +64,20 @@ TEST_P(myriadBlobTests_nightly, CanGetSameBlobsOnSameIR) { } } -INSTANTIATE_TEST_CASE_P(accuracy, myriadBlobTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadBlobTests_smoke, ::testing::Values(CONFIG_VALUE(YES), CONFIG_VALUE(NO)) ); -using myriadBlobExportTests_nightly = myriadLayersTests_nightly; +using myriadBlobExportTests_smoke = myriadLayersTests_nightly; -TEST_F(myriadBlobExportTests_nightly, CanNotDoImportOnNonExistFile) +TEST_F(myriadBlobExportTests_smoke, CanNotDoImportOnNonExistFile) { InferenceEngine::IExecutableNetwork::Ptr importedNetworkPtr; ASSERT_EQ(StatusCode::NETWORK_NOT_READ, _vpuPluginPtr->ImportNetwork(importedNetworkPtr, "I_dont_exist.blob", {}, nullptr)); } -TEST_F(myriadBlobExportTests_nightly, CanInferImportedNetworkOnExportedBlob) +TEST_F(myriadBlobExportTests_smoke, CanInferImportedNetworkOnExportedBlob) { auto fnPtr = ngraph::builder::subgraph::makeSplitConvConcat(); ASSERT_NO_THROW(_cnnNetwork = CNNNetwork(fnPtr)); @@ -95,7 +95,7 @@ TEST_F(myriadBlobExportTests_nightly, CanInferImportedNetworkOnExportedBlob) ASSERT_EQ(StatusCode::OK, inferRequest->Infer(&_resp)) << _resp.msg; } -TEST_F(myriadBlobExportTests_nightly, CanGetPerfCountsImportedNetwork) +TEST_F(myriadBlobExportTests_smoke, CanGetPerfCountsImportedNetwork) { auto fnPtr = ngraph::builder::subgraph::makeSplitConvConcat(); ASSERT_NO_THROW(_cnnNetwork = CNNNetwork(fnPtr)); @@ -125,7 +125,7 @@ TEST_F(myriadBlobExportTests_nightly, CanGetPerfCountsImportedNetwork) } } -class myriadConfigsWithBlobImportTests_nightly: public myriadLayersTests_nightly { +class myriadConfigsWithBlobImportTests_smoke: public myriadLayersTests_nightly { protected: // use this stream to redirect cout to it, // needs to be able check output on warnings @@ -148,7 +148,7 @@ private: }; -TEST_F(myriadConfigsWithBlobImportTests_nightly, TryingToSetCompileOptionPrintsWarning) +TEST_F(myriadConfigsWithBlobImportTests_smoke, TryingToSetCompileOptionPrintsWarning) { auto fnPtr = ngraph::builder::subgraph::makeSplitConvConcat(); ASSERT_NO_THROW(_cnnNetwork = CNNNetwork(fnPtr)); @@ -183,7 +183,7 @@ TEST_F(myriadConfigsWithBlobImportTests_nightly, TryingToSetCompileOptionPrintsW } } -TEST_F(myriadConfigsWithBlobImportTests_nightly, TryingToSetRuntimeOptionDoesNotPrintWarning) +TEST_F(myriadConfigsWithBlobImportTests_smoke, TryingToSetRuntimeOptionDoesNotPrintWarning) { auto fnPtr = ngraph::builder::subgraph::makeSplitConvConcat(); ASSERT_NO_THROW(_cnnNetwork = CNNNetwork(fnPtr)); @@ -215,9 +215,9 @@ TEST_F(myriadConfigsWithBlobImportTests_nightly, TryingToSetRuntimeOptionDoesNot } -using myriadBlobExportAccuracyDifferentCountInAndOutTests_nightly = myriadLayerTestBaseWithParam>; +using myriadBlobExportAccuracyDifferentCountInAndOutTests_smoke = myriadLayerTestBaseWithParam>; -TEST_F(myriadBlobExportAccuracyDifferentCountInAndOutTests_nightly, IsResultOfImportedAndGeneratedModelSame) +TEST_F(myriadBlobExportAccuracyDifferentCountInAndOutTests_smoke, IsResultOfImportedAndGeneratedModelSame) { SetSeed(DEFAULT_SEED_VALUE); @@ -287,9 +287,9 @@ TEST_F(myriadBlobExportAccuracyDifferentCountInAndOutTests_nightly, IsResultOfIm } -using myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_nightly = myriadLayerTestBaseWithParam>; +using myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_smoke = myriadLayerTestBaseWithParam>; -TEST_P(myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_nightly, IsResultOfImportedAndGeneratedModelSame) +TEST_P(myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_smoke, IsResultOfImportedAndGeneratedModelSame) { SetSeed(DEFAULT_SEED_VALUE); InferenceEngine::Precision inputPrecision = std::get<0>(GetParam()); @@ -354,9 +354,9 @@ TEST_P(myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_nightly, IsResu CompareCommonAbsolute(importedOutputBlobPtr, outputBlobPtr, 0.f); } -using myriadExtraTests_nightly = myriadLayersTests_nightly; +using myriadExtraTests_smoke = myriadLayersTests_nightly; -TEST_F(myriadExtraTests_nightly, ThereIsNoSegfaultOnZeroConvolutionWeights) { +TEST_F(myriadExtraTests_smoke, ThereIsNoSegfaultOnZeroConvolutionWeights) { if (!CheckMyriadX()) { SKIP() << "Non-MyriadX device"; } @@ -414,5 +414,5 @@ static const std::vector inputPrecisions = {Inferenc static const std::vector outputPrecisions = {InferenceEngine::Precision::FP16, InferenceEngine::Precision::FP32}; -INSTANTIATE_TEST_CASE_P(accuracy, myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadBlobExportAccuracyDifferentPrecisionOfInAndOutTests_smoke, ::testing::Combine(::testing::ValuesIn(inputPrecisions), ::testing::ValuesIn(outputPrecisions))); \ No newline at end of file diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.cpp index b848f2d60ca..bffd0972385 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_clamp_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsClampParams_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsClampParams_smoke, ::testing::Combine( ::testing::ValuesIn(s_clampTensors), ::testing::ValuesIn(s_clampParams)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.hpp index 306fe68e87b..4b28d9fc13a 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_clamp_test.hpp @@ -18,9 +18,9 @@ struct clamp_test_params { }; }; -typedef myriadLayerTestBaseWithParam> myriadLayersTestsClampParams_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsClampParams_smoke; -TEST_P(myriadLayersTestsClampParams_nightly, TestsClamp) { +TEST_P(myriadLayersTestsClampParams_smoke, TestsClamp) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto param = GetParam(); SizeVector tensor = std::get<0>(param); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.cpp index 2a385cf9544..07bf2868375 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_concat_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsConcat_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsConcat_smoke, ::testing::Combine( ::testing::ValuesIn(s_concatCores), ::testing::ValuesIn(s_axis), @@ -14,7 +14,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsConcat_nightly, getTestCaseName ); -TEST_F(myriadLayersTestsConcat_nightly, ConcatAfterNormalize) { +TEST_F(myriadLayersTestsConcat_smoke, ConcatAfterNormalize) { const std::string model = R"V0G0N( diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.hpp index d25918b945a..36c110ba394 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_concat_test.hpp @@ -7,7 +7,7 @@ using namespace InferenceEngine; using myriadConcatTestParams = std::tuple; -typedef myriadLayerTestBaseWithParam myriadLayersTestsConcat_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsConcat_smoke; void CheckOutput(const InferenceEngine::BlobMap& input, InferenceEngine::Blob::Ptr actual, int32_t axis) { int32_t OW = 1; @@ -49,7 +49,7 @@ void CheckOutput(const InferenceEngine::BlobMap& input, InferenceEngine::Blob::P ASSERT_NE(n_checks, 0); } -TEST_P(myriadLayersTestsConcat_nightly, Concat) { +TEST_P(myriadLayersTestsConcat_smoke, Concat) { auto param = GetParam(); auto core = std::get<0>(param); auto axis = std::get<1>(param); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.cpp index 75e3f27f580..78ef816d517 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.cpp @@ -12,7 +12,7 @@ using namespace testing; // //---------------------------------------------------------------------- -INSTANTIATE_TEST_CASE_P(tricky_ncdhw_userpad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(tricky_ncdhw_userpad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 19, 65, 47}), Values(KernelShape {1, 3, 5}), @@ -26,7 +26,7 @@ INSTANTIATE_TEST_CASE_P(tricky_ncdhw_userpad, myriadLayersConvNDTest_nightly, ) ); -INSTANTIATE_TEST_CASE_P(tricky_ncdhw_autopad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(tricky_ncdhw_autopad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 19, 65, 47}), Values(KernelShape {1, 3, 5}), @@ -48,7 +48,7 @@ INSTANTIATE_TEST_CASE_P(tricky_ncdhw_autopad, myriadLayersConvNDTest_nightly, // //---------------------------------------------------------------------- -INSTANTIATE_TEST_CASE_P(simple_ncdhw_userpad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(simple_ncdhw_userpad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -62,7 +62,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_userpad, myriadLayersConvNDTest_nightly, ) ); -INSTANTIATE_TEST_CASE_P(simple_ncdhw_autopad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(simple_ncdhw_autopad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -84,7 +84,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_autopad, myriadLayersConvNDTest_nightly, // //---------------------------------------------------------------------- -INSTANTIATE_TEST_CASE_P(tricky_nchw_userpad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(tricky_nchw_userpad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 65, 47}), Values(KernelShape {1, 3}), @@ -98,7 +98,7 @@ INSTANTIATE_TEST_CASE_P(tricky_nchw_userpad, myriadLayersConvNDTest_nightly, ) ); -INSTANTIATE_TEST_CASE_P(tricky_nchw_autopad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(tricky_nchw_autopad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 65, 47}), Values(KernelShape {1, 3}), @@ -120,7 +120,7 @@ INSTANTIATE_TEST_CASE_P(tricky_nchw_autopad, myriadLayersConvNDTest_nightly, // //---------------------------------------------------------------------- -INSTANTIATE_TEST_CASE_P(simple_nchw_userpad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(simple_nchw_userpad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -134,7 +134,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_userpad, myriadLayersConvNDTest_nightly, ) ); -INSTANTIATE_TEST_CASE_P(simple_nchw_autopad, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(simple_nchw_autopad, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -158,7 +158,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_autopad, myriadLayersConvNDTest_nightly, // NB: requires 1GB of RAM on device (e.g. ma2085 board) // Stress test: large image with large depth, large kernel -INSTANTIATE_TEST_CASE_P(i3d_id6, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id6, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 79, 224, 224}), Values(KernelShape {7, 7, 7}), @@ -171,7 +171,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id6, myriadLayersConvNDTest_nightly, Values(Groups(1)))); // Like `i3d_id6` test but with smaller image (so must fit in Myriad X) -INSTANTIATE_TEST_CASE_P(i3d_id6_shrink, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id6_shrink, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 3, 39, 112, 112}), Values(KernelShape {7, 7, 7}), @@ -184,7 +184,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id6_shrink, myriadLayersConvNDTest_nightly, Values(Groups(1)))); // Average-size image, trivial kernel 1x1x1 -INSTANTIATE_TEST_CASE_P(i3d_id12, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id12, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 64, 40, 56, 56}), Values(KernelShape {1, 1, 1}), @@ -197,7 +197,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id12, myriadLayersConvNDTest_nightly, Values(Groups(1)))); // Average-size image, non-trivial kernel 3x3x3 -INSTANTIATE_TEST_CASE_P(i3d_id17, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id17, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 64, 40, 56, 56}), Values(KernelShape {3, 3, 3}), @@ -210,7 +210,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id17, myriadLayersConvNDTest_nightly, Values(Groups(1)))); // Small image (7x7), trivial kernel -INSTANTIATE_TEST_CASE_P(i3d_id249, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id249, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 832, 10, 7, 7}), Values(KernelShape {1, 1, 1}), @@ -223,7 +223,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id249, myriadLayersConvNDTest_nightly, Values(Groups(1)))); // Small image (7x7), non-trivial kernel -INSTANTIATE_TEST_CASE_P(i3d_id301, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id301, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 48, 10, 7, 7}), Values(KernelShape {3, 3, 3}), @@ -236,7 +236,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id301, myriadLayersConvNDTest_nightly, Values(Groups(1)))); // Trivial image (1x1), trivial kernel -INSTANTIATE_TEST_CASE_P(i3d_id314, myriadLayersConvNDTest_nightly, +INSTANTIATE_TEST_CASE_P(i3d_id314, myriadLayersConvNDTest_smoke, Combine( Values(InputShape {1, 1024, 9, 1, 1}), Values(KernelShape {1, 1, 1}), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.hpp index 98bfeaa85c7..42bedcd42c5 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_conv_nd_test.hpp @@ -752,8 +752,8 @@ private: } }; -class myriadLayersConvNDTest_nightly: public ConvNDTest {}; +class myriadLayersConvNDTest_smoke: public ConvNDTest {}; -TEST_P(myriadLayersConvNDTest_nightly, ConvND) { +TEST_P(myriadLayersConvNDTest_smoke, ConvND) { testConvND(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.cpp index e64e72bd226..556f9fd8ef9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.cpp @@ -5,15 +5,25 @@ #include "myriad_layers_convert_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsIOConvert_nightly, + accuracy, myriadLayersTestsIOConvert_smoke, ::testing::Combine( ::testing::ValuesIn(inputsDims), - ::testing::ValuesIn(precisionsIO) + ::testing::ValuesIn(precisionsIO), + ::testing::Values("") ) ); INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsConvertWithFP16_nightly, + accuracy_customu8f16, myriadLayersTestsIOConvert_smoke, + ::testing::Combine( + ::testing::ValuesIn(inputsDims4D), + ::testing::Values(PrecisionPair{Precision::U8, Precision::FP16}), + ::testing::Values(s_CustomConfig) + ) +); + +INSTANTIATE_TEST_CASE_P( + accuracy, myriadLayersTestsConvertWithFP16_smoke, ::testing::Combine( ::testing::ValuesIn(inputsDims), ::testing::ValuesIn(withFP16Precisions) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.hpp index 03b62911148..b068a5b6594 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convert_test.hpp @@ -12,47 +12,65 @@ using namespace InferenceEngine; +PRETTY_PARAM(CustomConfig, std::string); + typedef std::pair PrecisionPair; -typedef std::tuple ConvertIOTestParam; +typedef std::tuple ConvertIOTestParam; typedef std::tuple ConvertWithFP16TestParam; -class myriadLayersTestsIOConvert_nightly: public myriadLayersTests_nightly, - public testing::WithParamInterface { +static CustomConfig s_CustomConfig = { +#ifdef VPU_HAS_CUSTOM_KERNELS + {getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml"} +#endif }; -TEST_P(myriadLayersTestsIOConvert_nightly, TestsIOConvert) -{ - const auto& param = ::testing::WithParamInterface::GetParam(); - const auto& inputDims = std::get<0>(param); - const auto& precisions = std::get<1>(param); - const auto& inputPrecision = precisions.first; - const auto& outputPrecision = precisions.second; +typedef myriadLayerTestBaseWithParam myriadLayersTestsIOConvert_smoke; - SetInputTensors({inputDims}); - SetOutputTensors({inputDims}); +TEST_P(myriadLayersTestsIOConvert_smoke, TestsIOConvert) { + const SizeVector& dims = std::get<0>(GetParam()); + const PrecisionPair& precision = std::get<1>(GetParam()); + const std::string& customConfig = std::get<2>(GetParam()); + const auto& inputPrecision = precision.first; + const auto& outputPrecision = precision.second; + + if(!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; + } + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; + + _config[VPU_CONFIG_KEY(DISABLE_CONVERT_STAGES)] = CONFIG_VALUE(YES); + + SetInputTensors({dims}); + SetOutputTensors({dims}); + + std::map params = { + {"precision", std::to_string(outputPrecision)}, + {"scale", std::to_string(1.0)}, // scale and bias are needed for custom layer + {"bias", std::to_string(0.0)} + }; + + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork( + LayerInitParams("Convert") + .params(params) + .in({dims}) + .out({dims}) + .outPrecision(outputPrecision), + NetworkInitParams() + .layoutPreference(vpu::LayoutPreference::ChannelMajor) + .inputPrecision(inputPrecision) + .outputPrecision(outputPrecision) + .lockLayout(true))); - makeSingleLayerNetwork(LayerInitParams("Copy"), - NetworkInitParams() - .inputPrecision(inputPrecision) - .outputPrecision(outputPrecision)); ASSERT_TRUE(Infer()); - auto tensorDesc = InferenceEngine::TensorDesc( - outputPrecision, _outputMap.begin()->second->getTensorDesc().getDims(), - _outputMap.begin()->second->getTensorDesc().getLayout()); - auto refBlob = make_blob_with_precision(outputPrecision, tensorDesc); - refBlob->allocate(); + ASSERT_NO_FATAL_FAILURE(ref_convert(_inputMap.begin()->second, _refBlob)); - ref_convert(_inputMap.begin()->second, refBlob); - - CompareCommonAbsolute(_outputMap.begin()->second, refBlob, ERROR_BOUND); + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, ERROR_BOUND); } -class myriadLayersTestsConvertWithFP16_nightly: public myriadLayersTests_nightly, - public testing::WithParamInterface { -}; +typedef myriadLayerTestBaseWithParam myriadLayersTestsConvertWithFP16_smoke; -TEST_P(myriadLayersTestsConvertWithFP16_nightly, TestsConvertWithFP16) +TEST_P(myriadLayersTestsConvertWithFP16_smoke, TestsConvertWithFP16) { const auto& param = ::testing::WithParamInterface::GetParam(); const auto& inputDims = std::get<0>(param); @@ -91,7 +109,7 @@ TEST_P(myriadLayersTestsConvertWithFP16_nightly, TestsConvertWithFP16) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND); } -std::vector inputsDims = { +std::vector inputsDims = { { 224, 224 }, { 3, 224, 224 }, { 1, 1, 224, 224 }, @@ -104,6 +122,15 @@ std::vector inputsDims = { { 2, 2, 3, 224, 224 }, }; +std::vector inputsDims4D = { + {{ 1, 1, 224, 224 }}, + {{ 1, 1, 416, 416 }}, + {{ 1, 1, 62, 62 }}, + {{ 1, 1, 227, 227 }}, + {{ 1, 3, 224, 224 }}, + {{ 1, 3, 360, 480 }}, +}; + std::vector precisionsIO = { {Precision::U8, Precision::FP16}, {Precision::FP32, Precision::FP16}, diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.cpp index da29ff1bd1c..6143dbbed3f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_convolution1x1.hpp" -INSTANTIATE_TEST_CASE_P(myriad, myriadConvolution1x1LayerTests_nightly, +INSTANTIATE_TEST_CASE_P(myriad, myriadConvolution1x1LayerTests_smoke, ::testing::Combine( ::testing::Values(CONFIG_VALUE(NO)), ::testing::ValuesIn(s_isHWC), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.hpp index 379817cc7a1..4ddafc8a679 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution1x1.hpp @@ -20,7 +20,7 @@ PRETTY_PARAM(hwAcceleration, std::string); PRETTY_PARAM(dimsConfig, dims_config); PRETTY_PARAM(isHWC, int); -typedef myriadLayerTestBaseWithParam> myriadConvolution1x1LayerTests_nightly; +typedef myriadLayerTestBaseWithParam> myriadConvolution1x1LayerTests_smoke; void refConvolution1x1(const Blob::Ptr src, InferenceEngine::TBlob::Ptr weights, Blob::Ptr dst, int isHWC) { ie_fp16 *in = static_cast(src->buffer()); @@ -39,7 +39,7 @@ void refConvolution1x1(const Blob::Ptr src, InferenceEngine::TBlob::Ptr size_t IW = in_width; size_t IH = in_height; size_t IC = in_channels; - + const auto& out_dims = dst->getTensorDesc().getDims(); size_t out_width = out_dims[out_dims.size() - 1]; size_t out_height = out_dims[out_dims.size() - 2]; @@ -67,16 +67,17 @@ void refConvolution1x1(const Blob::Ptr src, InferenceEngine::TBlob::Ptr continue; } uint32_t indx; - if(isHWC == 1){ - indx = ic + iw * IC + ih * IC * IW; + if (isHWC == 1) { + indx = ic + iw * IC + ih * IC * IW; valYXZ = (valYXZ) + (PrecisionUtils::f16tof32(in[indx]) * PrecisionUtils::f16tof32(w[oc*IC + ic])); } - else { + else + { indx = iw + ih * IW + ic * IW * IH; valZYX = PrecisionUtils::f32tof16(PrecisionUtils::f16tof32(valZYX) + PrecisionUtils::f16tof32(PrecisionUtils::f32tof16(PrecisionUtils::f16tof32(in[indx]) * PrecisionUtils::f16tof32(w[oc*IC + ic])))); } } - if(isHWC == 1){ + if (isHWC == 1) { out[oc*OH*OW + oh*OW + ow] = PrecisionUtils::f32tof16(valYXZ); } else { @@ -87,7 +88,7 @@ void refConvolution1x1(const Blob::Ptr src, InferenceEngine::TBlob::Ptr } } -TEST_P(myriadConvolution1x1LayerTests_nightly, Convolution1x1) { +TEST_P(myriadConvolution1x1LayerTests_smoke, Convolution1x1) { std::string model = R"V0G0N( @@ -102,7 +103,7 @@ TEST_P(myriadConvolution1x1LayerTests_nightly, Convolution1x1) { - + @IB@ @@ -133,6 +134,7 @@ TEST_P(myriadConvolution1x1LayerTests_nightly, Convolution1x1) { std::string HWConfigValue = std::get<0>(GetParam()); int isHWC = std::get<1>(GetParam()); dims_config customConfig = std::get<2>(GetParam()); + const auto layout = isHWC ? Layout::NHWC : Layout::NCHW; if(!customConfig.custom_config.empty() && !CheckMyriadX()) { GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; @@ -152,8 +154,6 @@ TEST_P(myriadConvolution1x1LayerTests_nightly, Convolution1x1) { size_t num_weights = IC * OC; - model.replace( model.find("@isHWC@"), sizeof("@isHWC@") -1, std::to_string(isHWC)); - model.replace( model.find("@IB@"), sizeof("@IB@") -1, std::to_string(IB)); model.replace( model.find("@IB@"), sizeof("@IB@") -1, std::to_string(IB)); model.replace( model.find("@IC@"), sizeof("@IC@") -1, std::to_string(IC)); @@ -171,7 +171,7 @@ TEST_P(myriadConvolution1x1LayerTests_nightly, Convolution1x1) { model.replace( model.find("@size_weights@"), sizeof("@size_weights@") -1, std::to_string(num_weights * sizeof(ie_fp16))); InferenceEngine::TBlob::Ptr weights_ptr = InferenceEngine::TBlob::Ptr(GenWeights(num_weights)); - + StatusCode st; InferenceEngine::Core ie; @@ -179,11 +179,11 @@ TEST_P(myriadConvolution1x1LayerTests_nightly, Convolution1x1) { _inputsInfo = network.getInputsInfo(); _inputsInfo["data"]->setPrecision(Precision::FP16); - (isHWC) ? _inputsInfo["data"]->setLayout(NHWC) : _inputsInfo["data"]->setLayout(NCHW); + _inputsInfo["data"]->setLayout(layout); _outputsInfo = network.getOutputsInfo(); _outputsInfo["conv1x1"]->setPrecision(Precision::FP16); - _outputsInfo["conv1x1"]->setLayout(NCHW); + _outputsInfo["conv1x1"]->setLayout(layout); ASSERT_NO_THROW(st = _vpuPluginPtr->LoadNetwork(_exeNetwork, network, {{VPU_CONFIG_KEY(CUSTOM_LAYERS), customConfig.custom_config}, {VPU_CONFIG_KEY(HW_STAGES_OPTIMIZATION), HWConfigValue}}, &_resp)); @@ -254,4 +254,4 @@ static std::vector s_isHWC = { #ifdef VPU_HAS_CUSTOM_KERNELS {0, 1} #endif -}; \ No newline at end of file +}; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.cpp index 967ed364ff2..df2fecebc9c 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_convolution3x3.hpp" -INSTANTIATE_TEST_CASE_P(myriad, myriadConvolution3x3LayerTests_nightly, +INSTANTIATE_TEST_CASE_P(myriad, myriadConvolution3x3LayerTests_smoke, ::testing::Combine( ::testing::Values(CONFIG_VALUE(NO)), ::testing::ValuesIn(s_DimsConfig))); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.hpp index 6f7307e0a88..bf06022988c 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution3x3.hpp @@ -19,7 +19,7 @@ typedef struct { PRETTY_PARAM(hwAcceleration, std::string); PRETTY_PARAM(dimsConfig, dims_config_con3x3); -typedef myriadLayerTestBaseWithParam> myriadConvolution3x3LayerTests_nightly; +typedef myriadLayerTestBaseWithParam> myriadConvolution3x3LayerTests_smoke; void refConvolution3x3(const Blob::Ptr src, InferenceEngine::TBlob::Ptr weights, Blob::Ptr dst, int stride_x, int stride_y, int pad_x, int pad_y, int dilation_x, int dilation_y) { @@ -102,7 +102,7 @@ void refConvolution3x3(const Blob::Ptr src, InferenceEngine::TBlob::Ptr } } -TEST_P(myriadConvolution3x3LayerTests_nightly, Convolution3x3) { +TEST_P(myriadConvolution3x3LayerTests_smoke, Convolution3x3) { std::string model = R"V0G0N( @@ -117,7 +117,7 @@ TEST_P(myriadConvolution3x3LayerTests_nightly, Convolution3x3) { - + @IB@ diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp index dbb8ad0e758..f4cfb447731 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_convolution_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy_chw_dilation, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_chw_dilation, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 37, 43, 43) , MAKE_STRUCT(tensor_test_params, 1, 37, 19, 19)) @@ -23,7 +23,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_chw_dilation, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_IR3_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_IR3_ConvTests_smoke, ::testing::Combine( ::testing::Values({1, 3, 32, 24}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -35,7 +35,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_IR3_ConvTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_0, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_0, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 5, 1, 1}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -47,7 +47,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_0, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_1, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_1, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 576, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -59,7 +59,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_1, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_2, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_2, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 128, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -71,7 +71,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_2, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_3, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_3, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 4, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -83,7 +83,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_3, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_4, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_4, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 256, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -95,7 +95,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_4, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_5, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_5, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 1024, 4, 4}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -107,7 +107,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_5, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_6, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_6, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 192, 4, 4}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -119,7 +119,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_6, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_7, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_7, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 160, 4, 4}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -131,7 +131,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_7, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_8, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_8, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 224, 4, 4}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -143,7 +143,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_8, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_9, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_9, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 1024, 4, 4}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -155,7 +155,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_9, myriadLayers_BatchTest_ConvTests_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_10, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_10, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({1, 64, 56, 56}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -167,7 +167,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_10, myriadLayers_BatchTest_ConvTests_nigh ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_11, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_11, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 192, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -179,7 +179,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_11, myriadLayers_BatchTest_ConvTests_nigh ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_12, myriadLayers_BatchTest_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_12, myriadLayers_BatchTest_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 576, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -191,7 +191,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_12, myriadLayers_BatchTest_ConvTests_nigh ) ); -INSTANTIATE_TEST_CASE_P(accuracy_Batch_1, myriadLayers_BatchTest2_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_Batch_1, myriadLayers_BatchTest2_ConvTests_smoke, ::testing::Combine( ::testing::Values({10, 576, 7, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -203,7 +203,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_Batch_1, myriadLayers_BatchTest2_ConvTests_nigh ) ); -INSTANTIATE_TEST_CASE_P(accuracy_3X3, myriadLayers_IR3_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3X3, myriadLayers_IR3_ConvTests_smoke, ::testing::Combine( ::testing::Values({1, 3, 32, 24}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -215,7 +215,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3X3, myriadLayers_IR3_ConvTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_3X1, myriadLayers_IR3_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3X1, myriadLayers_IR3_ConvTests_smoke, ::testing::Combine( ::testing::Values({1, 3, 32, 24}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 1)) @@ -227,7 +227,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3X1, myriadLayers_IR3_ConvTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_1X3, myriadLayers_IR3_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_1X3, myriadLayers_IR3_ConvTests_smoke, ::testing::Combine( ::testing::Values({1, 4, 16, 16}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 3)) @@ -239,7 +239,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1X3, myriadLayers_IR3_ConvTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_3X3X3_ConstInput_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_3X3X3_ConstInput_smoke, ::testing::Combine( ::testing::Values({1, 3, 10, 10}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -251,7 +251,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_3X3X3_ConstInput_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_crossroad_spatialConv, myriadLayerConvolutionTensorFlow_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_crossroad_spatialConv, myriadLayerConvolutionTensorFlow_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 3, 1024, 1024)) , ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 3, 512, 512)) @@ -264,7 +264,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_crossroad_spatialConv, myriadLayerConvolutionTe ) ); -INSTANTIATE_TEST_CASE_P(accuracy_inception_v2, myriadLayerConvolutionTensorFlow_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_inception_v2, myriadLayerConvolutionTensorFlow_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 16, 28, 28)) , ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 64, 14, 14)) @@ -277,7 +277,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_inception_v2, myriadLayerConvolutionTensorFlow_ ) ); -INSTANTIATE_TEST_CASE_P(accuracy_inception_v1, myriadLayerConvolutionTensorFlow_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_inception_v1, myriadLayerConvolutionTensorFlow_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 3, 224, 224), MAKE_STRUCT(tensor_test_params, 1, 32, 224, 224) @@ -292,7 +292,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_inception_v1, myriadLayerConvolutionTensorFlow_ ) ); -INSTANTIATE_TEST_CASE_P(test_3x3_SSD_dilation, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_3x3_SSD_dilation, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 512, 19, 19)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -305,7 +305,7 @@ INSTANTIATE_TEST_CASE_P(test_3x3_SSD_dilation, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(test_TF_Resnet_50, myriadLayers_IR3_ConvTests_nightly, +INSTANTIATE_TEST_CASE_P(test_TF_Resnet_50, myriadLayers_IR3_ConvTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 38, 38}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -317,7 +317,7 @@ INSTANTIATE_TEST_CASE_P(test_TF_Resnet_50, myriadLayers_IR3_ConvTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(test_3x3_icvnet_dilation, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_3x3_icvnet_dilation, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 24, 20, 20)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -332,7 +332,7 @@ INSTANTIATE_TEST_CASE_P(test_3x3_icvnet_dilation, myriadLayerConvolution_nightly ) ); -INSTANTIATE_TEST_CASE_P(test_5x5_with_dilation, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_5x5_with_dilation, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77)) , ::testing::Values(MAKE_STRUCT(param_size, 5, 5)) @@ -348,7 +348,7 @@ INSTANTIATE_TEST_CASE_P(test_5x5_with_dilation, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(test_7x7_with_dilation, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_7x7_with_dilation, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77)) , ::testing::Values(MAKE_STRUCT(param_size, 7, 7)) @@ -365,7 +365,7 @@ INSTANTIATE_TEST_CASE_P(test_7x7_with_dilation, myriadLayerConvolution_nightly, ); -INSTANTIATE_TEST_CASE_P(test_conv1x1, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_conv1x1, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 10, 13, 13)) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -379,7 +379,7 @@ INSTANTIATE_TEST_CASE_P(test_conv1x1, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_3x3_convolution, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_3x3_convolution, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 512, 13, 13)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -392,7 +392,7 @@ INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_3x3_convolution, myriadLa ) ); -INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_1x1_convolution, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_1x1_convolution, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 4608, 13, 13)) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -405,7 +405,7 @@ INSTANTIATE_TEST_CASE_P(test_yolo_tiny_2_512x13x13_use_1x1_convolution, myriadLa ) ); -INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77) , MAKE_STRUCT(tensor_test_params, 1, 32, 112, 96)) @@ -424,7 +424,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_group_large_input, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_group_large_input, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 32, 192, 336)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -439,7 +439,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_group_large_input, myriadLayerConvolution_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_any_group, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_any_group, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 32, 64, 77)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -456,7 +456,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_any_group, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3_with_group, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3_with_group, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 24, 80, 80) , MAKE_STRUCT(tensor_test_params, 1, 36, 80, 80)) @@ -471,7 +471,7 @@ INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3_with_group, myriadLayerConvolut ) ); -INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3s1, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3s1, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 24, 80, 80)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -484,7 +484,7 @@ INSTANTIATE_TEST_CASE_P(set_optimization_for_3x3s1, myriadLayerConvolution_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_1x1, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_1x1, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 16, 64, 64) , MAKE_STRUCT(tensor_test_params, 1, 32, 1, 1)) @@ -501,7 +501,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1x1, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 8, 16, 16) , MAKE_STRUCT(tensor_test_params, 1, 8, 59, 73)) @@ -517,7 +517,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_1x3, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_1x3, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 8, 59, 73)) , ::testing::Values(MAKE_STRUCT(param_size, 1, 3), MAKE_STRUCT(param_size, 3, 1)) @@ -531,7 +531,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1x3, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_5x5, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_5x5, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 16, 32, 32) /*, MAKE_STRUCT(tensor_test_params, 1, 8, 511, 399) failed*/) @@ -549,7 +549,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_5x5, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_7x7, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_7x7, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 8, 32, 32) /*, MAKE_STRUCT(tensor_test_params, 1, 8, 511, 399) failed*/) @@ -566,7 +566,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_7x7, myriadLayerConvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_1, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_1, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 3, 720, 1280)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -579,7 +579,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_1, myriadLayerConvolution_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_2, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_2, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 24, 357, 637)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -593,7 +593,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_2, myriadLayerConvolution_night ); -INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_3, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_3, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 16, 359, 639)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -606,7 +606,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3x3_large_input_3, myriadLayerConvolution_night ) ); -INSTANTIATE_TEST_CASE_P(accuracy_1x1_large_input, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_1x1_large_input, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 24, 355, 635)) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) @@ -619,7 +619,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1x1_large_input, myriadLayerConvolution_nightly ) ); -INSTANTIATE_TEST_CASE_P(accuracy_small_input_0, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_small_input_0, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 128, 38, 38)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -631,7 +631,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_small_input_0, myriadLayerConvolution_nightly, , ::testing::Values(vpu::LayoutPreference::ChannelMinor) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_small_input_1, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_small_input_1, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 256, 2, 3)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -643,7 +643,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_small_input_1, myriadLayerConvolution_nightly, , ::testing::Values(vpu::LayoutPreference::ChannelMinor) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_small_input_2, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_small_input_2, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 256, 2, 2)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -655,7 +655,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_small_input_2, myriadLayerConvolution_nightly, , ::testing::Values(vpu::LayoutPreference::ChannelMinor) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_small_input_3, myriadLayerConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_small_input_3, myriadLayerConvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 256, 1, 1)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.hpp index 18e16ffe0c4..9beac09790d 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_convolution_test.hpp @@ -22,12 +22,12 @@ PRETTY_PARAM(dilation_factor, param_size); PRETTY_PARAM(layoutPreference, vpu::LayoutPreference); typedef myriadLayerTestBaseWithParam> myriadLayerConvolution_nightly; + , out_channels, group, dilation_factor, layoutPreference >> myriadLayerConvolution_smoke; typedef myriadLayerTestBaseWithParam> myriadLayerConvolutionTensorFlow_nightly; + , group, dilation_factor, layoutPreference >> myriadLayerConvolutionTensorFlow_smoke; -TEST_P(myriadLayerConvolution_nightly, Convolution) { +TEST_P(myriadLayerConvolution_smoke, Convolution) { tensor_test_params input_dims = get<0>(GetParam()); param_size kernel = get<1>(GetParam()); param_size stride = get<2>(GetParam()); @@ -89,7 +89,7 @@ TEST_P(myriadLayerConvolution_nightly, Convolution) { CompareCommonAbsolute(outputBlob, _refBlob, maxerr); } -TEST_P(myriadLayerConvolutionTensorFlow_nightly, Convolution) { +TEST_P(myriadLayerConvolutionTensorFlow_smoke, Convolution) { tensor_test_params input_dims = get<0>(GetParam()); tensor_test_params output_dims = get<1>(GetParam()); param_size kernel = get<2>(GetParam()); @@ -173,10 +173,10 @@ void loadConstData(InferenceEngine::Blob::Ptr blob) { } } -class myriadLayers_3X3X3_ConstInput_nightly: public ConvolutionTest{ +class myriadLayers_3X3X3_ConstInput_smoke: public ConvolutionTest{ }; -TEST_P(myriadLayers_3X3X3_ConstInput_nightly, Convolution) { +TEST_P(myriadLayers_3X3X3_ConstInput_smoke, Convolution) { auto p = ::testing::WithParamInterface>::GetParam(); const auto layoutPreference = std::get<6>(p); @@ -215,11 +215,11 @@ TEST_P(myriadLayers_3X3X3_ConstInput_nightly, Convolution) { /* IR version 3 tests, main difference is a changes in padding parameters definitions */ typedef std::tuple IR3_params; -class myriadLayers_IR3_ConvTests_nightly: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ +class myriadLayers_IR3_ConvTests_smoke: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ public testing::WithParamInterface { }; -TEST_P(myriadLayers_IR3_ConvTests_nightly, Conv) { +TEST_P(myriadLayers_IR3_ConvTests_smoke, Conv) { std::map params; InferenceEngine::SizeVector output_tensor; int32_t IW = 0; @@ -280,11 +280,11 @@ TEST_P(myriadLayers_IR3_ConvTests_nightly, Conv) { CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), maxerr); } -class myriadLayers_BatchTest_ConvTests_nightly: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ +class myriadLayers_BatchTest_ConvTests_smoke: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ public testing::WithParamInterface { }; -class myriadLayers_BatchTest2_ConvTests_nightly: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ +class myriadLayers_BatchTest2_ConvTests_smoke: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ public testing::WithParamInterface { }; @@ -335,7 +335,7 @@ static void genTestData(InferenceEngine::Blob::Ptr blob) { } } -TEST_P(myriadLayers_BatchTest_ConvTests_nightly, Conv) { +TEST_P(myriadLayers_BatchTest_ConvTests_smoke, Conv) { std::map params; InferenceEngine::SizeVector output_tensor; int32_t IW = 0; @@ -586,7 +586,7 @@ static const std::string MODEL_RFCNN = R"V0G0N( )V0G0N"; -TEST_P(myriadLayers_BatchTest2_ConvTests_nightly, Conv) { +TEST_P(myriadLayers_BatchTest2_ConvTests_smoke, Conv) { std::map params; InferenceEngine::SizeVector output_tensor; int32_t IW = 0; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.cpp index f5c70a53bda..e2526fbab8c 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_copy_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerCopy_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerCopy_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(nd_tensor_test_params, {36, 19, 20, 21}) , MAKE_STRUCT(nd_tensor_test_params, {7, 8, 5, 12}) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.hpp index bf3f4cc6ae2..c93488d525f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_copy_test.hpp @@ -14,9 +14,9 @@ using namespace InferenceEngine; PRETTY_PARAM(NDims, nd_tensor_test_params); -typedef myriadLayerTestBaseWithParam> myriadLayerCopy_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerCopy_smoke; -TEST_P(myriadLayerCopy_nightly, Copy) { +TEST_P(myriadLayerCopy_smoke, Copy) { nd_tensor_test_params input_dims = get<0>(GetParam()); int ndims = get<1>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.cpp index fd28beb7e5c..c7e2de4bc12 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.cpp @@ -5,7 +5,7 @@ #include "myriad_layers_crop_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy_Crop, myriadLayerCropOneInputAndDim_nightly, + accuracy_Crop, myriadLayerCropOneInputAndDim_smoke, ::testing::Combine( ::testing::ValuesIn(s_tileTensors1), ::testing::ValuesIn(s_tileTensors2), @@ -15,7 +15,7 @@ INSTANTIATE_TEST_CASE_P( ); INSTANTIATE_TEST_CASE_P( - accuracy_Crop1, myriadLayerCropOneInput_nightly, + accuracy_Crop1, myriadLayerCropOneInput_smoke, ::testing::Combine( ::testing::ValuesIn(s_tileTensors1), ::testing::ValuesIn(s_tileTensors2), @@ -25,7 +25,7 @@ INSTANTIATE_TEST_CASE_P( ); INSTANTIATE_TEST_CASE_P( - accuracy_Crop2, myriadLayerCropTwoInputs_nightly, + accuracy_Crop2, myriadLayerCropTwoInputs_smoke, ::testing::Combine( ::testing::ValuesIn(s_tileTensors1), ::testing::ValuesIn(s_tileTensors2), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.hpp index c9d136000f7..8f9fa91b6b2 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_crop_test.hpp @@ -14,9 +14,9 @@ PRETTY_PARAM(dim, InferenceEngine::SizeVector) PRETTY_PARAM(crop_begin, InferenceEngine::SizeVector) PRETTY_PARAM(crop_end, InferenceEngine::SizeVector) -typedef myriadLayerTestBaseWithParam> myriadLayerCropOneInputAndDim_nightly; -typedef myriadLayerTestBaseWithParam> myriadLayerCropOneInput_nightly; -typedef myriadLayerTestBaseWithParam> myriadLayerCropTwoInputs_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerCropOneInputAndDim_smoke; +typedef myriadLayerTestBaseWithParam> myriadLayerCropOneInput_smoke; +typedef myriadLayerTestBaseWithParam> myriadLayerCropTwoInputs_smoke; static void ref_crop(const Blob::Ptr src, Blob::Ptr dst, @@ -67,7 +67,7 @@ static void ref_crop(const Blob::Ptr src, } } -TEST_P(myriadLayerCropOneInputAndDim_nightly, CropWithOneInputAndDim) { +TEST_P(myriadLayerCropOneInputAndDim_smoke, CropWithOneInputAndDim) { auto param = GetParam(); tensor_test_params tensor1 = std::get<0>(param); tensor_test_params tensor2 = std::get<1>(param); @@ -101,7 +101,7 @@ TEST_P(myriadLayerCropOneInputAndDim_nightly, CropWithOneInputAndDim) { CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, ERROR_BOUND); } -TEST_P(myriadLayerCropOneInput_nightly, CropWithOneInput) { +TEST_P(myriadLayerCropOneInput_smoke, CropWithOneInput) { auto param = GetParam(); tensor_test_params tensor1 = std::get<0>(param); tensor_test_params tensor2 = std::get<1>(param); @@ -135,7 +135,7 @@ TEST_P(myriadLayerCropOneInput_nightly, CropWithOneInput) { CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, ERROR_BOUND); } -TEST_P(myriadLayerCropTwoInputs_nightly, CropWithTwoInputs) { +TEST_P(myriadLayerCropTwoInputs_smoke, CropWithTwoInputs) { auto param = GetParam(); tensor_test_params tensor1 = std::get<0>(param); tensor_test_params tensor2 = std::get<1>(param); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.cpp index d1413efa6a4..a8352dbfa52 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.cpp @@ -4,26 +4,27 @@ #include "myriad_layers_custom_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsShuffleChannel_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsShuffleChannel_smoke, ::testing::Combine( ::testing::ValuesIn(s_ShuffleChannelTensors), ::testing::ValuesIn(s_ShuffleChannelGroup), ::testing::ValuesIn(s_CustomConfig))); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsQuantize_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFakeQuantize_smoke, ::testing::Combine( ::testing::ValuesIn(s_QuantizeTensors), ::testing::ValuesIn(s_QuantizeLevels), + ::testing::Values(IRVersion::v7, IRVersion::v10), ::testing::ValuesIn(s_CustomConfig))); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsQuantizeBinarize_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsQuantizeBinarize_smoke, ::testing::Combine( ::testing::ValuesIn(s_QuantizeTensors), ::testing::ValuesIn(s_QuantizeLevels), ::testing::ValuesIn(s_QuantizeSwitchOut), ::testing::ValuesIn(s_CustomConfig))); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsBinaryConvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsBinaryConvolution_smoke, ::testing::Combine( ::testing::ValuesIn(s_BinaryConvolutionTensors), ::testing::ValuesIn(s_BinaryConvolutionDilations), @@ -32,7 +33,18 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsBinaryConvolution_nightly, ::testing::ValuesIn(s_BinaryConvolutionStrides), ::testing::ValuesIn(s_CustomConfig))); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExperimentalDetectronPriorGridGenerator_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExperimentalDetectronPriorGridGenerator_smoke, ::testing::Combine( ::testing::ValuesIn(s_ExperimentalDetectronPriorGridGeneratorImageDims), ::testing::ValuesIn(s_CustomConfig))); + +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsCorrelate_smoke, + ::testing::Combine( + ::testing::ValuesIn(s_CorrelateParams), + ::testing::ValuesIn(s_CustomConfig))); + +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSpatialTransform_smoke, + ::testing::Combine( + ::testing::ValuesIn(s_SpatialTransformInputs), + ::testing::ValuesIn(s_SpatialTransformTheta), + ::testing::ValuesIn(s_CustomConfig))); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.hpp index d50e170db1b..2e838f71a26 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_custom_test.hpp @@ -2,7 +2,12 @@ // SPDX-License-Identifier: Apache-2.0 // +#pragma once + #include "myriad_layers_tests.hpp" +#include +#include +#include using namespace InferenceEngine; @@ -67,6 +72,7 @@ static void refQuantize(const Blob::Ptr src, int32_t W = 1; int32_t H = 1; int32_t C = 1; + get_dims(src, W, H, C); for (int c = 0; c < C; c++) { @@ -75,6 +81,10 @@ static void refQuantize(const Blob::Ptr src, float olow = PrecisionUtils::f16tof32(output_low->size() == 1 ? output_low_data[0] : output_low_data[c]); float ohigh = PrecisionUtils::f16tof32(output_high->size() == 1 ? output_high_data[0] : output_high_data[c]); + // emulate half math to be close to half float SHAVE implementation + float a = PrecisionUtils::f16tof32(PrecisionUtils::f32tof16((float)(levels - 1) / (ihigh - ilow))); + float b = PrecisionUtils::f16tof32(PrecisionUtils::f32tof16((ohigh - olow) / (float)(levels - 1))); + for (int h = 0; h < H; h++) { for (int w = 0; w < W; w++) { int idx = (isCHW) ? c*W*H + h*W + w : c + h*W*C + w*C; @@ -85,9 +95,15 @@ static void refQuantize(const Blob::Ptr src, dst_val = olow; } else if (src_val > ihigh) { dst_val = ohigh; - } else { - dst_val = round((src_val - ilow) * ((float)(levels - 1) / (ihigh - ilow))) * ((ohigh - olow) / (float)(levels - 1))+ olow; - //dst_val = round((src_val - ilow) / (ihigh - ilow) * (levels - 1)) / (levels - 1) * (ohigh - olow) + olow; + } else { + if(!(ihigh - ilow) || !(levels - 1)) { + dst_val = olow; + } else { + // quantization pass + float quantized = PrecisionUtils::f16tof32(PrecisionUtils::f32tof16((src_val - ilow) * a)); + // de-quantization pass + dst_val = PrecisionUtils::f16tof32(PrecisionUtils::f32tof16(roundf(quantized) * b)) + olow; + } } dst_data[idx] = PrecisionUtils::f32tof16(dst_val); @@ -308,6 +324,290 @@ static void refExperimentalDetectronPriorGridGenerator( } } } + +static void rearrange(const ie_fp16* in, ie_fp16* out, int num, int channels, int width, int height, + int widthheight, int padding, int pwidthheight) +{ + (void) height; + (void) pwidthheight; + + ASSERT_TRUE(num == 1) << "batch is not supported for Myriad"; + + for (int xy = 0; xy < widthheight; xy++) + { + for (int ch = 0; ch < channels; ch++) + { + ie_fp16 value = in[ch * widthheight + xy]; + + int xpad = (xy % width + padding); + int ypad = (xy / width + padding); + int xypad = ypad * (width + 2 * padding) + xpad; + + out[xypad * channels + ch] = value; + } + } +} + +static void correlate(int nthreads, int num, int topwidth, int topheight, int topchannels, int topcount, + int max_displacement, int neighborhood_grid_radius, int neighborhood_grid_width, + int kernel_radius, int kernel_size, int stride1, int stride2, + int bottomwidth, int bottomheight, int bottomchannels, + const ie_fp16* bottom0, const ie_fp16* bottom1, ie_fp16* top) +{ + (void) nthreads; + (void) kernel_radius; + (void) topcount; + (void) bottomheight; + (void) num; + + const int sumelems = kernel_size * kernel_size * bottomchannels; + + auto patch_data = std::vector(sumelems); + + for (int blockIdx_y = 0; blockIdx_y < topheight; blockIdx_y++) + { + for (int blockIdx_x = 0; blockIdx_x < topwidth; blockIdx_x++) + { + int x1 = blockIdx_x * stride1 + max_displacement; + int y1 = blockIdx_y * stride1 + max_displacement; + // Load 3D patch into shared memory + for (int j = 0; j < kernel_size; j++) + { + for (int i = 0; i < kernel_size; i++) + { + int idx1 = ( j * kernel_size + i) * bottomchannels; + int idx2 = ((y1 + j) * bottomwidth + x1 + i) * bottomchannels; + + for (int ch = 0; ch < bottomchannels; ch++) + patch_data[idx1 + ch] = bottom0[idx2 + ch]; + } + } + + for (int top_channel = 0; top_channel < topchannels; top_channel++) + { + int x2 = x1 + (top_channel % neighborhood_grid_width - neighborhood_grid_radius) * stride2; + int y2 = y1 + (top_channel / neighborhood_grid_width - neighborhood_grid_radius) * stride2; + + float sum = (0.0f); + for (int j = 0; j < kernel_size; j++) + { + for (int i = 0; i < kernel_size; i++) + { + int idx1 = ( j * kernel_size + i) * bottomchannels; + int idx2 = ((y2 + j) * bottomwidth + x2 + i) * bottomchannels; + + for (int ch = 0; ch < bottomchannels; ch++) + sum += PrecisionUtils::f16tof32(patch_data[idx1 + ch]) * PrecisionUtils::f16tof32(bottom1[idx2 + ch]); + } + } + top[top_channel * topheight * topwidth + blockIdx_y * topwidth + blockIdx_x] + = PrecisionUtils::f32tof16(sum / (float)sumelems); + } + } + } +} + +static void refCorrelate(const Blob::Ptr in0, + const Blob::Ptr in1, + Blob::Ptr out, + int kernel_size, int max_displacement, int pad_size, + int stride1, int stride2) { + // Correlation type = MULTIPLY + ASSERT_NE(in0, nullptr); + ASSERT_NE(in1, nullptr); + ASSERT_NE(out, nullptr); + + const ie_fp16 *in0_data = in0->buffer(); + const ie_fp16 *in1_data = in1->buffer(); + ie_fp16 *out_data = out->buffer(); + ASSERT_NE(in0_data, nullptr); + ASSERT_NE(in1_data, nullptr); + ASSERT_NE(out_data, nullptr); + + int32_t IW0 = 1; + int32_t IH0 = 1; + int32_t IC0 = 1; + get_dims(in0, IW0, IH0, IC0); + int32_t IW1 = 1; + int32_t IH1 = 1; + int32_t IC1 = 1; + get_dims(in1, IW1, IH1, IC1); + ASSERT_EQ(IW0, IW1); + ASSERT_EQ(IH0, IH1); + ASSERT_EQ(IC0, IC1); + + int32_t OW = 1; + int32_t OH = 1; + int32_t OC = 1; + get_dims(out, OW, OH, OC); + + const int bottomchannels = IC0; + + const int paddedbottomwidth = IW0 + 2 * pad_size; + const int paddedbottomheight = IH0 + 2 * pad_size; + + const int kernel_radius = kernel_size / 2; //size of unreachable border region (on each side) + const int border_size = max_displacement + kernel_radius; //size of unreachable border region (on each side) + + const int top_width = (int)ceilf((float)(paddedbottomwidth - border_size * 2) / (float)stride1); + const int top_height = (int)ceilf((float)(paddedbottomheight - border_size * 2) / (float)stride1); + + ASSERT_TRUE(top_width >= 1 && top_height >= 1) + << "Correlation cannot be done with current settings. Neighborhood and kernel don't fit in blob"; + + // Given a center position in image 1, + // how many displaced positions in -x / +x direction do we consider in image 2 (neighborhoodGridWidth): + const int neighborhood_grid_radius = max_displacement / stride2; + const int neighborhood_grid_width = 2 * neighborhood_grid_radius + 1; + + const int top_channels = neighborhood_grid_width * neighborhood_grid_width; + + ASSERT_TRUE(OC == top_channels && OH == top_height && OW == top_width) + << "input and output blobs have incompatible shapes"; + + auto rbot1 = std::vector(paddedbottomheight * paddedbottomwidth * bottomchannels); + auto rbot2 = std::vector(paddedbottomheight * paddedbottomwidth * bottomchannels); + + const int bnum = 1; + const int topcount = top_width * top_height * top_channels; + + const int pwidthheight = (IW0 + 2 * pad_size) * (IH0 + 2 * pad_size); + + rearrange(in0_data, rbot1.data(), bnum, IC0, IW0, IH0, IW0 * IH0, pad_size, pwidthheight); + rearrange(in1_data, rbot2.data(), bnum, IC0, IW0, IH0, IW0 * IH0, pad_size, pwidthheight); + + const int height = IH0 + 2 * pad_size; + const int width = IW0 + 2 * pad_size; + correlate(topcount, bnum, top_width, top_height, top_channels, topcount, + max_displacement, neighborhood_grid_radius, neighborhood_grid_width, + kernel_radius, kernel_size, stride1, stride2, width, height, IC0, + rbot1.data(), rbot2.data(), out_data); +} + +static float transform_forward_cpu(const ie_fp16* pic, const float px, const float py, int W, int H) { + float res = 0.0f; + float x = (px + 1) / 2 * H; + float y = (py + 1) / 2 * W; + int m, n, k, l; + float w; + k = (floorf(x)); + l = (floorf(y)); + m = floorf(x); + n = floorf(y); + w = 0; + + if (k >= 0 && k < H && l >= 0 && l < W) { + w = fmaxf(0.0f, 1 - fabsf(x - m)) * fmaxf(0.0f, 1 - fabsf(y - n)); + res += w * PrecisionUtils::f16tof32(pic[k * W + l]); + } + + k = (floorf(x) + 1); + l = (floorf(y)); + m = floorf(x) + 1; + n = floorf(y); + + w = 0; + if (k >= 0 && k < H && l >= 0 && l < W) { + w = fmaxf(0.0f, 1 - fabsf(x - m)) * fmaxf(0.0f, 1 - fabsf(y - n)); + res += w * PrecisionUtils::f16tof32(pic[k * W + l]); + } + k = (floorf(x)); + l = (floorf(y) + 1); + m = floorf(x); + n = floorf(y) + 1; + w = 0; + if (k >= 0 && k < H && l >= 0 && l < W) { + w = fmaxf(0.0f, 1 - fabsf(x - m)) * fmaxf(0.0f, 1 - fabsf(y - n)); + res += w * PrecisionUtils::f16tof32(pic[k * W + l]); + } + k = (floorf(x) + 1); + l = (floorf(y) + 1); + m = floorf(x) + 1; + n = floorf(y) + 1; + w = 0; + + if (k >= 0 && k < H && l >= 0 && l < W) { + w = fmaxf(0.0f, 1 - fabsf(x - m)) * fmaxf(0.0f, 1 - fabsf(y - n)); + res += w * PrecisionUtils::f16tof32(pic[k * W + l]); + } + + return PrecisionUtils::f32tof16(res); +} + +static void matrixMult(const std::vector& A, const std::vector& B, std::vector& C, + const int m, const int n, const int k, const int transposeB) { + if (transposeB) { + for (int rowA = 0; rowA < m; rowA++) { + for (int rowB = 0; rowB < n; rowB++) { + float sum = 0; + for (int colA = 0; colA < k; colA++) { + sum += A[rowA * k + colA] * B[rowB * k + colA]; + } + C[rowA * n + rowB] = sum; + } + } + } else { + for (int rowA = 0; rowA < m; rowA++) { + for (int colB = 0; colB < n; colB++) { + float sum = 0; + for (int colA = 0; colA < k; colA++) { + sum += A[rowA * k + colA] * B[colA * n + colB]; + } + C[rowA * n + colB] = sum; + } + } + } +} + +static void refSpatialTransform(const Blob::Ptr& src, const Blob::Ptr& theta, Blob::Ptr dst) { + ASSERT_NE(src, nullptr); + ASSERT_NE(theta, nullptr); + ASSERT_NE(dst, nullptr); + + const ie_fp16 *src_data = src->buffer(); + const ie_fp16 *theta_data = theta->buffer(); + ie_fp16 *dst_data = dst->buffer(); + ASSERT_NE(src_data, nullptr); + ASSERT_NE(theta_data, nullptr); + ASSERT_NE(dst_data, nullptr); + ASSERT_EQ(theta->size(), 6); + + int C = src->getTensorDesc().getDims()[1]; + int H = src->getTensorDesc().getDims()[2]; + int W = src->getTensorDesc().getDims()[3]; + + auto input_grid_data = std::vector(2*H*W); + auto output_grid_data = std::vector(3*H*W); + auto theta_float = std::vector(6); + for (size_t i = 0; i < 6; i++) { + theta_float[i] = PrecisionUtils::f16tof32(theta_data[i]); + } + + for (int i = 0; i < H * W; ++i) { + output_grid_data[3 * i] = ((i / W) * 1.0f / H * 2.0f - 1.0f); + output_grid_data[3 * i + 1] = ((i % W) * 1.0f / W * 2.0f - 1.0f); + output_grid_data[3 * i + 2] = 1.0f; + } + // Actually execute + int M_size = H * W; + int N_size = 2; + int K_size = 3; + matrixMult(output_grid_data, theta_float, input_grid_data, M_size, N_size, K_size, 1); + for (int j = 0; j < C; ++j) { + for (int s = 0; s < H; ++s) { + for (int t = 0; t < W; ++t) { + int row_idx = W * s + t; + float px = input_grid_data[row_idx * 2 + 0]; + float py = input_grid_data[row_idx * 2 + 1]; + + size_t dst_offset = (j * H + s) * W + t; + size_t src_offset = (j * H + 0) * W + 0; + dst_data[dst_offset] = transform_forward_cpu(src_data + src_offset, px, py, W, H); + } + } + } +} + static std::vector s_CustomConfig = { #ifdef VPU_HAS_CUSTOM_KERNELS getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" @@ -321,15 +621,26 @@ PRETTY_PARAM(Dilations, int) PRETTY_PARAM(Kernel, param_size) PRETTY_PARAM(Strides, int) -typedef myriadLayerTestBaseWithParam> myriadLayersTestsShuffleChannel_nightly; -typedef myriadLayerTestBaseWithParam> myriadLayersTestsQuantize_nightly; -typedef myriadLayerTestBaseWithParam> myriadLayersTestsQuantizeBinarize_nightly; -typedef myriadLayerTestBaseWithParam> myriadLayersTestsBinaryConvolution_nightly; -typedef myriadLayerTestBaseWithParam, std::string>> -myriadLayersTestsExperimentalDetectronPriorGridGenerator_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsShuffleChannel_smoke; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsFakeQuantize_smoke; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsQuantizeBinarize_smoke; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsBinaryConvolution_smoke; +typedef myriadLayerTestBaseWithParam, std::string>> myriadLayersTestsExperimentalDetectronPriorGridGenerator_smoke; +typedef myriadLayerTestBaseWithParam, std::string>> myriadLayersTestsSpatialTransform_smoke; -TEST_P(myriadLayersTestsShuffleChannel_nightly, ShuffleChannel) { - tensor_test_params dims = std::get<0>(GetParam()); +struct CorrelateParams { + tensor_test_params dims; + int kernel_size; + int pad_size; + int max_displacement; + int stride1; + int stride2; +}; + +typedef myriadLayerTestBaseWithParam> myriadLayersTestsCorrelate_smoke; + +TEST_P(myriadLayersTestsShuffleChannel_smoke, ShuffleChannel) { + tensor_test_params dims = std::get<0>(GetParam()); int group = std::get<1>(GetParam()); std::string customConfig = std::get<2>(GetParam()); @@ -363,62 +674,78 @@ static std::vector s_ShuffleChannelGroup = { 2 }; -TEST_P(myriadLayersTestsQuantize_nightly, Quantize) { +TEST_P(myriadLayersTestsFakeQuantize_smoke, FakeQuantize) { tensor_test_params dims = std::get<0>(GetParam()); int levels = std::get<1>(GetParam()); - std::string customConfig = std::get<2>(GetParam()); + _irVersion = std::get<2>(GetParam()); + std::string customConfig = std::get<3>(GetParam()); - if(!customConfig.empty() && !CheckMyriadX()) { - GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; + if (!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; } _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; - IN_OUT_desc inpt(5); - for (int i = 0; i < inpt.size(); ++i) { - inpt[i].resize(4); - inpt[i][0] = dims.n; - inpt[i][1] = 1; - inpt[i][2] = 1; - inpt[i][3] = 1; - } - inpt[0][1] = dims.c; - inpt[0][2] = dims.h; - inpt[0][3] = dims.w; - for (int i = 1; i < inpt.size(); ++i) { - if (rand()%2 > 0) { - inpt[i][1] = dims.c; - } - } + srand(42); - SetInputTensors(inpt); + const auto inputFqSize = rand() % 2 ? 1 : dims.c; + const auto outputFqSize = rand() % 2 ? 1 : dims.c; + + const auto inputDims = IN_OUT_desc{dims.asVector(), + {1, inputFqSize, 1, 1}, + {1, inputFqSize, 1, 1}, + {1, outputFqSize, 1, 1}, + {1, outputFqSize, 1, 1} + }; + + SetInputTensors(inputDims); SetOutputTensor(dims); std::map params; params["levels"] = std::to_string(levels); - ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("FakeQuantize").params(params))); + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork( + LayerInitParams("FakeQuantize").params(params), + NetworkInitParams() + .layoutPreference(vpu::LayoutPreference::ChannelMajor) + .lockLayout(true))); + + auto inputBlobs = std::vector{}; + inputBlobs.reserve(5); + for (const auto& inputBlob : _inputMap) { + inputBlobs.push_back(inputBlob.second); + } + + const auto generateQuantBounds = [](const Blob::Ptr& lowBlob, const Blob::Ptr& highBlob) { + IE_ASSERT(lowBlob->size() == highBlob->size()); + IE_ASSERT(lowBlob->getTensorDesc().getDims() == highBlob->getTensorDesc().getDims()); + + const auto lowBound = lowBlob->buffer().as(); + const auto highBound = highBlob->buffer().as(); + for (std::size_t i = 0; i < lowBlob->size(); i++) { + const float val1 = rand() % 256; + const float val2 = 255.0f - fabs(val1); + lowBound[i] = PrecisionUtils::f32tof16(std::min(val1, val2)); + highBound[i] = PrecisionUtils::f32tof16(std::max(val1, val2)); + } + }; + + generateQuantBounds(inputBlobs[1], inputBlobs[2]); + generateQuantBounds(inputBlobs[3], inputBlobs[4]); ASSERT_TRUE(Infer()); - std::vector inputBlobs(inpt.size()); - auto inptIter = _inputMap.begin(); - for (int i = 0; i < inpt.size(); i++) { - inputBlobs[i] = inptIter->second; - inptIter++; - } - ASSERT_NO_FATAL_FAILURE(refQuantize(inputBlobs[0], inputBlobs[1], inputBlobs[2], inputBlobs[3], inputBlobs[4], _refBlob, - levels, false)); + levels, true)); - CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, 0.01f); + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, 1.f); } -TEST_P(myriadLayersTestsQuantizeBinarize_nightly, Quantize_Binarization) { +TEST_P(myriadLayersTestsQuantizeBinarize_smoke, Quantize_Binarization) { std::string model = R"V0G0N( @@ -548,10 +875,10 @@ TEST_P(myriadLayersTestsQuantizeBinarize_nightly, Quantize_Binarization) { int OH = dims.h; int OW = dims.w; - int input_low_size = (rand()%2>0) ? dims.c : 1; - int input_high_size = (levels == 2) ? input_low_size : ((rand()%2>0) ? dims.c : 1); - int output_low_size = (rand()%2>0) ? dims.c : 1; - int output_high_size = (levels == 2) ? output_low_size : ((rand()%2>0) ? dims.c : 1); + int input_low_size = (rand()%2>0) ? dims.c : 1; + int input_high_size = (levels == 2) ? input_low_size : ((rand()%2>0) ? dims.c : 1); + int output_low_size = (rand()%2>0) ? dims.c : 1; + int output_high_size = (levels == 2) ? output_low_size : ((rand()%2>0) ? dims.c : 1); model.replace( model.find("@IB@"), sizeof("@IB@") -1, std::to_string(IB)); model.replace( model.find("@IB@"), sizeof("@IB@") -1, std::to_string(IB)); @@ -717,7 +1044,7 @@ static std::vector s_QuantizeSwitchOut = { 1 }; -TEST_P(myriadLayersTestsBinaryConvolution_nightly, BinaryConvolution) { +TEST_P(myriadLayersTestsBinaryConvolution_smoke, BinaryConvolution) { tensor_test_params dims = std::get<0>(GetParam()); int dilations = std::get<1>(GetParam()); int group = std::get<2>(GetParam()); @@ -791,7 +1118,7 @@ static std::vector s_BinaryConvolutionStrides = { 1, 2 }; -TEST_P(myriadLayersTestsExperimentalDetectronPriorGridGenerator_nightly, +TEST_P(myriadLayersTestsExperimentalDetectronPriorGridGenerator_smoke, ExperimentalDetectronPriorGridGenerator) { // Setup parameters and configuration. @@ -852,3 +1179,137 @@ s_ExperimentalDetectronPriorGridGeneratorImageDims = { {1, 128, 30, 30} }; +TEST_P(myriadLayersTestsCorrelate_smoke, Correlate) { + const auto test = std::get<0>(GetParam()); + const auto dims = test.dims; + const int kernel_size = test.kernel_size; + const int pad_size = test.pad_size; + const int max_displacement = test.max_displacement; + const int stride1 = test.stride1; + const int stride2 = test.stride2; + const std::string customConfig = std::get<1>(GetParam()); + + if(!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; + } + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; + + const int paddedbottomwidth = dims.w + 2 * pad_size; + const int paddedbottomheight = dims.h + 2 * pad_size; + + const int kernel_radius = kernel_size / 2; //size of unreachable border region (on each side) + const int border_size = max_displacement + kernel_radius; //size of unreachable border region (on each side) + + const int neighborhood_grid_radius = max_displacement / stride2; + const int neighborhood_grid_width = 2 * neighborhood_grid_radius + 1; + + const int top_width = (int)ceilf((float) (paddedbottomwidth - border_size * 2) / (float) stride1); + const int top_height = (int)ceilf((float)(paddedbottomheight - border_size * 2) / (float)stride1); + const int top_channels = (test.max_displacement + 1) * (test.max_displacement + 1);// neighborhood_grid_width * neighborhood_grid_width; + + const auto inputTensors = IN_OUT_desc{dims.asVector(), dims.asVector()}; + const auto outputTensors = IN_OUT_desc{{1, (uint32_t)top_channels, (uint32_t)top_height, (uint32_t)top_width}}; + + SetInputTensors(inputTensors); + SetOutputTensors(outputTensors); + + std::map params = { + {"top_width", std::to_string(top_width)}, + {"top_height", std::to_string(top_height)}, + {"width", std::to_string(dims.w)}, + {"height", std::to_string(dims.h)}, + {"channels", std::to_string(dims.c)}, + {"displacement", std::to_string(max_displacement)}, + {"pad", std::to_string(pad_size)}, + {"neighborhood_grid_radius", std::to_string(neighborhood_grid_radius)}, + {"neighborhood_grid_width", std::to_string(neighborhood_grid_width)}, + {"kernel_size", std::to_string(kernel_size)}, + {"stride", std::to_string(stride1) + "," + std::to_string(stride2)}, + }; + + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork( + LayerInitParams("Correlate").params(params), + NetworkInitParams() + .layoutPreference(vpu::LayoutPreference::ChannelMajor) + .lockLayout(true))); + + std::vector input_blobs{}; + input_blobs.reserve(_inputMap.size()); + for (auto& input : _inputMap) { + // generate input data + for (int i = 0; i < dims.c * dims.h * dims. w; i++) { + const float corr_min = -1.744443f; + const float corr_max = 11.167725f; + float val = (corr_min + (float) rand() / ((float) RAND_MAX / (corr_max - corr_min + 1.f) + 1.f)); + + auto buf = input.second->buffer().as(); + buf[i] = PrecisionUtils::f32tof16(val); + } + + input_blobs.push_back(input.second); + } + const int output_size = top_width * top_height * top_channels; + for (int i = 0; i < output_size; i++) { + _outputMap.begin()->second->buffer().as()[i] = 0; + _refBlob->buffer().as()[i] = 0; + } + + ASSERT_TRUE(Infer()); + + refCorrelate(input_blobs[0], input_blobs[1], _refBlob, kernel_size, max_displacement, pad_size, stride1, stride2); + + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, 0.1f); +} + +static const std::vector s_CorrelateParams = { + { {1, 64, 48, 64}, 1, 8, 8, 1, 2 }, + { {1, 127, 12, 64}, 3, 8, 8, 1, 2 }, + { {1, 256, 48, 64}, 1, 20, 20, 1, 2 } +}; + +TEST_P(myriadLayersTestsSpatialTransform_smoke, SpatialTransform) { + const tensor_test_params dims = std::get<0>(GetParam()); + const std::array theta = std::get<1>(GetParam()); + const std::string customConfig = std::get<2>(GetParam()); + + if(!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; + } + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; + + SetInputTensors({dims.asVector(), {1, 1, 2, 3}}); + SetOutputTensor(dims); + + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork( + LayerInitParams("SpatialTransform"), + NetworkInitParams() + .layoutPreference(vpu::LayoutPreference::ChannelMajor) + .lockLayout(true))); + + auto theta_half = std::next(_inputMap.begin())->second; + for (int i = 0; i < 6; i++) { + theta_half->buffer().as()[i] = PrecisionUtils::f32tof16(theta[i]); + } + + ASSERT_TRUE(Infer()); + + ASSERT_NO_FATAL_FAILURE(refSpatialTransform(_inputMap.begin()->second, + std::next(_inputMap.begin())->second, + _refBlob)); + + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, 0.001f); +} + +static const std::vector s_SpatialTransformInputs = { + {{ 1, 3, 24, 94 }}, + {{ 1, 3, 96, 188 }}, + {{ 1, 3, 97, 189 }}, + {{ 1, 3, 98, 190 }}, + {{ 1, 3, 384, 512 }}, + {{ 1, 3, 24, 640 }}, +}; + +static const std::vector> s_SpatialTransformTheta = { + {1.2f, 0.2f, -0.2f, 0.2f, 1.2f, -0.2f}, + {1.f, 0.f, 0.f, 0.0f, 1.f, 0.f} +}; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.cpp index 9a4703a0db1..b1f42b1bc76 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_deconvolution_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy_deconv_to_conv, myriadLayerDeconvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_deconv_to_conv, myriadLayerDeconvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 6, 5, 6)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 1), MAKE_STRUCT(param_size, 3, 3)) @@ -17,7 +17,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_deconv_to_conv, myriadLayerDeconvolution_nightl ) ); -INSTANTIATE_TEST_CASE_P(accuracy_deconv_to_conv_2, myriadLayerDeconvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_deconv_to_conv_2, myriadLayerDeconvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 2, 256, 14, 14)) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2), MAKE_STRUCT(param_size, 3, 3)) @@ -30,7 +30,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_deconv_to_conv_2, myriadLayerDeconvolution_nigh ) ); -INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerDeconvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerDeconvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 384, 4, 2)) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2) @@ -47,7 +47,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_group, myriadLayerDeconvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_depthDeconv, myriadLayerDeconvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_depthDeconv, myriadLayerDeconvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 384, 4, 2)) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2) @@ -84,7 +84,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerDeconvolution_asymm_pad, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerDeconvolution_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerDeconvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 2, 37, 59) , MAKE_STRUCT(tensor_test_params, 1, 21, 16, 16) @@ -109,7 +109,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerDeconvolution_nightly, ) ); -INSTANTIATE_TEST_CASE_P(extra3x3s1, myriadLayerDeconvolution_nightly, +INSTANTIATE_TEST_CASE_P(extra3x3s1, myriadLayerDeconvolution_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 256, 1, 1)) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.hpp index 87ebb68e5c4..7c3af16da77 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_deconvolution_test.hpp @@ -40,12 +40,12 @@ PRETTY_PARAM(layoutPreference, vpu::LayoutPreference) PRETTY_PARAM(hw_optimization, bool) typedef myriadLayerTestBaseWithParam> myriadLayerDeconvolution_nightly; + , out_channels, group, layoutPreference, hw_optimization >> myriadLayerDeconvolution_smoke; typedef myriadLayerTestBaseWithParam> myriadLayerDeconvolution_asymm_pad; -TEST_P(myriadLayerDeconvolution_nightly, Deconvolution) { +TEST_P(myriadLayerDeconvolution_smoke, Deconvolution) { tensor_test_params input_dims = get<0>(GetParam()); param_size kernel = get<1>(GetParam()); param_size stride = get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp index 4da2d75add6..50c254557e9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_detection_output_test.cpp @@ -838,7 +838,7 @@ void checkDetectionObjectArrays(std::vector gold, std::vector gen_locations; std::vector gen_confidence; @@ -995,7 +995,7 @@ public: } }; -TEST_F(myriadDetectionOutputTests_nightly, NoConst) { +TEST_F(myriadDetectionOutputTests_smoke, NoConst) { ASSERT_NO_FATAL_FAILURE(PrepareInput()); ASSERT_NO_FATAL_FAILURE(CalcRefOutput(false)); @@ -1053,7 +1053,7 @@ TEST_F(myriadDetectionOutputTests_nightly, NoConst) { CheckResults(); } -TEST_F(myriadDetectionOutputTests_nightly, MxNet) { +TEST_F(myriadDetectionOutputTests_smoke, MxNet) { ASSERT_NO_FATAL_FAILURE(PrepareInput()); ASSERT_NO_FATAL_FAILURE(CalcRefOutput(true)); @@ -1109,7 +1109,7 @@ TEST_F(myriadDetectionOutputTests_nightly, MxNet) { CheckResults(); } -TEST_F(myriadDetectionOutputTests_nightly, WithConst) { +TEST_F(myriadDetectionOutputTests_smoke, WithConst) { ASSERT_NO_FATAL_FAILURE(PrepareInput()); ASSERT_NO_FATAL_FAILURE(CalcRefOutput(false)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.cpp index 467dd42d390..62031c6f8dc 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.cpp @@ -4,168 +4,168 @@ #include "myriad_layers_eltwise_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMax_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMax_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSum_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSum_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSub_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSub_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMul_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMul_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSumWithCoeff_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSumWithCoeff_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSumWithBroadcast_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSumWithBroadcast_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::Values(4)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSubWithCoeff_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSubWithCoeff_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSubWithBroadcast_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSubWithBroadcast_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::Values(4)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseDiv_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseDiv_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMin_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMin_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSqDiff_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseSqDiff_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwisePow_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwisePow_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseFloorMod_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseFloorMod_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseEqual_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseEqual_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseNotEqual_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseNotEqual_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseGreater_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseGreater_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseGreaterEqual_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseGreaterEqual_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLess_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLess_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLessEqual_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLessEqual_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalNot_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalNot_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyOneInput), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalAnd_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalAnd_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalOr_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalOr_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalXor_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseLogicalXor_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseInputs), ::testing::ValuesIn(s_eltwiseDims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMean_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsEltwiseMean_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseOnlyTwoInputs), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp index ae216bbcc35..c4201201bdd 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_eltwise_test.hpp @@ -375,51 +375,51 @@ protected: } }; -class myriadTestsEltwiseMax_nightly: public EltwiseTest +class myriadTestsEltwiseMax_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSum_nightly: public EltwiseTest +class myriadTestsEltwiseSum_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSub_nightly: public EltwiseTest +class myriadTestsEltwiseSub_smoke: public EltwiseTest { }; -class myriadTestsEltwiseMul_nightly: public EltwiseTest +class myriadTestsEltwiseMul_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSumWithCoeff_nightly: public EltwiseTest +class myriadTestsEltwiseSumWithCoeff_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSubWithCoeff_nightly: public EltwiseTest +class myriadTestsEltwiseSubWithCoeff_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSumWithBroadcast_nightly: public EltwiseTest +class myriadTestsEltwiseSumWithBroadcast_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSubWithBroadcast_nightly: public EltwiseTest +class myriadTestsEltwiseSubWithBroadcast_smoke: public EltwiseTest { }; -class myriadTestsEltwiseDiv_nightly: public EltwiseTest +class myriadTestsEltwiseDiv_smoke: public EltwiseTest { }; -class myriadTestsEltwiseMin_nightly: public EltwiseTest +class myriadTestsEltwiseMin_smoke: public EltwiseTest { }; -class myriadTestsEltwiseSqDiff_nightly: public EltwiseTest +class myriadTestsEltwiseSqDiff_smoke: public EltwiseTest { }; -class myriadTestsEltwisePow_nightly: public EltwiseTest +class myriadTestsEltwisePow_smoke: public EltwiseTest { void SetUp() override { EltwiseTest::SetUp(); @@ -427,35 +427,35 @@ class myriadTestsEltwisePow_nightly: public EltwiseTest } }; -class myriadTestsEltwiseFloorMod_nightly: public EltwiseTest +class myriadTestsEltwiseFloorMod_smoke: public EltwiseTest { }; -class myriadTestsEltwiseEqual_nightly: public EltwiseTest +class myriadTestsEltwiseEqual_smoke: public EltwiseTest { }; -class myriadTestsEltwiseNotEqual_nightly: public EltwiseTest +class myriadTestsEltwiseNotEqual_smoke: public EltwiseTest { }; -class myriadTestsEltwiseGreater_nightly: public EltwiseTest +class myriadTestsEltwiseGreater_smoke: public EltwiseTest { }; -class myriadTestsEltwiseGreaterEqual_nightly: public EltwiseTest +class myriadTestsEltwiseGreaterEqual_smoke: public EltwiseTest { }; -class myriadTestsEltwiseLess_nightly: public EltwiseTest +class myriadTestsEltwiseLess_smoke: public EltwiseTest { }; -class myriadTestsEltwiseLessEqual_nightly: public EltwiseTest +class myriadTestsEltwiseLessEqual_smoke: public EltwiseTest { }; -class myriadTestsEltwiseLogicalNot_nightly: public EltwiseTest +class myriadTestsEltwiseLogicalNot_smoke: public EltwiseTest { void SetUp() override { EltwiseTest::SetUp(); @@ -463,7 +463,7 @@ class myriadTestsEltwiseLogicalNot_nightly: public EltwiseTest +class myriadTestsEltwiseLogicalAnd_smoke: public EltwiseTest { void SetUp() override { EltwiseTest::SetUp(); @@ -471,7 +471,7 @@ class myriadTestsEltwiseLogicalAnd_nightly: public EltwiseTest +class myriadTestsEltwiseLogicalOr_smoke: public EltwiseTest { void SetUp() override { EltwiseTest::SetUp(); @@ -479,7 +479,7 @@ class myriadTestsEltwiseLogicalOr_nightly: public EltwiseTest +class myriadTestsEltwiseLogicalXor_smoke: public EltwiseTest { void SetUp() override { EltwiseTest::SetUp(); @@ -487,126 +487,126 @@ class myriadTestsEltwiseLogicalXor_nightly: public EltwiseTest +class myriadTestsEltwiseMean_smoke: public EltwiseTest { }; -TEST_P(myriadTestsEltwiseMax_nightly, Max) +TEST_P(myriadTestsEltwiseMax_smoke, Max) { InitBody(); } -TEST_P(myriadTestsEltwiseSum_nightly, Sum) +TEST_P(myriadTestsEltwiseSum_smoke, Sum) { InitBody(); } -TEST_P(myriadTestsEltwiseSub_nightly, Sub) +TEST_P(myriadTestsEltwiseSub_smoke, Sub) { InitBody(); } -TEST_P(myriadTestsEltwiseMul_nightly, Mul) +TEST_P(myriadTestsEltwiseMul_smoke, Mul) { InitBody(); } -TEST_P(myriadTestsEltwiseSumWithCoeff_nightly, Sum) +TEST_P(myriadTestsEltwiseSumWithCoeff_smoke, Sum) { InitBody(true); } -TEST_P(myriadTestsEltwiseSubWithCoeff_nightly, Sub) +TEST_P(myriadTestsEltwiseSubWithCoeff_smoke, Sub) { InitBody(true); } -TEST_P(myriadTestsEltwiseSumWithBroadcast_nightly, Sum) +TEST_P(myriadTestsEltwiseSumWithBroadcast_smoke, Sum) { InitBody(false, true); } -TEST_P(myriadTestsEltwiseSubWithBroadcast_nightly, Sub) +TEST_P(myriadTestsEltwiseSubWithBroadcast_smoke, Sub) { InitBody(false, true); } -TEST_P(myriadTestsEltwiseDiv_nightly, Div) +TEST_P(myriadTestsEltwiseDiv_smoke, Div) { InitBody(); } -TEST_P(myriadTestsEltwiseMin_nightly, Min) +TEST_P(myriadTestsEltwiseMin_smoke, Min) { InitBody(); } -TEST_P(myriadTestsEltwiseSqDiff_nightly, SqDiff) +TEST_P(myriadTestsEltwiseSqDiff_smoke, SqDiff) { InitBody(); } -TEST_P(myriadTestsEltwisePow_nightly, Pow) +TEST_P(myriadTestsEltwisePow_smoke, Pow) { InitBody(); } -TEST_P(myriadTestsEltwiseFloorMod_nightly, FloorMod) +TEST_P(myriadTestsEltwiseFloorMod_smoke, FloorMod) { InitBody(); } -TEST_P(myriadTestsEltwiseEqual_nightly, Equal) +TEST_P(myriadTestsEltwiseEqual_smoke, Equal) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseNotEqual_nightly, NotEqual) +TEST_P(myriadTestsEltwiseNotEqual_smoke, NotEqual) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseGreater_nightly, Greater) +TEST_P(myriadTestsEltwiseGreater_smoke, Greater) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseGreaterEqual_nightly, GreaterEqual) +TEST_P(myriadTestsEltwiseGreaterEqual_smoke, GreaterEqual) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseLess_nightly, Less) +TEST_P(myriadTestsEltwiseLess_smoke, Less) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseLessEqual_nightly, LessEqual) +TEST_P(myriadTestsEltwiseLessEqual_smoke, LessEqual) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseLogicalNot_nightly, LogicalNot) +TEST_P(myriadTestsEltwiseLogicalNot_smoke, LogicalNot) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseLogicalAnd_nightly, LogicalAnd) +TEST_P(myriadTestsEltwiseLogicalAnd_smoke, LogicalAnd) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseLogicalOr_nightly, LogicalOr) +TEST_P(myriadTestsEltwiseLogicalOr_smoke, LogicalOr) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseLogicalXor_nightly, LogicalXor) +TEST_P(myriadTestsEltwiseLogicalXor_smoke, LogicalXor) { InitBody(false, false, true); } -TEST_P(myriadTestsEltwiseMean_nightly, Mean) +TEST_P(myriadTestsEltwiseMean_smoke, Mean) { InitBody(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.cpp index c40101bcfda..bd7f236d7b9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_elu_test.hpp" -INSTANTIATE_TEST_CASE_P( accuracy, myriadLayersTestsELUParams, +INSTANTIATE_TEST_CASE_P( accuracy, myriadLayersTestsELUParams_smoke, ::testing::Combine( ::testing::ValuesIn(s_powerTensors), ::testing::ValuesIn(s_powerParams)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.hpp index 9a80695c828..83b7e4eb998 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_elu_test.hpp @@ -29,9 +29,9 @@ void gen_ref_elu(const InferenceEngine::Blob::Ptr src, } } -typedef myriadLayerTestBaseWithParam> myriadLayersTestsELUParams; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsELUParams_smoke; -TEST_P(myriadLayersTestsELUParams, TestsELU) { +TEST_P(myriadLayersTestsELUParams_smoke, TestsELU) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto param = GetParam(); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.cpp index 9034960cb99..8f5ef6a2caf 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.cpp @@ -5,5 +5,5 @@ #include "myriad_layers_erf_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsErf_nightly, + accuracy, myriadLayersTestsErf_smoke, ::testing::ValuesIn(s_ErfDims)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.hpp index d9986746617..07de3354c60 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_erf_test.hpp @@ -26,12 +26,12 @@ void ref_erf(const InferenceEngine::Blob::Ptr src, } } -class myriadLayersTestsErf_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsErf_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { public: }; -TEST_P(myriadLayersTestsErf_nightly, TestsErf) +TEST_P(myriadLayersTestsErf_smoke, TestsErf) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto p = ::testing::WithParamInterface::GetParam(); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.cpp index d0b928788f7..969ffdc03b0 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.cpp @@ -18,7 +18,7 @@ static const std::vector s_layerParams_list = {{ 10.0, 10.0, 5.0, 5.0 }, 4.135166645050049, 0.5, 0.05, _MaxDetections, _NumClasses, 2000, 0 }, }; -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsExpDetectionOutput_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsExpDetectionOutput_smoke, ::testing::Combine( ::testing::ValuesIn(s_sizeParams_list), ::testing::ValuesIn(s_layerParams_list)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp index 12eb0481ef1..5d168f6ff97 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_detectionoutput_test.hpp @@ -381,11 +381,11 @@ protected: } }; -class myriadTestsExpDetectionOutput_nightly: public ExpDetectionOutputTest +class myriadTestsExpDetectionOutput_smoke: public ExpDetectionOutputTest { }; -TEST_P(myriadTestsExpDetectionOutput_nightly, ExpDetectionOutput) +TEST_P(myriadTestsExpDetectionOutput_smoke, ExpDetectionOutput) { testExpDetectionOutput(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals.cpp index 546e74ef07d..bedf24f7f15 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_exp_generateproposals_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExpGenerateProposals_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExpGenerateProposals_smoke, ::testing::Combine( ::testing::ValuesIn(s_ExpGenerateProposalsLayerScores), ::testing::ValuesIn(s_ExpGenerateProposalsLayerImInfo), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp index 976b445c682..433caca1c71 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_generateproposals_test.hpp @@ -30,7 +30,7 @@ struct GenerateProposalsParam { using ExpGenerateProposalsTestParams = std::tuple, GenerateProposalsParam>; -typedef myriadLayerTestBaseWithParam myriadLayersTestsExpGenerateProposals_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsExpGenerateProposals_smoke; static void genInputs(InferenceEngine::BlobMap inputMap, const int numProposals, @@ -140,7 +140,7 @@ static void genInputs(InferenceEngine::BlobMap inputMap, inputIMinfo[1] = PrecisionUtils::f32tof16( (float) imgW ); } -TEST_P(myriadLayersTestsExpGenerateProposals_nightly, ExpGenerateProposals) { +TEST_P(myriadLayersTestsExpGenerateProposals_smoke, ExpGenerateProposals) { tensor_test_params scoresDims = std::get<0>(GetParam()); std::vector im_info = std::get<1>(GetParam()); GenerateProposalsParam opParams = std::get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.cpp index 12283f03355..f0aa7bb1c0e 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_exp_priorgridgenerator_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExpPriorGridGenerator_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExpPriorGridGenerator_smoke, ::testing::Combine( ::testing::ValuesIn(s_ExpPriorGridGeneratorLayerInputs), ::testing::ValuesIn(s_ExpPriorGridGeneratorLayerParam)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.hpp index 780b614ff7f..18177d8e69b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_priorgridgenerator_test.hpp @@ -42,7 +42,7 @@ struct InputDims { using ExpPriorGridGeneratorTestParams = std::tuple; -typedef myriadLayerTestBaseWithParam myriadLayersTestsExpPriorGridGenerator_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsExpPriorGridGenerator_smoke; static void genPriors(InferenceEngine::Blob::Ptr rois, const tensor_test_params& params, @@ -64,7 +64,7 @@ static void genPriors(InferenceEngine::Blob::Ptr rois, } } -TEST_P(myriadLayersTestsExpPriorGridGenerator_nightly, ExpPriorGridGenerator) { +TEST_P(myriadLayersTestsExpPriorGridGenerator_smoke, ExpPriorGridGenerator) { InputDims inputTensorsDims = std::get<0>(GetParam()); PriorGridGeneratorParam opParams = std::get<1>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.cpp index 71a38140b60..f5ce39ba41b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.cpp @@ -5,5 +5,5 @@ #include "myriad_layers_exp_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsExp_nightly, + accuracy, myriadLayersTestsExp_smoke, ::testing::ValuesIn(s_expParams)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.hpp index 1a95aaf7f65..c8ea03e20bb 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_test.hpp @@ -11,10 +11,10 @@ using namespace InferenceEngine; -class myriadLayersTestsExp_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsExp_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface {}; -TEST_P(myriadLayersTestsExp_nightly, TestsExp) +TEST_P(myriadLayersTestsExp_smoke, TestsExp) { auto p = ::testing::WithParamInterface::GetParam(); SetInputTensor(p); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.cpp index 80793c66518..bd8a826672e 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_exp_topkrois_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExpTopKROIs_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsExpTopKROIs_smoke, ::testing::Combine( ::testing::ValuesIn(s_ExpTopKROIsInputRoisNum), ::testing::ValuesIn(s_ExpTopKROIsMaxRoisNum)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp index f00be2409e3..a220de35ae2 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_exp_topkrois_test.hpp @@ -22,7 +22,7 @@ struct TopKROIsParam { using ExpTopKROIsTestParams = std::tuple; -typedef myriadLayerTestBaseWithParam myriadLayersTestsExpTopKROIs_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsExpTopKROIs_smoke; static void genInputs(InferenceEngine::BlobMap inputMap) { const std::string INPUT_ROIS = "input0"; @@ -101,7 +101,7 @@ static void genInputs(InferenceEngine::BlobMap inputMap) { } } -TEST_P(myriadLayersTestsExpTopKROIs_nightly, ExpTopKROIs) { +TEST_P(myriadLayersTestsExpTopKROIs_smoke, ExpTopKROIs) { int inputRoisNum = std::get<0>(GetParam()); TopKROIsParam opParams = std::get<1>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.cpp index 261a6824060..c98d36fff21 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_flatten_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFlatten_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFlatten_smoke, ::testing::Combine( ::testing::ValuesIn(s_flattenTensors), ::testing::ValuesIn(s_flattenAxis) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.hpp index 833bf8729ea..7dde0103154 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_flatten_test.hpp @@ -4,7 +4,7 @@ #include "myriad_layers_tests.hpp" -typedef myriadLayerTestBaseWithParam> myriadLayersTestsFlatten_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsFlatten_smoke; static void ref_flatten(const InferenceEngine::Blob::Ptr src, InferenceEngine::Blob::Ptr dst) { @@ -53,7 +53,7 @@ static void ref_flatten(const InferenceEngine::Blob::Ptr src, } } -TEST_P(myriadLayersTestsFlatten_nightly, Flatten) { +TEST_P(myriadLayersTestsFlatten_smoke, Flatten) { auto input = std::get<0>(GetParam()); int32_t axis_val = std::get<1>(GetParam()); IN_OUT_desc input_tensor; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.cpp index 9342ee03882..351dae0bf6f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.cpp @@ -5,5 +5,5 @@ #include "myriad_layers_floor_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsFloor_nightly, + accuracy, myriadLayersTestsFloor_smoke, ::testing::ValuesIn(s_FloorParams)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.hpp index a086ac96a6c..8b52f4cf088 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_floor_test.hpp @@ -25,12 +25,12 @@ void ref_floor(const InferenceEngine::Blob::Ptr src, } } -class myriadLayersTestsFloor_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsFloor_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { public: }; -TEST_P(myriadLayersTestsFloor_nightly, TestsFloor) +TEST_P(myriadLayersTestsFloor_smoke, TestsFloor) { auto p = ::testing::WithParamInterface::GetParam(); SetInputTensor(p); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.cpp index fa2f928ee93..31e8a73e866 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.cpp @@ -4,18 +4,18 @@ #include "myriad_layers_fully_connected_tests.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsFullyConnected_nightly, + accuracy, myriadLayersTestsFullyConnected_smoke, ::testing::ValuesIn(s_fcTestParams) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFullyConnectedBatch_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFullyConnectedBatch_smoke, ::testing::Combine( ::testing::ValuesIn(s_fcTestBatchParams) , ::testing::ValuesIn(s_fcTestBatchOutSizes) ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFullyConnectedPVA_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsFullyConnectedPVA_smoke, ::testing::Combine( ::testing::ValuesIn(s_fcTestPVAParams) , ::testing::ValuesIn(s_fcTestPVAOutSizes) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.hpp index 791e1a8b936..b5c9ef536f0 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_fully_connected_tests.hpp @@ -7,16 +7,16 @@ using namespace InferenceEngine; -class myriadLayersTestsFullyConnected_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsFullyConnected_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; typedef std::tuple IR3_FC_params; -class myriadLayersTestsFullyConnectedBatch_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsFullyConnectedBatch_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersTestsFullyConnected_nightly, TestsFullyConnected) +TEST_P(myriadLayersTestsFullyConnected_smoke, TestsFullyConnected) { fcon_test_params p = ::testing::WithParamInterface::GetParam(); @@ -83,7 +83,7 @@ static void genTestData1(InferenceEngine::Blob::Ptr blob) { } -TEST_P(myriadLayersTestsFullyConnectedBatch_nightly, TestsFullyConnected) +TEST_P(myriadLayersTestsFullyConnectedBatch_smoke, TestsFullyConnected) { auto p = ::testing::WithParamInterface::GetParam(); auto input_tensor = std::get<0>(p); @@ -117,11 +117,11 @@ TEST_P(myriadLayersTestsFullyConnectedBatch_nightly, TestsFullyConnected) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), 0.02); } -class myriadLayersTestsFullyConnectedPVA_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsFullyConnectedPVA_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersTestsFullyConnectedPVA_nightly, TestsFullyConnected) +TEST_P(myriadLayersTestsFullyConnectedPVA_smoke, TestsFullyConnected) { auto p = ::testing::WithParamInterface::GetParam(); auto input_tensor = std::get<0>(p); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.cpp index 251c65d446b..e86055727ed 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.cpp @@ -6,7 +6,7 @@ using namespace testing; -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerGather_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerGather_smoke, Values(GatherTestParams { {36549, 1024}, {16}, 0, "FP16" }, GatherTestParams { {10}, {10}, 0, "FP16" }, GatherTestParams { {36549, 1024}, {10}, 0, "FP16" }, diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.hpp index 868858fd44e..512fda7585c 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gather_test.hpp @@ -24,7 +24,7 @@ using GatherTestParams = std::tuple; -class myriadLayerGather_nightly : +class myriadLayerGather_smoke : public myriadLayerTestBaseWithParam { protected: @@ -305,6 +305,6 @@ private: } }; -TEST_P(myriadLayerGather_nightly, Gather) { +TEST_P(myriadLayerGather_smoke, Gather) { testGather(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.cpp index 8baa645a208..2f0aafdea56 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_gemm_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerGEMM, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerGEMM_smoke, ::testing::Combine( ::testing::Values( MAKE_STRUCT(gemm_parameters, 4.7f, 2.3f, 5, 7, 11, 1, 2, 3, 4, 5, 6, 7, 8), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.hpp index a9ed573f1ef..65380edc079 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_gemm_test.hpp @@ -103,9 +103,9 @@ static void gemm_ref(int M, int N, int K, } } -typedef myriadLayerTestBaseWithParam> myriadLayerGEMM; +typedef myriadLayerTestBaseWithParam> myriadLayerGEMM_smoke; -TEST_P(myriadLayerGEMM, GEMM) { +TEST_P(myriadLayerGEMM_smoke, GEMM) { gemm_parameters gemm_parameter = get<0>(GetParam()); auto layoutPreference = get<1>(GetParam()); auto hasThreeInputs = get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.cpp index 7a004ed7b92..3d2f15df078 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.cpp @@ -4,96 +4,11 @@ #include "myriad_layers_grn_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsGRN_nightly, - ::testing::Combine( - ::testing::ValuesIn(s_GRNTensors), - ::testing::ValuesIn(s_GRN_bias), - ::testing::ValuesIn(s_MVNCustomConfig))); - - -TEST_F(myriadLayersTests_nightly, GRN_CHW_Input) -{ - std::string model = R"V0G0N( - - - - - - 1 - 24 - 128 - 224 - - - - - - - - 1 - 24 - 128 - 224 - - - - - 1 - 24 - 128 - 224 - - - - - - - - - )V0G0N"; - - StatusCode st; - - ASSERT_NO_THROW(readNetwork(model)); - - const auto& network = _cnnNetwork; - - _inputsInfo = network.getInputsInfo(); - _inputsInfo["data"]->setPrecision(Precision::FP16); - - _outputsInfo = network.getOutputsInfo(); - _outputsInfo["grn"]->setPrecision(Precision::FP16); - - ASSERT_NO_THROW(st = _vpuPluginPtr->LoadNetwork(_exeNetwork, network, {}, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - ASSERT_NE(_exeNetwork, nullptr) << _resp.msg; - - ASSERT_NO_THROW(st = _exeNetwork->CreateInferRequest(_inferRequest, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - auto tensorDesc = TensorDesc(Precision::FP16, _inputsInfo["data"]->getTensorDesc().getDims(), Layout::NCHW); - - auto inputNCHW = make_shared_blob(tensorDesc); - ASSERT_NO_THROW(inputNCHW->allocate()); - - auto outputNCHW = make_shared_blob(tensorDesc); - ASSERT_NO_THROW(outputNCHW->allocate()); - - auto output_ref = make_shared_blob(tensorDesc); - ASSERT_NO_THROW(output_ref->allocate()); - - ASSERT_NO_THROW(GenRandomData(inputNCHW)); - - ASSERT_NO_THROW(st = _inferRequest->SetBlob("data", inputNCHW, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - ASSERT_NO_THROW(st = _inferRequest->SetBlob("grn", outputNCHW, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - ASSERT_NO_THROW(st = _inferRequest->Infer(&_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - ASSERT_NO_FATAL_FAILURE(refGRN(inputNCHW, output_ref, 0.5f, true)); - - CompareCommonAbsolute(outputNCHW, output_ref, 0.003); -} +INSTANTIATE_TEST_CASE_P( + accuracy, myriadLayersTestsGRN_smoke, + ::testing::Combine( + ::testing::ValuesIn(s_GRNInputs), + ::testing::Values(0.5f, 10.f, 1.f), + ::testing::Values(IRVersion::v7, IRVersion::v10), + ::testing::ValuesIn(s_CustomConfig) +)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.hpp index 768cf523595..5a2cb7eba17 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_grn_test.hpp @@ -43,46 +43,47 @@ static void refGRN(const Blob::Ptr src, } PRETTY_PARAM(Bias, float) +PRETTY_PARAM(CustomConfig, std::string) -typedef myriadLayerTestBaseWithParam> myriadLayersTestsGRN_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsGRN_smoke; -TEST_P(myriadLayersTestsGRN_nightly, GRN) { - tensor_test_params dims = std::get<0>(GetParam()); - float bias = std::get<1>(GetParam()); - std::string customConfig = std::get<2>(GetParam()); +TEST_P(myriadLayersTestsGRN_smoke, GRN) { + const SizeVector dims = std::get<0>(GetParam()); + const float bias = std::get<1>(GetParam()); + _irVersion = std::get<2>(GetParam()); + const std::string customConfig = std::get<3>(GetParam()); - if(!customConfig.empty() && !CheckMyriadX()) { - GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; + if (!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; } _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; - SetInputTensor(dims); - SetOutputTensor(dims); + SetInputTensors({dims}); + SetOutputTensors({dims}); std::map params; params["bias"] = std::to_string(bias); - ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("GRN").params(params))); + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("GRN").params(params), + NetworkInitParams() + .layoutPreference(vpu::LayoutPreference::ChannelMajor) + .lockLayout(true))); ASSERT_TRUE(Infer()); - ASSERT_NO_FATAL_FAILURE(refGRN(_inputMap.begin()->second, _refBlob, bias, false)); + ASSERT_NO_FATAL_FAILURE(refGRN(_inputMap.begin()->second, _refBlob, bias, true)); CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, ERROR_BOUND); } -static std::vector s_GRNTensors = { - {{1, 3, 16, 224}}, - {{1, 24, 128, 224}}, -}; - -static std::vector s_GRN_bias = { - 0.5f, 10.f -}; - -static std::vector s_MVNCustomConfig = { - "" , +static std::vector s_CustomConfig = { + {""} , #ifdef VPU_HAS_CUSTOM_KERNELS getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" #endif }; + +static std::vector s_GRNInputs = { + {1, 3, 16, 224}, + {1, 24, 128, 224}, +}; \ No newline at end of file diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.cpp index 44eb70f6302..1628d021f2e 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.cpp @@ -5,7 +5,7 @@ #include "myriad_layers_interp_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsInterp_nightly, + accuracy, myriadLayersTestsInterp_smoke, ::testing::Combine( ::testing::Values( diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.hpp index 533940bddcd..834461632eb 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_interp_test.hpp @@ -14,7 +14,7 @@ PRETTY_PARAM(layoutPreference, vpu::LayoutPreference) PRETTY_PARAM(SizeInputOutput, interp_test_params) PRETTY_PARAM(align_corners, bool) -typedef myriadLayerTestBaseWithParam> myriadLayersTestsInterp_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsInterp_smoke; void ref_interp(const Blob::Ptr src, @@ -103,7 +103,7 @@ void ref_interp(const Blob::Ptr src, } } -TEST_P(myriadLayersTestsInterp_nightly, Interp) +TEST_P(myriadLayersTestsInterp_smoke, Interp) { interp_test_params test_params = get<0>(GetParam()); auto layoutPreference = get<1>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.cpp index 3b23651a776..24d235e3d18 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.cpp @@ -5,5 +5,5 @@ #include "myriad_layers_log_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsLog_nightly, + accuracy, myriadLayersTestsLog_smoke, ::testing::ValuesIn(s_logParams)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.hpp index 14c4db5071d..7a6abd749c9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_log_test.hpp @@ -12,12 +12,12 @@ using namespace InferenceEngine; -class myriadLayersTestsLog_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsLog_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { public: }; -TEST_P(myriadLayersTestsLog_nightly, TestsLog) +TEST_P(myriadLayersTestsLog_smoke, TestsLog) { auto p = ::testing::WithParamInterface::GetParam(); SetInputTensor(p); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.cpp index ff6f0cd260d..c0d9b153ca1 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_lrn_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsLRN_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsLRN_smoke, ::testing::Combine( ::testing::ValuesIn(s_LRNTensors), ::testing::ValuesIn(s_LRNlocal_size), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.hpp index 7137ffca295..dcb4f765244 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lrn_test.hpp @@ -98,9 +98,9 @@ PRETTY_PARAM(k_val, float) PRETTY_PARAM(alpha, float) PRETTY_PARAM(beta, float) -typedef myriadLayerTestBaseWithParam> myriadLayersTestsLRN_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsLRN_smoke; -TEST_P(myriadLayersTestsLRN_nightly, LRN) { +TEST_P(myriadLayersTestsLRN_smoke, LRN) { tensor_test_params dims = std::get<0>(GetParam()); uint32_t local_v = std::get<1>(GetParam()); float k = std::get<2>(GetParam()); @@ -128,7 +128,7 @@ TEST_P(myriadLayersTestsLRN_nightly, LRN) { CompareCommonAbsolute(dst, _refBlob, ERROR_BOUND); } -TEST_P(myriadLayersTestsLRN_nightly, InnerLRN) { +TEST_P(myriadLayersTestsLRN_smoke, InnerLRN) { tensor_test_params dims = std::get<0>(GetParam()); uint32_t local_v = std::get<1>(GetParam()); float k = std::get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.cpp index 2eebd191d41..1b779792952 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.cpp @@ -172,6 +172,6 @@ TEST_F(myriadLayersTests_nightly, LSTMCellSequenceNet) { CompareCommonAbsolute(output, refOut0, ERROR_BOUND); } -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsLSTMCell_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsLSTMCell_smoke, ::testing::Values(MAKE_STRUCT(lstmcell_test_params, 512, 128)), ); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.hpp index 1491f728be9..860b8089223 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_lstm_cell.hpp @@ -162,7 +162,7 @@ struct lstmcell_test_params { << ", state size = " << tst.state_size; }; }; -typedef myriadLayerTestBaseWithParam myriadLayersTestsLSTMCell_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsLSTMCell_smoke; #define f32Tof16 PrecisionUtils::f32tof16 #define f16Tof32 PrecisionUtils::f16tof32 @@ -304,7 +304,7 @@ static void matrix_copy_transpose_repack(const ie_fp16 *psrc, ie_fp16 *pdst, int } } -TEST_P(myriadLayersTestsLSTMCell_nightly, LSTMCell) { +TEST_P(myriadLayersTestsLSTMCell_smoke, LSTMCell) { auto param = GetParam(); lstmcell_test_params test_params = param; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.cpp index a54ed4a0836..825b5773e01 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.cpp @@ -4,12 +4,13 @@ #include "myriad_layers_mvn_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMVN_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMVN_smoke, ::testing::Combine( ::testing::ValuesIn(s_MVNTensors), ::testing::ValuesIn(s_MVN_acrossChannels), ::testing::ValuesIn(s_MVN_normalize), ::testing::ValuesIn(s_MVN_epsilon), + ::testing::Values(IRVersion::v7, IRVersion::v10), ::testing::ValuesIn(s_MVNCustomConfig))); TEST_F(myriadLayersTests_nightly, MVN_CHW_Input) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.hpp index 79def375dd4..ae7741b6ffd 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_mvn_test.hpp @@ -136,15 +136,16 @@ PRETTY_PARAM(AcrossChannels, int) PRETTY_PARAM(Normalize, int) PRETTY_PARAM(Epsilon, float) -typedef myriadLayerTestBaseWithParam> myriadLayersTestsMVN_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsMVN_smoke; -TEST_P(myriadLayersTestsMVN_nightly, MVN) +TEST_P(myriadLayersTestsMVN_smoke, MVN) { tensor_test_params dims = std::get<0>(GetParam()); int acrossChannels = std::get<1>(GetParam()); int normalize = std::get<2>(GetParam()); float eps = std::get<3>(GetParam()); - std::string customConfig = std::get<4>(GetParam()); + _irVersion = std::get<4>(GetParam()); + std::string customConfig = std::get<5>(GetParam()); if(!customConfig.empty() && !CheckMyriadX()) { GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.cpp index d93cfebd34c..a8c7fc63fb9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_nms_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsNonMaxSuppression_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsNonMaxSuppression_smoke, ::testing::Values( MAKE_STRUCT(NMS_testParams, {6, 1, 1}, // {spatial_dimension, num_classes, num_batches} diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.hpp index ec6340f3e80..6acf0e7c3d7 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nms_test.hpp @@ -174,9 +174,9 @@ static void copyReference(const Blob::Ptr& blob, const refType src) { } } -typedef myriadLayerTestBaseWithParam myriadLayersTestsNonMaxSuppression_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsNonMaxSuppression_smoke; -TEST_P(myriadLayersTestsNonMaxSuppression_nightly, NonMaxSuppression) { +TEST_P(myriadLayersTestsNonMaxSuppression_smoke, NonMaxSuppression) { const auto params = GetParam(); const int spatDim = params.dims[0]; const int numClasses = params.dims[1]; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.cpp index a75812d01e9..bbcd9e37314 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.cpp @@ -4,5 +4,5 @@ #include "myriad_layers_nonzero_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestNonZero_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestNonZero_smoke, ::testing::ValuesIn(inputDims)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp index c2af76b24f9..d294a183ced 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_nonzero_test.hpp @@ -11,7 +11,7 @@ using namespace InferenceEngine; -class myriadLayerTestNonZero_nightly: public myriadLayersTests_nightly, +class myriadLayerTestNonZero_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { public: void testNonZero(vpu::LayoutPreference preference, Precision precision); @@ -71,7 +71,7 @@ protected: } }; -void myriadLayerTestNonZero_nightly::testNonZero(vpu::LayoutPreference preference, Precision precision) { +void myriadLayerTestNonZero_smoke::testNonZero(vpu::LayoutPreference preference, Precision precision) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); const auto& inputDims = GetParam(); @@ -111,19 +111,19 @@ void myriadLayerTestNonZero_nightly::testNonZero(vpu::LayoutPreference preferenc CompareNonZero(outputIndicesBlob, refIndicesBlob, outputDimsBlob, refDimsBlob); } -TEST_P(myriadLayerTestNonZero_nightly, NonZero) { +TEST_P(myriadLayerTestNonZero_smoke, NonZero) { testNonZero(vpu::LayoutPreference::ChannelMajor, Precision::FP16); } -TEST_P(myriadLayerTestNonZero_nightly, NonZeroNHWC) { +TEST_P(myriadLayerTestNonZero_smoke, NonZeroNHWC) { testNonZero(vpu::LayoutPreference::ChannelMinor, Precision::FP16); } -TEST_P(myriadLayerTestNonZero_nightly, NonZeroI32) { +TEST_P(myriadLayerTestNonZero_smoke, NonZeroI32) { testNonZero(vpu::LayoutPreference::ChannelMajor, Precision::I32); } -TEST_P(myriadLayerTestNonZero_nightly, NonZeroU8) { +TEST_P(myriadLayerTestNonZero_smoke, NonZeroU8) { testNonZero(vpu::LayoutPreference::ChannelMajor, Precision::U8); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.cpp index 35277909d71..a9a70a92611 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_normalize_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsNormalize_nightly, ::testing::Combine( +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsNormalize_smoke, ::testing::Combine( ::testing::Values( // small size, num_channels is not divisible by 8 MAKE_STRUCT(tensor_test_params, 1, 33, 1, 1), @@ -21,7 +21,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsNormalize_nightly, ::testing: )); -INSTANTIATE_TEST_CASE_P(accuracy_more, myriadLayersTestsNormalize_nightly, ::testing::Combine( +INSTANTIATE_TEST_CASE_P(accuracy_more, myriadLayersTestsNormalize_smoke, ::testing::Combine( ::testing::Values( //more tests MAKE_STRUCT(tensor_test_params, 1, 1, 38, 38), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.hpp index e060fab8ed6..64c24edaff9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_normalize_test.hpp @@ -99,9 +99,9 @@ PRETTY_PARAM(AcrossSpatial, bool) PRETTY_PARAM(ChannelSharedNormalize, bool) PRETTY_PARAM(EPS, float) -typedef myriadLayerTestBaseWithParam> myriadLayersTestsNormalize_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsNormalize_smoke; -TEST_P(myriadLayersTestsNormalize_nightly, Normalize) { +TEST_P(myriadLayersTestsNormalize_smoke, Normalize) { tensor_test_params dims = std::get<0>(GetParam()); int across_spatial = std::get<1>(GetParam()); int channel_shared = std::get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.cpp index 3834b2b6317..59fa5635dae 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_oneHot_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestOneHot_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestOneHot_smoke, ::testing::Values( MAKE_STRUCT(OneHotParams, {64}, 2, {0}, {}, {}), MAKE_STRUCT(OneHotParams, {64}, 2, {-1}, {}, {}), @@ -22,7 +22,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestOneHot_nightly, MAKE_STRUCT(OneHotParams, {4, 8, 16, 32, 64}, 2, {-1}, {}, {}) )); -INSTANTIATE_TEST_CASE_P(accuracy_add, myriadLayerTestOneHot_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_add, myriadLayerTestOneHot_smoke, ::testing::Values( MAKE_STRUCT(OneHotParams, {16, 32, 64}, 2, {2}, {}, {}), MAKE_STRUCT(OneHotParams, {8, 16, 32,64}, 2, {2}, {}, {}), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.hpp index a12982f0459..58f4fc6193a 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_oneHot_test.hpp @@ -51,9 +51,9 @@ void ref_oneHot(const InferenceEngine::Blob::Ptr src, } } -typedef myriadLayerTestBaseWithParam myriadLayerTestOneHot_nightly; +typedef myriadLayerTestBaseWithParam myriadLayerTestOneHot_smoke; -TEST_P(myriadLayerTestOneHot_nightly, OneHot) { +TEST_P(myriadLayerTestOneHot_smoke, OneHot) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); OneHotParams testParams = GetParam(); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.cpp index fbbbf928246..abe65e9f722 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_pad_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerPad, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerPad_smoke, ::testing::Combine( ::testing::Values(MAKE_STRUCT(tensor_test_params, 1, 64, 16, 16)), ::testing::Values(MAKE_STRUCT(pad_parameters, 0, 32, 1, 2, 0, 32, 3, 4)), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.hpp index 82a375cf05b..29b61b981d6 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pad_test.hpp @@ -31,7 +31,7 @@ struct pad_parameters { PRETTY_PARAM(layoutPreference, vpu::LayoutPreference); PRETTY_PARAM(pad_mode, std::string); -typedef myriadLayerTestBaseWithParam> myriadLayerPad; +typedef myriadLayerTestBaseWithParam> myriadLayerPad_smoke; const float pad_value = 42.0f; @@ -126,7 +126,7 @@ void ref_pad(const Blob::Ptr src, } } -TEST_P(myriadLayerPad, Pad) { +TEST_P(myriadLayerPad_smoke, Pad) { tensor_test_params input_dims = get<0>(GetParam()); pad_parameters pad_parameter = get<1>(GetParam()); auto layoutPreference = get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.cpp index 8d8894611b4..453e15552f9 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_permute_nd_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy_2D, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2D, myriadLayersPermuteNDTests_smoke, ::testing::Combine( ::testing::ValuesIn(s_inTensors_2D) , ::testing::ValuesIn(s_permuteTensors_2D) @@ -12,7 +12,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2D, myriadLayersPermuteNDTests_nightly, , ::testing::ValuesIn(s_permutePrecisions) )); -INSTANTIATE_TEST_CASE_P(accuracy_3D, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3D, myriadLayersPermuteNDTests_smoke, ::testing::Combine( ::testing::ValuesIn(s_inTensors_3D) , ::testing::ValuesIn(s_permuteTensors_3D) @@ -20,7 +20,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_3D, myriadLayersPermuteNDTests_nightly, , ::testing::ValuesIn(s_permutePrecisions) )); -INSTANTIATE_TEST_CASE_P(accuracy_4D, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_4D, myriadLayersPermuteNDTests_smoke, ::testing::Combine( ::testing::ValuesIn(s_inTensors_4D) , ::testing::ValuesIn(s_permuteTensors_4D) @@ -28,7 +28,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_4D, myriadLayersPermuteNDTests_nightly, , ::testing::ValuesIn(s_permutePrecisions) )); -INSTANTIATE_TEST_CASE_P(accuracy_5D, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_5D, myriadLayersPermuteNDTests_smoke, ::testing::Combine( ::testing::ValuesIn(s_inTensors_5D) , ::testing::ValuesIn(s_permuteTensors_5D) @@ -36,7 +36,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_5D, myriadLayersPermuteNDTests_nightly, , ::testing::ValuesIn(s_permutePrecisions) )); -INSTANTIATE_TEST_CASE_P(fc_to_conv_case, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(fc_to_conv_case, myriadLayersPermuteNDTests_smoke, ::testing::Values( std::make_tuple( SizeVector{8, 50, 256, 7, 7}, @@ -53,7 +53,7 @@ INSTANTIATE_TEST_CASE_P(fc_to_conv_case, myriadLayersPermuteNDTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_FasterRCNN, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_FasterRCNN, myriadLayersPermuteNDTests_smoke, ::testing::Combine( ::testing::Values({1, 24, 14, 14}) ,::testing::Values({0, 2, 3, 1}) @@ -61,7 +61,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_FasterRCNN, myriadLayersPermuteNDTests_nightly, ,::testing::ValuesIn(s_permutePrecisions) )); -INSTANTIATE_TEST_CASE_P(accuracy_MaskRCNN, myriadLayersPermuteNDTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_MaskRCNN, myriadLayersPermuteNDTests_smoke, ::testing::Combine( ::testing::Values({4, 3, 1, 88, 120}) ,::testing::Values({0, 3, 4, 1, 2}) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.hpp index aac7cc2b8ae..736716d2b1f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_nd_test.hpp @@ -64,13 +64,13 @@ using PermuteNDParams = std::tuple; -class myriadLayersPermuteNDTests_nightly: +class myriadLayersPermuteNDTests_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersPermuteNDTests_nightly, Permute) { +TEST_P(myriadLayersPermuteNDTests_smoke, Permute) { const auto& testParams = GetParam(); const auto& inputTensorSizes = std::get<0>(testParams); const auto& permutationVector = std::get<1>(testParams); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.cpp index 3638bd2da8f..41973e348c3 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.cpp @@ -4,13 +4,13 @@ #include "myriad_layers_permute_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersPermuteTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersPermuteTests_smoke, ::testing::Combine( ::testing::ValuesIn(s_inTensors) , ::testing::ValuesIn(s_permuteTensors) )); -INSTANTIATE_TEST_CASE_P(accuracyFasterRCNN, myriadLayersPermuteTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracyFasterRCNN, myriadLayersPermuteTests_smoke, ::testing::Combine( ::testing::Values({1, 24, 14, 14}) ,::testing::Values({0, 2, 3, 1}) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.hpp index 17110869863..cbc8bc8b5af 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_permute_test.hpp @@ -22,7 +22,7 @@ static inline void PrintTo(const offset_test_params& param, ::std::ostream* os) } typedef std::tuple PermuteParams; -class myriadLayersPermuteTests_nightly: public myriadLayersTests_nightly, /*input tensor, order */ +class myriadLayersPermuteTests_smoke: public myriadLayersTests_nightly, /*input tensor, order */ public testing::WithParamInterface { }; @@ -56,7 +56,7 @@ static void genRefData(InferenceEngine::Blob::Ptr blob) { } } -TEST_P(myriadLayersPermuteTests_nightly, Permute) { +TEST_P(myriadLayersPermuteTests_smoke, Permute) { std::map params; InferenceEngine::SizeVector output_tensor; int32_t IW = 0; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.cpp index a004fb70bb1..e696c2b4b07 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.cpp @@ -13,7 +13,7 @@ using namespace testing; //====================================================================== INSTANTIATE_TEST_CASE_P(tricky_ncdhw_avg_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 19, 65, 47}), Values(KernelShape {1, 3, 5}), @@ -29,7 +29,7 @@ INSTANTIATE_TEST_CASE_P(tricky_ncdhw_avg_userpad, ); INSTANTIATE_TEST_CASE_P(tricky_ncdhw_max_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 19, 65, 47}), Values(KernelShape {1, 3, 5}), @@ -44,7 +44,7 @@ INSTANTIATE_TEST_CASE_P(tricky_ncdhw_max_userpad, ); INSTANTIATE_TEST_CASE_P(tricky_ncdhw_avg_autopad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 19, 65, 47}), Values(KernelShape {1, 3, 5}), @@ -62,7 +62,7 @@ INSTANTIATE_TEST_CASE_P(tricky_ncdhw_avg_autopad, ); INSTANTIATE_TEST_CASE_P(tricky_ncdhw_max_autopad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 19, 65, 47}), Values(KernelShape {1, 3, 5}), @@ -85,7 +85,7 @@ INSTANTIATE_TEST_CASE_P(tricky_ncdhw_max_autopad, //====================================================================== INSTANTIATE_TEST_CASE_P(simple_ncdhw_avg_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -101,7 +101,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_avg_userpad, ); INSTANTIATE_TEST_CASE_P(simple_ncdhw_max_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -125,7 +125,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_max_userpad, //---------------------------------------------------------------------- INSTANTIATE_TEST_CASE_P(simple_ncdhw_avg_autopad_1, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -142,7 +142,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_avg_autopad_1, ); INSTANTIATE_TEST_CASE_P(simple_ncdhw_avg_autopad_2, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -159,7 +159,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_avg_autopad_2, //---------------------------------------------------------------------- INSTANTIATE_TEST_CASE_P(simple_ncdhw_max_autopad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 20, 64, 48}), Values(KernelShape {3, 3, 3}), @@ -182,7 +182,7 @@ INSTANTIATE_TEST_CASE_P(simple_ncdhw_max_autopad, //====================================================================== INSTANTIATE_TEST_CASE_P(tricky_nchw_avg_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 65, 47}), Values(KernelShape {1, 5}), @@ -198,7 +198,7 @@ INSTANTIATE_TEST_CASE_P(tricky_nchw_avg_userpad, ); INSTANTIATE_TEST_CASE_P(tricky_nchw_max_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 65, 47}), Values(KernelShape {1, 5}), @@ -213,7 +213,7 @@ INSTANTIATE_TEST_CASE_P(tricky_nchw_max_userpad, ); INSTANTIATE_TEST_CASE_P(tricky_nchw_avg_autopad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 65, 47}), Values(KernelShape {1, 5}), @@ -231,7 +231,7 @@ INSTANTIATE_TEST_CASE_P(tricky_nchw_avg_autopad, ); INSTANTIATE_TEST_CASE_P(tricky_nchw_max_autopad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 65, 47}), Values(KernelShape {1, 5}), @@ -254,7 +254,7 @@ INSTANTIATE_TEST_CASE_P(tricky_nchw_max_autopad, //====================================================================== INSTANTIATE_TEST_CASE_P(simple_nchw_avg_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -270,7 +270,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_avg_userpad, ); INSTANTIATE_TEST_CASE_P(simple_nchw_max_userpad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -294,7 +294,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_max_userpad, //---------------------------------------------------------------------- INSTANTIATE_TEST_CASE_P(simple_nchw_avg_autopad_1, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -311,7 +311,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_avg_autopad_1, ); INSTANTIATE_TEST_CASE_P(simple_nchw_avg_autopad_2, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -328,7 +328,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_avg_autopad_2, //---------------------------------------------------------------------- INSTANTIATE_TEST_CASE_P(simple_nchw_max_autopad, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 3, 64, 48}), Values(KernelShape {3, 3}), @@ -351,7 +351,7 @@ INSTANTIATE_TEST_CASE_P(simple_nchw_max_autopad, //====================================================================== INSTANTIATE_TEST_CASE_P(i3d_id10, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 64, 40, 112, 112}), Values(KernelShape {1, 3, 3}), @@ -364,7 +364,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id10, Values(ExcludePad(true)))); INSTANTIATE_TEST_CASE_P(i3d_id47, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 192, 40, 28, 28}), Values(KernelShape {3, 3, 3}), @@ -377,7 +377,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id47, Values(ExcludePad(true)))); INSTANTIATE_TEST_CASE_P(i3d_id247, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 832, 20, 14, 14}), Values(KernelShape {2, 2, 2}), @@ -390,7 +390,7 @@ INSTANTIATE_TEST_CASE_P(i3d_id247, Values(ExcludePad(true)))); INSTANTIATE_TEST_CASE_P(i3d_id312, - myriadLayersPoolNDTest_nightly, + myriadLayersPoolNDTest_smoke, Combine( Values(InputShape {1, 1024, 10, 7, 7}), Values(KernelShape {2, 7, 7}), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.hpp index b1043930e86..232953e223b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pool_nd_test.hpp @@ -775,8 +775,8 @@ private: } }; -class myriadLayersPoolNDTest_nightly: public PoolNDTest {}; +class myriadLayersPoolNDTest_smoke: public PoolNDTest {}; -TEST_P(myriadLayersPoolNDTest_nightly, PoolND) { +TEST_P(myriadLayersPoolNDTest_smoke, PoolND) { testPoolND(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.cpp index b312c137408..1564201cb34 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_pooling_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({10, 192, 56, 56}) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) /* kernel */ @@ -17,7 +17,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayers_IR3_BatchPoolingTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_1, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_1, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({10, 576, 14, 14}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -31,7 +31,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1, myriadLayers_IR3_BatchPoolingTests_nightly, ); -INSTANTIATE_TEST_CASE_P(accuracy_4X4, myriadLayers_IR3_PoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_4X4, myriadLayers_IR3_PoolingTests_smoke, ::testing::Combine( ::testing::Values({10, 1024, 4, 4}) , ::testing::Values(MAKE_STRUCT(param_size, 4, 4)) /* kernel */ @@ -44,7 +44,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_4X4, myriadLayers_IR3_PoolingTests_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_1X1, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_1X1, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 3, 5, 7}) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) /* kernel */ @@ -57,7 +57,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_1X1, myriadLayers_IR3_BatchPoolingTests_nightly ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p0000, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p0000, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -70,7 +70,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p0000, myriadLayers_IR3_BatchPoolingTests_ni ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p0001, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p0001, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -82,7 +82,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p0001, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p0011, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p0011, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -94,7 +94,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p0011, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p0111, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p0111, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -106,7 +106,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p0111, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p1111, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p1111, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -118,7 +118,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p1111, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p1110, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p1110, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -130,7 +130,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p1110, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p1100, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p1100, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -142,7 +142,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p1100, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p1000, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p1000, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -154,7 +154,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p1000, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p1101, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p1101, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -166,7 +166,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p1101, myriadLayers_IR3_BatchPoolingTests_ni , ::testing::ValuesIn(s_poolingMethod) ) ); -INSTANTIATE_TEST_CASE_P(accuracy_2X2p1011, myriadLayers_IR3_BatchPoolingTests_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_2X2p1011, myriadLayers_IR3_BatchPoolingTests_smoke, ::testing::Combine( ::testing::Values({1, 512, 26, 26}) , ::testing::Values(MAKE_STRUCT(param_size, 2, 2)) /* kernel */ @@ -179,14 +179,14 @@ INSTANTIATE_TEST_CASE_P(accuracy_2X2p1011, myriadLayers_IR3_BatchPoolingTests_ni ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMax_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMax_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsFull), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxOverlappedByKernel_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxOverlappedByKernel_smoke, ::testing::Combine( ::testing::Values({1, 1024, 6, 6}), ::testing::Values(MAKE_STRUCT(param_size, 7, 7)), @@ -195,7 +195,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxOverlappedByKernel_nightly ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPad4_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPad4_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInputPad4), ::testing::ValuesIn(g_poolingKernelPad4), @@ -204,7 +204,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPad4_nightly, ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPad4_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPad4_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInputPad4), ::testing::ValuesIn(g_poolingKernelPad4), @@ -213,24 +213,24 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPad4_nightly, ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsGlobalMax_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsGlobalMax_smoke, ::testing::ValuesIn(g_GlobalPoolingInput )); -INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayersTestsMax_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayersTestsMax_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(s_poolingLayerParams_k3x3), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvg_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvg_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsFull), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgOverlappedByKernel_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgOverlappedByKernel_smoke, ::testing::Combine( ::testing::Values({1, 1024, 6, 6}), ::testing::Values(MAKE_STRUCT(param_size, 7, 7)), @@ -239,12 +239,12 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgOverlappedByKernel_nightly ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayersTestsAvg_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_3x3, myriadLayersTestsAvg_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(s_poolingLayerParams_k3x3), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsGlobalAvg_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsGlobalAvg_smoke, ::testing::ValuesIn(g_GlobalPoolingInput)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.hpp index 73123ac0d27..c1923c65257 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_pooling_test.hpp @@ -15,35 +15,35 @@ extern const char POOLING_MAX[] = "max"; extern const char POOLING_AVG[] = "avg"; -class myriadLayersTestsMax_nightly: public PoolingTest +class myriadLayersTestsMax_smoke: public PoolingTest { }; -class myriadLayersTestsMaxOverlappedByKernel_nightly: public PoolingTestPad4 +class myriadLayersTestsMaxOverlappedByKernel_smoke: public PoolingTestPad4 { }; -class myriadLayersTestsMaxPad4_nightly: public PoolingTestPad4 +class myriadLayersTestsMaxPad4_smoke: public PoolingTestPad4 { }; -class myriadLayersTestsGlobalMax_nightly: public GlobalPoolingTest +class myriadLayersTestsGlobalMax_smoke: public GlobalPoolingTest { }; -class myriadLayersTestsAvg_nightly: public PoolingTest +class myriadLayersTestsAvg_smoke: public PoolingTest { }; -class myriadLayersTestsAvgOverlappedByKernel_nightly: public PoolingTestPad4 +class myriadLayersTestsAvgOverlappedByKernel_smoke: public PoolingTestPad4 { }; -class myriadLayersTestsAvgPad4_nightly: public PoolingTestPad4 +class myriadLayersTestsAvgPad4_smoke: public PoolingTestPad4 { }; -class myriadLayersTestsGlobalAvg_nightly: public GlobalPoolingTest +class myriadLayersTestsGlobalAvg_smoke: public GlobalPoolingTest { }; @@ -51,7 +51,7 @@ class myriadLayersTestsGlobalAvg_nightly: public GlobalPoolingTest /* input tensor, kernel, stride, pads_begin, pads_end, auto_pad, exclude_pad method */ typedef std::tuple IR3_PoolParams; -class myriadLayers_IR3_PoolingTests_nightly: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ +class myriadLayers_IR3_PoolingTests_smoke: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ public testing::WithParamInterface { }; @@ -90,7 +90,7 @@ static void genTestData(InferenceEngine::Blob::Ptr blob) { } -TEST_P(myriadLayers_IR3_PoolingTests_nightly, Pooling) { +TEST_P(myriadLayers_IR3_PoolingTests_smoke, Pooling) { std::map params; InferenceEngine::SizeVector output_tensor; int32_t IW = 0; @@ -166,11 +166,11 @@ TEST_P(myriadLayers_IR3_PoolingTests_nightly, Pooling) { CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), maxerr); } -class myriadLayers_IR3_BatchPoolingTests_nightly: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ +class myriadLayers_IR3_BatchPoolingTests_smoke: public myriadLayersTests_nightly, /*input tensor, kernel, stride, pads_begin, pads_end, out_channel, group */ public testing::WithParamInterface { }; -TEST_P(myriadLayers_IR3_BatchPoolingTests_nightly, Pooling) { +TEST_P(myriadLayers_IR3_BatchPoolingTests_smoke, Pooling) { std::map params; InferenceEngine::SizeVector output_tensor; int32_t IW = 0; @@ -259,52 +259,52 @@ static const std::vector s_poolingMethod = { "max" }; -TEST_P(myriadLayersTestsMax_nightly, MaxPooling) +TEST_P(myriadLayersTestsMax_smoke, MaxPooling) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(_layout_preference))); CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND); } -TEST_P(myriadLayersTestsMaxOverlappedByKernel_nightly, MaxPooling) +TEST_P(myriadLayersTestsMaxOverlappedByKernel_smoke, MaxPooling) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(_layout_preference))); CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND); } -TEST_P(myriadLayersTestsMaxPad4_nightly, MaxPoolingPad4) +TEST_P(myriadLayersTestsMaxPad4_smoke, MaxPoolingPad4) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(_layout_preference))); auto refBlob = getReferenceOutput(); CompareCommonAbsolute(_outputMap.begin()->second, refBlob, ERROR_BOUND); } -TEST_P(myriadLayersTestsAvg_nightly, AvgPooling) +TEST_P(myriadLayersTestsAvg_smoke, AvgPooling) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(_layout_preference))); CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND); } -TEST_P(myriadLayersTestsAvgOverlappedByKernel_nightly, AvgPooling) +TEST_P(myriadLayersTestsAvgOverlappedByKernel_smoke, AvgPooling) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(_layout_preference))); CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND); } -TEST_P(myriadLayersTestsAvgPad4_nightly, AvgPoolingPad4) +TEST_P(myriadLayersTestsAvgPad4_smoke, AvgPoolingPad4) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(_layout_preference))); auto refBlob = getReferenceOutput(); CompareCommonAbsolute(_outputMap.begin()->second, refBlob, ERROR_BOUND); } -TEST_P(myriadLayersTestsGlobalMax_nightly, GlobalMaxPooling) +TEST_P(myriadLayersTestsGlobalMax_smoke, GlobalMaxPooling) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams())); auto refBlob = getReferenceOutput(); CompareCommonAbsolute(_outputMap.begin()->second, refBlob, ERROR_BOUND); } -TEST_P(myriadLayersTestsGlobalAvg_nightly, GlobalAvgPooling) +TEST_P(myriadLayersTestsGlobalAvg_smoke, GlobalAvgPooling) { if(_pad_val.x != 0 || _pad_val.y != 0) { GTEST_SKIP() << "paddings should not be exist for GlobalAvgPool"; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.cpp index 5ed5eb65dbe..69142a11c05 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.cpp @@ -3,7 +3,7 @@ // #include "myriad_layers_power_test.hpp" -INSTANTIATE_TEST_CASE_P( accuracy, myriadLayersTestsPowerParams_nightly, +INSTANTIATE_TEST_CASE_P( accuracy, myriadLayersTestsPowerParams_smoke, ::testing::Combine( ::testing::ValuesIn(s_powerTensors), ::testing::ValuesIn(s_powerParams)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.hpp index ebe4eb90961..c2086c75727 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_power_test.hpp @@ -39,9 +39,9 @@ static void gen_ref_power(const InferenceEngine::Blob::Ptr src, } } -typedef myriadLayerTestBaseWithParam> myriadLayersTestsPowerParams_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsPowerParams_smoke; -TEST_P(myriadLayersTestsPowerParams_nightly, TestsPower) { +TEST_P(myriadLayersTestsPowerParams_smoke, TestsPower) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto param = GetParam(); SizeVector tensor = std::get<0>(param); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.cpp index 3f952ce1aea..f15f0f2eb47 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_prelu_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy_PReLU, myriadLayerPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_PReLU, myriadLayerPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(s_PReLUTensors) , ::testing::Values(0, 1) @@ -12,7 +12,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_PReLU, myriadLayerPReLU_nightly, ); INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayerFullyConnectedWithPReLU_nightly, + accuracy, myriadLayerFullyConnectedWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_fcTestParamsSubset), ::testing::Values(g_dimensionsFC[0]), @@ -21,7 +21,7 @@ INSTANTIATE_TEST_CASE_P( ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), @@ -29,7 +29,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithPReLU_nightly, ::testing::ValuesIn(s_PReluLayerParams)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), @@ -37,7 +37,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithPReLU_nightly, ::testing::ValuesIn(s_PReluLayerParams)) ); -INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsMaxPoolingWithPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsMaxPoolingWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput_postOp), ::testing::Values(MAKE_STRUCT(pooling_layer_params, {3, 3}, {1, 1}, {1, 1})), @@ -45,7 +45,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsMaxPoolingWithPReLU_ni ::testing::Values(MAKE_STRUCT(PReLULayerDef, {{{PRELU_PARAM, "0"}}}))) ); -INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsAvgPoolingWithPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsAvgPoolingWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput_postOp), ::testing::Values(MAKE_STRUCT(pooling_layer_params, {3, 3}, {1, 1}, {1, 1})), @@ -53,7 +53,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsAvgPoolingWithPReLU_ni ::testing::Values(MAKE_STRUCT(PReLULayerDef, {{{PRELU_PARAM, "0"}}}))) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_convolutionTensors) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -65,7 +65,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithPReLU_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayerConvolutionWithPReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayerConvolutionWithPReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput_postOp) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.hpp index ca1a14e16b1..78394dd209b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prelu_test.hpp @@ -12,9 +12,9 @@ using std::get; using namespace InferenceEngine; PRETTY_PARAM(ChannelSharedPrelu, int); -typedef myriadLayerTestBaseWithParam> myriadLayerPReLU_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerPReLU_smoke; -TEST_P(myriadLayerPReLU_nightly, PReLU) { +TEST_P(myriadLayerPReLU_smoke, PReLU) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); SizeVector dims = get<0>(GetParam()); @@ -62,7 +62,7 @@ static std::vector s_PReluLayerParams = { {{{PRELU_PARAM, "1"}}} }; -class myriadLayerFullyConnectedWithPReLU_nightly: public FCTest{ +class myriadLayerFullyConnectedWithPReLU_smoke: public FCTest{ }; #define TEST_BODY \ @@ -89,7 +89,7 @@ class myriadLayerFullyConnectedWithPReLU_nightly: public FCTest{ ref_PReLU_wrap);\ ASSERT_TRUE(generateNetAndInfer(NetworkInitParams())); -TEST_P(myriadLayerFullyConnectedWithPReLU_nightly, TestsFullyConnected) +TEST_P(myriadLayerFullyConnectedWithPReLU_smoke, TestsFullyConnected) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<3>(p); @@ -99,13 +99,13 @@ TEST_P(myriadLayerFullyConnectedWithPReLU_nightly, TestsFullyConnected) #define ERROR_BOUND_WITH_RELU (4.e-3f) -class myriadLayersTestsMaxPoolingWithPReLU_nightly: public PoolingTest{ +class myriadLayersTestsMaxPoolingWithPReLU_smoke: public PoolingTest{ }; -class myriadLayersTestsAvgPoolingWithPReLU_nightly: public PoolingTest{ +class myriadLayersTestsAvgPoolingWithPReLU_smoke: public PoolingTest{ }; -TEST_P(myriadLayersTestsMaxPoolingWithPReLU_nightly, TestsMaxPoolingWithPReLU) +TEST_P(myriadLayersTestsMaxPoolingWithPReLU_smoke, TestsMaxPoolingWithPReLU) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<3>(p); @@ -113,7 +113,7 @@ TEST_P(myriadLayersTestsMaxPoolingWithPReLU_nightly, TestsMaxPoolingWithPReLU) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_RELU); } -TEST_P(myriadLayersTestsAvgPoolingWithPReLU_nightly, TestsAvgPoolingWithPReLU) +TEST_P(myriadLayersTestsAvgPoolingWithPReLU_smoke, TestsAvgPoolingWithPReLU) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<3>(p); @@ -121,10 +121,10 @@ TEST_P(myriadLayersTestsAvgPoolingWithPReLU_nightly, TestsAvgPoolingWithPReLU) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_RELU); } -class myriadLayerConvolutionWithPReLU_nightly: public ConvolutionTest{ +class myriadLayerConvolutionWithPReLU_smoke: public ConvolutionTest{ }; -TEST_P(myriadLayerConvolutionWithPReLU_nightly, Convolution) { +TEST_P(myriadLayerConvolutionWithPReLU_smoke, Convolution) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<6>(p); TEST_BODY; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prior_box_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prior_box_test.cpp index 34aad160a92..c0334fc710f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prior_box_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_prior_box_test.cpp @@ -267,7 +267,7 @@ void refPriorBox(Blob::Ptr dst, const PriorBoxParams &p) { } } -class myriadLayersPriorBoxTests_nightly : public myriadLayersTests_nightly { +class myriadLayersPriorBoxTests_smoke : public myriadLayersTests_nightly { public: Blob::Ptr getFp16Blob(const Blob::Ptr& in) { if (in->getTensorDesc().getPrecision() == Precision::FP16) @@ -342,7 +342,7 @@ public: } }; -TEST_F(myriadLayersPriorBoxTests_nightly, NotLastLayer) +TEST_F(myriadLayersPriorBoxTests_smoke, NotLastLayer) { std::string model = R"V0G0N( @@ -476,7 +476,7 @@ TEST_F(myriadLayersPriorBoxTests_nightly, NotLastLayer) RunOnModel(model, "priorbox_copy"); } -TEST_F(myriadLayersPriorBoxTests_nightly, LastLayer_FP16) +TEST_F(myriadLayersPriorBoxTests_smoke, LastLayer_FP16) { std::string model = R"V0G0N( @@ -592,7 +592,7 @@ TEST_F(myriadLayersPriorBoxTests_nightly, LastLayer_FP16) RunOnModel(model, "priorbox", Precision::FP16); } -TEST_F(myriadLayersPriorBoxTests_nightly, LastLayer_FP32) +TEST_F(myriadLayersPriorBoxTests_smoke, LastLayer_FP32) { std::string model = R"V0G0N( @@ -1532,7 +1532,7 @@ TEST_F(myriadLayersTests_nightly, PriorBox_WithConcat) CompareCommonAbsolute(_refBlob, outputBlob, 0.0); } -TEST_F(myriadLayersPriorBoxTests_nightly, FaceBoxLayer) +TEST_F(myriadLayersPriorBoxTests_smoke, FaceBoxLayer) { std::string model = R"V0G0N( @@ -1664,7 +1664,7 @@ TEST_F(myriadLayersPriorBoxTests_nightly, FaceBoxLayer) RunOnModelWithParams(model, "priorbox", params, Precision::FP16); } -TEST_F(myriadLayersPriorBoxTests_nightly, TwoPriorBoxLayersWithUnusedInput) +TEST_F(myriadLayersPriorBoxTests_smoke, TwoPriorBoxLayersWithUnusedInput) { std::string model = R"V0G0N( diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp index e70318b5a64..9844cee0c95 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_proposal_test.cpp @@ -13,7 +13,7 @@ using namespace InferenceEngine; #define OUTPUT_SAMPLING_NUM (20) // Validate only top 20 rois #define OUTPUT_ROI_MATCH_THRESHOLD (18) // At least 18 rois should be matched -class myriadLayersTestsProposal_nightly : public myriadLayersTests_nightly { +class myriadLayersTestsProposal_smoke : public myriadLayersTests_nightly { protected: std::string model; @@ -277,7 +277,7 @@ std::string caffeModel() { )V0G0N"; } -TEST_F(myriadLayersTestsProposal_nightly, Caffe) { +TEST_F(myriadLayersTestsProposal_smoke, Caffe) { // Verify only 20 ranked proposal output with GT values std::vector gt_values = { @@ -313,7 +313,7 @@ TEST_F(myriadLayersTestsProposal_nightly, Caffe) { ASSERT_NO_FATAL_FAILURE(compareOutputSampleToRef(gt_values, 0.26f)); } -TEST_F(myriadLayersTestsProposal_nightly, CaffeNoClipBeforeNms) { +TEST_F(myriadLayersTestsProposal_smoke, CaffeNoClipBeforeNms) { // Verify only 20 ranked proposal output with GT values - reference get from MKLDNN plugin std::vector gt_values = { @@ -352,7 +352,7 @@ TEST_F(myriadLayersTestsProposal_nightly, CaffeNoClipBeforeNms) { ASSERT_NO_FATAL_FAILURE(compareOutputSampleToRef(gt_values, 0.26f)); } -TEST_F(myriadLayersTestsProposal_nightly, CaffeClipAfterNms) { +TEST_F(myriadLayersTestsProposal_smoke, CaffeClipAfterNms) { // Verify only 20 ranked proposal output with GT values std::vector gt_values = { @@ -394,7 +394,7 @@ TEST_F(myriadLayersTestsProposal_nightly, CaffeClipAfterNms) { ASSERT_NO_FATAL_FAILURE(compareOutputSampleToRef(gt_values, 0.26f)); } -TEST_F(myriadLayersTestsProposal_nightly, CaffeNormalizedOutput) { +TEST_F(myriadLayersTestsProposal_smoke, CaffeNormalizedOutput) { // Verify only 20 ranked proposal output with GT values std::vector gt_values = { @@ -436,7 +436,7 @@ TEST_F(myriadLayersTestsProposal_nightly, CaffeNormalizedOutput) { ASSERT_NO_FATAL_FAILURE(compareOutputSampleToRef(gt_values, 0.026f)); } -TEST_F(myriadLayersTestsProposal_nightly, TensorFlow) { +TEST_F(myriadLayersTestsProposal_smoke, TensorFlow) { model = R"V0G0N( diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.cpp index ae8f1015496..535cdf1284e 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_psroipooling_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsPSROIPooling_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsPSROIPooling_smoke, ::testing::Combine( ::testing::ValuesIn(s_PSROIPoolingLayerInput), ::testing::ValuesIn(s_PSROIPoolingLayerParam), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.hpp index abc95848110..66195e6bb30 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_psroipooling_test.hpp @@ -27,7 +27,7 @@ static inline void PrintTo(const PSROIPoolingParams& param, ::std::ostream* os) using PSROIPoolingTestParams = std::tuple; -class myriadLayersTestsPSROIPooling_nightly: public myriadLayerTestBaseWithParam { +class myriadLayersTestsPSROIPooling_smoke: public myriadLayerTestBaseWithParam { public: void genROIs(InferenceEngine::Blob::Ptr rois, const PSROIPoolingParams& params, @@ -200,7 +200,7 @@ static std::vector s_PSROIPoolingNumROIs = { 1, 10, 30, 50, 100, 300 }; -TEST_P(myriadLayersTestsPSROIPooling_nightly, PSROIPooling) { +TEST_P(myriadLayersTestsPSROIPooling_smoke, PSROIPooling) { #if defined(_WIN32) || defined(WIN32) SKIP() << "Disabled for Windows. CVS-13239"; #endif diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.cpp index f9620a34120..525691f3a39 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.cpp @@ -4,42 +4,42 @@ #include "myriad_layers_reduce_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceAnd_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceAnd_smoke, ::testing::Combine( - ::testing::ValuesIn(s_input_dims), + ::testing::ValuesIn(s_input_pair), ::testing::ValuesIn(s_axes_list), ::testing::ValuesIn(s_data_precision), ::testing::ValuesIn(s_keep_dims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceMin_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceMin_smoke, ::testing::Combine( - ::testing::ValuesIn(s_input_dims), + ::testing::ValuesIn(s_input_pair), ::testing::ValuesIn(s_axes_list), ::testing::ValuesIn(s_data_precision), ::testing::ValuesIn(s_keep_dims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceMax_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceMax_smoke, ::testing::Combine( - ::testing::ValuesIn(s_input_dims), + ::testing::ValuesIn(s_input_pair), ::testing::ValuesIn(s_axes_list), ::testing::ValuesIn(s_data_precision), ::testing::ValuesIn(s_keep_dims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceSum_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceSum_smoke, ::testing::Combine( - ::testing::ValuesIn(s_input_dims), + ::testing::ValuesIn(s_input_pair), ::testing::ValuesIn(s_axes_list), ::testing::ValuesIn(s_data_precision), ::testing::ValuesIn(s_keep_dims)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceMean_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsReduceMean_smoke, ::testing::Combine( - ::testing::ValuesIn(s_input_dims), + ::testing::ValuesIn(s_input_pair), ::testing::ValuesIn(s_axes_list), ::testing::ValuesIn(s_data_precision), ::testing::ValuesIn(s_keep_dims)) -); +); \ No newline at end of file diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.hpp index aa49a09cca0..4de534921b6 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reduce_test.hpp @@ -245,7 +245,7 @@ static const std::map> refMapI32 = {REDUCE_MEAN, {&refReduceMeanI32, 0.0f, RefReduceMean::generateData}}, }; -using ReduceTestParams = std::tuple; +using ReduceTestParams = std::tuple, SizeVector, Precision, bool>; static const Precision axesPrecision = Precision::I32; @@ -349,24 +349,13 @@ public: for (int i : list) { if (i < 0) // handle negative indices - i = ndims - i; + i = ndims - std::abs(i); EXPECT_TRUE((i >= 0) && (i < ndims)); mask |= (1 << i); } return mask; } - static Layout defaultLayout(int ndims) - { - switch (ndims) - { - case 5: return NCDHW; - case 4: return NCHW; - case 3: return CHW; - case 2: return NC; - case 1: return C; - } - return ANY; - } + static void getAxesBlob(const SizeVector& axesList, TBlob::Ptr& weightsBlob, TBlob::Ptr& axesBlob) { size_t axes_size = axesList.size(); @@ -400,11 +389,14 @@ protected: _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); const auto params = GetParam(); - const auto inputDims = std::get<0>(params); - const auto axesList = std::get<1>(params); + const auto inputPair = std::get<0>(params); + auto axesList = std::get<1>(params); const auto dataPrecision = std::get<2>(params); const int keepDims = std::get<3>(params) ? 1 : 0; + const auto inputDims = inputPair.first; + const auto layoutPreference = inputPair.second; + const auto outputDims = ReduceUtils::calcOutputDims(inputDims, axesList, keepDims); const auto model = ReduceUtils::getModel(inputDims, axesList, outputDims, ReduceType, dataPrecision, keepDims); @@ -412,19 +404,18 @@ protected: TBlob::Ptr axesBlob; ReduceUtils::getAxesBlob(axesList, weightsBlob, axesBlob); ASSERT_NE(weightsBlob, nullptr); - + ASSERT_NO_THROW(readNetwork(model, weightsBlob)); const auto& network = _cnnNetwork; _inputsInfo = network.getInputsInfo(); _inputsInfo["reduce_input"]->setPrecision(dataPrecision); - _inputsInfo["reduce_input"]->setLayout(ReduceUtils::defaultLayout(inputDims.size())); + _inputsInfo["reduce_input"]->setLayout(vpu::deviceLayout(TensorDesc::getLayoutByDims(inputDims), layoutPreference)); _outputsInfo = network.getOutputsInfo(); _outputsInfo["reduce"]->setPrecision(dataPrecision); - _outputsInfo["reduce"]->setLayout(ReduceUtils::defaultLayout(outputDims.size())); - + _outputsInfo["reduce"]->setLayout(vpu::deviceLayout(TensorDesc::getLayoutByDims(outputDims), layoutPreference)); StatusCode st = OK; ASSERT_NO_THROW(st = _vpuPluginPtr->LoadNetwork(_exeNetwork, network, _config, &_resp)); @@ -457,87 +448,95 @@ protected: refBlob = make_shared_blob(outputBlob->getTensorDesc()); refBlob->allocate(); - ref_reduce(inputBlob, axesBlob, refBlob, keepDims, reduceOp); + ref_reduce(inputBlob, axesBlob, refBlob, keepDims, layoutPreference, reduceOp); CompareCommonAbsolute(outputBlob, refBlob, compareThreshold); - } else if (dataPrecision == Precision::I32) { - auto opIt = refMapI32.find(ReduceType); - ASSERT_TRUE(opIt != refMapI32.end()); - auto reduceOp = opIt->second.op; - auto generateData = opIt->second.generateData; - generateData(inputBlob); + } else if (dataPrecision == Precision::I32) { + auto opIt = refMapI32.find(ReduceType); + ASSERT_TRUE(opIt != refMapI32.end()); + auto reduceOp = opIt->second.op; + auto generateData = opIt->second.generateData; + generateData(inputBlob); - ASSERT_NO_THROW(st = _inferRequest->Infer(&_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; + ASSERT_NO_THROW(st = _inferRequest->Infer(&_resp)); + ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - refBlob = make_shared_blob(outputBlob->getTensorDesc()); - refBlob->allocate(); - ref_reduce(inputBlob, axesBlob, refBlob, keepDims, reduceOp); - CompareCommonExact(outputBlob, refBlob); - } + refBlob = make_shared_blob(outputBlob->getTensorDesc()); + refBlob->allocate(); + ref_reduce(inputBlob, axesBlob, refBlob, keepDims, layoutPreference, reduceOp); + CompareCommonExact(outputBlob, refBlob); + } } }; -class myriadTestsReduceAnd_nightly: public ReduceTest +class myriadTestsReduceAnd_smoke: public ReduceTest { }; -class myriadTestsReduceMin_nightly: public ReduceTest +class myriadTestsReduceMin_smoke: public ReduceTest { }; -class myriadTestsReduceMax_nightly: public ReduceTest +class myriadTestsReduceMax_smoke: public ReduceTest { }; -class myriadTestsReduceSum_nightly: public ReduceTest +class myriadTestsReduceSum_smoke: public ReduceTest { }; -class myriadTestsReduceMean_nightly: public ReduceTest +class myriadTestsReduceMean_smoke: public ReduceTest { }; // Tests are disabled due to hang: #-28315 -TEST_P(myriadTestsReduceAnd_nightly, And) +TEST_P(myriadTestsReduceAnd_smoke, And) { testReduce(); } -TEST_P(myriadTestsReduceMin_nightly, Min) +TEST_P(myriadTestsReduceMin_smoke, Min) { testReduce(); } -TEST_P(myriadTestsReduceMax_nightly, Max) +TEST_P(myriadTestsReduceMax_smoke, Max) { testReduce(); } -TEST_P(myriadTestsReduceSum_nightly, Sum) +TEST_P(myriadTestsReduceSum_smoke, Sum) { testReduce(); } -TEST_P(myriadTestsReduceMean_nightly, Mean) +TEST_P(myriadTestsReduceMean_smoke, Mean) { testReduce(); } -static const std::vector s_input_dims = +static const std::vector> s_input_pair = { - {1, 3, 2, 14, 32}, - {2, 2, 2, 14, 32}, - {3, 5, 4, 8, 16}, - {4, 2, 16, 16, 8}, + {{1, 3, 2, 14, 32}, vpu::LayoutPreference::ChannelMinor}, + {{1, 3, 2, 14, 32}, vpu::LayoutPreference::ChannelMajor}, + {{2, 2, 2, 14, 32}, vpu::LayoutPreference::ChannelMinor}, + {{2, 2, 2, 14, 32}, vpu::LayoutPreference::ChannelMajor}, + {{3, 5, 4, 8, 16}, vpu::LayoutPreference::ChannelMinor}, + {{3, 5, 4, 8, 16}, vpu::LayoutPreference::ChannelMajor}, + {{4, 2, 16, 16, 8}, vpu::LayoutPreference::ChannelMinor}, + {{4, 2, 16, 16, 8}, vpu::LayoutPreference::ChannelMajor}, - {3, 2, 14, 32}, - {2, 2, 14, 32}, - {5, 4, 8, 16}, - {2, 16, 16, 8}, + {{3, 2, 14, 32}, vpu::LayoutPreference::ChannelMinor}, + {{3, 2, 14, 32}, vpu::LayoutPreference::ChannelMajor}, + {{2, 2, 14, 32}, vpu::LayoutPreference::ChannelMinor}, + {{2, 2, 14, 32}, vpu::LayoutPreference::ChannelMajor}, + {{5, 4, 8, 16}, vpu::LayoutPreference::ChannelMinor}, + {{5, 4, 8, 16}, vpu::LayoutPreference::ChannelMajor}, + {{2, 16, 16, 8}, vpu::LayoutPreference::ChannelMinor}, + {{2, 16, 16, 8}, vpu::LayoutPreference::ChannelMajor}, - {3, 2, 14}, - {2, 2, 14}, - {5, 4, 8}, - {2, 16, 16}, + {{3, 2, 14}, vpu::LayoutPreference::ChannelMajor}, + {{2, 2, 14}, vpu::LayoutPreference::ChannelMajor}, + {{5, 4, 8}, vpu::LayoutPreference::ChannelMajor}, + {{2, 16, 16}, vpu::LayoutPreference::ChannelMajor}, - { 7, 3, 5, 1, 7, 11, 12}, + {{7, 3, 5, 1, 7, 11, 12}, vpu::LayoutPreference::ChannelMajor}, }; static const std::vector s_axes_list = diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.cpp index cad6b54d219..50eb4eb5541 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.cpp @@ -5,32 +5,14 @@ #include "myriad_layers_region_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayerRegionYolo_nightly, - ::testing::ValuesIn(s_regionData) -); - -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsRegion_CHW_HW_nightly, - ::testing::Combine( - ::testing::Values({1, 125, 13, 13}) - , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) - , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) - , ::testing::Values(MAKE_STRUCT(param_size, 0, 0)) - , ::testing::Values(125) - , ::testing::Values(1) - ) -); - -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsRegion_CHW_HW_80cl_nightly, - ::testing::Combine( - ::testing::Values({1, 425, 13, 13}) - , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) - , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) - , ::testing::Values(MAKE_STRUCT(param_size, 0, 0)) - , ::testing::Values(425) - , ::testing::Values(1) - ) -); - -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerRegionYolo_CHW_nightly, - ::testing::ValuesIn(s_classes) -); + accuracy, myriadLayersTestsRegionYolo_smoke, + ::testing::Combine( + ::testing::Values(4), + ::testing::Values(20, 80), + ::testing::Values(5, 10), + ::testing::Values(3), + ::testing::Values(1, 0), + ::testing::Values(vpu::LayoutPreference::ChannelMajor, vpu::LayoutPreference::ChannelMinor), + ::testing::Values(IRVersion::v7, IRVersion::v10), + ::testing::ValuesIn(s_CustomConfig) +)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.hpp index 768aba55c62..71090b00ea6 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_region_test.hpp @@ -4,327 +4,82 @@ #include #include "myriad_layers_tests.hpp" -#include +#include using namespace InferenceEngine; -struct region_test_params { - tensor_test_params in; - int coords; - int classes; - int num; - int maskSize; - int doSoftMax; - std::string customLayers; - friend std::ostream& operator<<(std::ostream& os, region_test_params const& tst) - { - return os << "tensor (" << tst.in - << "),coords=" << tst.coords - << ", classes=" << tst.classes - << ", num=" << tst.num - << ", maskSize=" << tst.maskSize - << ", doSoftMax=" << tst.doSoftMax - << ", by using custom layer=" << (tst.customLayers.empty() ? "no" : "yes"); - }; -}; +#define ERROR_BOUND 0.0005f -class myriadLayerRegionYolo_nightly: public myriadLayersTests_nightly, - public testing::WithParamInterface { -}; +PRETTY_PARAM(Coords, int) +PRETTY_PARAM(Classes, int) +PRETTY_PARAM(Num, int) +PRETTY_PARAM(MaskSize, int) +PRETTY_PARAM(DoSoftmax, int) +PRETTY_PARAM(CustomConfig, std::string) -TEST_P(myriadLayerRegionYolo_nightly, BaseTestsRegion) { - region_test_params p = ::testing::WithParamInterface::GetParam(); +typedef myriadLayerTestBaseWithParam> myriadLayersTestsRegionYolo_smoke; - // TODO: M2 mode is not working for OpenCL compiler - if(!p.customLayers.empty() && !CheckMyriadX()) { - GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; +TEST_P(myriadLayersTestsRegionYolo_smoke, RegionYolo) { + const int coords = std::get<0>(GetParam()); + const int classes = std::get<1>(GetParam()); + const int num = std::get<2>(GetParam()); + const int maskSize = std::get<3>(GetParam()); + const int doSoftmax = std::get<4>(GetParam()); + const auto layoutPreference = std::get<5>(GetParam()); + _irVersion = std::get<6>(GetParam()); + const std::string customConfig = std::get<7>(GetParam()); + + if (!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; } + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; + + const auto mask = [&] { + std::string mask; + for (int i = 0; i < maskSize; i++) { + mask += std::to_string(i) + ','; + } + if (!mask.empty()) mask.pop_back(); + return mask; + }(); + std::map params; + params["coords"] = std::to_string(coords); + params["classes"] = std::to_string(classes); + params["num"] = std::to_string(num); + params["mask"] = mask; + params["do_softmax"] = std::to_string(doSoftmax); + params["axis"] = "0"; + params["end_axis"] = "0"; - params["coords"] = std::to_string(p.coords); - params["classes"] = std::to_string(p.classes); - params["num"] = std::to_string(p.num); - params["mask"] = "0,1,2"; - params["do_softmax"] = std::to_string(p.doSoftMax); + const auto dims = [&] { + const auto regions = doSoftmax ? num : maskSize; + const uint32_t channels = (coords + classes + 1) * regions; + IE_ASSERT(channels > 0); + return tensor_test_params{1, channels, 13, 13}; + }(); - InferenceEngine::SizeVector tensor; - tensor.resize(4); - tensor[3] = p.in.w; - tensor[2] = p.in.h; - tensor[1] = p.in.c; - tensor[0] = 1; - _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = p.customLayers; - _testNet.addLayer(LayerInitParams("RegionYolo") - .params(params) - .in({tensor}) - .out({tensor}), - ref_RegionYolo_wrap); - ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().layoutPreference(vpu::LayoutPreference::ChannelMinor))); - CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), 0.0025); + SetInputTensor(dims); + SetOutputTensor(dims); + + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("RegionYolo").params(params), + NetworkInitParams() + .layoutPreference(layoutPreference) + .lockLayout(true))); + + ASSERT_TRUE(Infer()); + + ASSERT_NO_FATAL_FAILURE(ref_RegionYolo(_inputMap.begin()->second, _refBlob, + coords, classes, num, maskSize, doSoftmax)); + + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, ERROR_BOUND); } -static std::vector s_regionData = { - region_test_params{{1, (4+20+1)*5, 13, 13}, 4, 20, 5, 3, 1, ""}, - region_test_params{{1, (4+80+1)*5, 13, 13}, 4, 80, 5, 3, 1, ""}, - region_test_params{{1, (4+20+1)*3, 13, 13}, 4, 20, 9, 3, 0, ""}, - region_test_params{{1, (4+80+1)*3, 13, 13}, 4, 80, 9, 3, 0, ""}, - +std::vector s_CustomConfig = { + {""}, #ifdef VPU_HAS_CUSTOM_KERNELS - region_test_params{{1, (4+20+1)*5, 13, 13}, 4, 20, 5, 3, 1, getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml"}, - region_test_params{{1, (4+80+1)*5, 13, 13}, 4, 80, 5, 3, 1, getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml"}, - region_test_params{{1, (4+20+1)*3, 13, 13}, 4, 20, 9, 3, 0, getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml"}, - region_test_params{{1, (4+80+1)*3, 13, 13}, 4, 80, 9, 3, 0, getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml"}, + getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" #endif -}; - -/* HW network needs to be created to test strides influence to RegionYolo input */ -/* so convolution layer added as the first layer to this test */ -class myriadLayersTestsRegion_CHW_HW_nightly: public ConvolutionTest<>{ -}; - -/*80 input classes */ -class myriadLayersTestsRegion_CHW_HW_80cl_nightly: public ConvolutionTest<>{ -}; - -/* to passthrough "original" data */ -template -void constWeightsRange(uint16_t* ptr, size_t weightsSize) { - ASSERT_NE(ptr, nullptr); - ASSERT_EQ(weightsSize, width * width); - std::memset(ptr, 0, sizeof(uint16_t) * (weightsSize)); - for (int i = 0; i < weightsSize/width; ++i) { - ptr[i * width + i] = PrecisionUtils::f32tof16(1.0f); - } -} - -void constBiasesRange(uint16_t* ptr, size_t weightsSize) { - std::memset(ptr, 0, sizeof(uint16_t) * (weightsSize)); -} - -void loadData(InferenceEngine::Blob::Ptr blob) { - /* input blob has predefined size and CHW layout */ - ASSERT_NE(blob, nullptr); - auto inDims = blob->getTensorDesc().getDims(); - InferenceEngine::Blob::Ptr inputBlobRef = - InferenceEngine::make_shared_blob({InferenceEngine::Precision::FP32, inDims, InferenceEngine::NCHW}); - inputBlobRef->allocate(); - const float* ref_values = inputBlobRef->buffer(); - - std::string inputTensorBinary = TestDataHelpers::get_data_path(); - inputTensorBinary += "/vpu/InputYoLoV2Tiny.bin"; - ASSERT_TRUE(fromBinaryFile(inputTensorBinary, inputBlobRef)); - uint16_t *inputBlobRawDataFp16 = static_cast(blob->buffer()); - ASSERT_NE(inputBlobRawDataFp16, nullptr); - - switch(blob->getTensorDesc().getLayout()) { - case InferenceEngine::NCHW: - for (int indx = 0; indx < blob->size(); indx++) { - inputBlobRawDataFp16[indx] = PrecisionUtils::f32tof16(ref_values[indx]); - } - break; - case InferenceEngine::NHWC: - for (int h = 0 ; h < inDims[2]; ++h) { - for (int w = 0 ; w < inDims[3]; ++w) { - for (int c = 0 ; c < inDims[1]; ++c) { - int src_i = w + inDims[3] * h + inDims[3] * inDims[2] * c; - int dst_i = c + inDims[1] * w + inDims[3] * inDims[1] * h; - inputBlobRawDataFp16[dst_i] = PrecisionUtils::f32tof16(ref_values[src_i]); - } - } - } - break; - default: - FAIL() << "unsupported layout: " << blob->getTensorDesc().getLayout(); - } -} - -void loadData_80cl(InferenceEngine::Blob::Ptr blob) { - /* input blob has predefined size and CHW layout */ - ASSERT_NE(blob, nullptr); - auto inDims = blob->getTensorDesc().getDims(); - InferenceEngine::Blob::Ptr inputBlobRef = - InferenceEngine::make_shared_blob({InferenceEngine::Precision::FP32, inDims, InferenceEngine::NCHW}); - inputBlobRef->allocate(); - const float* ref_values = inputBlobRef->buffer(); - - std::string inputTensorBinary = TestDataHelpers::get_data_path(); - inputTensorBinary += "/vpu/InputYoLoV2_80cl.bin"; - ASSERT_TRUE(fromBinaryFile(inputTensorBinary, inputBlobRef)); - uint16_t *inputBlobRawDataFp16 = static_cast(blob->buffer()); - ASSERT_NE(inputBlobRawDataFp16, nullptr); - - switch(blob->getTensorDesc().getLayout()) { - case InferenceEngine::NCHW: - for (int indx = 0; indx < blob->size(); indx++) { - inputBlobRawDataFp16[indx] = PrecisionUtils::f32tof16(ref_values[indx]); - } - break; - case InferenceEngine::NHWC: - for (int h = 0 ; h < inDims[2]; ++h) { - for (int w = 0 ; w < inDims[3]; ++w) { - for (int c = 0 ; c < inDims[1]; ++c) { - int src_i = w + inDims[3] * h + inDims[3] * inDims[2] * c; - int dst_i = c + inDims[1] * w + inDims[3] * inDims[1] * h; - inputBlobRawDataFp16[dst_i] = PrecisionUtils::f32tof16(ref_values[src_i]); - } - } - } - break; - default: - FAIL() << "unsupported layout: " << blob->getTensorDesc().getLayout(); - } -} - -TEST_P(myriadLayersTestsRegion_CHW_HW_nightly, RegionYolo) { - std::map params; - params["coords"] = "4"; - params["classes"] = "20"; - params["num"] = "5"; - params["mask"] = std::string("0,1,2"); - params["do_softmax"] = "1"; - _testNet.addLayer(LayerInitParams("RegionYolo") - .params(params) - .in({_output_tensor}) - .out({{1, _output_tensor[0] * _output_tensor[1] * _output_tensor[2] * _output_tensor[3]}}), - ref_RegionYolo_wrap); - _testNet.setWeightsCallbackForLayer(0, constWeightsRange<125>); - _testNet.setBiasesCallbackForLayer(0, constBiasesRange); - _genDataCallback = loadData; - ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().useHWOpt(true))); - CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), 0.0035); -} - -TEST_P(myriadLayersTestsRegion_CHW_HW_80cl_nightly, RegionYolol) { - std::map params; - params["coords"] = "4"; - params["classes"] = "80"; - params["num"] = "5"; - params["mask"] = std::string("0,1,2"); - params["do_softmax"] = "1"; - _testNet.addLayer(LayerInitParams("RegionYolo") - .params(params) - .in({_output_tensor}) - .out({{1, _output_tensor[0] * _output_tensor[1] * _output_tensor[2] * _output_tensor[3]}}), - ref_RegionYolo_wrap); - _testNet.setWeightsCallbackForLayer(0, constWeightsRange<425>); - _testNet.setBiasesCallbackForLayer(0, constBiasesRange); - _genDataCallback = loadData_80cl; - ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().useHWOpt(true))); - CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), 0.0060); -} - -class myriadLayerRegionYolo_CHW_nightly: public myriadLayersTests_nightly, - public testing::WithParamInterface { -}; - -TEST_P(myriadLayerRegionYolo_CHW_nightly, TestsRegion) { - auto classes = GetParam(); - InferenceEngine::SizeVector input_dims = {1, 125, 13, 13}; - if (classes == 80) { - input_dims[1] = 425; - } - IN_OUT_desc input_tensor; - input_tensor.push_back(input_dims); - - std::map params; - params["coords"] = "4"; - params["classes"] = std::to_string(classes); - params["num"] = "5"; - params["mask"] = std::string("0,1,2"); - params["do_softmax"] = "1"; - _testNet.addLayer(LayerInitParams("RegionYolo") - .params(params) - .in(input_tensor) - .out({{1, input_dims[0] * input_dims[1] * input_dims[2] * input_dims[3]}}), - ref_RegionYolo_wrap); - _genDataCallback = loadData; - if (classes == 80) { - _genDataCallback = loadData_80cl; - } - ASSERT_TRUE(generateNetAndInfer(NetworkInitParams())); - /* bound is too high , set for M2 tests */ - CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), 0.006); -} - -TEST_P(myriadLayerRegionYolo_CHW_nightly, Test_CHW_HWC_Compare) { - auto classes = GetParam(); - IN_OUT_desc input_tensor; - InferenceEngine::SizeVector input_dims = {1, 125, 13, 13}; - if (classes == 80) { - input_dims[1] = 425; - } - - input_tensor.push_back(input_dims); - - std::map params; - params["coords"] = "4"; - params["classes"] = std::to_string(classes); - params["num"] = "5"; - params["mask"] = std::string("0,1,2"); - params["do_softmax"] = "1"; - _testNet.addLayer(LayerInitParams("RegionYolo") - .params(params) - .in(input_tensor) - .out({{1, input_dims[0] * input_dims[1] * input_dims[2] * input_dims[3]}}), - ref_RegionYolo_wrap); - if (classes == 80) { - _genDataCallback = loadData_80cl; - } - _config[VPU_CONFIG_KEY(HW_STAGES_OPTIMIZATION)] = CONFIG_VALUE(NO); - ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().useHWOpt(false).runRefGraph(false))); - /* create NHWC version */ - /* we cannot use the same generateNetAndInfer call due */ - /* to IE bug. */ - InferenceEngine::InputsDataMap inputsInfo; - InferenceEngine::BlobMap outputMap; - InferenceEngine::OutputsDataMap outputsInfo; - InferenceEngine::IExecutableNetwork::Ptr exeNetwork; - InferenceEngine::IInferRequest::Ptr inferRequest; - - _inputsInfo.begin()->second->setLayout(NHWC); - _outputsInfo.begin()->second->setLayout(NC); - - InferenceEngine::StatusCode st = InferenceEngine::StatusCode::GENERAL_ERROR; - ASSERT_NO_THROW(st = _vpuPluginPtr->LoadNetwork(exeNetwork, _cnnNetwork, _config, &_resp)); - ASSERT_NE(exeNetwork, nullptr) << _resp.msg; - ASSERT_NO_THROW(exeNetwork->CreateInferRequest(inferRequest, &_resp)) << _resp.msg; - ASSERT_NE(inferRequest, nullptr) << _resp.msg; - ASSERT_NO_THROW(inputsInfo = _cnnNetwork.getInputsInfo()); - auto inIt = _inputsInfo.begin(); - for (auto in = _inputsInfo.begin(); in != _inputsInfo.end(); in++) { - Blob::Ptr inpt; - ASSERT_NO_THROW(_inferRequest->GetBlob(inIt->first.c_str(), inpt, &_resp)); - ASSERT_NO_THROW(inferRequest->SetBlob(inIt->first.c_str(), inpt, &_resp)); - ++inIt; - } - ASSERT_NO_THROW(outputsInfo = _cnnNetwork.getOutputsInfo()); - auto outIt = _outputsInfo.begin(); - for (auto outputInfo : outputsInfo) { - outputInfo.second->setPrecision(outIt->second->getTensorDesc().getPrecision()); - InferenceEngine::SizeVector outputDims = outputInfo.second->getTensorDesc().getDims(); - Blob::Ptr outputBlob = nullptr; - Layout layout = outIt->second->getTensorDesc().getLayout(); - // work only with NHWC layout if size of the input dimensions == NHWC - switch (outputInfo.second->getPrecision()) { - case Precision::FP16: - outputBlob = InferenceEngine::make_shared_blob({Precision::FP16, outputDims, layout}); - break; - case Precision::FP32: - outputBlob = InferenceEngine::make_shared_blob({Precision::FP32, outputDims, layout}); - break; - default: - THROW_IE_EXCEPTION << "Unsupported precision for output. Supported FP16, FP32"; - } - outputBlob->allocate(); - st = inferRequest->SetBlob(outputInfo.first.c_str(), outputBlob, &_resp); - outputMap[outputInfo.first] = outputBlob; - ASSERT_EQ((int) InferenceEngine::StatusCode::OK, st) << _resp.msg; - ++outIt; - } - ASSERT_EQ(inferRequest->Infer(&_resp), InferenceEngine::OK); - /* bound is too high !!!! investigation TBD */ - CompareCommonAbsolute(_outputMap.begin()->second, outputMap.begin()->second, 0.001); -} - -const std::vector s_classes = {20, 80}; +}; \ No newline at end of file diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.cpp index 63218e57b79..0fee57bbf64 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_relu_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReLU_smoke, ::testing::Combine( ::testing::ValuesIn(s_copyTensors), ::testing::ValuesIn(s_reluLayerParams) @@ -12,7 +12,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReLU_nightly, ); INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayerFullyConnectedWithReLU_nightly, + accuracy, myriadLayerFullyConnectedWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_fcTestParamsSubset), ::testing::Values(g_dimensionsFC[0]), @@ -21,7 +21,7 @@ INSTANTIATE_TEST_CASE_P( ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), @@ -29,7 +29,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithReLU_nightly, ::testing::ValuesIn(s_reluLayerParams)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), @@ -37,7 +37,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithReLU_nightly, ::testing::ValuesIn(s_reluLayerParams)) ); -INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsMaxPoolingWithReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsMaxPoolingWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput_postOp), ::testing::Values(MAKE_STRUCT(pooling_layer_params, {3, 3}, {1, 1}, {1, 1})), @@ -45,7 +45,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsMaxPoolingWithReLU_nig ::testing::Values(MAKE_STRUCT(ReLULayerDef, {{{"negative_slope", "0.0"}}}))) ); -INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsAvgPoolingWithReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsAvgPoolingWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput_postOp), ::testing::Values(MAKE_STRUCT(pooling_layer_params, {3, 3}, {1, 1}, {1, 1})), @@ -53,7 +53,7 @@ INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayersTestsAvgPoolingWithReLU_nig ::testing::Values(MAKE_STRUCT(ReLULayerDef, {{{"negative_slope", "0.0"}}}))) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_convolutionTensors) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -65,7 +65,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithReLU_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayerConvolutionWithReLU_nightly, +INSTANTIATE_TEST_CASE_P(accuracy_postop, myriadLayerConvolutionWithReLU_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput_postOp) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.hpp index eb1e2cea9e2..4337eda7440 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_relu_test.hpp @@ -11,7 +11,7 @@ using namespace InferenceEngine; const std::string relu_param = "negative_slope"; -class myriadLayersTestsReLUMergeWithBias_nightly : public myriadLayersTests_nightly { +class myriadLayersTestsReLUMergeWithBias_smoke : public myriadLayersTests_nightly { public: void RunTest(const std::string& model, size_t num_weights, size_t num_bias) { StatusCode st; @@ -65,9 +65,9 @@ static std::vector s_reluLayerParams = { {{{"negative_slope", "0.1"}}}, }; -typedef myriadLayerTestBaseWithParam> myriadLayerReLU_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerReLU_smoke; -TEST_P(myriadLayerReLU_nightly, ReLU) { +TEST_P(myriadLayerReLU_smoke, ReLU) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto input_dims = std::get<0>(GetParam()); auto extraLayerParams = std::get<1>(GetParam()); @@ -99,10 +99,10 @@ static std::vector s_copyTensors = { }, }; -class myriadLayerFullyConnectedWithReLU_nightly: public FCTest{ +class myriadLayerFullyConnectedWithReLU_smoke: public FCTest{ }; -TEST_P(myriadLayerFullyConnectedWithReLU_nightly, TestsFullyConnected) +TEST_P(myriadLayerFullyConnectedWithReLU_smoke, TestsFullyConnected) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<3>(p); @@ -117,13 +117,13 @@ TEST_P(myriadLayerFullyConnectedWithReLU_nightly, TestsFullyConnected) #define ERROR_BOUND_WITH_RELU (4.e-3f) -class myriadLayersTestsMaxPoolingWithReLU_nightly: public PoolingTest{ +class myriadLayersTestsMaxPoolingWithReLU_smoke: public PoolingTest{ }; -class myriadLayersTestsAvgPoolingWithReLU_nightly: public PoolingTest{ +class myriadLayersTestsAvgPoolingWithReLU_smoke: public PoolingTest{ }; -TEST_P(myriadLayersTestsMaxPoolingWithReLU_nightly, TestsMaxPoolingWithReLU) +TEST_P(myriadLayersTestsMaxPoolingWithReLU_smoke, TestsMaxPoolingWithReLU) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<3>(p); @@ -136,7 +136,7 @@ TEST_P(myriadLayersTestsMaxPoolingWithReLU_nightly, TestsMaxPoolingWithReLU) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_RELU); } -TEST_P(myriadLayersTestsAvgPoolingWithReLU_nightly, TestsAvgPoolingWithReLU) +TEST_P(myriadLayersTestsAvgPoolingWithReLU_smoke, TestsAvgPoolingWithReLU) { auto p = ::testing::WithParamInterface>::GetParam(); auto extraLayerParams = std::get<3>(p); @@ -149,10 +149,10 @@ TEST_P(myriadLayersTestsAvgPoolingWithReLU_nightly, TestsAvgPoolingWithReLU) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_RELU); } -class myriadLayerConvolutionWithReLU_nightly: public ConvolutionTest{ +class myriadLayerConvolutionWithReLU_smoke: public ConvolutionTest{ }; -TEST_P(myriadLayerConvolutionWithReLU_nightly, Convolution) { +TEST_P(myriadLayerConvolutionWithReLU_smoke, Convolution) { auto p = ::testing::WithParamInterface>::GetParam(); auto ReLUParam = std::get<6>(p); _testNet.addLayer(LayerInitParams("ReLU") diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.cpp index bd604f426b7..d60a7d4b55a 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.cpp @@ -4,29 +4,10 @@ #include "myriad_layers_reorg_test.hpp" -static std::vector s_CustomConfig = { - "", -#ifdef VPU_HAS_CUSTOM_KERNELS - getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" -#endif -}; - -static std::vector layoutPreferences = { - vpu::LayoutPreference::ChannelMajor, -#ifndef VPU_HAS_CUSTOM_KERNELS - vpu::LayoutPreference::ChannelMinor -#endif -}; - -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsReorg_nightly, ::testing::Combine( - ::testing::Values( - MAKE_STRUCT(tensor_test_params, 1, 64, 26, 26), - MAKE_STRUCT(tensor_test_params, 1, 192, 6 * 26, 6 * 26), - MAKE_STRUCT(tensor_test_params, 1, 4, 6, 6) - ), - ::testing::Values(2), +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsReorg_smoke, ::testing::Combine( + ::testing::ValuesIn(s_ReorgInputs), ::testing::Values(2), - ::testing::ValuesIn(layoutPreferences), - ::testing::ValuesIn(s_CustomConfig), - ::testing::Values(IRVersion::v7, IRVersion::v10) + ::testing::Values(vpu::LayoutPreference::ChannelMinor, vpu::LayoutPreference::ChannelMajor), + ::testing::Values(IRVersion::v7, IRVersion::v10), + ::testing::ValuesIn(s_CustomConfig) )); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.hpp index be5cdad9e24..1fa387b6420 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reorg_test.hpp @@ -5,121 +5,109 @@ #include #include "myriad_layers_tests.hpp" -using std::tuple; -using std::get; - using namespace InferenceEngine; -static void reorg_calculate(short *inp, int w, int h, int c, int batch, int stride, float *out) +static void reorg_calculate(const Blob::Ptr src, Blob::Ptr dst, int stride) { - int out_c = c / (stride*stride); + ASSERT_NE(src, nullptr); + ASSERT_NE(dst, nullptr); + const uint16_t *src_data = src->buffer(); + uint16_t *dst_data = dst->buffer(); + ASSERT_NE(src_data, nullptr); + ASSERT_NE(dst_data, nullptr); - int oc = c * (stride*stride); - int oh = h / stride; - int ow = w / stride; + const auto inputDims = src->getTensorDesc().getDims(); + const int C = inputDims[1]; + const int H = inputDims[2]; + const int W = inputDims[3]; - for(int b = 0; b < batch; ++b) - { - for(int k = 0; k < c; ++k) - { - for(int j = 0; j < h; ++j) - { - for(int i = 0; i < w; ++i) - { - int in_index = i + w * (j + h * (k + c * b)); + const auto inputCHW = [&] { + auto inputCHW = std::vector(C*H*W); + if (Layout::NCHW == src->getTensorDesc().getLayout()) { + std::copy(src_data, src_data + C*H*W, begin(inputCHW)); + } else { + for (int c = 0; c < C; c++) { + for (int h = 0; h < H; h++) { + for (int w = 0; w < W; w++) { + inputCHW[c*H*W + h*W + w] = src_data[h*W*C + w*C + c]; + } + } + } + } + return inputCHW; + }(); - int new_z = in_index / (oh*ow); - int new_y = (in_index %(oh*ow)) / ow; - int new_x = (in_index %(oh*ow)) % ow; - int new_index = new_z + new_x * oc + new_y * oc * ow; + const int C2 = C/(stride*stride); + const int H2 = H*stride; + const int W2 = W*stride; - int c2 = k % out_c; - int offset = k / out_c; - int w2 = i*stride + offset % stride; - int h2 = j*stride + offset / stride; - int out_index = w2 + w*stride*(h2 + h*stride*(c2 + out_c*b)); + for (int c = 0; c < C; ++c) { + for (int h = 0; h < H; ++h) { + for (int w = 0; w < W; ++w) { + const int offset = c/C2; + const int c2 = c - C2*offset; + const int h2 = h*stride + offset/stride; + const int w2 = w*stride + offset - stride*(offset/stride); - out[new_index] = PrecisionUtils::f16tof32(inp[out_index]); - } - } - } - } + dst_data[c*H*W + h*W + w] = inputCHW[c2*H2*W2 + h2*W2 + w2]; + } + } + } + + dst->getTensorDesc().setLayout(Layout::NCHW); } PRETTY_PARAM(Stride, int); -PRETTY_PARAM(ScaleOutput, int); PRETTY_PARAM(layoutPreference, vpu::LayoutPreference); +PRETTY_PARAM(CustomConfig, std::string) +typedef myriadLayerTestBaseWithParam> + myriadLayersTestsReorg_smoke; -typedef myriadLayerTestBaseWithParam> myriadLayersTestsReorg_nightly; +TEST_P(myriadLayersTestsReorg_smoke, TestsReorg) { + const SizeVector dimsInput = std::get<0>(GetParam()); + const int stride = std::get<1>(GetParam()); + const auto layoutPreference = std::get<2>(GetParam()); + _irVersion = std::get<3>(GetParam()); + const std::string customConfig = std::get<4>(GetParam()); -TEST_P(myriadLayersTestsReorg_nightly, TestsReorg) { + if(!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; + } + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; - // TODO: M2 mode is not working for OpenCL compiler - if(!get<4>(GetParam()).empty() && !CheckMyriadX()) { - GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; - } + const auto dimsOutput = SizeVector{dimsInput[0], + dimsInput[1] * (stride * stride), + dimsInput[2] / stride, + dimsInput[3] / stride}; - tensor_test_params dimsInput = get<0>(GetParam()); + SetInputTensors({dimsInput}); + SetOutputTensors({dimsOutput}); - int scaleOutput = get<1>(GetParam()); - tensor_test_params dimsOutput = {dimsInput.n, dimsInput.c * (scaleOutput * scaleOutput), dimsInput.h / scaleOutput, dimsInput.w / scaleOutput}; - - int stride = get<2>(GetParam()); - auto layoutPreference = get<3>(GetParam()); - _irVersion = get<5>(GetParam()); std::map params; - std::string type = "ReorgYolo"; - params["stride"] = std::to_string(stride); - SetInputTensor(dimsInput); - SetOutputTensor(dimsOutput); - _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = get<4>(GetParam()); - ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams(type) - .params(params), - NetworkInitParams().layoutPreference(layoutPreference) - .outputPrecision(InferenceEngine::Precision::FP32))); - /* input data preparation */ - SetInputInOrder(); + + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("ReorgYolo").params(params), + NetworkInitParams() + .layoutPreference(layoutPreference) + .lockLayout(true))); ASSERT_TRUE(Infer()); - InferenceEngine::SizeVector inputDims = _inputsInfo.begin()->second->getTensorDesc().getDims(); - InferenceEngine::Blob::Ptr inputBlobRef = - InferenceEngine::make_shared_blob({InferenceEngine::Precision::FP16, inputDims, InferenceEngine::NHWC}); - inputBlobRef->allocate(); - short *inputBlobRefRawData = inputBlobRef->buffer(); - int c = inputDims[1]; - int h = inputDims[2]; - int w = inputDims[3]; + ASSERT_NO_FATAL_FAILURE(reorg_calculate(_inputMap.begin()->second, _refBlob, stride)); - auto inputBlob =_inputMap[_inputsInfo.begin()->first]; - short * inputBlob_data = inputBlob->buffer(); - - /* Preliminary repacking */ - for(int k = 0; k < c; k++) - { - for(int j = 0; j < h; j++) - { - for(int i = 0; i < w; i++) - { - int dst_index = i + w * j + w * h * k; - int src_index = k + c * i + c * w * j; - - inputBlobRefRawData[dst_index] = inputBlob_data[src_index]; - } - } - } - - auto outputBlob =_outputMap[_outputsInfo.begin()->first]; - InferenceEngine::SizeVector outputDims = _outputsInfo.begin()->second->getTensorDesc().getDims(); - - InferenceEngine::TBlob::Ptr outputBlobRef = - InferenceEngine::make_shared_blob(TensorDesc(InferenceEngine::Precision::FP32, outputDims, InferenceEngine::NCHW)); - outputBlobRef->allocate(); - float *outputBlobRefRawData = outputBlobRef->buffer(); - - reorg_calculate(inputBlobRefRawData, w, h, c, 1, stride, outputBlobRefRawData); - - compare(outputBlob->buffer(), outputBlobRef->buffer(), outputBlob->size(), 0.0); + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, 0); } + +static std::vector s_CustomConfig = { + {""}, +#ifdef VPU_HAS_CUSTOM_KERNELS + getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" +#endif +}; + +static std::vector s_ReorgInputs = { + {1, 64, 26, 26}, + {1, 192, 6 * 26, 6 * 26}, + {1, 4, 6, 6} +}; \ No newline at end of file diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.cpp index 7528199af11..6030976b32e 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.cpp @@ -4,8 +4,13 @@ #include "myriad_layers_resample_test.hpp" -INSTANTIATE_TEST_CASE_P(myriad, myriadResampleLayerTests_nightly, - ::testing::Combine( - ::testing::Values(CONFIG_VALUE(NO), CONFIG_VALUE(YES)), - ::testing::ValuesIn(s_ResampleCustomConfig), - ::testing::ValuesIn(s_ResampleAntialias))); +// #-31522 +INSTANTIATE_TEST_CASE_P( + DISABLED_accuracy, myriadResampleLayerTests_smoke, + ::testing::Combine( + ::testing::ValuesIn(s_ResampleInput), + ::testing::Values(2.0f, 0.5f), + ::testing::Values(false, true), + ::testing::Values(false, true), + ::testing::ValuesIn(s_CustomConfig)) +); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.hpp index 40b2e76e8dd..ada3a740900 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_resample_test.hpp @@ -4,16 +4,24 @@ #include #include "myriad_layers_tests.hpp" -// #include using namespace InferenceEngine; #define ERROR_BOUND 1e-3 +PRETTY_PARAM(Factor, float) +PRETTY_PARAM(Antialias, int) +PRETTY_PARAM(HwOptimization, bool); +PRETTY_PARAM(CustomConfig, std::string); + +typedef myriadLayerTestBaseWithParam> + myriadResampleLayerTests_smoke; + static inline float triangleCoeff(float x) { return (1.0f - fabsf(x)); } + void refResample(const Blob::Ptr src, Blob::Ptr dst, int antialias) { ie_fp16 *src_data = static_cast(src->buffer()); ie_fp16 *output_sequences = static_cast(dst->buffer()); @@ -31,14 +39,7 @@ void refResample(const Blob::Ptr src, Blob::Ptr dst, int antialias) { if (IH == OH && IW == OW) { - int b = 0; - for (int c = 0; c < C; c++) - for (int h = 0; h < IH; h++) - for (int w = 0; w < IW; w++){ - int dst_index = w + IW * h + IW * IH * c; - int src_index = dst_index; - output_sequences[dst_index] = src_data[src_index]; - } + std::copy(src_data, src_data + C*IH*IW, output_sequences); return; } @@ -96,115 +97,59 @@ void refResample(const Blob::Ptr src, Blob::Ptr dst, int antialias) { } } -PRETTY_PARAM(hwAcceleration, std::string); -PRETTY_PARAM(customConfig, std::string); -PRETTY_PARAM(Antialias, int) +TEST_P(myriadResampleLayerTests_smoke, Resample) { + const SizeVector inputDims = std::get<0>(GetParam()); + const float factor = std::get<1>(GetParam()); + const bool antialias = std::get<2>(GetParam()); + const bool hwOptimization = std::get<3>(GetParam()); + const std::string customConfig = std::get<4>(GetParam()); -typedef myriadLayerTestBaseWithParam> myriadResampleLayerTests_nightly; + ASSERT_GT(factor, 0); -TEST_P(myriadResampleLayerTests_nightly, Resample) { - std::string model = R"V0G0N( - - - - - - 1 - 128 - 26 - 26 - - - - - - - - 1 - 128 - 26 - 26 - - - - - 1 - 128 - 52 - 52 - - - - - - - - - )V0G0N"; - - SetSeed(DEFAULT_SEED_VALUE + 6); - - std::string HWConfigValue = std::get<0>(GetParam()); - std::string customConfig = std::get<1>(GetParam()); - int antialias = std::get<2>(GetParam()); - - model.replace( model.find("@TEST@"), sizeof("@TEST@") -1, std::to_string(antialias)); - if((customConfig != "") || (antialias != 1)){ - if(!customConfig.empty() && !CheckMyriadX()) { - GTEST_SKIP()<<"Custom layers for MYRIAD2 not supported"; - } - _config[VPU_CONFIG_KEY(HW_STAGES_OPTIMIZATION)] = HWConfigValue; - _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; - StatusCode st; - - ASSERT_NO_THROW(readNetwork(model)); - - const auto& network = _cnnNetwork; - - _inputsInfo = network.getInputsInfo(); - _inputsInfo["data"]->setPrecision(Precision::FP16); - _inputsInfo["data"]->setLayout(NCHW); - - _outputsInfo = network.getOutputsInfo(); - _outputsInfo["detector/yolo-v3/ResizeNearestNeighbor"]->setPrecision(Precision::FP16); - - ASSERT_NO_THROW(st = _vpuPluginPtr->LoadNetwork(_exeNetwork, network, - {{VPU_CONFIG_KEY(CUSTOM_LAYERS), customConfig}, {VPU_CONFIG_KEY(HW_STAGES_OPTIMIZATION), HWConfigValue}}, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - ASSERT_NE(_exeNetwork, nullptr) << _resp.msg; - - ASSERT_NO_THROW(st = _exeNetwork->CreateInferRequest(_inferRequest, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - Blob::Ptr data; - ASSERT_NO_THROW(st = _inferRequest->GetBlob("data", data, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - GenRandomData(data); - - ASSERT_NO_THROW(st = _inferRequest->Infer(&_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - Blob::Ptr outputBlob; - ASSERT_NO_THROW(_inferRequest->GetBlob("detector/yolo-v3/ResizeNearestNeighbor", outputBlob, &_resp)); - ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - - _refBlob = make_shared_blob(TensorDesc(Precision::FP16, outputBlob->getTensorDesc().getDims(), NCHW)); - _refBlob->allocate(); - - refResample(data, _refBlob, antialias); - - CompareCommonAbsolute(outputBlob, _refBlob, ERROR_BOUND); + if (customConfig.empty() && antialias) { + GTEST_SKIP() << "Native Resample with antialiasing is not supported"; } + + if (!customConfig.empty() && !CheckMyriadX()) { + GTEST_SKIP() << "Custom layers for MYRIAD2 not supported"; + } + + _config[VPU_CONFIG_KEY(CUSTOM_LAYERS)] = customConfig; + + const auto outputDims = SizeVector{inputDims[0], + inputDims[1], + (size_t)(inputDims[2] * factor), + (size_t)(inputDims[3] * factor)}; + + SetInputTensors({inputDims}); + SetOutputTensors({outputDims}); + + std::map params; + params["antialias"] = std::to_string((int)antialias); + params["factor"] = std::to_string(factor); + + ASSERT_NO_FATAL_FAILURE(makeSingleLayerNetwork(LayerInitParams("Resample").params(params), + NetworkInitParams() + .useHWOpt(hwOptimization) + .lockLayout(true))); + + ASSERT_TRUE(Infer()); + + ASSERT_NO_FATAL_FAILURE(refResample(_inputMap.begin()->second, _refBlob, antialias)); + + CompareCommonAbsolute(_outputMap.begin()->second, _refBlob, ERROR_BOUND); } -static std::vector s_ResampleCustomConfig = { - "", +static std::vector s_ResampleInput = { + {1, 128, 26, 26}, + {1, 64, 52, 52}, + {1, 23, 14, 14} +}; + +static std::vector s_CustomConfig = { + {""}, #ifdef VPU_HAS_CUSTOM_KERNELS getIELibraryPath() + "/vpu_custom_kernels/customLayerBindings.xml" #endif }; -static std::vector s_ResampleAntialias = { - {0, 1} -}; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.cpp index 3a9f302cded..a5abd89aeba 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.cpp @@ -5,7 +5,7 @@ #include "ngraph_functions/subgraph_builders.hpp" #include "myriad_layers_reshape_test.hpp" -TEST_F(myriadEliminateReshapeTests_nightly, SplitConvConcat) { +TEST_F(myriadEliminateReshapeTests_smoke, SplitConvConcat) { ASSERT_NO_THROW(_cnnNetwork = InferenceEngine::CNNNetwork(ngraph::builder::subgraph::makeSplitConvConcat())); StatusCode st; @@ -265,7 +265,7 @@ TEST_F(myriadLayersTests_nightly, ReshapeAfterConcat_Eliminate) { EXPECT_EQ(InferenceEngineProfileInfo::NOT_RUN, layerInfo.status); } -TEST_F(myriadLayerReshapeFasterRCNN_nightly, Reshape) { +TEST_F(myriadLayerReshapeFasterRCNN_smoke, Reshape) { InferenceEngine::SizeVector input_tensor = {1, 14, 14, 24}; InferenceEngine::SizeVector output_tensor = {1, 2352, 2}; std::map layer_params = { @@ -281,13 +281,13 @@ TEST_F(myriadLayerReshapeFasterRCNN_nightly, Reshape) { ASSERT_TRUE(generateNetAndInfer(NetworkInitParams().useHWOpt( CheckMyriadX()))); } -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReshape_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReshape_smoke, ::testing::Combine( ::testing::ValuesIn(s_reshapeInParams), ::testing::ValuesIn(s_reshapeOutParams)) ); -INSTANTIATE_TEST_CASE_P(fc_to_conv_case, myriadLayerReshape_nightly, +INSTANTIATE_TEST_CASE_P(fc_to_conv_case, myriadLayerReshape_smoke, ::testing::Values( std::make_tuple( SizeVector{400, 12544}, @@ -308,11 +308,11 @@ INSTANTIATE_TEST_CASE_P(fc_to_conv_case, myriadLayerReshape_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsReshapeBeforeFC_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsReshapeBeforeFC_smoke, ::testing::Values(CONFIG_VALUE(YES), CONFIG_VALUE(NO)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsReshapeFasterRCNN_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsReshapeFasterRCNN_smoke, ::testing::Combine( ::testing::ValuesIn(s_convTensor) , ::testing::Values(MAKE_STRUCT(param_size, 1, 1)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.hpp index 22fa2540e5c..942c6551e66 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reshape_test.hpp @@ -10,11 +10,11 @@ using namespace InferenceEngine; -using myriadEliminateReshapeTests_nightly = myriadLayersTests_nightly; +using myriadEliminateReshapeTests_smoke = myriadLayersTests_nightly; -typedef myriadLayerTestBaseWithParam> myriadLayerReshape_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerReshape_smoke; -TEST_P(myriadLayerReshape_nightly, Reshape) { +TEST_P(myriadLayerReshape_smoke, Reshape) { auto input_tensor = std::get<0>(GetParam()); auto output_tensor = std::get<1>(GetParam()); @@ -38,7 +38,7 @@ TEST_P(myriadLayerReshape_nightly, Reshape) { } -typedef myriadLayersTests_nightly myriadLayerReshapeFasterRCNN_nightly; +typedef myriadLayersTests_nightly myriadLayerReshapeFasterRCNN_smoke; static std::vector s_reshapeInParams = { {{1, 4, 2, 16}}, @@ -198,9 +198,9 @@ std::string MODEL_WITHOUT_FLATTEN = R"V0G0N( )V0G0N"; -typedef myriadLayerTestBaseWithParam myriadLayersTestsReshapeBeforeFC_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsReshapeBeforeFC_smoke; -TEST_P(myriadLayersTestsReshapeBeforeFC_nightly, OptimizeReshapeIfItIsPlacedBeforeFC) { +TEST_P(myriadLayersTestsReshapeBeforeFC_smoke, OptimizeReshapeIfItIsPlacedBeforeFC) { std::string HWConfigValue = GetParam(); if (!CheckMyriadX() && HWConfigValue == CONFIG_VALUE(YES)) { std::cout << "Disable for non-MyriadX devices" << std::endl; @@ -247,11 +247,11 @@ TEST_P(myriadLayersTestsReshapeBeforeFC_nightly, OptimizeReshapeIfItIsPlacedBefo EXPECT_EQ(InferenceEngineProfileInfo::NOT_RUN, layerInfo.status); } -class myriadLayersTestsReshapeFasterRCNN_nightly: public ConvolutionTest<>{ +class myriadLayersTestsReshapeFasterRCNN_smoke: public ConvolutionTest<>{ }; // FIXME: rewrite the test (it doesn't use Convolution) avoid HWC layout for 3D tensor in reference code -TEST_P(myriadLayersTestsReshapeFasterRCNN_nightly, DISABLED_Convolution) { +TEST_P(myriadLayersTestsReshapeFasterRCNN_smoke, DISABLED_Convolution) { std::map permute_params = { {"order", "0,2,3,1"} }; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.cpp index 2a9dfab9bb5..bc5c38e449e 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_reverse_sequence_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReverseSequence_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerReverseSequence_smoke, ::testing::Combine( ::testing::Values( MAKE_STRUCT(reverse_sequence_test_params, {5, 6, 18}, 0, 0) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.hpp index 3481c9a5cc5..829f3e9821d 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_reverse_sequence_test.hpp @@ -30,7 +30,7 @@ struct reverse_sequence_test_params { }; PRETTY_PARAM(ReverseSequence, reverse_sequence_test_params); -typedef myriadLayerTestBaseWithParam> myriadLayerReverseSequence_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerReverseSequence_smoke; static int nchw_to_nhwc(InferenceEngine::SizeVector dims, int ind) { @@ -104,7 +104,7 @@ static void ref_reverse_sequence( } } -TEST_P(myriadLayerReverseSequence_nightly, ReverseSequence) { +TEST_P(myriadLayerReverseSequence_smoke, ReverseSequence) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); reverse_sequence_test_params input_dims = std::get<0>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_rfcn_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_rfcn_test.cpp index 627b4597d60..8204fbf5882 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_rfcn_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_rfcn_test.cpp @@ -647,7 +647,7 @@ static void refGlobalAvgPooling7x7Rfcn(const Blob::Ptr src, } } -class myriadLayersRfcnTests_nightly: public myriadLayersTests_nightly { +class myriadLayersRfcnTests_smoke: public myriadLayersTests_nightly { public: void GenROIs(InferenceEngine::Blob::Ptr rois, const uint32_t in_width, const uint32_t in_height, @@ -796,7 +796,7 @@ public: Blob::Ptr outputBlob; }; -TEST_F(myriadLayersRfcnTests_nightly, ReshapeRfcn) +TEST_F(myriadLayersRfcnTests_smoke, ReshapeRfcn) { StatusCode st = GENERAL_ERROR; @@ -812,7 +812,7 @@ TEST_F(myriadLayersRfcnTests_nightly, ReshapeRfcn) CompareCommonAbsolute(outputBlob, prior_network_output, 0.0f); } -TEST_F(myriadLayersRfcnTests_nightly, SoftmaxRfcn) +TEST_F(myriadLayersRfcnTests_smoke, SoftmaxRfcn) { StatusCode st = GENERAL_ERROR; @@ -830,7 +830,7 @@ TEST_F(myriadLayersRfcnTests_nightly, SoftmaxRfcn) CompareCommonAbsolute(outputBlob, _refBlob, ERROR_BOUND); } -TEST_F(myriadLayersRfcnTests_nightly, GlobalAvgPooling7x7Rfcn) +TEST_F(myriadLayersRfcnTests_smoke, GlobalAvgPooling7x7Rfcn) { StatusCode st = GENERAL_ERROR; diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.cpp index 84649ea0940..b9b7a7f5387 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_roi_align_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsROIAlign_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsROIAlign_smoke, ::testing::Combine( ::testing::ValuesIn(s_ROIAlignLayerInput), ::testing::ValuesIn(s_ROIAlignLayerParam), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.hpp index 0326e834aff..f2c3021ae52 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_align_test.hpp @@ -35,7 +35,7 @@ PRETTY_PARAM(roi_align_mode, std::string); PRETTY_PARAM(number_rois, uint32_t); using ROIAlignTestParams = std::tuple; -typedef myriadLayerTestBaseWithParam myriadLayersTestsROIAlign_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsROIAlign_smoke; const int roi_cols = 4; @@ -161,7 +161,7 @@ static std::string getModel(const int batches, const int channels, const int hei return model; } -TEST_P(myriadLayersTestsROIAlign_nightly, ROIAlign) { +TEST_P(myriadLayersTestsROIAlign_smoke, ROIAlign) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); const tensor_test_params dims_layer_in = std::get<0>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.cpp index bfe13074f4a..4c44ef6a380 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_roi_feature_extractor_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsROIFeatureExtractor_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsROIFeatureExtractor_smoke, ::testing::Combine( ::testing::ValuesIn(s_ROIFeatureExtractorLayerInput), ::testing::ValuesIn(s_ROIFeatureExtractorLayerParam), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.hpp index 6ad35a63a2d..2d3933e3ba4 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_feature_extractor_test.hpp @@ -30,7 +30,7 @@ PRETTY_PARAM(number_rois, uint32_t); using ROIFeatureExtractorTestParams = std::tuple; -typedef myriadLayerTestBaseWithParam myriadLayersTestsROIFeatureExtractor_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsROIFeatureExtractor_smoke; static void genROIs(InferenceEngine::Blob::Ptr rois, const roi_feature_extractor_param& params, @@ -55,7 +55,7 @@ static void genROIs(InferenceEngine::Blob::Ptr rois, } } -TEST_P(myriadLayersTestsROIFeatureExtractor_nightly, ROIFeatureExtractor) { +TEST_P(myriadLayersTestsROIFeatureExtractor_smoke, ROIFeatureExtractor) { tensor_test_params dims_layer_in = std::get<0>(GetParam()); roi_feature_extractor_param test_params = std::get<1>(GetParam()); const uint32_t num_rois = std::get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.cpp index 8f875a1bfc9..ff45363c66d 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_roi_pooling_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsROIPooling_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsROIPooling_smoke, ::testing::Combine( ::testing::ValuesIn(s_ROIPoolingLayerInput), ::testing::ValuesIn(s_ROIPoolingLayerParam), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.hpp index 6b8d98835c5..68b89f87b7b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_roi_pooling_test.hpp @@ -42,7 +42,7 @@ static inline void PrintTo(const t_ROIPooling_method& param, ::std::ostream* os) using ROIPoolingTestParams = std::tuple; -class myriadLayersTestsROIPooling_nightly: public myriadLayerTestBaseWithParam { +class myriadLayersTestsROIPooling_smoke: public myriadLayerTestBaseWithParam { public: void genROIs(InferenceEngine::Blob::Ptr rois, const ROIPoolingParams& params, @@ -260,7 +260,7 @@ public: } }; -TEST_P(myriadLayersTestsROIPooling_nightly, ROIPooling) { +TEST_P(myriadLayersTestsROIPooling_smoke, ROIPooling) { tensor_test_params dims_layer_in = std::get<0>(GetParam()); ROIPoolingParams test_params = std::get<1>(GetParam()); const uint32_t num_rois = std::get<2>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.cpp index 05cedf0eb37..8c8de925791 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.cpp @@ -5,7 +5,7 @@ #include "myriad_layers_scale_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsScale_nightly, + accuracy, myriadLayersTestsScale_smoke, ::testing::Combine( ::testing::ValuesIn(s_inputScaleTensors), ::testing::ValuesIn(s_inputBiasScale))); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.hpp index cdad5d77157..956e025080b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scale_test.hpp @@ -97,11 +97,11 @@ void ref_scale(const InferenceEngine::Blob::Ptr src, typedef std::tuple TestScaleShift; -class myriadLayersTestsScale_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsScale_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersTestsScale_nightly, TestsScale) +TEST_P(myriadLayersTestsScale_smoke, TestsScale) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.cpp index 8ecc122bd6f..7d7b6a3054f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.cpp @@ -53,12 +53,12 @@ static const std::vector dataShapeList_useCases = { { 16, 512, 56, 56 }, }; -INSTANTIATE_TEST_CASE_P(nd_tensors, myriadLayersScatterElementsUpdateTest_nightly, +INSTANTIATE_TEST_CASE_P(nd_tensors, myriadLayersScatterElementsUpdateTest_smoke, Combine( ValuesIn(dataShapeList_ndTensors), ValuesIn(dataTypeList))); -INSTANTIATE_TEST_CASE_P(use_cases, myriadLayersScatterElementsUpdateTest_nightly, +INSTANTIATE_TEST_CASE_P(use_cases, myriadLayersScatterElementsUpdateTest_smoke, Combine( ValuesIn(dataShapeList_useCases), ValuesIn(dataTypeList))); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.hpp index acfa1b6b35b..57b49de0fea 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_elements_update_test.hpp @@ -41,7 +41,7 @@ using DataType = std::string; // "FP16", "I32" using ScatterElementsUpdateTestParams = std::tuple; -class myriadLayersScatterElementsUpdateTest_nightly : +class myriadLayersScatterElementsUpdateTest_smoke : public myriadLayerTestBaseWithParam { protected: @@ -392,6 +392,6 @@ private: std::mt19937 m_gen; }; -TEST_P(myriadLayersScatterElementsUpdateTest_nightly, accuracy) { +TEST_P(myriadLayersScatterElementsUpdateTest_smoke, accuracy) { testScatterElementsUpdate(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.cpp index 1d3d7afb71f..6f5fda6d856 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.cpp @@ -14,7 +14,7 @@ using namespace testing; INSTANTIATE_TEST_CASE_P( nd_tensors, - myriadLayersScatterUpdateTest_nightly, + myriadLayersScatterUpdateTest_smoke, Values( // 1-dimensional `indices` ScatterUpdateTestParams { { 1000 }, { 100000 } }, @@ -51,7 +51,7 @@ INSTANTIATE_TEST_CASE_P( INSTANTIATE_TEST_CASE_P( use_cases, - myriadLayersScatterUpdateTest_nightly, + myriadLayersScatterUpdateTest_smoke, Values( // use case from Mask R-CNN: N = 1000, C = 256, HxW = 7x7 ScatterUpdateTestParams { { 32 }, { 1000, 256, 7, 7} }, diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.hpp index 6685bbecd3b..f9352f39a00 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_scatter_update_test.hpp @@ -43,7 +43,7 @@ using IndicesShape = SizeVector; using ScatterUpdateTestParams = std::tuple; -class myriadLayersScatterUpdateTest_nightly: +class myriadLayersScatterUpdateTest_smoke: public myriadLayerTestBaseWithParam { protected: @@ -459,6 +459,6 @@ private: } }; -TEST_P(myriadLayersScatterUpdateTest_nightly, accuracy) { +TEST_P(myriadLayersScatterUpdateTest_smoke, accuracy) { testScatterUpdate(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.cpp index d5db411ff11..0d63d0fc03f 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_select_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsSelect_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsSelect_smoke, ::testing::Combine( ::testing::ValuesIn(s_eltwiseTensors), ::testing::ValuesIn(s_eltwiseDims)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.hpp index 560728b68ab..ac7d8d84e88 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_select_test.hpp @@ -116,7 +116,7 @@ protected: } }; -class myriadTestsSelect_nightly: public SelectTest +class myriadTestsSelect_smoke: public SelectTest { void SetUp() override { SelectTest::SetUp(); @@ -124,7 +124,7 @@ class myriadTestsSelect_nightly: public SelectTest } }; -TEST_P(myriadTestsSelect_nightly, Select) +TEST_P(myriadTestsSelect_smoke, Select) { InitBody(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.cpp index 8d79d804f56..89d8f407d25 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.cpp @@ -5,24 +5,24 @@ #include "myriad_layers_sigmoid_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsSigmoid_nightly, + accuracy, myriadLayersTestsSigmoid_smoke, ::testing::ValuesIn(s_sigmoidParams)); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithSigmoid_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithSigmoid_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithSigmoid_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithSigmoid_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithSigmoid_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithSigmoid_smoke, ::testing::Combine( ::testing::ValuesIn(g_convolutionTensors) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -35,7 +35,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithSigmoid_nightly, ); INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayerFullyConnectedWithSigmoid_nightly, + accuracy, myriadLayerFullyConnectedWithSigmoid_smoke, ::testing::Combine( ::testing::ValuesIn(g_fcTestParamsSubset), ::testing::Values(g_dimensionsFC[0]), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.hpp index dc25cb652ba..c4ec91e16b1 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_sigmoid_test.hpp @@ -13,12 +13,12 @@ using namespace InferenceEngine; -class myriadLayersTestsSigmoid_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsSigmoid_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { public: }; -TEST_P(myriadLayersTestsSigmoid_nightly, TestsSigmoid) +TEST_P(myriadLayersTestsSigmoid_smoke, TestsSigmoid) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); @@ -52,13 +52,13 @@ static std::vector s_sigmoidParams = { {{1, 3, 277, 230}} }; -class myriadLayersTestsMaxPoolingWithSigmoid_nightly: public PoolingTest{ +class myriadLayersTestsMaxPoolingWithSigmoid_smoke: public PoolingTest{ }; -class myriadLayersTestsAvgPoolingWithSigmoid_nightly: public PoolingTest{ +class myriadLayersTestsAvgPoolingWithSigmoid_smoke: public PoolingTest{ }; -TEST_P(myriadLayersTestsMaxPoolingWithSigmoid_nightly, TestsMaxPoolingWithSigmoid) +TEST_P(myriadLayersTestsMaxPoolingWithSigmoid_smoke, TestsMaxPoolingWithSigmoid) { _testNet.addLayer(LayerInitParams("Sigmoid") .in({_output_tensor}) @@ -68,7 +68,7 @@ TEST_P(myriadLayersTestsMaxPoolingWithSigmoid_nightly, TestsMaxPoolingWithSigmoi CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_SIGMOID); } -TEST_P(myriadLayersTestsAvgPoolingWithSigmoid_nightly, TestsAvgPoolingWithSigmoid) +TEST_P(myriadLayersTestsAvgPoolingWithSigmoid_smoke, TestsAvgPoolingWithSigmoid) { _testNet.addLayer(LayerInitParams("Sigmoid") .in({_output_tensor}) @@ -78,10 +78,10 @@ TEST_P(myriadLayersTestsAvgPoolingWithSigmoid_nightly, TestsAvgPoolingWithSigmoi CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_SIGMOID); } -class myriadLayerConvolutionWithSigmoid_nightly: public ConvolutionTest{ +class myriadLayerConvolutionWithSigmoid_smoke: public ConvolutionTest{ }; -TEST_P(myriadLayerConvolutionWithSigmoid_nightly, Convolution) { +TEST_P(myriadLayerConvolutionWithSigmoid_smoke, Convolution) { _irVersion = std::get<6>(GetParam()); _testNet.addLayer(LayerInitParams("Sigmoid") .in({_output_tensor}) @@ -97,10 +97,10 @@ TEST_P(myriadLayerConvolutionWithSigmoid_nightly, Convolution) { CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), maxerr); } -class myriadLayerFullyConnectedWithSigmoid_nightly: public FCTest<>{ +class myriadLayerFullyConnectedWithSigmoid_smoke: public FCTest<>{ }; -TEST_P(myriadLayerFullyConnectedWithSigmoid_nightly, TestsFullyConnected) +TEST_P(myriadLayerFullyConnectedWithSigmoid_smoke, TestsFullyConnected) { _testNet.addLayer(LayerInitParams("Sigmoid") .in({_output_tensor}) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.cpp index 71eae36f9dc..d5a5ee1d220 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_slice_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSlice_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSlice_smoke, ::testing::Values( MAKE_STRUCT(SliceParams, {4, 8, 16, 32, 64}, {{4, 8, 16, 10, 64}, {4, 8, 16, 22, 64}}, 3), MAKE_STRUCT(SliceParams, {4, 8, 16, 32}, {{4, 8, 2, 32}, {4, 8, 14, 32}}, 2)) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.hpp index 6f794767919..1a8f2094a1a 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_slice_test.hpp @@ -16,9 +16,9 @@ struct SliceParams { PRETTY_PARAM(SliceTestParams, SliceParams); -typedef myriadLayerTestBaseWithParam myriadLayersTestsSlice_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsSlice_smoke; -TEST_P(myriadLayersTestsSlice_nightly, Slice) { +TEST_P(myriadLayersTestsSlice_smoke, Slice) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); const SliceParams testParams = GetParam(); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.cpp index 236329a1ea7..8e3bd5683c8 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.cpp @@ -5,7 +5,7 @@ #include "myriad_layers_softmax_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsSoftMax_nightly, + accuracy, myriadLayersTestsSoftMax_smoke, ::testing::Combine( ::testing::ValuesIn(s_softMaxTensors) , ::testing::Values(IRVersion::v7, IRVersion::v10) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.hpp index 3f2729350e8..93d8eb76b66 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_softmax_test.hpp @@ -18,20 +18,20 @@ void PrintTo(const SoftmaxAxisSizes& p, std::ostream* os) { *os << "axis=" << p.axis << ", sizes=" << testing::PrintToString(p.sizes); } -using myriadLayersTestsSoftMaxParams_nightly = myriadLayerTestBaseWithParam>; +using myriadLayersTestsSoftMaxParams_smoke = myriadLayerTestBaseWithParam>; -class myriadLayersTestsSoftMax_nightly: public myriadLayersTestsSoftMaxParams_nightly { +class myriadLayersTestsSoftMax_smoke: public myriadLayersTestsSoftMaxParams_smoke { protected: SoftmaxAxisSizes _testingInput; void SetUp() override { - myriadLayersTestsSoftMaxParams_nightly::SetUp(); + myriadLayersTestsSoftMaxParams_smoke::SetUp(); _testingInput = std::get<0>(GetParam()); _irVersion = std::get<1>(GetParam()); } }; -TEST_P(myriadLayersTestsSoftMax_nightly, TestsSoftMax) +TEST_P(myriadLayersTestsSoftMax_smoke, TestsSoftMax) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); SetInputTensors({_testingInput.sizes}); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.cpp index 45bdd62d9d4..72ba0c199dc 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_split_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSplit_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSplit_smoke, ::testing::Values( MAKE_STRUCT(SplitParams, {4, 8, 16, 32, 64}, 2, 6), MAKE_STRUCT(SplitParams, {4, 8, 16, 32}, 2, 6), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.hpp index bba4236ab8b..a4cbe23b30c 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_split_test.hpp @@ -16,9 +16,9 @@ struct SplitParams { PRETTY_PARAM(SplitTestParams, SplitParams); -typedef myriadLayerTestBaseWithParam myriadLayersTestsSplit_nightly; +typedef myriadLayerTestBaseWithParam myriadLayersTestsSplit_smoke; -TEST_P(myriadLayersTestsSplit_nightly, Split) { +TEST_P(myriadLayersTestsSplit_smoke, Split) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); const SplitParams testParams = GetParam(); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.cpp index c8c2eeeb6ca..3ab61dfd70c 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_squeeze_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC1, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC1_smoke, ::testing::Combine( ::testing::ValuesIn(s_squeezeTensorsTC1), ::testing::ValuesIn(s_squeezeIndicesTC1), @@ -13,7 +13,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC1, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC2, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC2_smoke, ::testing::Combine( ::testing::ValuesIn(s_squeezeTensorsTC2), ::testing::ValuesIn(s_squeezeIndicesTC2), @@ -22,7 +22,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC2, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC3, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC3_smoke, ::testing::Combine( ::testing::ValuesIn(s_squeezeTensorsTC3), ::testing::ValuesIn(s_squeezeIndicesTC3), @@ -31,7 +31,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC3, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC4, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC4_smoke, ::testing::Combine( ::testing::ValuesIn(s_squeezeTensorsTC4), ::testing::ValuesIn(s_squeezeIndicesTC4), @@ -40,7 +40,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC4, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC5, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsSqueezeTC5_smoke, ::testing::Combine( ::testing::ValuesIn(s_squeezeTensorsTC5), ::testing::ValuesIn(s_squeezeIndicesTC5), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.hpp index fc510ebc277..c0d8ef8e8df 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_squeeze_test.hpp @@ -169,47 +169,47 @@ protected: } }; -class myriadLayersTestsSqueezeTC1 : public myriadLayersTestsSqueezeBase +class myriadLayersTestsSqueezeTC1_smoke : public myriadLayersTestsSqueezeBase { }; -class myriadLayersTestsSqueezeTC2 : public myriadLayersTestsSqueezeBase +class myriadLayersTestsSqueezeTC2_smoke : public myriadLayersTestsSqueezeBase { }; -class myriadLayersTestsSqueezeTC3 : public myriadLayersTestsSqueezeBase +class myriadLayersTestsSqueezeTC3_smoke : public myriadLayersTestsSqueezeBase { }; -class myriadLayersTestsSqueezeTC4 : public myriadLayersTestsSqueezeBase +class myriadLayersTestsSqueezeTC4_smoke : public myriadLayersTestsSqueezeBase { }; -class myriadLayersTestsSqueezeTC5 : public myriadLayersTestsSqueezeBase +class myriadLayersTestsSqueezeTC5_smoke : public myriadLayersTestsSqueezeBase { }; -TEST_P(myriadLayersTestsSqueezeTC1, Squeeze) { +TEST_P(myriadLayersTestsSqueezeTC1_smoke, Squeeze) { DISABLE_IF(!CheckMyriadX()); InitBody(); } -TEST_P(myriadLayersTestsSqueezeTC2, Squeeze) { +TEST_P(myriadLayersTestsSqueezeTC2_smoke, Squeeze) { DISABLE_IF(!CheckMyriadX()); InitBody(); } -TEST_P(myriadLayersTestsSqueezeTC3, Squeeze) { +TEST_P(myriadLayersTestsSqueezeTC3_smoke, Squeeze) { DISABLE_IF(!CheckMyriadX()); InitBody(); } -TEST_P(myriadLayersTestsSqueezeTC4, Squeeze) { +TEST_P(myriadLayersTestsSqueezeTC4_smoke, Squeeze) { DISABLE_IF(!CheckMyriadX()); InitBody(); } -TEST_P(myriadLayersTestsSqueezeTC5, Squeeze) { +TEST_P(myriadLayersTestsSqueezeTC5_smoke, Squeeze) { DISABLE_IF(!CheckMyriadX()); InitBody(); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.cpp index 249c5893916..5be47e61122 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.cpp @@ -5,5 +5,5 @@ #include "myriad_layers_strided_slice_test.h" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsStridedSlice_nightly, + accuracy, myriadLayersTestsStridedSlice_smoke, ::testing::ValuesIn(s_stridedSliceParams)); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.h b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.h index e62dfbf4ce2..4f324e979cd 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.h +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_strided_slice_test.h @@ -25,7 +25,7 @@ struct strided_slice_test_param { InferenceEngine::SizeVector out_shape; }; -class myriadLayersTestsStridedSlice_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsStridedSlice_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { public: std::string model_t = R"V0G0N( @@ -222,7 +222,7 @@ std::string stridesEdge = R"V0G0N( } }; -TEST_P(myriadLayersTestsStridedSlice_nightly, TestsStridedSlice) { +TEST_P(myriadLayersTestsStridedSlice_smoke, TestsStridedSlice) { auto p = ::testing::WithParamInterface::GetParam(); std::string model = getModel(p); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.cpp index c2129b5fba7..20268ffa540 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.cpp @@ -5,10 +5,10 @@ #include "myriad_layers_tanh_test.hpp" INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayersTestsTanh_nightly, + accuracy, myriadLayersTestsTanh_smoke, ::testing::ValuesIn(s_tanhParams)); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithTanH_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithTanH_smoke, ::testing::Combine( ::testing::ValuesIn(g_convolutionTensors) , ::testing::Values(MAKE_STRUCT(param_size, 3, 3)) @@ -20,14 +20,14 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerConvolutionWithTanH_nightly, ) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithTanh_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsMaxPoolingWithTanh_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), ::testing::ValuesIn(g_poolingLayout)) ); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithTanh_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithTanh_smoke, ::testing::Combine( ::testing::ValuesIn(g_poolingInput), ::testing::ValuesIn(g_poolingLayerParamsLite), @@ -35,7 +35,7 @@ INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsAvgPoolingWithTanh_nightly, ); INSTANTIATE_TEST_CASE_P( - accuracy, myriadLayerFullyConnectedWithTanH_nightly, + accuracy, myriadLayerFullyConnectedWithTanH_smoke, ::testing::Combine( ::testing::ValuesIn(g_fcTestParamsSubset), ::testing::Values(g_dimensionsFC[0]), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.hpp index af95678efd9..a72e3d022c5 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tanh_test.hpp @@ -10,11 +10,11 @@ #define ERROR_BOUND_WITH_TANH (1.0e-3f) using namespace InferenceEngine; -class myriadLayersTestsTanh_nightly: public myriadLayersTests_nightly, +class myriadLayersTestsTanh_smoke: public myriadLayersTests_nightly, public testing::WithParamInterface { }; -TEST_P(myriadLayersTestsTanh_nightly, TestsTanh) +TEST_P(myriadLayersTestsTanh_smoke, TestsTanh) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); auto p = ::testing::WithParamInterface::GetParam(); @@ -71,10 +71,10 @@ static std::vector s_fcTestParamsSubset = { {{1, 16, 8, 8}, 8, 0.065f} }; -class myriadLayerConvolutionWithTanH_nightly: public ConvolutionTest{ +class myriadLayerConvolutionWithTanH_smoke: public ConvolutionTest{ }; -TEST_P(myriadLayerConvolutionWithTanH_nightly, Convolution) { +TEST_P(myriadLayerConvolutionWithTanH_smoke, Convolution) { auto param = GetParam(); _irVersion = std::get<6>(param); @@ -92,13 +92,13 @@ TEST_P(myriadLayerConvolutionWithTanH_nightly, Convolution) { CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), maxerr); } -class myriadLayersTestsMaxPoolingWithTanh_nightly: public PoolingTest{ +class myriadLayersTestsMaxPoolingWithTanh_smoke: public PoolingTest{ }; -class myriadLayersTestsAvgPoolingWithTanh_nightly: public PoolingTest{ +class myriadLayersTestsAvgPoolingWithTanh_smoke: public PoolingTest{ }; -TEST_P(myriadLayersTestsMaxPoolingWithTanh_nightly, TestsMaxPoolingWithTanh) +TEST_P(myriadLayersTestsMaxPoolingWithTanh_smoke, TestsMaxPoolingWithTanh) { _testNet.addLayer(LayerInitParams("TanH") .in({_output_tensor}) @@ -108,7 +108,7 @@ TEST_P(myriadLayersTestsMaxPoolingWithTanh_nightly, TestsMaxPoolingWithTanh) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_TANH); } -TEST_P(myriadLayersTestsAvgPoolingWithTanh_nightly, TestsAvgPoolingWithTanh) +TEST_P(myriadLayersTestsAvgPoolingWithTanh_smoke, TestsAvgPoolingWithTanh) { _testNet.addLayer(LayerInitParams("TanH") .in({_output_tensor}) @@ -118,10 +118,10 @@ TEST_P(myriadLayersTestsAvgPoolingWithTanh_nightly, TestsAvgPoolingWithTanh) CompareCommonAbsolute(_outputMap.begin()->second, getReferenceOutput(), ERROR_BOUND_WITH_TANH); } -class myriadLayerFullyConnectedWithTanH_nightly: public FCTest<>{ +class myriadLayerFullyConnectedWithTanH_smoke: public FCTest<>{ }; -TEST_P(myriadLayerFullyConnectedWithTanH_nightly, TestsFullyConnected) +TEST_P(myriadLayerFullyConnectedWithTanH_smoke, TestsFullyConnected) { _testNet.addLayer(LayerInitParams("TanH") .in({_output_tensor}) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tensor_iterator_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tensor_iterator_test.cpp deleted file mode 100644 index e18562dcdc2..00000000000 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tensor_iterator_test.cpp +++ /dev/null @@ -1,66 +0,0 @@ -// Copyright (C) 2019 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -#include "vpu_layers_tests.hpp" -#include "vpu_case_params.hpp" -#include "common/include/vpu/utils/error.hpp" - -#include "single_layer_common.hpp" - -#include "gtest/gtest.h" - -#include -#include -#include -#include -#include - -namespace { - -class MyriadLayersTestsTensorIterator : public CommonTestUtils::TestsCommon { -public: - void SetUp() override { - fn_ptr = ngraph::builder::subgraph::makeTIwithLSTMcell(); - } -protected: - std::shared_ptr fn_ptr; -}; - -// TODO: Issue: 29485 -TEST_F(MyriadLayersTestsTensorIterator, CompareNativeVersionWithUnrolledLoop) { - DISABLE_IF(!CheckMyriadX () && !CheckMA2085()); - CNNNetwork network(fn_ptr); - network.getInputsInfo().begin()->second->setPrecision(Precision::FP16); - - - auto ie = PluginCache::get().ie(); - - ExecutableNetwork exeNetworkWithConfig = ie->LoadNetwork(network, CommonTestUtils::DEVICE_MYRIAD, - {{VPU_CONFIG_KEY(FORCE_PURE_TENSOR_ITERATOR), CONFIG_VALUE(NO)}, - {VPU_CONFIG_KEY(ENABLE_TENSOR_ITERATOR_UNROLLING), CONFIG_VALUE(YES)}}); - InferRequest inferRequestWithConfig = exeNetworkWithConfig.CreateInferRequest(); - auto blobWithConfig = FuncTestUtils::createAndFillBlob(network.getInputsInfo().begin()->second->getTensorDesc()); - inferRequestWithConfig.SetBlob(network.getInputsInfo().begin()->first, blobWithConfig); - inferRequestWithConfig.Infer(); - auto* outRawDataWithConfig = inferRequestWithConfig.GetBlob(network.getOutputsInfo().begin()->first)->cbuffer().as(); - - ExecutableNetwork exeNetworkWithoutConfig = ie->LoadNetwork(network, CommonTestUtils::DEVICE_MYRIAD, - {{VPU_CONFIG_KEY(FORCE_PURE_TENSOR_ITERATOR), CONFIG_VALUE(YES)}, - {VPU_CONFIG_KEY(ENABLE_TENSOR_ITERATOR_UNROLLING), CONFIG_VALUE(NO)}}); - InferRequest inferRequestWithoutConfig = exeNetworkWithoutConfig.CreateInferRequest(); - auto blobWithoutConfig = FuncTestUtils::createAndFillBlob(network.getInputsInfo().begin()->second->getTensorDesc()); - inferRequestWithoutConfig.SetBlob(network.getInputsInfo().begin()->first, blobWithoutConfig); - inferRequestWithoutConfig.Infer(); - auto* outRawDataWithoutConfig = inferRequestWithoutConfig.GetBlob(network.getOutputsInfo().begin()->first)->cbuffer().as(); - - auto thr = FuncTestUtils::GetComparisonThreshold(InferenceEngine::Precision::FP16); - size_t outElementsCount = std::accumulate(begin(fn_ptr->get_output_shape(0)), end(fn_ptr->get_output_shape(0)), 1, - std::multiplies()); - - FuncTestUtils::compareRawBuffers(outRawDataWithoutConfig, outRawDataWithConfig, outElementsCount, - outElementsCount, - thr); - -} -} diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.cpp index bf23c58ac49..e4eb5bcd251 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_tile_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracyAdd, myriadLayerTestTile_nightly, +INSTANTIATE_TEST_CASE_P(accuracyAdd, myriadLayerTestTile_smoke, ::testing::Combine( ::testing::Values( MAKE_STRUCT(tile_test::nd_tensor_test_params, {4, 5, 6}, 0) @@ -21,7 +21,7 @@ INSTANTIATE_TEST_CASE_P(accuracyAdd, myriadLayerTestTile_nightly, , ::testing::Values(2, 3, 5) )); -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestTile_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayerTestTile_smoke, ::testing::Combine( ::testing::Values( MAKE_STRUCT(tile_test::nd_tensor_test_params, {4, 5, 6}, 1) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.hpp index 1d20b1ae718..8424212fa24 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_tile_test.hpp @@ -106,9 +106,9 @@ void ref_tile(const InferenceEngine::Blob::Ptr src, } } -typedef myriadLayerTestBaseWithParam> myriadLayerTestTile_nightly; +typedef myriadLayerTestBaseWithParam> myriadLayerTestTile_smoke; -TEST_P(myriadLayerTestTile_nightly, Tile) { +TEST_P(myriadLayerTestTile_smoke, Tile) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); tile_test::nd_tensor_test_params input_dims = get<0>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.cpp index e31adc85f3a..6f5f795495a 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.cpp @@ -32,7 +32,7 @@ static const std::vector s_sorts_list = // "none", // currently is not supported by firmware }; -INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsTopK_nightly, +INSTANTIATE_TEST_CASE_P(accuracy, myriadTestsTopK_smoke, ::testing::Combine( ::testing::ValuesIn(s_geometries_list), ::testing::ValuesIn(s_modes_list), diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.hpp index 53baba2e36a..6b589068ed8 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_topk_test.hpp @@ -32,17 +32,6 @@ static const Precision indexPrecision = Precision::I32; class TopKTest: public myriadLayerTestBaseWithParam { protected: - std::set getExecutedStagesTypes() const { - std::set result; - std::map perfMap; - _inferRequest->GetPerformanceCounts(perfMap, nullptr); - - for (const auto& perf : perfMap) - result.emplace(perf.second.exec_type); - - return result; - } - void testTopK(const IRVersion irVersion, const bool outputValues, const bool outputIndices) { _config[VPU_CONFIG_KEY(DETECT_NETWORK_BATCH)] = CONFIG_VALUE(NO); _config[CONFIG_KEY(PERF_COUNT)] = CONFIG_VALUE(YES); @@ -105,16 +94,6 @@ protected: ASSERT_NO_THROW(st = _inferRequest->Infer(&_resp)); ASSERT_EQ(StatusCode::OK, st) << _resp.msg; - const auto executedTypes = getExecutedStagesTypes(); - - // This logic must be synchronized with TopKStage class. - const bool useArgMaxOptimization = (!outputValues || !outputIndices) - && mode == "max" - && ((sort == "value" && outputValues) || (sort == "index" && outputIndices)); - - ASSERT_EQ(executedTypes.count("ArgMax"), useArgMaxOptimization); - ASSERT_EQ(executedTypes.count("TopK"), !useArgMaxOptimization); - Blob::Ptr outputValuesBlob, outputIndicesBlob; if (outputValues) { ASSERT_NO_THROW(st = _inferRequest->GetBlob("topk.0", outputValuesBlob, &_resp)); @@ -326,26 +305,26 @@ protected: } }; -class myriadTestsTopK_nightly: public TopKTest +class myriadTestsTopK_smoke: public TopKTest { }; -TEST_P(myriadTestsTopK_nightly, TopKv7) +TEST_P(myriadTestsTopK_smoke, TopKv7) { testTopK(IRVersion::v7, true, true); } -TEST_P(myriadTestsTopK_nightly, TopKv10_All) +TEST_P(myriadTestsTopK_smoke, TopKv10_All) { testTopK(IRVersion::v10, true, true); } -TEST_P(myriadTestsTopK_nightly, TopKv10_ArgMaxValues) +TEST_P(myriadTestsTopK_smoke, TopKv10_ArgMaxValues) { testTopK(IRVersion::v10, true, false); } -TEST_P(myriadTestsTopK_nightly, TopKv10_ArgMaxIndices) +TEST_P(myriadTestsTopK_smoke, TopKv10_ArgMaxIndices) { testTopK(IRVersion::v10, false, true); } diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.cpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.cpp index 3b254e9610f..78bfc189e9b 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.cpp @@ -4,7 +4,7 @@ #include "myriad_layers_unsqueeze_test.hpp" -INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsUnsqueeze, +INSTANTIATE_TEST_CASE_P(accuracy, myriadLayersTestsUnsqueeze_smoke, ::testing::Combine( ::testing::ValuesIn(s_squeezeTensors), ::testing::ValuesIn(s_squeezeIndices) diff --git a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.hpp b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.hpp index 2b4ae6d7ab3..71604b149ec 100644 --- a/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/common/layers/myriad_layers_unsqueeze_test.hpp @@ -12,7 +12,7 @@ using namespace InferenceEngine; typedef std::vector IndicesVector; -typedef myriadLayerTestBaseWithParam> myriadLayersTestsUnsqueeze; +typedef myriadLayerTestBaseWithParam> myriadLayersTestsUnsqueeze_smoke; static void ref_unsqueeze(const InferenceEngine::Blob::Ptr src, InferenceEngine::Blob::Ptr dst) { @@ -44,7 +44,7 @@ static void ref_unsqueeze(const InferenceEngine::Blob::Ptr src, ie_memcpy(dst_data, dst_size * sizeof(ie_fp16), src_data, src_size * sizeof(ie_fp16)); } -TEST_P (myriadLayersTestsUnsqueeze, Unsqueeze){ +TEST_P(myriadLayersTestsUnsqueeze_smoke, Unsqueeze){ auto input = std::get<0>(GetParam()); auto indices = std::get<1>(GetParam()); diff --git a/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.cpp b/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.cpp index 27df402e0dd..1aced5f00bb 100644 --- a/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.cpp @@ -12,6 +12,7 @@ #include "pool_ref.hpp" #include "ie_memcpy.h" #include +#include #include "common_test_utils/common_layers_params.hpp" #include "vpu/utils/error.hpp" @@ -1034,7 +1035,6 @@ namespace reduceImpl for (int i = 1; i < ndims; ++i) offset = offset * dims[i] + indices[i]; } - return data[offset]; } @@ -1182,55 +1182,65 @@ namespace reduceImpl } } -template<> -void ref_reduce(const Blob::Ptr& in, - const Blob::Ptr& axes, - Blob::Ptr& out, - int keep_dims, - IReduceKernel* op) +template void ref_reduce(const Blob::Ptr& in, + const Blob::Ptr& axes, + Blob::Ptr& out, + int keep_dims, + vpu::LayoutPreference layoutPreference, + IReduceKernel* op); + +template void ref_reduce(const Blob::Ptr& in, + const Blob::Ptr& axes, + Blob::Ptr& out, + int keep_dims, + vpu::LayoutPreference layoutPreference, + IReduceKernel* op); + +template +void ref_reduce(const Blob::Ptr& in, + const Blob::Ptr& axes, + Blob::Ptr& out, + int keep_dims, + vpu::LayoutPreference layoutPreference, + IReduceKernel* op) { ASSERT_NE(in, nullptr); ASSERT_NE(axes, nullptr); ASSERT_NE(out, nullptr); - const int16_t* inData = in->cbuffer().as(); - int16_t* outData = out->buffer().as(); - - ASSERT_NE(inData, nullptr); - ASSERT_NE(outData, nullptr); - const auto axesDims = axes->getTensorDesc().getDims(); ASSERT_EQ(axesDims.size(), 1); const auto axesSize = axesDims[0]; - const int32_t* axesData = axes->cbuffer().as(); - ASSERT_TRUE(!(axesSize > 0) || (axesData != nullptr)); + int32_t* axesData = axes->cbuffer().as(); - reduceImpl::refReduce(in, out, axesSize, axesData, keep_dims, op); -} + if (layoutPreference == vpu::LayoutPreference::ChannelMinor) { + auto inDims = in->getTensorDesc().getDims(); + const auto ndims = inDims.size(); + auto newDims = inDims; -template<> -void ref_reduce(const Blob::Ptr& in, - const Blob::Ptr& axes, - Blob::Ptr& out, - int keep_dims, - IReduceKernel* op) -{ - ASSERT_NE(in, nullptr); - ASSERT_NE(axes, nullptr); - ASSERT_NE(out, nullptr); + const auto dimsOrder = vpu::DimsOrder::fromLayout(in->getTensorDesc().getLayout()); + const auto defPerm = vpu::DimsOrder::fromNumDims(ndims).toPermutation(); - const int32_t* inData = in->cbuffer().as(); - int32_t* outData = out->buffer().as(); + for (int i = 0; i < ndims; ++i) { + auto newInd = ndims - 1 - dimsOrder.dimInd(defPerm[ndims - i - 1]); + newDims[newInd] = inDims[i]; + } - ASSERT_NE(inData, nullptr); - ASSERT_NE(outData, nullptr); + in->getTensorDesc().setDims(newDims); - const auto axesDims = axes->getTensorDesc().getDims(); - ASSERT_EQ(axesDims.size(), 1); + for (int i = 0; i < axesSize; ++i) { + axesData[i] = ndims - 1 - dimsOrder.dimInd(defPerm[ndims - axesData[i] - 1]); + newDims[axesData[i]] = keep_dims ? 1 : 0; + } + + if (!keep_dims) { + newDims.erase(std::remove(newDims.begin(), newDims.end(), 0), newDims.end()); + } + + out->getTensorDesc().setDims(newDims); + } - const auto axesSize = axesDims[0]; - const int32_t* axesData = axes->cbuffer().as(); ASSERT_TRUE(!(axesSize > 0) || (axesData != nullptr)); reduceImpl::refReduce(in, out, axesSize, axesData, keep_dims, op); diff --git a/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.hpp b/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.hpp index edbbe17dc65..6c8a746f142 100644 --- a/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/vpu_base/myriad_layers_reference_functions.hpp @@ -9,6 +9,9 @@ #include // std::setw +#include +#include + typedef std::map ParamsStruct; typedef float (*eltwise_kernel)(float a, float b, float c); @@ -273,6 +276,7 @@ void ref_reduce(const InferenceEngine::Blob::Ptr& src, const InferenceEngine::Blob::Ptr& axes, InferenceEngine::Blob::Ptr& dst, int keep_dims, + vpu::LayoutPreference layoutPreference, IReduceKernel* op); void ref_topk(const InferenceEngine::Blob::Ptr& srcValues, diff --git a/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.cpp b/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.cpp index d58909742ec..81196de0abd 100644 --- a/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.cpp +++ b/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.cpp @@ -92,6 +92,15 @@ void vpuLayersTests::dumpPerformance() { } } +bool vpuLayersTests::wasCustomLayerInferred() const { + auto perfMap = std::map{}; + _inferRequest->GetPerformanceCounts(perfMap, nullptr); + const auto isCustomLayer = [&](const std::pair& info) { + return !strcmp(info.second.exec_type, "Custom"); + }; + return std::any_of(begin(perfMap), end(perfMap), isCustomLayer); +} + namespace { template @@ -240,7 +249,11 @@ bool vpuLayersTests::Infer() { return false; const auto st = _inferRequest->Infer(&_resp); EXPECT_EQ(InferenceEngine::StatusCode::OK, st) << _resp.msg; - //dumpPerformance(); +// dumpPerformance(); + if (!_config[VPU_CONFIG_KEY(CUSTOM_LAYERS)].empty()) { + EXPECT_TRUE(wasCustomLayerInferred()) + << "CustomBindings.xml has been provided but Custom layer was not inferred"; + } return true; } diff --git a/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.hpp b/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.hpp index e67d3aea41a..2f4c8d47cb4 100644 --- a/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.hpp +++ b/inference-engine/tests_deprecated/functional/vpu/vpu_base/vpu_layers_tests.hpp @@ -69,6 +69,7 @@ protected: void TearDown() override; bool CheckMyriadX(); void dumpPerformance(); + bool wasCustomLayerInferred() const; // For historical reasons, gen-blob functions use to 'hack' blob layout: // replace NCHW with NHWC even if you explicitly setup layout preference diff --git a/inference-engine/tests_deprecated/helpers/CMakeLists.txt b/inference-engine/tests_deprecated/helpers/CMakeLists.txt index 281f2838e7a..1777ac9071c 100644 --- a/inference-engine/tests_deprecated/helpers/CMakeLists.txt +++ b/inference-engine/tests_deprecated/helpers/CMakeLists.txt @@ -37,7 +37,14 @@ function(add_helpers target_name) endfunction() add_helpers(${TARGET_NAME}) + target_link_libraries(${TARGET_NAME} PUBLIC commonTestUtils) add_helpers(${TARGET_NAME}_s USE_STATIC_IE) + target_link_libraries(${TARGET_NAME}_s PUBLIC commonTestUtils_s) + +if (ENABLE_DATA) + add_dependencies(${TARGET_NAME} data) + add_dependencies(${TARGET_NAME}_s data) +endif() diff --git a/inference-engine/tests_deprecated/helpers/single_layer_common.hpp b/inference-engine/tests_deprecated/helpers/single_layer_common.hpp index 622af44e8b3..7b23da41a5c 100644 --- a/inference-engine/tests_deprecated/helpers/single_layer_common.hpp +++ b/inference-engine/tests_deprecated/helpers/single_layer_common.hpp @@ -5,6 +5,7 @@ #pragma once #include +#include #include #include #include @@ -75,9 +76,8 @@ struct MapStrStr { }; template -inline InferenceEngine::details::CNNNetworkImplPtr -buildSingleLayerNetworkCommon(InferenceEngine::details::IFormatParser *parser, - const std::string &layerType, +inline InferenceEngine::CNNNetwork +buildSingleLayerNetworkCommon(const std::string &layerType, const CommonTestUtils::InOutShapes &inOutShapes, std::map *params, const std::string &layerDataName = "data", @@ -85,8 +85,6 @@ buildSingleLayerNetworkCommon(InferenceEngine::details::IFormatParser *parser, size_t weightsSize = 0, size_t biasesSize = 0, const InferenceEngine::TBlob::Ptr &weights = nullptr) { - IE_ASSERT(parser); - testing::XMLHelper xmlHelper(parser); std::string precisionStr = precision.name(); auto netBuilder = CommonTestUtils::XmlNetBuilder::buildNetworkWithOneInput("Mock", inOutShapes.inDims[0], precisionStr); @@ -105,10 +103,9 @@ buildSingleLayerNetworkCommon(InferenceEngine::details::IFormatParser *parser, } else { testContent = netBuilder.finish(); } - xmlHelper.loadContent(testContent); - auto result = xmlHelper.parseWithReturningNetwork(); - if (weights) xmlHelper.setWeights(weights); - return result; + + InferenceEngine::Core ie; + return ie.ReadNetwork(testContent, weights); } void GenRandomDataCommon(InferenceEngine::Blob::Ptr blob); diff --git a/inference-engine/tests_deprecated/helpers/tests_utils.hpp b/inference-engine/tests_deprecated/helpers/tests_utils.hpp deleted file mode 100644 index eed7e142a68..00000000000 --- a/inference-engine/tests_deprecated/helpers/tests_utils.hpp +++ /dev/null @@ -1,26 +0,0 @@ -// Copyright (C) 2018-2020 Intel Corporation -// SPDX-License-Identifier: Apache-2.0 -// - -/** -* \brief TODO: short file description -* \file file_utils.h -*/ -#pragma once - -#include -#include - -namespace { - bool strContains(const std::string & str, const std::string & substr) { - return str.find(substr) != std::string::npos; - } - bool strDoesnotContain(const std::string & str, const std::string & substr) { - (void)strDoesnotContain; // to overcome unused warning - return !strContains(str, substr); - } -} - -#define ASSERT_STR_CONTAINS(str, substr) ASSERT_PRED2(&strContains, str, substr) -#define ASSERT_STR_DOES_NOT_CONTAIN(str, substr) ASSERT_PRED2 (&strDoesnotContain, str, substr) -#define EXPECT_STR_CONTAINS(str, substr) EXPECT_PRED2(&strContains, str, substr) \ No newline at end of file diff --git a/inference-engine/tests_deprecated/mock_engine/mock_plugin.cpp b/inference-engine/tests_deprecated/mock_engine/mock_plugin.cpp index 2012c8b15af..ae2ee0117ab 100644 --- a/inference-engine/tests_deprecated/mock_engine/mock_plugin.cpp +++ b/inference-engine/tests_deprecated/mock_engine/mock_plugin.cpp @@ -37,6 +37,7 @@ void MockPlugin::SetLogCallback(InferenceEngine::IErrorListener &listener) noexc } void MockPlugin::GetVersion(const Version *&versionInfo) noexcept { + versionInfo = &version; } StatusCode MockPlugin::AddExtension(IExtensionPtr extension, InferenceEngine::ResponseDesc *resp) noexcept { diff --git a/inference-engine/tests_deprecated/mock_engine/mock_plugin.hpp b/inference-engine/tests_deprecated/mock_engine/mock_plugin.hpp index c6e38f6c8fc..abf793431de 100644 --- a/inference-engine/tests_deprecated/mock_engine/mock_plugin.hpp +++ b/inference-engine/tests_deprecated/mock_engine/mock_plugin.hpp @@ -14,6 +14,7 @@ IE_SUPPRESS_DEPRECATED_START class MockPlugin : public InferenceEngine::IInferencePlugin { InferenceEngine::IInferencePlugin * _target = nullptr; + InferenceEngine::Version version; public: explicit MockPlugin(InferenceEngine::IInferencePlugin*target); diff --git a/inference-engine/tests_deprecated/unit/CMakeLists.txt b/inference-engine/tests_deprecated/unit/CMakeLists.txt index dac4a97d0d7..0f6a70005f2 100644 --- a/inference-engine/tests_deprecated/unit/CMakeLists.txt +++ b/inference-engine/tests_deprecated/unit/CMakeLists.txt @@ -167,7 +167,7 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fuse-ld=gold") endif() -add_test(NAME ${TARGET_NAME} - COMMAND ${TARGET_NAME}) +add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME}) +set_property(TEST ${TARGET_NAME} PROPERTY LABELS IE) add_dependencies(${TARGET_NAME} mock_engine) diff --git a/inference-engine/tests_deprecated/unit/cnn_network/cnn_net_reader_impl_test.cpp b/inference-engine/tests_deprecated/unit/cnn_network/cnn_net_reader_impl_test.cpp index 1ffba477fa8..7c940a6fe06 100644 --- a/inference-engine/tests_deprecated/unit/cnn_network/cnn_net_reader_impl_test.cpp +++ b/inference-engine/tests_deprecated/unit/cnn_network/cnn_net_reader_impl_test.cpp @@ -3,7 +3,6 @@ // #include -#include #include #include #include "cnn_network_impl.hpp" diff --git a/inference-engine/tests_deprecated/unit/engines/vpu/eliminate_copy_tests.cpp b/inference-engine/tests_deprecated/unit/engines/vpu/eliminate_copy_tests.cpp index eeccb27c3aa..6fa314b58c5 100644 --- a/inference-engine/tests_deprecated/unit/engines/vpu/eliminate_copy_tests.cpp +++ b/inference-engine/tests_deprecated/unit/engines/vpu/eliminate_copy_tests.cpp @@ -89,6 +89,6 @@ TEST_F(VPU_EliminateCopyTest, OneInputTwoConcats) { ASSERT_EQ(hwOutput->parentData(), outputCopy1); ASSERT_EQ(hwOutput->numConsumers(), 2); - ASSERT_TRUE(contains(hwOutput->consumers(), [](const Stage& stage) { return stage->type() == StageType::Concat; })); + ASSERT_TRUE(contains(hwOutput->consumers(), [](const Stage& stage) { return stage->type() == StageType::StubConcat; })); ASSERT_TRUE(contains(hwOutput->consumers(), [](const Stage& stage) { return stage->type() == StageType::Copy; })); } diff --git a/inference-engine/tests_deprecated/unit/graph_tools/graph_copy_tests.cpp b/inference-engine/tests_deprecated/unit/graph_tools/graph_copy_tests.cpp index 21370f4d68d..0541458b06d 100644 --- a/inference-engine/tests_deprecated/unit/graph_tools/graph_copy_tests.cpp +++ b/inference-engine/tests_deprecated/unit/graph_tools/graph_copy_tests.cpp @@ -4,7 +4,7 @@ #include #include -#include "test_assertions.hpp" +#include #include #include #include diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/cnn_network_test.cpp b/inference-engine/tests_deprecated/unit/inference_engine_tests/cnn_network_test.cpp index f9ed79c1289..a3efb8944de 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/cnn_network_test.cpp +++ b/inference-engine/tests_deprecated/unit/inference_engine_tests/cnn_network_test.cpp @@ -7,19 +7,69 @@ using namespace InferenceEngine; -class CNNNetworkTests : public ::testing::Test { -protected: - virtual void TearDown() { - } - - virtual void SetUp() { - } - -public: - -}; +using CNNNetworkTests = ::testing::Test; TEST_F(CNNNetworkTests, throwsOnInitWithNull) { std::shared_ptr nlptr = nullptr; ASSERT_THROW(CNNNetwork network(nlptr), InferenceEngine::details::InferenceEngineException); } + +TEST_F(CNNNetworkTests, throwsOnInitWithNullNgraph) { + std::shared_ptr nlptr = nullptr; + ASSERT_THROW(CNNNetwork network(nlptr), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedGetPrecision) { + CNNNetwork network; + ASSERT_THROW(network.getPrecision(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedGetOutputsInfo) { + CNNNetwork network; + ASSERT_THROW(network.getOutputsInfo(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedGetInputsInfo) { + CNNNetwork network; + ASSERT_THROW(network.getInputsInfo(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedLayerCount) { + CNNNetwork network; + ASSERT_THROW(network.layerCount(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedGetName) { + CNNNetwork network; + ASSERT_THROW(network.getName(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedCastToICNNNetwork) { + CNNNetwork network; + ASSERT_THROW(auto & net = static_cast(network), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnConstUninitializedCastToICNNNetwork) { + const CNNNetwork network; + ASSERT_THROW(const auto & net = static_cast(network), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnUninitializedGetFunction) { + CNNNetwork network; + ASSERT_THROW(network.getFunction(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnConstUninitializedGetFunction) { + const CNNNetwork network; + ASSERT_THROW(network.getFunction(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnConstUninitializedBegin) { + CNNNetwork network; + ASSERT_THROW(network.getFunction(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(CNNNetworkTests, throwsOnConstUninitializedGetInputShapes) { + CNNNetwork network; + ASSERT_THROW(network.getInputShapes(), InferenceEngine::details::InferenceEngineException); +} diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/async_infer_request_tests.cpp b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/async_infer_request_tests.cpp index d26ba87d372..311cc34e229 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/async_infer_request_tests.cpp +++ b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/async_infer_request_tests.cpp @@ -291,3 +291,76 @@ TEST_F(InferRequestTests, failToSetInputWithEmptyDimensions) { auto exceptionMessage = getExceptionMessage([&]() { InferRequest->SetInput(blobMap); }); ASSERT_EQ(_inputDataIsEmptyError, exceptionMessage.substr(0, _inputDataIsEmptyError.size())); } + +using InferRequestCPPTests = ::testing::Test; + +TEST_F(InferRequestCPPTests, throwsOnInitWithNull) { + IInferRequest::Ptr nlptr = nullptr; + ASSERT_THROW(InferRequest req(nlptr), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedSetBlob) { + InferRequest req; + ASSERT_THROW(req.SetBlob({}, {}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedGetBlob) { + InferRequest req; + ASSERT_THROW(req.GetBlob({}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedSetBlobPreproc) { + InferRequest req; + ASSERT_THROW(req.SetBlob({}, {}, {}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedGetPreProcess) { + InferRequest req; + ASSERT_THROW(req.GetPreProcess({}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedInfer) { + InferRequest req; + ASSERT_THROW(req.Infer(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedGetPerformanceCounts) { + InferRequest req; + ASSERT_THROW(req.GetPerformanceCounts(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedSetInput) { + InferRequest req; + ASSERT_THROW(req.SetInput({{}}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedSetOutput) { + InferRequest req; + ASSERT_THROW(req.SetOutput({{}}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedSetBatch) { + InferRequest req; + ASSERT_THROW(req.SetBatch({}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedStartAsync) { + InferRequest req; + ASSERT_THROW(req.StartAsync(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedWait) { + InferRequest req; + ASSERT_THROW(req.Wait({}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedSetCompletionCallback) { + InferRequest req; + std::function f; + ASSERT_THROW(req.SetCompletionCallback(f), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(InferRequestCPPTests, throwsOnUninitializedCast) { + InferRequest req; + ASSERT_THROW(auto & ireq = static_cast(req), InferenceEngine::details::InferenceEngineException); +} diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/executable_network_base_tests.cpp b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/executable_network_base_tests.cpp index 35b60936d66..10c97f8856e 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/executable_network_base_tests.cpp +++ b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/executable_network_base_tests.cpp @@ -66,3 +66,65 @@ TEST_F(ExecutableNetworkBaseTests, canCatchUnknownErrorInExport) { EXPECT_CALL(*mock_impl.get(), Export(_)).WillOnce(Throw(5)); ASSERT_EQ(UNEXPECTED, exeNetwork->Export({}, nullptr)); } + +using ExecutableNetworkTests = ::testing::Test; + +TEST_F(ExecutableNetworkTests, throwsOnInitWithNull) { + std::shared_ptr nlptr = nullptr; + ASSERT_THROW(ExecutableNetwork exec(nlptr), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedGetOutputsInfo) { + ExecutableNetwork exec; + ASSERT_THROW(exec.GetOutputsInfo(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedGetInputsInfo) { + ExecutableNetwork exec; + ASSERT_THROW(exec.GetInputsInfo(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedExport) { + ExecutableNetwork exec; + ASSERT_THROW(exec.Export(std::string()), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedExportStream) { + ExecutableNetwork exec; + ASSERT_THROW(exec.Export(std::cout), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, nothrowsOnUninitializedCast) { + ExecutableNetwork exec; + ASSERT_NO_THROW(auto & enet = static_cast(exec)); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedGetExecGraphInfo) { + ExecutableNetwork exec; + ASSERT_THROW(exec.GetExecGraphInfo(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedQueryState) { + ExecutableNetwork exec; + ASSERT_THROW(exec.QueryState(), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedSetConfig) { + ExecutableNetwork exec; + ASSERT_THROW(exec.SetConfig({{}}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedGetConfig) { + ExecutableNetwork exec; + ASSERT_THROW(exec.GetConfig({}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedGetMetric) { + ExecutableNetwork exec; + ASSERT_THROW(exec.GetMetric({}), InferenceEngine::details::InferenceEngineException); +} + +TEST_F(ExecutableNetworkTests, throwsOnUninitializedGetContext) { + ExecutableNetwork exec; + ASSERT_THROW(exec.GetContext(), InferenceEngine::details::InferenceEngineException); +} diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/iinference_plugin_internal_tests.cpp b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/iinference_plugin_internal_tests.cpp index de0694f1201..43981f740e9 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/iinference_plugin_internal_tests.cpp +++ b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/iinference_plugin_internal_tests.cpp @@ -61,7 +61,7 @@ protected: mockNotEmptyNet.getOutputsInfo(outputsInfo); mockInferRequestInternal = make_shared(inputsInfo, outputsInfo); mockExeNetworkTS = make_shared(); - EXPECT_CALL(*mock_plugin_impl.get(), LoadExeNetworkImpl(_, _, _)).WillOnce(Return(mockExeNetworkTS)); + EXPECT_CALL(*mock_plugin_impl.get(), LoadExeNetworkImpl(_, _)).WillOnce(Return(mockExeNetworkTS)); EXPECT_CALL(*mockExeNetworkTS.get(), CreateInferRequestImpl(_, _)).WillOnce(Return(mockInferRequestInternal)); sts = plugin->LoadNetwork(exeNetwork, mockNotEmptyNet, {}, &dsc); ASSERT_EQ((int) StatusCode::OK, sts) << dsc.msg; diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/plugin_base_tests.cpp b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/plugin_base_tests.cpp index 9e457836c04..a63acb4b7e6 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/plugin_base_tests.cpp +++ b/inference-engine/tests_deprecated/unit/inference_engine_tests/cpp_interfaces/plugin_base_tests.cpp @@ -5,6 +5,7 @@ #include #include #include +#include #include "cpp_interfaces/base/ie_plugin_base.hpp" #include "unit_test_utils/mocks/cpp_interfaces/mock_plugin_impl.hpp" @@ -84,3 +85,42 @@ TEST_F(PluginBaseTests, canCatchUnknownErrorInSetConfig) { const std::map config; ASSERT_EQ(UNEXPECTED, plugin->SetConfig(config, nullptr)); } + +using InferencePluginTests = testing::Test; + +TEST_F(InferencePluginTests, throwsOnNullptrCreation) { + InferenceEnginePluginPtr nulptr; + InferencePlugin plugin; + ASSERT_THROW(plugin = InferencePlugin(nulptr), details::InferenceEngineException); +} + +TEST_F(InferencePluginTests, throwsOnUninitializedGetVersion) { + InferencePlugin plg; + ASSERT_THROW(plg.GetVersion(), details::InferenceEngineException); +} + +TEST_F(InferencePluginTests, throwsOnUninitializedLoadNetwork) { + InferencePlugin plg; + QueryNetworkResult r; + ASSERT_THROW(plg.LoadNetwork(CNNNetwork(), {}), details::InferenceEngineException); +} + +TEST_F(InferencePluginTests, throwsOnUninitializedImportNetwork) { + InferencePlugin plg; + ASSERT_THROW(plg.ImportNetwork({}, {}), details::InferenceEngineException); +} + +TEST_F(InferencePluginTests, throwsOnUninitializedAddExtension) { + InferencePlugin plg; + ASSERT_THROW(plg.AddExtension(IExtensionPtr()), details::InferenceEngineException); +} + +TEST_F(InferencePluginTests, throwsOnUninitializedSetConfig) { + InferencePlugin plg; + ASSERT_THROW(plg.SetConfig({{}}), details::InferenceEngineException); +} + +TEST_F(InferencePluginTests, nothrowsUninitializedCast) { + InferencePlugin plg; + ASSERT_NO_THROW(auto plgPtr = static_cast(plg)); +} diff --git a/inference-engine/tests_deprecated/unit/inference_engine_tests/locked_memory_test.cpp b/inference-engine/tests_deprecated/unit/inference_engine_tests/locked_memory_test.cpp index 31cd97885e9..6eccab9dde4 100644 --- a/inference-engine/tests_deprecated/unit/inference_engine_tests/locked_memory_test.cpp +++ b/inference-engine/tests_deprecated/unit/inference_engine_tests/locked_memory_test.cpp @@ -5,23 +5,13 @@ #include "tests_common.hpp" #include "unit_test_utils/mocks/mock_allocator.hpp" -using namespace std; - -class LockedMemoryTest : public TestsCommon { -protected: - unique_ptr createMockAllocator() { - return unique_ptr(new MockAllocator()); - } -}; - using namespace InferenceEngine; using namespace ::testing; +using LockedMemoryTest = testing::Test; TEST_F(LockedMemoryTest, canUnlockMemoryAfterUsage) { - - auto allocator = createMockAllocator(); - + std::unique_ptr allocator(new MockAllocator()); char array [] = {1,2,3}; EXPECT_CALL(*allocator.get(), lock((void*)1, _)).WillRepeatedly(Return((void*)array)); @@ -34,11 +24,8 @@ TEST_F(LockedMemoryTest, canUnlockMemoryAfterUsage) { } } - TEST_F(LockedMemoryTest, canReadFromLockedMemory) { - - auto allocator = createMockAllocator(); - + std::unique_ptr allocator(new MockAllocator()); char array [] = {1,2,3,4,5}; EXPECT_CALL(*allocator.get(), lock((void*)1, _)).WillRepeatedly(Return((void*)array)); @@ -50,11 +37,8 @@ TEST_F(LockedMemoryTest, canReadFromLockedMemory) { } } - TEST_F(LockedMemoryTest, canWriteToLockedMemory) { - - auto allocator = createMockAllocator(); - + std::unique_ptr allocator(new MockAllocator()); char array [] = {1,2,3,4,5}; EXPECT_CALL(*allocator.get(), lock((void*)1, _)).WillRepeatedly(Return((void*)array)); diff --git a/inference-engine/thirdparty/clDNN/api/tensor.hpp b/inference-engine/thirdparty/clDNN/api/tensor.hpp index b662f018951..cc2ddec47ca 100644 --- a/inference-engine/thirdparty/clDNN/api/tensor.hpp +++ b/inference-engine/thirdparty/clDNN/api/tensor.hpp @@ -184,6 +184,7 @@ struct format { g_os_iyx_osv16, ///< format used for weights for 2D convolution g_os_iyx_osv32, ///< format used for weights for 2D convolution gs_oiyx_gsv16, ///< format used for weights for 2D convolution + gs_oizyx_gsv16, ///< format used for weights for 3D convolution gs_oiyx_gsv32, ///< format used for weights for 2D convolution g_is_os_zyx_osv16_isv16, ///< format used for grouped weights for blocked 3D deconvolution g_os_is_yx_osv16_isv4, @@ -191,6 +192,12 @@ struct format { g_os_is_zyx_isv8_osv16_isv2, g_os_is_yx_isv8_osv16_isv2, g_os_is_zyx_isv16_osv16, + g_os_zyx_is_osv16_isv4, ///< format for imad deconvolution + g_os_zyx_is_osv16_isv16, ///< format for imad deconvolution + g_os_zyx_is_osv16_isv32, ///< format for imad deconvolution + g_os_zyx_is_osv32_isv4, ///< format for imad deconvolution + g_os_zyx_is_osv32_isv16, ///< format for imad deconvolution + g_os_zyx_is_osv32_isv32, ///< format for imad deconvolution format_num, ///< number of format types any = -1 @@ -264,6 +271,7 @@ struct format { { os_is_yx_osv32_isv32p, { 1, 1, 1, 0, 0, "bfxy", "bfxy?", {}}}, { os_is_zyx_isv16_osv16, { 1, 1, 3, 0, 0, "bfzyx", "bfxyz", {{0, 16}, {1, 16}}}}, { is_os_zyx_osv16_isv16, { 1, 1, 3, 0, 0, "fbzyx", "bfxyz", {{0, 16}, {1, 16}}}}, + { is_os_yx_osv16_isv16, { 1, 1, 2, 0, 0, "fbyx", "bfxyz", {{0, 16}, {1, 16}}}}, { os_is_osv32_isv32_swizzled_by_4, { 1, 1, 0, 0, 0, "bfxy", "bfxy?", {{0, 32}, {1, 32}}}}, { os_is_zyx_isv8_osv16_isv2, { 1, 1, 3, 0, 0, "bfzyx", "bfxyz", {{1, 8}, {0, 16}, {1, 2}}}}, { os_zyxi_osv16, { 1, 1, 3, 0, 0, "bzyxf", "bfxyz", {{0, 16}}}}, @@ -275,6 +283,7 @@ struct format { { g_os_iyx_osv16, { 1, 1, 2, 0, 1, "gbfyx", "bfxy????g", {{0, 16}}}}, { g_os_iyx_osv32, { 1, 1, 2, 0, 1, "gbfyx", "bfxy????g", {{0, 32}}}}, { gs_oiyx_gsv16, { 1, 1, 2, 0, 1, "gbfyx", "bfxy????g", {{8, 16}}}}, + { gs_oizyx_gsv16, { 1, 1, 3, 0, 1, "gbfzyx", "bfxyz???g", {{8, 16}}}}, { gs_oiyx_gsv32, { 1, 1, 2, 0, 1, "gbfyx", "bfxy????g", {{8, 32}}}}, { gyxio, { 1, 1, 2, 0, 1, "gyxfb", "bfxy????g", {}}}, { g_is_os_zyx_osv16_isv16, { 1, 1, 3, 0, 1, "gfbzyx", "bfxyz???g", {{0, 16}, {1, 16}}}}, @@ -283,6 +292,12 @@ struct format { { g_os_is_yx_isv8_osv16_isv2, { 1, 1, 2, 0, 1, "gbfyx", "bfxy????g", {{1, 8}, {0, 16}, {1, 2}}}}, { g_os_is_zyx_isv16_osv16, { 1, 1, 3, 0, 1, "gbfzyx", "bfxyz???g", {{0, 16}, {1, 16}}}}, { g_os_is_yx_osv16_isv4, { 1, 1, 2, 0, 1, "gbfxy", "bfxy????g", {{0, 16}, {1, 4}}}}, + { g_os_zyx_is_osv16_isv4, { 1, 1, 3, 0, 1, "gbzyxi", "bfxyz???g", {{0, 16}, {1, 4}}}}, + { g_os_zyx_is_osv16_isv16, { 1, 1, 3, 0, 1, "gbzyxi", "bfxyz???g", {{0, 16}, {1, 16}}}}, + { g_os_zyx_is_osv16_isv32, { 1, 1, 3, 0, 1, "gbzyxi", "bfxyz???g", {{0, 16}, {1, 32}}}}, + { g_os_zyx_is_osv32_isv4, { 1, 1, 3, 0, 1, "gbzyxi", "bfxyz???g", {{0, 32}, {1, 4}}}}, + { g_os_zyx_is_osv32_isv16, { 1, 1, 3, 0, 1, "gbzyxi", "bfxyz???g", {{0, 32}, {1, 16}}}}, + { g_os_zyx_is_osv32_isv32, { 1, 1, 3, 0, 1, "gbzyxi", "bfxyz???g", {{0, 32}, {1, 32}}}}, }; return traits.at(fmt); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp index cb29d22ff95..02ade1b5b6d 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp @@ -129,6 +129,12 @@ WeightsTensor::WeightsChannelArray WeightsTensor::weightsChannelArray {{ { WeightsLayout::gs_oi_yxs_gsv16_yxsv4, { 0, 1, -1, 2, 3, -1, -1, 4 } }, { WeightsLayout::gs_oi_yxs_gsv32_yxsv4, { 0, 1, -1, 2, 3, -1, -1, 4 } }, { WeightsLayout::g_os_is_yx_osv16_isv4, { 0, 1, -1, 2, 3, -1, -1, 4 } }, + { WeightsLayout::g_os_zyx_is_osv16_isv4, { 1, 2, 3, 0, 4, -1, -1, 5 } }, + { WeightsLayout::g_os_zyx_is_osv16_isv16, { 1, 2, 3, 0, 4, -1, -1, 5 } }, + { WeightsLayout::g_os_zyx_is_osv16_isv32, { 1, 2, 3, 0, 4, -1, -1, 5 } }, + { WeightsLayout::g_os_zyx_is_osv32_isv4, { 1, 2, 3, 0, 4, -1, -1, 5 } }, + { WeightsLayout::g_os_zyx_is_osv32_isv16, { 1, 2, 3, 0, 4, -1, -1, 5 } }, + { WeightsLayout::g_os_zyx_is_osv32_isv32, { 1, 2, 3, 0, 4, -1, -1, 5 } }, }}; NDims DataTensor::GetSimpleDims(const std::vector& d, DataLayout l) { @@ -638,6 +644,30 @@ NDims WeightsTensor::GetSimpleDims(const std::vector& d, WeightsLayout l newDims[2] = RoundUp(newDims[2], 4); newDims[3] = RoundUp(newDims[3], 16); break; + case g_os_zyx_is_osv16_isv4: + newDims[0] = RoundUp(newDims[0], 4); + newDims[4] = RoundUp(newDims[4], 16); + break; + case g_os_zyx_is_osv16_isv16: + newDims[0] = RoundUp(newDims[0], 16); + newDims[4] = RoundUp(newDims[4], 16); + break; + case g_os_zyx_is_osv16_isv32: + newDims[0] = RoundUp(newDims[0], 32); + newDims[4] = RoundUp(newDims[4], 16); + break; + case g_os_zyx_is_osv32_isv4: + newDims[0] = RoundUp(newDims[0], 4); + newDims[4] = RoundUp(newDims[4], 32); + break; + case g_os_zyx_is_osv32_isv16: + newDims[0] = RoundUp(newDims[0], 16); + newDims[4] = RoundUp(newDims[4], 32); + break; + case g_os_zyx_is_osv32_isv32: + newDims[0] = RoundUp(newDims[0], 32); + newDims[4] = RoundUp(newDims[4], 32); + break; default: break; } @@ -760,6 +790,12 @@ WeightsTensor WeightsTensor::TransformIgnorePadding(WeightsLayout l, WeightsType vec[Channelndex(l, WeightsChannelName::OFM)] = OFM().v; vec[Channelndex(l, WeightsChannelName::LX)] = LX().v; vec[Channelndex(l, WeightsChannelName::LY)] = LY().v; + } else if (src_channels == 4 && dst_channels == 5) { + vec[Channelndex(l, WeightsChannelName::X)] = X().v; + vec[Channelndex(l, WeightsChannelName::Y)] = Y().v; + vec[Channelndex(l, WeightsChannelName::Z)] = 1; + vec[Channelndex(l, WeightsChannelName::IFM)] = IFM().v; + vec[Channelndex(l, WeightsChannelName::OFM)] = OFM().v; } else { assert(0); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h index 4932bbcec4b..7b7064fe2ed 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h @@ -147,6 +147,14 @@ enum WeightsLayout { gs_oi_yxs_gsv32_yxsv4, // grouped weights for depthwise IMAD convolution (b_fs_yx_fsv32 format) g_os_is_yx_osv16_isv4, + + g_os_zyx_is_osv16_isv4, + g_os_zyx_is_osv16_isv16, + g_os_zyx_is_osv16_isv32, + g_os_zyx_is_osv32_isv4, + g_os_zyx_is_osv32_isv16, + g_os_zyx_is_osv32_isv32, + WeightsLayoutCount // NUMBER OF ELEMENTS IN ENUM }; @@ -212,32 +220,7 @@ inline bool SimpleLayout(DataLayout l) { } } -inline bool GroupedLayout(WeightsLayout l) { - switch (l) { - case WeightsLayout::goiyx: - case WeightsLayout::goizyx: - case WeightsLayout::g_os_iyx_osv16: - case WeightsLayout::g_os_iyx_osv32: - case WeightsLayout::gs_oiyx_gsv16: - case WeightsLayout::gs_oizyx_gsv16: - case WeightsLayout::gs_oiyx_gsv32: - case WeightsLayout::g_os_iyx_osv16_rotate_180: - case WeightsLayout::gyxio: - case WeightsLayout::gi_yxs_os_yxsv2_osv16: - case WeightsLayout::g_is_os_zyx_osv16_isv16: - case WeightsLayout::g_is_os_yx_osv16_isv16: - case WeightsLayout::g_os_is_zyx_isv8_osv16_isv2: - case WeightsLayout::g_os_is_yx_isv8_osv16_isv2: - case WeightsLayout::g_os_is_zyx_isv16_osv16: - case WeightsLayout::giy_xs_os_xsv2_osv16__ao32: - case WeightsLayout::giy_xs_os_xsv2_osv8__ao32: - case WeightsLayout::gs_oi_yxs_gsv4_yxsv4: - case WeightsLayout::g_os_is_yx_osv16_isv4: - return true; - default: - return false; - } -} +inline bool GroupedLayout(WeightsLayout l); inline bool GroupedLayout(DataLayout) { return false; @@ -588,5 +571,10 @@ private: static WeightsChannelArray weightsChannelArray; static NDims GetSimpleDims(const std::vector& d, WeightsLayout l); }; + +inline bool GroupedLayout(WeightsLayout l) { + return WeightsTensor::DoesGroupDimExist(l); +} + } // namespace Tensor } // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp index 41ab10199db..e85a6e0eaaf 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_base.cpp @@ -26,17 +26,14 @@ ActivationKernelBase::DispatchData ActivationKernelBase::SetDefault(const activa DispatchData runInfo; std::vector global; std::vector local; - if (out.GetLayout() == DataLayout::bfzyx) { - global = {out.X().v, out.Y().v * out.Z().v, out.Feature().v * out.Batch().v}; - local = GetOptimalLocalWorkGroupSizes(global, arg.engineInfo); - } else if (out.GetLayout() == DataLayout::yxfb) { + if (out.GetLayout() == DataLayout::yxfb) { global = {out.Feature().v * out.Batch().v, out.X().v, out.Y().v}; local = GetOptimalLocalWorkGroupSizes(global, arg.engineInfo); } else if (out.GetLayout() == DataLayout::b_fs_yx_fsv16) { global = {Align(out.Feature().v, 16) * out.Batch().v, out.X().v, out.Y().v}; local = {16, 1, 1}; } else { - global = {out.X().v, out.Y().v, out.Feature().v * out.Batch().v}; + global = {out.X().v, out.Y().v * out.Z().v, out.Feature().v * out.Batch().v}; local = GetOptimalLocalWorkGroupSizes(global, arg.engineInfo); } @@ -77,6 +74,12 @@ bool ActivationKernelBase::Validate(const Params& p, const optional_params& o) c o.GetType() != KernelType::ACTIVATION) { return false; } + const activation_params& orgParams = static_cast(p); + + for (auto& fused_op : orgParams.fused_ops) { + if (!IsFusedPrimitiveSupported(fused_op)) + return false; + } return true; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.cpp index 29d7742a974..f48b0e275fe 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.cpp @@ -28,6 +28,7 @@ ParamsKey ActivationKernelOpt::GetSupportedKey() const { k.EnableOutputDataType(Datatype::INT32); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); + k.EnableDifferentTypes(); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); k.EnableTensorOffset(); @@ -75,34 +76,33 @@ bool ActivationKernelOpt::Validate(const Params& p, const optional_params& o) co return false; } - if (params.gradient) { - if (params.inputs[0].GetLayout() != params.inputs[1].GetLayout()) - return false; - } + if (params.output.GetLayout() != params.inputs[0].GetLayout()) + return false; - // Opt kernel supports fused activations without extra inputs, since - // it can't calculate correct offset for tensors with different layout. - for (auto& op : params.fused_ops) { - if (!op.tensors.empty()) { - for (auto& t : op.tensors) { - if (!(t == params.inputs[0])) - return false; - } - } - } + if (!params.fused_ops.empty() && params.output.GetLayout() != DataLayout::bfyx && + params.output.GetLayout() != DataLayout::bfzyx) + return false; return true; } JitConstants ActivationKernelOpt::GetJitConstants(const activation_params& params, DispatchData kd) const { auto jit = ActivationKernelBase::GetJitConstants(params, kd); + auto input_dt = params.inputs[0].GetDType(); jit.AddConstant(MakeJitConstant("NUM_COLS_WI", NUM_COLS_WI)); if (!params.fused_ops.empty()) { - auto input_dt = GetUnitType(params); - FusedOpsConfiguration conf = {"", {"x"}, "v", input_dt, 4, LoadType::LT_UNALIGNED, BoundaryCheck::DISABLED, IndexType::LINEAR_OFFSET }; + std::vector idx_order; + if (params.inputs[0].GetDims().size() <= 4) { + idx_order = {"fo_b", "fo_f", "fo_y", "fo_x"}; + } else if (params.inputs[0].GetDims().size() == 5) { + idx_order = {"fo_b", "fo_f", "fo_z", "fo_y", "fo_x"}; + } + FusedOpsConfiguration conf = + {"", idx_order, "v", input_dt, 4, LoadType::LT_UNALIGNED, BoundaryCheck::DISABLED, IndexType::TENSOR_COORD}; jit.Merge(MakeFusedOpsJitConstants(params, {conf})); } + jit.Merge(MakeActivationJitConstants(params.activations, input_dt, "_KERNEL")); return jit; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.h index 67a2a7c59af..e2db8123f26 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_opt.h @@ -17,6 +17,8 @@ #include "activation_kernel_base.h" +#include + namespace kernel_selector { class ActivationKernelOpt : public ActivationKernelBase { public: @@ -32,5 +34,8 @@ protected: DispatchData SetDefault(const activation_params& arg) const override; bool Validate(const Params& p, const optional_params& o) const override; JitConstants GetJitConstants(const activation_params& params, DispatchData kd) const override; + std::vector GetSupportedFusedOps() const override { + return { FusedOpType::QUANTIZE }; + } }; } // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.cpp index 41f9729cbdc..cc3231946a7 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.cpp @@ -31,6 +31,7 @@ ParamsKey ActivationKernelRef::GetSupportedKey() const { k.EnableOutputDataType(Datatype::UINT8); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); + k.EnableDifferentTypes(); k.EnableActivationAdditionalParamsAsInput(); k.EnableAllInputLayout(); k.EnableAllOutputLayout(); @@ -43,13 +44,20 @@ ParamsKey ActivationKernelRef::GetSupportedKey() const { JitConstants ActivationKernelRef::GetJitConstants(const activation_params& params, DispatchData kd) const { auto jit = ActivationKernelBase::GetJitConstants(params, kd); + auto input_dt = params.inputs[0].GetDType(); if (!params.fused_ops.empty()) { - auto input_dt = GetUnitType(params); - FusedOpsConfiguration conf = {"", {"batch", "feature", "y", "x"}, "dst", input_dt, 1 }; + std::vector idx_order; + if (params.inputs[0].GetDims().size() <= 4) { + idx_order = {"batch", "feature", "y", "x"}; + } else if (params.inputs[0].GetDims().size() == 5) { + idx_order = {"batch", "feature", "z", "y", "x"}; + } + FusedOpsConfiguration conf = {"", idx_order, "dst", input_dt, 1}; jit.Merge(MakeFusedOpsJitConstants(params, {conf})); } + jit.Merge(MakeActivationJitConstants(params.activations, input_dt, "_KERNEL")); return jit; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.h index a0d8850fb50..ad15f0a5fae 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/activation/activation_kernel_ref.h @@ -17,6 +17,8 @@ #include "activation_kernel_base.h" +#include + namespace kernel_selector { class ActivationKernelRef : public ActivationKernelBase { public: @@ -26,5 +28,8 @@ public: KernelsData GetKernelsData(const Params& params, const optional_params& options) const override; ParamsKey GetSupportedKey() const override; JitConstants GetJitConstants(const activation_params& params, DispatchData kd) const override; + std::vector GetSupportedFusedOps() const override { + return { FusedOpType::QUANTIZE }; + } }; } // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp index aadb1c38390..b0284dbaf56 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_1x1.cpp @@ -118,8 +118,9 @@ bool ConvolutionKernel_b_fs_yx_fsv16_1x1::Validate(const Params& p, const option output.X().v != input.X().v || output.Y().v != input.Y().v || output.Feature().v % 16 != 0; const bool bFilterSize = params.filterSize.x != 1 || params.filterSize.y != 1; const bool bStride = params.stride.x != 1 || params.stride.y != 1; + const bool bPadding = input.Feature().pad.before % feature_block_size != 0 || output.Feature().pad.before % feature_block_size != 0; - if (bOutputSizes || bFilterSize || bStride) { + if (bOutputSizes || bFilterSize || bStride || bPadding) { return false; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_depthwise.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_depthwise.cpp index 05f35c51c1f..16782e098e8 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_depthwise.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_yx_fsv16_depthwise.cpp @@ -55,6 +55,10 @@ bool ConvolutionKernel_b_fs_yx_fsv16_depthwise::Validate(const Params& p, const if (cp.inputs[0].Feature().v != cp.groups || cp.output.Feature().v != cp.groups) return false; + // Check that padding features doesn't miss-align the blocks + if (cp.inputs[0].Feature().pad.before % feature_block_size != 0 || cp.output.Feature().pad.before % feature_block_size != 0) + return false; + return true; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_zyx_fsv16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_zyx_fsv16.cpp index 4e26fc000d8..756269686a6 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_zyx_fsv16.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_b_fs_zyx_fsv16.cpp @@ -33,7 +33,7 @@ FusedOpsConfiguration GenerateFusedOpsConfiguration_f16(size_t conf_id, std::str if (is_vector) idx_order = {"(mb)", "(oc*OC_BLOCK + g*OC)", "od", "oh", "(ow + " + std::to_string(conf_id * 8) + ")"}; else - idx_order = {"(mb)", "(oc*OC_BLOCK + g*OC + local_id)", "od", "oh", "(ow + i)"}; + idx_order = {"(mb)", "(oc*OC_BLOCK + g*OC + local_id)", "od", "oh", "(ow + " + std::to_string(conf_id * 8) + " + i)"}; return { suffix, idx_order, diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp index d2167508d07..b15ad49c4cd 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_base.cpp @@ -232,7 +232,8 @@ KernelsData ConvolutionKernelBase::GetCommonKernelsData(const Params& params, GetPreferredWeightsLayout(newParams), kd.weightsReorderParams, GetSupportedKey(), - newParams.groups); + newParams.groups, + newParams.transposed); if (!succeed) { return {}; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp index a15d21d5d8d..eb641bc3c27 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16.cpp @@ -63,7 +63,6 @@ ParamsKey ConvolutionKernel_bfyx_os_iyx_osv16::GetSupportedKey() const { k.EnableBatching(); k.EnableSplitSupport(); k.EnableDilation(); - k.EnableTranspose(); k.EnableGroupedConvolution(); return k; } @@ -237,11 +236,7 @@ JitConstants ConvolutionKernel_bfyx_os_iyx_osv16::GetJitConstants(const convolut WeightsLayout ConvolutionKernel_bfyx_os_iyx_osv16::GetPreferredWeightsLayout( const convolution_params ¶ms) const { - if (!params.transposed) { - return (params.groups > 1) ? WeightsLayout::g_os_iyx_osv16 : WeightsLayout::os_iyx_osv16; - } else { - return (params.groups > 1) ? WeightsLayout::g_os_iyx_osv16_rotate_180 : WeightsLayout::os_iyx_osv16_rotate_180; - } + return (params.groups > 1) ? WeightsLayout::g_os_iyx_osv16 : WeightsLayout::os_iyx_osv16; } KernelsData ConvolutionKernel_bfyx_os_iyx_osv16::GetKernelsData(const Params& params, diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16_2_sg.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16_2_sg.cpp index 047148cebbb..152fb25241e 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16_2_sg.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_bfyx_os_iyx_osv16_2_sg.cpp @@ -63,7 +63,6 @@ ParamsKey ConvolutionKernel_bfyx_os_iyx_osv16_2_sg::GetSupportedKey() const { k.EnableBatching(); k.EnableSplitSupport(); k.EnableDilation(); - k.EnableTranspose(); return k; } @@ -235,11 +234,7 @@ JitConstants ConvolutionKernel_bfyx_os_iyx_osv16_2_sg::GetJitConstants(const con WeightsLayout ConvolutionKernel_bfyx_os_iyx_osv16_2_sg::GetPreferredWeightsLayout( const convolution_params ¶ms) const { - if (!params.transposed) { - return WeightsLayout::os_iyx_osv16; - } else { - return WeightsLayout::os_iyx_osv16_rotate_180; - } + return params.groups == 1 ? WeightsLayout::os_iyx_osv16 : WeightsLayout::g_os_iyx_osv16; } KernelsData ConvolutionKernel_bfyx_os_iyx_osv16_2_sg::GetKernelsData(const Params& params, @@ -265,4 +260,4 @@ KernelsData ConvolutionKernel_bfyx_os_iyx_osv16_2_sg::GetKernelsDataForAutoTune( return res; } -} // namespace kernel_selector \ No newline at end of file +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_imad.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_imad.cpp index 20e2c9d2c2e..53ed2c97d25 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_imad.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_kernel_imad.cpp @@ -194,7 +194,7 @@ bool ConvolutionKernel_imad::Validate(const Params& params, const optional_param } auto& newParams = static_cast(params); - if ((newParams.inputs[0].Feature().v / newParams.groups) % 4 != 0) + if (newParams.groups > 1 && newParams.weights.IFM().v % 4 != 0) return false; size_t min_block_size_x = (newParams.weights.X().v - 1) * newParams.dilation.x + 1; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_params.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_params.cpp index 2e253bea912..44bd574cc91 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_params.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/convolution/convolution_params.cpp @@ -67,10 +67,6 @@ ParamsKey convolution_params::GetParamsKey() const { k.EnableDepthwiseSeparableOpt(); } - if (transposed) { - k.EnableTranspose(); - } - if (local_convolution) { k.EnableLocalConvolution(); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.cpp index 2b5c961cd04..dbbc4a9538c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.cpp @@ -52,13 +52,16 @@ DeconvolutionKernelBase::DispatchData DeconvolutionKernel_b_fs_zyx_fsv16::SetDef const auto& out = params.output; + bool ver_bsv16_fsv16 = params.output.GetLayout() == DataLayout::bs_fs_yx_bsv16_fsv16 + || params.output.GetLayout() == DataLayout::bs_fs_zyx_bsv16_fsv16; + auto x = out.X().v; auto y = out.Y().v; auto z = out.Z().v; auto f = Align(out.Feature().v, 16); auto b = out.Batch().v; - if (out.Batch().v % 16 == 0) { + if (ver_bsv16_fsv16) { if (params.depthwise_separable_opt) { kd.gws0 = x * y * z; kd.gws1 = f; @@ -120,6 +123,17 @@ bool DeconvolutionKernel_b_fs_zyx_fsv16::Validate(const Params& p, const optiona if (!DeconvolutionKernelBase::Validate(p, o)) { return false; } + auto& deconv_params = static_cast(p); + + if (deconv_params.output.GetLayout() != deconv_params.inputs[0].GetLayout()) + return false; + + const auto& params = static_cast(p); + const auto feature_block_size = 16; + + // Check that padding features doesn't miss-align the blocks + if (params.inputs[0].Feature().pad.before % feature_block_size != 0 || params.output.Feature().pad.before % feature_block_size != 0) + return false; return true; } @@ -129,7 +143,10 @@ JitConstants DeconvolutionKernel_b_fs_zyx_fsv16::GetJitConstants(const deconvolu auto output = params.output; auto jit = Parent::GetJitConstants(params); - if (output.Batch().v % 16 == 0) { + bool ver_bsv16_fsv16 = params.output.GetLayout() == DataLayout::bs_fs_yx_bsv16_fsv16 + || params.output.GetLayout() == DataLayout::bs_fs_zyx_bsv16_fsv16; + + if (ver_bsv16_fsv16) { jit.AddConstant(MakeJitConstant("VER_16MB16C", 1)); } else { jit.AddConstant(MakeJitConstant("VER_8OW16C", 1)); @@ -150,7 +167,7 @@ JitConstants DeconvolutionKernel_b_fs_zyx_fsv16::GetJitConstants(const deconvolu icb /= 2; } - if (output.Batch().v % 16 == 0) { + if (ver_bsv16_fsv16) { mb_block = 16; jit.AddConstant(MakeJitConstant("MB_BLOCK", mb_block)); jit.AddConstant(MakeJitConstant("IC_BLOCK", ic_block)); @@ -218,6 +235,47 @@ JitConstants DeconvolutionKernel_b_fs_zyx_fsv16::GetJitConstants(const deconvolu jit.AddConstant(MakeJitConstant("LWS_1", runInfo.lws1)); jit.AddConstant(MakeJitConstant("LWS_2", runInfo.lws2)); + if (!params.fused_ops.empty()) { + auto fused_dt = GetActivationType(params); + std::vector idx_order_block_c00; + std::vector idx_order_block_c01; + std::vector idx_order_block_ci; + + if (params.output.Dimentions() <= 4) { + idx_order_block_c00 = { "mb", "(g * IC + gic * IC_BLOCK)", "ih", "iw" }; + idx_order_block_c01 = { "(mb + 8)", "(g * IC + gic * IC_BLOCK)", "ih", "iw" }; + idx_order_block_ci = { "mb", "(g * IC + gic * IC_BLOCK)", "ih", "(iw + i)" }; + } else { + idx_order_block_c00 = { "mb", "(g * IC + gic * IC_BLOCK)", "id", "ih", "iw" }; + idx_order_block_c01 = { "(mb + 8)", "(g * IC + gic * IC_BLOCK)", "id", "ih", "iw" }; + idx_order_block_ci = { "mb", "(g * IC + gic * IC_BLOCK)", "id", "ih", "(iw + i)" }; + } + + FusedOpsConfiguration conf_c00 = { + "_BLOCK_C00", + idx_order_block_c00, + "blockC00", + fused_dt, + 8, + LoadType::LT_ALIGNED_READ, + BoundaryCheck::ENABLED, + IndexType::TENSOR_COORD, + Tensor::DataChannelName::BATCH }; + FusedOpsConfiguration conf_c01 = { + "_BLOCK_C01", + idx_order_block_c01, + "blockC01", + fused_dt, + 8, + LoadType::LT_ALIGNED_READ, + BoundaryCheck::ENABLED, + IndexType::TENSOR_COORD, + Tensor::DataChannelName::BATCH }; + FusedOpsConfiguration conf_ci = { "_BLOCK_CI", idx_order_block_ci, "blockC00[i]", fused_dt, 1, LoadType::LT_ALIGNED_READ }; + + jit.Merge(MakeFusedOpsJitConstants(params, { conf_c00, conf_c01, conf_ci })); + } + return jit; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.h index ba7bd58a45c..7785877a3b9 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16.h @@ -31,7 +31,7 @@ public: protected: WeightsLayout GetPreferredWeightsLayout(const deconvolution_params& p) const override { - if (p.output.GetLayout() == DataLayout::b_fs_yx_fsv16) + if (p.output.Dimentions() == 4) return WeightsLayout::is_os_yx_osv16_isv16; else return WeightsLayout::is_os_zyx_osv16_isv16; @@ -39,5 +39,14 @@ protected: bool Validate(const Params& p, const optional_params& o) const override; CommonDispatchData SetDefault(const deconvolution_params& arg) const override; JitConstants GetJitConstants(const deconvolution_params& params) const override; + + std::vector GetSupportedFusedOps() const override { + return { + FusedOpType::ACTIVATION, + FusedOpType::ELTWISE, + FusedOpType::SCALE, + FusedOpType::QUANTIZE + }; + } }; } // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.cpp index 083766f611b..cafa959944b 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.cpp @@ -18,29 +18,108 @@ #include "kernel_selector_utils.h" #include +#include +#include +#include namespace kernel_selector { static const size_t sub_group_size = 16; static const size_t feature_block_size = 16; +static const float max_reg_pressure = 3.f / 4.f; -size_t DeconvolutionKernel_b_fs_zyx_fsv16_dw::GetBlockSizeX(const deconvolution_params& params) const { - std::vector blockWidths = {8, 4, 2, 1}; - for (auto& blockSize : blockWidths) - if (params.output.X().v % blockSize == 0) { - return blockSize; +float DeconvolutionKernel_b_fs_zyx_fsv16_dw::EstimateRegPressure(const deconvolution_params& params, + const dispatch_params& d_params) const { + size_t usage_bytes = 0; + + usage_bytes += d_params.block_size_x * BytesPerElement(GetAccumulatorType(params)); + + if (d_params.preload_weights == weights_preload::all) { + usage_bytes += params.weights.X().v * params.weights.Y().v * params.weights.Z().v * BytesPerElement(params.weights.GetDType()); + } else if (d_params.preload_weights == weights_preload::line) { + usage_bytes += params.weights.X().v * BytesPerElement(params.weights.GetDType()); + } else { + usage_bytes += BytesPerElement(params.weights.GetDType()); + } + + if (d_params.preload_input == input_preload::line) { + size_t input_line_size = CeilDiv(d_params.block_size_x + params.weights.X().v - 1, params.stride.x); + usage_bytes += input_line_size * BytesPerElement(params.inputs[0].GetDType()); + } else { + usage_bytes += BytesPerElement(params.inputs[0].GetDType()); + } + + constexpr size_t register_num = 128; + constexpr size_t register_bytes = 32; + constexpr size_t max_register_bytes = register_num * register_bytes; + + return static_cast(usage_bytes * sub_group_size) / static_cast(max_register_bytes); +} + +DeconvolutionKernel_b_fs_zyx_fsv16_dw::dispatch_params +DeconvolutionKernel_b_fs_zyx_fsv16_dw::GetDispatchParams(const deconvolution_params& params) const { + std::vector ordered_params; + if (params.inputs[0].GetDType() == Datatype::F16 || params.inputs[0].GetDType() == Datatype::F32) { + ordered_params = { + // Preload weights + dispatch_params{8, input_preload::none, weights_preload::all}, + dispatch_params{4, input_preload::none, weights_preload::all}, + dispatch_params{2, input_preload::none, weights_preload::all}, + dispatch_params{1, input_preload::none, weights_preload::all}, + // No preloading + dispatch_params{8, input_preload::none, weights_preload::none}, + dispatch_params{4, input_preload::none, weights_preload::none}, + dispatch_params{2, input_preload::none, weights_preload::none}, + dispatch_params{1, input_preload::none, weights_preload::none}, + }; + } else { + ordered_params = { + dispatch_params{16, input_preload::line, weights_preload::line}, + dispatch_params{8, input_preload::line, weights_preload::line}, + dispatch_params{4, input_preload::line, weights_preload::line}, + dispatch_params{16, input_preload::line, weights_preload::none}, + dispatch_params{8, input_preload::line, weights_preload::none}, + dispatch_params{4, input_preload::line, weights_preload::none}, + dispatch_params{2, input_preload::line, weights_preload::line}, + dispatch_params{2, input_preload::line, weights_preload::none}, + dispatch_params{1, input_preload::line, weights_preload::none}, + dispatch_params{1, input_preload::none, weights_preload::none}, + }; + } + + dispatch_params best_params = dispatch_params{ 1, input_preload::none, weights_preload::none }; + + for (auto& d_params : ordered_params) { + bool good_block_size_x = params.output.X().v % d_params.block_size_x == 0 || params.output.X().v > d_params.block_size_x * 3; + bool good_reg_pressure = EstimateRegPressure(params, d_params) <= max_reg_pressure; + // No support for no input preload and weights line preload in kernel + bool good_preloads = !(d_params.preload_input == input_preload::none && d_params.preload_weights == weights_preload::line); + // At least one input preload + bool full_input_preload = d_params.preload_input != input_preload::line || + CeilDiv(d_params.block_size_x + params.filterSize.x - 1, params.stride.x) <= params.inputs[0].X().v; + + if (good_block_size_x && good_reg_pressure && good_preloads && full_input_preload) { + best_params = d_params; + break; } - return 1; + } + + return best_params; } ParamsKey DeconvolutionKernel_b_fs_zyx_fsv16_dw::GetSupportedKey() const { ParamsKey k; k.EnableInputDataType(Datatype::F32); - k.EnableOutputDataType(Datatype::F32); - k.EnableInputWeightsType(WeightsType::F32); k.EnableInputDataType(Datatype::F16); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + + k.EnableOutputDataType(Datatype::F32); k.EnableOutputDataType(Datatype::F16); - k.EnableInputWeightsType(WeightsType::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + + k.EnableAllInputWeightsType(); k.EnableInputLayout(DataLayout::b_fs_yx_fsv16); k.EnableOutputLayout(DataLayout::b_fs_yx_fsv16); k.EnableInputLayout(DataLayout::b_fs_zyx_fsv16); @@ -53,6 +132,8 @@ ParamsKey DeconvolutionKernel_b_fs_zyx_fsv16_dw::GetSupportedKey() const { k.EnableSubGroup(); k.EnableSubGroupShort(); k.EnableGroupedConvolution(); + k.EnableDifferentTypes(); + k.EnableDifferentInputWeightsTypes(); return k; } @@ -67,7 +148,7 @@ DeconvolutionKernelBase::DispatchData DeconvolutionKernel_b_fs_zyx_fsv16_dw::Set auto f = out.Feature().v; auto b = out.Batch().v; - kd.gws0 = (x / GetBlockSizeX(params)) * y * z; + kd.gws0 = CeilDiv(x, GetDispatchParams(params).block_size_x) * y * z; kd.gws1 = Align(f, feature_block_size); kd.gws2 = b; @@ -85,7 +166,7 @@ bool DeconvolutionKernel_b_fs_zyx_fsv16_dw::Validate(const Params& p, const opti return false; } - const deconvolution_params& params = static_cast(p); + const auto& params = static_cast(p); if (params.groups == 1) return false; @@ -93,6 +174,10 @@ bool DeconvolutionKernel_b_fs_zyx_fsv16_dw::Validate(const Params& p, const opti if (params.weights.IFM().v != 1 || params.weights.OFM().v != 1) return false; + // Check that padding features doesn't miss-align the blocks + if (params.inputs[0].Feature().pad.before % feature_block_size != 0 || params.output.Feature().pad.before % feature_block_size != 0) + return false; + return true; } @@ -101,11 +186,43 @@ JitConstants DeconvolutionKernel_b_fs_zyx_fsv16_dw::GetJitConstants(const deconv auto output = params.output; auto jit = Parent::GetJitConstants(params); - jit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", GetBlockSizeX(params))); + auto dp = GetDispatchParams(params); + auto& block_size_x = dp.block_size_x; + + jit.AddConstant(MakeJitConstant("X_BLOCK_SIZE", block_size_x)); jit.AddConstant(MakeJitConstant("SUB_GROUP_SIZE", sub_group_size)); if (params.output.Feature().v % feature_block_size != 0) { jit.AddConstant(MakeJitConstant("OUTPUT_LEFTOVERS", params.output.Feature().v % feature_block_size)); } + jit.AddConstant(MakeJitConstant("INPUT_BLOCK_SIZE_X", CeilDiv(block_size_x + params.filterSize.x - 1, params.stride.x))); + jit.AddConstant(MakeJitConstant("PRELOAD_INPUT_LINE", dp.preload_input == input_preload::line)); + jit.AddConstant(MakeJitConstant("PRELOAD_WEIGHTS", dp.preload_weights == weights_preload::all)); + jit.AddConstant(MakeJitConstant("PRELOAD_WEIGHTS_LINE", dp.preload_weights == weights_preload::line)); + + if (!params.fused_ops.empty()) { + auto fused_dt = GetActivationType(params); + std::vector idx_order; + if (params.output.Dimentions() <= 4) { + idx_order = {"b", "fg", "y", "x"}; + } else { + idx_order = { "b", "fg", "z", "y", "x" }; + } + auto boundary_check = BoundaryCheck::ENABLED; + if (params.output.Feature().v % feature_block_size == 0 && params.output.X().v % block_size_x == 0) { + boundary_check = BoundaryCheck::DISABLED; + } + FusedOpsConfiguration conf = { + "", + idx_order, + "dequantized", + fused_dt, + block_size_x, + LoadType::LT_ALIGNED_READ, + boundary_check, + IndexType::TENSOR_COORD, + Tensor::DataChannelName::X }; + jit.Merge(MakeFusedOpsJitConstants(params, { conf })); + } return jit; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.h index 518f6fc87ce..4b444504a51 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_b_fs_zyx_fsv16_dw.h @@ -39,6 +39,32 @@ protected: bool Validate(const Params& p, const optional_params& o) const override; CommonDispatchData SetDefault(const deconvolution_params& arg) const override; JitConstants GetJitConstants(const deconvolution_params& params) const override; - size_t GetBlockSizeX(const deconvolution_params& params) const; + + enum class weights_preload { + none, + line, + all + }; + enum class input_preload { + none, + line + }; + + struct dispatch_params { + size_t block_size_x; + input_preload preload_input; + weights_preload preload_weights; + }; + dispatch_params GetDispatchParams(const deconvolution_params& params) const; + float EstimateRegPressure(const deconvolution_params& params, const dispatch_params& disp_params) const; + + std::vector GetSupportedFusedOps() const override { + return { + FusedOpType::ACTIVATION, + FusedOpType::ELTWISE, + FusedOpType::SCALE, + FusedOpType::QUANTIZE + }; + } }; } // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp index a83013989f2..4a7d89ed325 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.cpp @@ -55,6 +55,11 @@ bool DeconvolutionKernelBase::Validate(const Params& p, const optional_params& o return false; } + for (auto& fused_op : params.fused_ops) { + if (!IsFusedPrimitiveSupported(fused_op)) + return false; + } + return true; } @@ -68,14 +73,15 @@ JitConstants DeconvolutionKernelBase::GetJitConstants(const deconvolution_params (dp.filterSize.y - 1 + padding.y) * input.Y().pitch; input_offset_with_padding = std::max(input_offset_with_padding, (int64_t)0); - jit.AddConstants({MakeJitConstant("STRIDE", dp.stride), - MakeJitConstant("PADDING", dp.padding), - MakeJitConstant("DILATION", dp.dilation), - MakeJitConstant("FILTER_ARRAY_NUM", dp.split), - MakeJitConstant("INPUT0_OFFSET_WITH_PADDING", input_offset_with_padding), - MakeJitConstant("DEPTHWISE_SEPARABLE_OPT", dp.depthwise_separable_opt), - MakeJitConstant("FUSED_ELTWISE", dp.fused_eltwise), - MakeJitConstant("GROUPED", (dp.groups > 1) ? 1 : 0)}); + jit.AddConstants({ MakeJitConstant("STRIDE", dp.stride), + MakeJitConstant("PADDING", dp.padding), + MakeJitConstant("DILATION", dp.dilation), + MakeJitConstant("FILTER_ARRAY_NUM", dp.split), + MakeJitConstant("INPUT0_OFFSET_WITH_PADDING", input_offset_with_padding), + MakeJitConstant("DEPTHWISE_SEPARABLE_OPT", dp.depthwise_separable_opt), + MakeJitConstant("GROUPED", (dp.groups > 1) ? 1 : 0) }); + jit.Merge(MakeTypeJitConstants(GetAccumulatorType(dp), "ACCUMULATOR")); + jit.Merge(MakeTypeJitConstants(GetActivationType(dp), "ACTIVATION")); return jit; } @@ -114,7 +120,12 @@ KernelsData DeconvolutionKernelBase::GetKernelsData(const Params& params, const KernelData kd = KernelData::Default(params); deconvolution_params& newParams = *static_cast(kd.params.get()); - bool succeed = UpdateWeightsParams(newParams, options, GetPreferredWeightsLayout(newParams), kd.weightsReorderParams); + bool succeed = UpdateWeightsParams(newParams, + options, + GetPreferredWeightsLayout(newParams), + kd.weightsReorderParams, + GetSupportedKey(), + newParams.groups); if (!succeed) { return {}; @@ -133,13 +144,33 @@ KernelsData DeconvolutionKernelBase::GetKernelsData(const Params& params, const entry_point, DEFAULT, true, - !newParams.bias.empty()); + !newParams.bias.empty(), + 1, + GetFusedPrimitiveInputsCount(params)); kernel.arguments.push_back({ArgumentDescriptor::Types::SPLIT, 0}); - if (orgParams.fused_eltwise) - kernel.arguments.push_back({ArgumentDescriptor::Types::INPUT, 1}); kd.estimatedTime = runInfo.efficiency; return {kd}; } -} // namespace kernel_selector \ No newline at end of file + +Datatype DeconvolutionKernelBase::GetAccumulatorType(const deconvolution_params& params) const { + if (params.inputs[0].GetDType() == Datatype::INT8 || params.inputs[0].GetDType() == Datatype::UINT8) + return Datatype::INT32; + + // input is either fp32 or fp16 + // for fp32->fp16 accumulate to fp16, otherwise accumulate to input type + if (params.output.GetDType() == Datatype::F16) + return Datatype::F16; + + return params.inputs[0].GetDType(); +} + +Datatype DeconvolutionKernelBase::GetActivationType(const deconvolution_params& params) const { + auto accumulator_dt = GetAccumulatorType(params); + if (accumulator_dt == Datatype::INT32) + return Datatype::F32; + return accumulator_dt; +} + +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h index 1a80205f9f9..a1da638fd78 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_base.h @@ -34,7 +34,6 @@ struct deconvolution_params : public weight_bias_params { uint32_t split = 1; uint32_t groups = 1; bool depthwise_separable_opt = false; - bool fused_eltwise = false; std::string to_string() const override; @@ -89,5 +88,8 @@ protected: return (params.groups > 1) ? WeightsLayout::goizyx : WeightsLayout::oizyx; } bool Validate(const Params& p, const optional_params& o) const override; + + virtual Datatype GetAccumulatorType(const deconvolution_params& params) const; + virtual Datatype GetActivationType(const deconvolution_params& params) const; }; -} // namespace kernel_selector \ No newline at end of file +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.cpp index 94fa41469b9..8ec74cd406f 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.cpp @@ -55,4 +55,23 @@ CommonDispatchData DeconvolutionKernel_bfyx_opt::SetDefault(const deconvolution_ kd.efficiency = FORCE_PRIORITY_6; return kd; } -} // namespace kernel_selector \ No newline at end of file + +JitConstants DeconvolutionKernel_bfyx_opt::GetJitConstants(const deconvolution_params& params) const { + auto jit = Parent::GetJitConstants(params); + + if (!params.fused_ops.empty()) { + auto fused_dt = GetActivationType(params); + FusedOpsConfiguration conf = { + "", + {"batch_offset", "ofm_offset", "id_y", "id_x"}, + "result", + fused_dt, + 1, + LoadType::LT_UNALIGNED, + BoundaryCheck::DISABLED }; + jit.Merge(MakeFusedOpsJitConstants(params, { conf })); + } + return jit; +} + +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.h index 85c945c9017..1276cbf375c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_bfyx_opt.h @@ -21,6 +21,7 @@ namespace kernel_selector { class DeconvolutionKernel_bfyx_opt : public DeconvolutionKernelBase { public: + using Parent = DeconvolutionKernelBase; DeconvolutionKernel_bfyx_opt() : DeconvolutionKernelBase("deconvolution_gpu_bfyx_opt") {} virtual ~DeconvolutionKernel_bfyx_opt() {} @@ -28,5 +29,15 @@ public: protected: CommonDispatchData SetDefault(const deconvolution_params& params) const override; + JitConstants GetJitConstants(const deconvolution_params& params) const override; + + std::vector GetSupportedFusedOps() const override { + return { + FusedOpType::ACTIVATION, + FusedOpType::ELTWISE, + FusedOpType::SCALE, + FusedOpType::QUANTIZE + }; + } }; -} // namespace kernel_selector \ No newline at end of file +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_along_f_tile_bfx.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_along_f_tile_bfx.cpp new file mode 100644 index 00000000000..4c4e77a06d3 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_along_f_tile_bfx.cpp @@ -0,0 +1,285 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "deconvolution_kernel_imad_along_f_tile_bfx.hpp" + +#include "kernel_selector_utils.h" + +#include +#include +#include +#include + +namespace kernel_selector { + +namespace { + constexpr size_t simd = 16; +} + +ParamsKey DeconvolutionKernel_imad_along_f_tile_bfx::GetSupportedKey() const { + ParamsKey k; + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + k.EnableInputWeightsType(WeightsType::INT8); + k.EnableInputWeightsType(WeightsType::UINT8); + + k.EnableInputLayout(DataLayout::b_fs_yx_fsv16); + k.EnableOutputLayout(DataLayout::b_fs_yx_fsv16); + k.EnableInputLayout(DataLayout::b_fs_zyx_fsv16); + k.EnableOutputLayout(DataLayout::b_fs_zyx_fsv16); + + k.EnableInputLayout(DataLayout::b_fs_yx_fsv32); + k.EnableOutputLayout(DataLayout::b_fs_yx_fsv32); + k.EnableInputLayout(DataLayout::b_fs_zyx_fsv32); + k.EnableOutputLayout(DataLayout::b_fs_zyx_fsv32); + + k.EnableInputLayout(DataLayout::bs_fs_yx_bsv16_fsv16); + k.EnableOutputLayout(DataLayout::bs_fs_yx_bsv16_fsv16); + k.EnableInputLayout(DataLayout::bs_fs_zyx_bsv16_fsv16); + k.EnableOutputLayout(DataLayout::bs_fs_zyx_bsv16_fsv16); + + k.EnableInputLayout(DataLayout::byxf_af32); + k.EnableOutputLayout(DataLayout::byxf_af32); + + k.EnableDifferentTypes(); + k.EnableDifferentInputWeightsTypes(); + k.EnableBatching(); + k.EnableTensorPitches(); + k.EnableTensorOffset(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableGroupedConvolution(); + + return k; +} + +bool DeconvolutionKernel_imad_along_f_tile_bfx::Validate(const Params& p, const optional_params& o) const { + if (!Parent::Validate(p, o)) + return false; + + auto& params = static_cast(p); + if (params.groups > 1 && params.weights.IFM().v % 4 != 0) + return false; + + // Consider loosening at the cost of performance + if (params.groups > 1 && params.weights.OFM().v % simd != 0) + return false; + + return true; +} + +WeightsLayout DeconvolutionKernel_imad_along_f_tile_bfx::GetPreferredWeightsLayout(const deconvolution_params& params) const { + // isv, osv + using layout_map_key = std::tuple; + using layout_map = std::map; + + layout_map lt_map = { + {layout_map_key((size_t)4, (size_t)16), WeightsLayout::g_os_zyx_is_osv16_isv4 }, + {layout_map_key((size_t)16, (size_t)16), WeightsLayout::g_os_zyx_is_osv16_isv16 }, + {layout_map_key((size_t)32, (size_t)16), WeightsLayout::g_os_zyx_is_osv16_isv32 }, + {layout_map_key((size_t)4, (size_t)32), WeightsLayout::g_os_zyx_is_osv32_isv4 }, + {layout_map_key((size_t)16, (size_t)32), WeightsLayout::g_os_zyx_is_osv32_isv16 }, + {layout_map_key((size_t)32, (size_t)32), WeightsLayout::g_os_zyx_is_osv32_isv32 }}; + + auto tile_ifm = GetTileIFM(params); + auto tile_ofm_simd = GetTileOFM(params) * simd; + auto key = layout_map_key(tile_ifm, tile_ofm_simd); + auto it = lt_map.find(key); + if (it == lt_map.end()) { + // Params are not valid for this implementation, return anything to allow Validate to reject + return WeightsLayout::goizyx; + } + auto layout = it->second; + return layout; +} + +DeconvolutionKernelBase::DispatchData DeconvolutionKernel_imad_along_f_tile_bfx::SetDefault(const deconvolution_params& params) const { + auto dispatch = Parent::SetDefault(params); + + auto tile_x = GetTileX(params); + auto tile_ofm = GetTileOFM(params); + auto tile_b = GetTileB(params); + + std::vector global = { + CeilDiv(params.output.X().v, tile_x) * params.output.Y().v * params.output.Z().v, + Align(CeilDiv(params.output.Feature().v, tile_ofm), simd), + CeilDiv(params.output.Batch().v, tile_b) + }; + + std::vector local = { 1, simd, 1 }; + + dispatch.gws0 = global[0]; + dispatch.gws1 = global[1]; + dispatch.gws2 = global[2]; + + dispatch.lws0 = local[0]; + dispatch.lws1 = local[1]; + dispatch.lws2 = local[2]; + + // Currently most optimized for fsv16 formats + if (params.inputs[0].GetLayout() == DataLayout::b_fs_yx_fsv16 || params.inputs[0].GetLayout() == DataLayout::b_fs_zyx_fsv16) { + dispatch.efficiency = FORCE_PRIORITY_7; + } else { + dispatch.efficiency = FORCE_PRIORITY_8; + } + + return dispatch; +} + +JitConstants DeconvolutionKernel_imad_along_f_tile_bfx::GetJitConstants(const deconvolution_params& params) const { + auto jit = Parent::GetJitConstants(params); + auto tile_ifm = GetTileIFM(params); + auto tile_x = GetTileX(params); + auto tile_ofm = GetTileOFM(params); + auto tile_b = GetTileB(params); + + jit.AddConstant(MakeJitConstant("TILE_IFM", tile_ifm)); + jit.AddConstant(MakeJitConstant("TILE_X", tile_x)); + jit.AddConstant(MakeJitConstant("TILE_OFM", tile_ofm)); + jit.AddConstant(MakeJitConstant("TILE_B", tile_b)); + jit.AddConstant(MakeJitConstant("SIMD", simd)); + + auto& in = params.inputs[0]; + auto in_layout = in.GetLayout(); + + // Layout specific params + size_t input_tile_ifm_pitch = 0; + size_t input_in_tile_batch_pitch = 0; + size_t zyx_pitch_factor = in.Z().LogicalDimPadded() * in.Y().LogicalDimPadded() * in.X().LogicalDimPadded(); + + if (in_layout == DataLayout::b_fs_yx_fsv16 || in_layout == DataLayout::b_fs_zyx_fsv16) { + if (tile_ifm == 16) { + input_tile_ifm_pitch = zyx_pitch_factor * 16; + } + input_in_tile_batch_pitch = Align(in.Feature().LogicalDimPadded(), 16) * zyx_pitch_factor; + } else if (in_layout == DataLayout::b_fs_yx_fsv32 || in_layout == DataLayout::b_fs_yx_fsv32) { + if (tile_ifm == 32) { + input_tile_ifm_pitch = zyx_pitch_factor * 32; + } + input_in_tile_batch_pitch = Align(in.Feature().LogicalDimPadded(), 32) * zyx_pitch_factor; + } else if (in_layout == DataLayout::bs_fs_yx_bsv16_fsv16 || in_layout == DataLayout::bs_fs_zyx_bsv16_fsv16) { + if (tile_ifm == 16) { + input_tile_ifm_pitch = zyx_pitch_factor * 16 * 16; + } + input_in_tile_batch_pitch = 16; + } else if (in_layout == DataLayout::byxf_af32) { + input_tile_ifm_pitch = tile_ifm; + input_in_tile_batch_pitch = zyx_pitch_factor * Align(in.Feature().LogicalDimPadded(), 32); + } + + jit.AddConstant(MakeJitConstant("INPUT_VALID_TILE_IFM_PITCH", input_tile_ifm_pitch != 0)); + jit.AddConstant(MakeJitConstant("INPUT_TILE_IFM_PITCH", input_tile_ifm_pitch)); + jit.AddConstant(MakeJitConstant("INPUT_IN_TILE_B_PITCH", input_in_tile_batch_pitch)); + + if (params.output.GetLayout() == DataLayout::b_fs_yx_fsv16 || params.output.GetLayout() == DataLayout::b_fs_zyx_fsv16) { + jit.AddConstant(MakeJitConstant("OUTPUT_BLOCK_X_STORE", true)); + } else { + jit.AddConstant(MakeJitConstant("OUTPUT_NAIVE_STORE", true)); + } + + if (!params.fused_ops.empty()) { + auto fused_in_dt = GetActivationType(params); + std::vector idx_order; + if (params.output.Dimentions() <= 4) { + idx_order = { "(out_b + ob)", "(out_f + of * SIMD)", "out_y", "(out_x + tx)" }; + } else { + idx_order = { "(out_b + ob)", "(out_f + of * SIMD)", "out_z", "out_y", "(out_x + tx)" }; + } + auto boundary_check = BoundaryCheck::DISABLED; + if (params.output.X().v % tile_x != 0 + || params.output.Feature().v % (tile_ofm * simd) != 0 + || params.output.Batch().v % tile_b != 0) { + boundary_check = BoundaryCheck::ENABLED; + } + std::vector loop_axes = { Tensor::DataChannelName::X }; + if (tile_b != 1) { + loop_axes.push_back(Tensor::DataChannelName::BATCH); + } else { + idx_order[0] = "out_b"; + } + + auto conf = FusedOpsConfiguration{ "", + idx_order, + "dequantized[ob][of][tx]", + fused_in_dt, + 1, + LoadType::LT_UNALIGNED, + boundary_check, + IndexType::TENSOR_COORD, + Tensor::DataChannelName::X, + loop_axes, + true }; + + jit.Merge(MakeFusedOpsJitConstants(params, { conf })); + } + + return jit; +} + +size_t DeconvolutionKernel_imad_along_f_tile_bfx::GetTileIFM(const deconvolution_params& params) const { + size_t fsv = 4; + if (params.inputs[0].GetLayout() == DataLayout::b_fs_yx_fsv16 + || params.inputs[0].GetLayout() == DataLayout::b_fs_zyx_fsv16 + || params.inputs[0].GetLayout() == DataLayout::bs_fs_yx_bsv16_fsv16 + || params.inputs[0].GetLayout() == DataLayout::bs_fs_zyx_bsv16_fsv16) { + fsv = 16; + } + if (params.inputs[0].GetLayout() == DataLayout::b_fs_yx_fsv32 + || params.inputs[0].GetLayout() == DataLayout::b_fs_zyx_fsv32 + || params.inputs[0].GetLayout() == DataLayout::byxf_af32) { + fsv = 32; + } + + auto ifm = params.weights.IFM().v; + bool grouped = params.groups > 1; + auto pref_tile_ifm = std::min(fsv, ifm); + + std::vector allowed_tile_ifm = { 4, 16, 32 }; + size_t tile_ifm = 1; + for (auto candidate : allowed_tile_ifm) { + if (candidate <= pref_tile_ifm + && (!grouped || ifm % candidate == 0)) + tile_ifm = candidate; + } + return tile_ifm; +} + +size_t DeconvolutionKernel_imad_along_f_tile_bfx::GetTileOFM(const deconvolution_params& params) const { + // TODO Loosen divisibility requirement for tile ofm 2 + if (params.weights.OFM().v % (simd * 2) == 0 && params.output.Batch().v % 2 != 0) + return 2; + + return 1; +} + +size_t DeconvolutionKernel_imad_along_f_tile_bfx::GetTileX(const deconvolution_params& params) const { + constexpr size_t max_tile_x = simd; + if (params.output.X().v <= max_tile_x) + return params.output.X().v; + + return max_tile_x; +} + +size_t DeconvolutionKernel_imad_along_f_tile_bfx::GetTileB(const deconvolution_params& params) const { + if (params.output.Batch().v % 2 == 0) + return 2; + + return 1; +} + +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_along_f_tile_bfx.hpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_along_f_tile_bfx.hpp new file mode 100644 index 00000000000..9732a1ca790 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_along_f_tile_bfx.hpp @@ -0,0 +1,51 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "deconvolution_kernel_base.h" +#include + +namespace kernel_selector { + +class DeconvolutionKernel_imad_along_f_tile_bfx : public DeconvolutionKernelBase { +public: + using Parent = DeconvolutionKernelBase; + DeconvolutionKernel_imad_along_f_tile_bfx() : DeconvolutionKernelBase("deconvolution_gpu_imad_along_f_tile_bfx") {} + virtual ~DeconvolutionKernel_imad_along_f_tile_bfx() = default; + + ParamsKey GetSupportedKey() const override; + +protected: + bool Validate(const Params& p, const optional_params& o) const override; + WeightsLayout GetPreferredWeightsLayout(const deconvolution_params ¶ms) const override; + CommonDispatchData SetDefault(const deconvolution_params& params) const override; + JitConstants GetJitConstants(const deconvolution_params& params) const override; + + std::vector GetSupportedFusedOps() const override { + return { + FusedOpType::ACTIVATION, + FusedOpType::ELTWISE, + FusedOpType::SCALE, + FusedOpType::QUANTIZE + }; + } + + size_t GetTileIFM(const deconvolution_params& params) const; + size_t GetTileOFM(const deconvolution_params& params) const; + size_t GetTileX(const deconvolution_params& params) const; + size_t GetTileB(const deconvolution_params& params) const; +}; + +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_ref.cpp new file mode 100644 index 00000000000..9edde95f13e --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_ref.cpp @@ -0,0 +1,110 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "deconvolution_kernel_imad_ref.hpp" + +#include "kernel_selector_utils.h" + +#include +#include + +namespace kernel_selector { + +ParamsKey DeconvolutionKernel_imad_ref::GetSupportedKey() const { + ParamsKey k; + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::F16); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + k.EnableInputWeightsType(WeightsType::INT8); + k.EnableInputWeightsType(WeightsType::UINT8); + + k.EnableInputLayout(DataLayout::b_fs_yx_fsv4); + k.EnableInputLayout(DataLayout::b_fs_yx_fsv16); + k.EnableInputLayout(DataLayout::b_fs_zyx_fsv16); + k.EnableInputLayout(DataLayout::bs_fs_yx_bsv16_fsv16); + k.EnableInputLayout(DataLayout::bs_fs_zyx_bsv16_fsv16); + k.EnableInputLayout(DataLayout::b_fs_yx_fsv32); + k.EnableInputLayout(DataLayout::b_fs_zyx_fsv32); + k.EnableInputLayout(DataLayout::byxf_af32); + k.EnableAllOutputLayout(); + + k.EnableDifferentTypes(); + k.EnableDifferentInputWeightsTypes(); + k.EnableBatching(); + k.EnableTensorPitches(); + k.EnableTensorOffset(); + k.EnableBiasPerFeature(); + k.EnableNonBiasTerm(); + k.EnableGroupedConvolution(); + + return k; +} + +WeightsLayout DeconvolutionKernel_imad_ref::GetPreferredWeightsLayout(const deconvolution_params&) const { + return WeightsLayout::g_os_zyx_is_osv32_isv4; +} + +DeconvolutionKernelBase::DispatchData DeconvolutionKernel_imad_ref::SetDefault(const deconvolution_params& params) const { + auto dispatch = Parent::SetDefault(params); + + std::vector global = { + params.output.Feature().v, + params.output.X().v * params.output.Y().v * params.output.Z().v, + params.output.Batch().v + }; + + auto local = GetOptimalLocalWorkGroupSizes(global, params.engineInfo); + + dispatch.gws0 = global[0]; + dispatch.gws1 = global[1]; + dispatch.gws2 = global[2]; + + dispatch.lws0 = local[0]; + dispatch.lws1 = local[1]; + dispatch.lws2 = local[2]; + + dispatch.efficiency = FORCE_PRIORITY_9; + + return dispatch; +} + +JitConstants DeconvolutionKernel_imad_ref::GetJitConstants(const deconvolution_params& params) const { + auto jit = Parent::GetJitConstants(params); + auto tile_ifm = GetTileIFM(params); + + jit.AddConstant(MakeJitConstant("TILE_IFM", tile_ifm)); + + if (!params.fused_ops.empty()) { + std::vector idx_order; + if (params.output.Dimentions() <= 4) { + idx_order = { "out_b", "out_f", "out_y", "out_x" }; + } else { + idx_order = { "out_b", "out_f", "out_z", "out_y", "out_x" }; + } + auto conf = FusedOpsConfiguration{ "", idx_order, "dequantized", GetActivationType(params), 1, LoadType::LT_UNALIGNED, BoundaryCheck::DISABLED }; + jit.Merge(MakeFusedOpsJitConstants(params, { conf })); + } + + return jit; +} + +size_t DeconvolutionKernel_imad_ref::GetTileIFM(const deconvolution_params&) const { + return 4; +} + + +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_ref.hpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_ref.hpp new file mode 100644 index 00000000000..297bb61bee2 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_imad_ref.hpp @@ -0,0 +1,47 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "deconvolution_kernel_base.h" +#include + +namespace kernel_selector { + +class DeconvolutionKernel_imad_ref : public DeconvolutionKernelBase { +public: + using Parent = DeconvolutionKernelBase; + DeconvolutionKernel_imad_ref() : DeconvolutionKernelBase("deconvolution_gpu_imad_ref") {} + virtual ~DeconvolutionKernel_imad_ref() = default; + + ParamsKey GetSupportedKey() const override; + +protected: + WeightsLayout GetPreferredWeightsLayout(const deconvolution_params ¶ms) const override; + CommonDispatchData SetDefault(const deconvolution_params& params) const override; + JitConstants GetJitConstants(const deconvolution_params& params) const override; + + std::vector GetSupportedFusedOps() const override { + return { + FusedOpType::ACTIVATION, + FusedOpType::ELTWISE, + FusedOpType::SCALE, + FusedOpType::QUANTIZE + }; + } + + size_t GetTileIFM(const deconvolution_params& params) const; +}; + +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.cpp index 90cb7f23e48..4f6bfc29ade 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.cpp @@ -20,10 +20,19 @@ ParamsKey DeconvolutionKernelRef::GetSupportedKey() const { ParamsKey k; k.EnableInputDataType(Datatype::F16); k.EnableInputDataType(Datatype::F32); + k.EnableInputDataType(Datatype::INT8); + k.EnableInputDataType(Datatype::UINT8); + k.EnableInputWeightsType(WeightsType::F16); k.EnableInputWeightsType(WeightsType::F32); + k.EnableInputWeightsType(WeightsType::INT8); + k.EnableInputWeightsType(WeightsType::UINT8); + k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); + k.EnableOutputDataType(Datatype::INT8); + k.EnableOutputDataType(Datatype::UINT8); + k.EnableInputLayout(DataLayout::yxfb); k.EnableInputLayout(DataLayout::bfyx); k.EnableInputLayout(DataLayout::byxf); @@ -38,6 +47,7 @@ ParamsKey DeconvolutionKernelRef::GetSupportedKey() const { k.EnableOutputLayout(DataLayout::b_fs_yx_fsv16); k.EnableOutputLayout(DataLayout::b_fs_zyx_fsv16); k.EnableOutputLayout(DataLayout::bs_fs_zyx_bsv16_fsv16); + k.EnableTensorOffset(); k.EnableTensorPitches(); k.EnableBiasPerFeature(); @@ -47,6 +57,8 @@ ParamsKey DeconvolutionKernelRef::GetSupportedKey() const { k.EnableDepthwiseSeparableOpt(); k.EnableGradient(); k.EnableGroupedConvolution(); + k.EnableDifferentTypes(); + k.EnableDifferentInputWeightsTypes(); return k; } @@ -73,6 +85,19 @@ JitConstants DeconvolutionKernelRef::GetJitConstants(const deconvolution_params& if (params.output.Feature().v * params.output.Batch().v <= 16) jit.AddConstant(MakeJitConstant("DIM_ORDER_XYBF", 1)); + if (!params.fused_ops.empty()) { + auto fused_dt = GetActivationType(params); + std::vector idx_order; + if (params.output.Dimentions() <= 4) { + idx_order = { "batch_offset", "ofm_offset", "out_y", "out_x" }; + } else { + idx_order = { "batch_offset", "ofm_offset", "out_z", "out_y", "out_x" }; + } + FusedOpsConfiguration conf = { "", idx_order, "post_activation", fused_dt, 1, LoadType::LT_UNALIGNED, BoundaryCheck::DISABLED }; + + jit.Merge(MakeFusedOpsJitConstants(params, { conf })); + } + return jit; } } // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.h index 34a4e2bec49..09c1dbd859c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_ref.h @@ -29,5 +29,14 @@ public: protected: CommonDispatchData SetDefault(const deconvolution_params& params) const override; JitConstants GetJitConstants(const deconvolution_params& params) const override; + + std::vector GetSupportedFusedOps() const override { + return { + FusedOpType::ACTIVATION, + FusedOpType::ELTWISE, + FusedOpType::SCALE, + FusedOpType::QUANTIZE + }; + } }; -} // namespace kernel_selector \ No newline at end of file +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_selector.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_selector.cpp index 6c2852c3500..b8a02480456 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_selector.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/deconvolution/deconvolution_kernel_selector.cpp @@ -18,6 +18,8 @@ #include "deconvolution_kernel_bfyx_opt.h" #include "deconvolution_kernel_b_fs_zyx_fsv16.h" #include "deconvolution_kernel_b_fs_zyx_fsv16_dw.h" +#include "deconvolution_kernel_imad_ref.hpp" +#include "deconvolution_kernel_imad_along_f_tile_bfx.hpp" namespace kernel_selector { deconvolution_kernel_selector::deconvolution_kernel_selector() { @@ -25,6 +27,8 @@ deconvolution_kernel_selector::deconvolution_kernel_selector() { Attach(); Attach(); Attach(); + Attach(); + Attach(); } KernelsData deconvolution_kernel_selector::GetBestKernels(const Params& params, const optional_params& options) const { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_b_fs_yx_fsv16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_b_fs_yx_fsv16.cpp index c1bbf41d94f..102fcf2a59f 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_b_fs_yx_fsv16.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/eltwise/eltwise_kernel_b_fs_yx_fsv16.cpp @@ -163,9 +163,18 @@ bool EltwiseKernel_b_fs_yx_fsv16::Validate(const Params& params, const optional_ auto input0 = ewParams.inputs[0]; + // Check that padding before features doesn't miss-align the blocks + auto feature_block_size = 16; + if (input0.Feature().pad.before % feature_block_size != 0 || output.Feature().pad.before % feature_block_size != 0) { + return false; + } + for (size_t i = 1; i < ewParams.inputs.size(); i++) { if (ewParams.inputs[i].LogicalSize() == input0.LogicalSize() && !(ewParams.inputs[i] == input0)) return false; + if (ewParams.inputs[i].Feature().pad.before % feature_block_size != 0) { + return false; + } } return true; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_b_fs_yx_fsv16.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_b_fs_yx_fsv16.cpp index 01e52ff7865..31a2ac39c40 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_b_fs_yx_fsv16.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/pooling/pooling_kernel_gpu_b_fs_yx_fsv16.cpp @@ -99,6 +99,13 @@ bool PoolingKernel_b_fs_yx_fsv16::Validate(const Params& p, const optional_param return false; } + const auto& params = static_cast(p); + const auto feature_block_size = 16; + + // Check that padding features doesn't miss-align the blocks + if (params.inputs[0].Feature().pad.before % feature_block_size != 0 || params.output.Feature().pad.before % feature_block_size != 0) + return false; + return true; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/quantize/quantize_kernel_params.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/quantize/quantize_kernel_params.h index 250b6cb56f3..388c0d6fb56 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/quantize/quantize_kernel_params.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/quantize/quantize_kernel_params.h @@ -54,6 +54,7 @@ struct quantize_fuse_params : fuse_params { bool has_post_scale, bool has_post_shift, bool has_pre_shift, + bool has_clamp, bool per_tensor_input_range, bool per_tensor_input_scale, bool per_tensor_input_shift, @@ -70,6 +71,7 @@ struct quantize_fuse_params : fuse_params { , has_post_scale(has_post_scale) , has_post_shift(has_post_shift) , has_pre_shift(has_pre_shift) + , has_clamp(has_clamp) , per_tensor_input_range(per_tensor_input_range) , per_tensor_input_scale(per_tensor_input_scale) , per_tensor_input_shift(per_tensor_input_shift) @@ -86,6 +88,7 @@ struct quantize_fuse_params : fuse_params { bool has_post_scale; bool has_post_shift; bool has_pre_shift; + bool has_clamp; bool per_tensor_input_range; bool per_tensor_input_scale; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp index 205cd16cd4d..b8536d75866 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.cpp @@ -90,6 +90,10 @@ inline JitConstants MakeReorderWeightsJitConstants(const reorder_weights_params& MakeJitConstant("OUTPUT", output), }; + if (params.rotate_180) { + jit.AddConstant(MakeJitConstant("REORDER_ROTATE", params.rotate_180)); + } + if (fp16Supported) { jit.Merge(MakeUnitTypeJitConstants(Datatype::F16)); } else { diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.h index 68775383687..f10460f303c 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_kernel_base.h @@ -63,6 +63,7 @@ struct reorder_weights_params : public Params { WeightsTensor input; WeightsTensor output; bool winograd = false; + bool rotate_180 = false; virtual ParamsKey GetParamsKey() const { ParamsKey k; @@ -82,6 +83,10 @@ struct reorder_weights_params : public Params { if (winograd) { k.EnableWinogradReorder(); } + + if (rotate_180) { + k.EnableRotateReorder(); + } return k; } }; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_weights_kernel.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_weights_kernel.cpp index 164ec00834e..7d158ddd253 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_weights_kernel.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/reorder/reorder_weights_kernel.cpp @@ -31,6 +31,7 @@ ParamsKey ReorderWeightsKernel::GetSupportedKey() const { k.EnableDifferentTypes(); k.EnableTensorOffset(); k.EnableTensorPitches(); + k.EnableRotateReorder(); return k; } @@ -38,4 +39,4 @@ KernelsData ReorderWeightsKernel::GetKernelsData(const Params& params, const opt const reorder_weights_params& orgParams = static_cast(params); return GetCommonKernelsData(orgParams, options, DONT_USE_IF_HAVE_SOMETHING_ELSE); } -} // namespace kernel_selector \ No newline at end of file +} // namespace kernel_selector diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl index 026d717263b..6e34d064956 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_opt.cl @@ -18,32 +18,40 @@ #include "include/data_types.cl" KERNEL(activation)( -#if GRADIENT - __global UNIT_TYPE* input_grad, - __global UNIT_TYPE* output, - __global UNIT_TYPE* input -#else - __global UNIT_TYPE* input, - __global UNIT_TYPE* output + __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* output +#if HAS_FUSED_OPS_DECLS + , FUSED_OPS_DECLS #endif ) { const unsigned int x = (uint)get_global_id(0) * NUM_COLS_WI; +#if OUTPUT_DIMS == 5 + const unsigned int fo_x = x % OUTPUT_SIZE_X; + const unsigned int fo_y = x / OUTPUT_SIZE_X % OUTPUT_SIZE_Y; + const unsigned int fo_z = x / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y) % OUTPUT_SIZE_Z; + const unsigned int fo_f = x / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y * OUTPUT_SIZE_Z) % OUTPUT_FEATURE_NUM; + const unsigned int fo_b = x / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y * OUTPUT_SIZE_Z* OUTPUT_FEATURE_NUM); +#elif OUTPUT_DIMS == 4 + const unsigned int fo_x = x % OUTPUT_SIZE_X; + const unsigned int fo_y = x / OUTPUT_SIZE_X % OUTPUT_SIZE_Y; + const unsigned int fo_f = x / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y) % OUTPUT_FEATURE_NUM; + const unsigned int fo_b = x / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y * OUTPUT_FEATURE_NUM); +#endif unsigned int input_offset = x + INPUT0_OFFSET; unsigned int output_offset = x + OUTPUT_OFFSET; - typedef CAT(UNIT_TYPE, 4) type_t; -#if GRADIENT - type_t g = ((__global type_t*) (input_grad + input_offset))[0]; -#endif - type_t v = ((__global type_t*) (input + input_offset))[0]; + typedef CAT(INPUT0_TYPE, 4) input_t; + typedef CAT(OUTPUT_TYPE, 4) output_t; -#if GRADIENT - v = ACTIVATION(g, v, ACTIVATION_PARAMS); + input_t v = ((__global input_t*) (input + input_offset))[0]; + + v = ACTIVATION_KERNEL(v, ACTIVATION_PARAMS_KERNEL); +#if HAS_FUSED_OPS + FUSED_OPS; + *((__global output_t*)(output + output_offset)) = FUSED_OPS_RESULT; #else - v = ACTIVATION(v, ACTIVATION_PARAMS); + *((__global output_t*)(output + output_offset)) = v; #endif - - *((__global type_t*)(output + output_offset)) = v; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl index 90660861f6c..9caafc47a59 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/activation_ref.cl @@ -26,16 +26,13 @@ // TODO: move it from layout based to memory based KERNEL(activation)( -#if GRADIENT - __global UNIT_TYPE* input_grad, - __global UNIT_TYPE* output_grad, - __global UNIT_TYPE* input -#else - __global UNIT_TYPE* input, - __global UNIT_TYPE* output -#endif + __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* output #ifdef PARAMETERIZED , __global ADDITIONAL_PARAMS_TYPE* params +#endif +#if HAS_FUSED_OPS_DECLS + , FUSED_OPS_DECLS #endif ) { @@ -45,7 +42,7 @@ KERNEL(activation)( #define ORDER batch,feature,y,x #endif -#if defined OUTPUT_LAYOUT_BFZYX +#if OUTPUT_DIMS == 5 const unsigned x = get_global_id(0); const uint y = (uint)get_global_id(1) % OUTPUT_SIZE_Y; const uint z = (uint)get_global_id(1) / OUTPUT_SIZE_Y; @@ -82,12 +79,7 @@ KERNEL(activation)( #endif #endif -#if GRADIENT - const unsigned src_grad_index = GET_INDEX(INPUT,0,ORDER); - const unsigned src_index = GET_INDEX(INPUT,1,ORDER); -#else const unsigned src_index = GET_INDEX(INPUT,0,ORDER); -#endif const unsigned dst_index = GET_INDEX(OUTPUT,,ORDER); #if defined PARAMETERIZED @@ -105,16 +97,20 @@ KERNEL(activation)( #endif #define PARAMETERIZED_ACTIVATION_PARAMS NL_M_PARAMETERIZED, NL_N_PARAMETERIZED - #if GRADIENT - output_grad[dst_index] = ACTIVATION(input_grad[src_grad_index], input[src_index], PARAMETERIZED_ACTIVATION_PARAMS); + INPUT0_TYPE dst = ACTIVATION_KERNEL(input[src_index], PARAMETERIZED_ACTIVATION_PARAMS); + #if HAS_FUSED_OPS + FUSED_OPS; + output[dst_index] = FUSED_OPS_RESULT; #else - output[dst_index] = ACTIVATION(input[src_index], PARAMETERIZED_ACTIVATION_PARAMS); + output[dst_index] = dst; #endif #else - #if GRADIENT - output_grad[dst_index] = ACTIVATION(input_grad[src_grad_index], input[src_index], ACTIVATION_PARAMS); + INPUT0_TYPE dst = ACTIVATION_KERNEL(input[src_index], ACTIVATION_PARAMS); + #if HAS_FUSED_OPS + FUSED_OPS; + output[dst_index] = FUSED_OPS_RESULT; #else - output[dst_index] = ACTIVATION(input[src_index], ACTIVATION_PARAMS); + output[dst_index] = dst; #endif #endif } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_to_bfyx_f16.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_to_bfyx_f16.cl index 302d75c0782..7c6b06daf71 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_to_bfyx_f16.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_bfyx_to_bfyx_f16.cl @@ -112,9 +112,9 @@ KERNEL(convolution_bfyx_to_bfyx_f16)( __attribute__((opencl_unroll_hint(INPUT_BLOCK_SIZE))) for (int i = 0; i < INPUT_BLOCK_SIZE; i++) { - const uint in_elem = i * SUB_GROUP_SIZE + lid; - const uint xb = in_elem % INPUT_LINE_SIZE; - const uint yb = in_elem / INPUT_LINE_SIZE; + const int in_elem = i * SUB_GROUP_SIZE + lid; + const int xb = in_elem % INPUT_LINE_SIZE; + const int yb = in_elem / INPUT_LINE_SIZE; if (input_y + yb >= 0 && input_y + yb < INPUT0_SIZE_Y && input_x + xb >= 0 && input_x + xb < INPUT0_SIZE_X) line_cache[ic * INPUT_BLOCK_SIZE + i] = input[input_offset + diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_bfyx_to_b_fs_yx_fsv32.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_bfyx_to_b_fs_yx_fsv32.cl index fe1c4064ba5..b30b27a4763 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_bfyx_to_b_fs_yx_fsv32.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/convolution_gpu_mmad_bfyx_to_b_fs_yx_fsv32.cl @@ -292,7 +292,7 @@ KERNEL(convolution_mmad_bfyx_to_b_fs_yx_fsv32)( #else // OUTPUT_FEATURE_NUM > 16 const uint dst_index = OUTPUT_GET_INDEX(b, fg*OSV + lid, y, x+i); if (x + i < OUTPUT_SIZE_X && fg*OSV + lid < OUTPUT_FEATURE_NUM) { - output[dst_index] = dst[ofm][i]; + output[dst_index] = dst[0][i]; } #endif // OUTPUT_FEATURE_NUM > 16 } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_b_fs_zyx_fsv16_dw.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_b_fs_zyx_fsv16_dw.cl index edb37951f1e..9b4408a4178 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_b_fs_zyx_fsv16_dw.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_b_fs_zyx_fsv16_dw.cl @@ -15,19 +15,58 @@ *******************************************************************************/ #include "include/include_all.cl" -#include "include/unit_type.cl" +#include "include/data_types.cl" + +#include "deconvolution_gpu_imad_common.cl" #define unroll_for __attribute__((opencl_unroll_hint)) for #define FEATURE_SLICE_SIZE 16 + #if X_BLOCK_SIZE == 1 - #define BLOCK_TYPE UNIT_TYPE - #define DST_VAR dst + #define GET_VEC_ELEM(var, idx) var #else - #define BLOCK_TYPE CAT(UNIT_TYPE, X_BLOCK_SIZE) - #define DST_VAR dst[x_block] + #define GET_VEC_ELEM(var, idx) var[idx] +#endif + +#define ACCUMULATOR_BLOCK_TYPE MAKE_VECTOR_TYPE(ACCUMULATOR_TYPE, X_BLOCK_SIZE) +#define ACTIVATION_BLOCK_TYPE MAKE_VECTOR_TYPE(ACTIVATION_TYPE, X_BLOCK_SIZE) +#define OUTPUT_BLOCK_TYPE MAKE_VECTOR_TYPE(OUTPUT_TYPE, X_BLOCK_SIZE) + +#define TO_ACTIVATION_BLOCK_TYPE(x) CAT(convert_, ACTIVATION_BLOCK_TYPE)(x) +#define TO_OUTPUT_BLOCK_TYPE(x) CAT(convert_, OUTPUT_BLOCK_TYPE)(x) + +#if FILTER_TYPE_SIZE == 1 +DECLARE_READ_BLOCK_16(load_weights, FILTER_TYPE) +#elif FILTER_TYPE_SIZE == 2 +DECLARE_READ_BLOCK_8(load_weights, FILTER_TYPE) +#else +DECLARE_READ_BLOCK_4(load_weights, FILTER_TYPE) +#endif + +#if OUTPUT_TYPE_SIZE == 1 +DECLARE_STORE_BLOCK_16(store_output, OUTPUT_TYPE) +#else +DECLARE_STORE_BLOCK_8(store_output, OUTPUT_TYPE) +#endif + +#if PRELOAD_INPUT_LINE +# if INPUT0_TYPE_SIZE +DECLARE_READ_BLOCK_16(preload_input, INPUT0_TYPE) +# else +DECLARE_READ_BLOCK_8(preload_input, INPUT0_TYPE) +# endif +#endif + +#if PRELOAD_WEIGHTS_LINE +# if FILTER_TYPE_SIZE == 1 +DECLARE_READ_BLOCK_16(preload_weights, FILTER_TYPE) +# else +DECLARE_READ_BLOCK_8(preload_weights, FILTER_TYPE) +# endif #endif __attribute__((intel_reqd_sub_group_size(FEATURE_SLICE_SIZE))) // attr:no-format +__attribute__((reqd_work_group_size(1, FEATURE_SLICE_SIZE, 1))) KERNEL(deconvolution_gpu_b_fs_zyx_fsv16_dw)( const __global INPUT0_TYPE *input, __global OUTPUT_TYPE *output, @@ -35,21 +74,26 @@ KERNEL(deconvolution_gpu_b_fs_zyx_fsv16_dw)( #if BIAS_TERM const __global BIAS_TYPE *bias, #endif - uint split_idx) +#if HAS_FUSED_OPS_DECLS + FUSED_OPS_DECLS, +#endif + uint split_idx + ) { const uint zyx = (uint)get_global_id(0); - const uint x = (zyx % (OUTPUT_SIZE_X / X_BLOCK_SIZE)) * X_BLOCK_SIZE; -#if INPUT0_LAYOUT_B_FS_YX_FSV16 - const uint y = zyx / (OUTPUT_SIZE_X / X_BLOCK_SIZE); + const uint x = (zyx % (CEIL_DIV(OUTPUT_SIZE_X, X_BLOCK_SIZE))) * X_BLOCK_SIZE; +#if OUTPUT_DIMS <= 4 + const uint y = zyx / (CEIL_DIV(OUTPUT_SIZE_X, X_BLOCK_SIZE)); const uint z = 0; #else - const uint zy = zyx / (OUTPUT_SIZE_X / X_BLOCK_SIZE); + const uint zy = zyx / (CEIL_DIV(OUTPUT_SIZE_X, X_BLOCK_SIZE)); const uint y = zy % OUTPUT_SIZE_Y; const uint z = zy / OUTPUT_SIZE_Y; #endif const uint f_block = get_group_id(1); const uint sglid = get_sub_group_local_id(); - const uint f = f_block * FEATURE_SLICE_SIZE + sglid; + const uint fg = f_block * FEATURE_SLICE_SIZE; + const uint f = fg + sglid; const uint b = (uint)get_global_id(2); const int input_x = x + PADDING_SIZE_X - (FILTER_SIZE_X - 1); @@ -75,27 +119,87 @@ KERNEL(deconvolution_gpu_b_fs_zyx_fsv16_dw)( const uint filter_offset = f_block * FEATURE_SLICE_SIZE * FILTER_SIZE_X * FILTER_SIZE_Y * FILTER_SIZE_Z; -#if BIAS_TERM - BLOCK_TYPE dst = (BLOCK_TYPE)(UNIT_BLOCK_READ(bias, f_block * FEATURE_SLICE_SIZE)); +#if BIAS_TERM && ACCUMULATOR_IS_FP + ACCUMULATOR_BLOCK_TYPE dst = (ACCUMULATOR_BLOCK_TYPE)(DT_BIAS_BLOCK_READ(bias, f_block * FEATURE_SLICE_SIZE)); #else - BLOCK_TYPE dst = (BLOCK_TYPE)(UNIT_VAL_ZERO); + ACCUMULATOR_BLOCK_TYPE dst = (ACCUMULATOR_BLOCK_TYPE)(ACCUMULATOR_VAL_ZERO); #endif - UNIT_TYPE wei[FILTER_SIZE_Z * FILTER_SIZE_Y * FILTER_SIZE_X]; +#if PRELOAD_WEIGHTS + FILTER_TYPE wei[FILTER_SIZE_Z * FILTER_SIZE_Y * FILTER_SIZE_X]; + + FUNC_CALL(load_weights)(weights, filter_offset, FILTER_SIZE_X * FILTER_SIZE_Y * FILTER_SIZE_Z, wei); +#endif + + INPUT0_TYPE src_val = INPUT0_VAL_ZERO; + +#if PRELOAD_INPUT_LINE + int first_input_x = input_x; + if (first_input_x % STRIDE_SIZE_X != 0) { + if (first_input_x >= 0) + first_input_x = ALIGN(first_input_x, STRIDE_SIZE_X); + else + first_input_x = first_input_x / STRIDE_SIZE_X * STRIDE_SIZE_X; + } + first_input_x = first_input_x / STRIDE_SIZE_X; unroll_for (uint k_z = 0; k_z < FILTER_SIZE_Z; k_z++) { + const int input_offset_z = input_z + k_z; + const bool zero_z = (input_offset_z >= INPUT0_SIZE_Z * STRIDE_SIZE_Z) || (input_offset_z < 0) || ((input_offset_z % STRIDE_SIZE_Z) != 0); unroll_for (uint k_y = 0; k_y < FILTER_SIZE_Y; k_y++) { - unroll_for (uint k_x = 0; k_x < FILTER_SIZE_X; k_x++) { - const uint wei_idx = (FILTER_SIZE_Z - k_z - 1) * FILTER_Z_PITCH + (FILTER_SIZE_Y - k_y - 1) * FILTER_Y_PITCH + (FILTER_SIZE_X - k_x - 1); - wei[wei_idx] = UNIT_BLOCK_READ(weights, filter_offset + k_z * FILTER_Z_PITCH * FEATURE_SLICE_SIZE - + k_y * FILTER_Y_PITCH * FEATURE_SLICE_SIZE - + k_x * FEATURE_SLICE_SIZE); - } - } - } + const int input_offset_y = input_y + k_y; + const bool zero_y = (input_offset_y >= INPUT0_SIZE_Y * STRIDE_SIZE_Y) || (input_offset_y < 0) || ((input_offset_y % STRIDE_SIZE_Y) != 0); + if (!zero_y && !zero_z) { + INPUT0_TYPE input_line[INPUT_BLOCK_SIZE_X] = { }; + uint fixed_input_offset_y = (uint)input_offset_y / STRIDE_SIZE_Y; + uint fixed_input_offset_z = (uint)input_offset_z / STRIDE_SIZE_Z; + uint preload_input_offset = input_offset + fixed_input_offset_z * input_z_pitch + + fixed_input_offset_y * input_y_pitch; - UNIT_TYPE src_val = UNIT_VAL_ZERO; + if (first_input_x >= 0) { + FUNC_CALL(preload_input)(input, preload_input_offset + first_input_x * input_x_pitch, INPUT_BLOCK_SIZE_X, input_line); + } else { + unroll_for (uint xi = 0; xi < INPUT_BLOCK_SIZE_X; ++xi) { + if (first_input_x + xi >= 0) { + input_line[xi] = DT_INPUT_BLOCK_READ(input, preload_input_offset + first_input_x * input_x_pitch + xi * input_x_pitch); + } else { + input_line[xi] = 0; + } + } + } +#if PRELOAD_WEIGHTS_LINE + FILTER_TYPE wei[FILTER_SIZE_X] = { }; + FUNC_CALL(preload_weights)(weights, + filter_offset + (FILTER_SIZE_Z - k_z - 1) * FILTER_Z_PITCH * FEATURE_SLICE_SIZE + + (FILTER_SIZE_Y - k_y - 1) * FILTER_Y_PITCH * FEATURE_SLICE_SIZE, + FILTER_SIZE_X, + wei); +#endif + + unroll_for (uint k_x = 0; k_x < FILTER_SIZE_X; k_x++) { +# if PRELOAD_WEIGHTS + const uint in_idx = (FILTER_SIZE_Z - k_z - 1) * FILTER_Z_PITCH + (FILTER_SIZE_Y - k_y - 1) * FILTER_Y_PITCH + (FILTER_SIZE_X - k_x - 1); + FILTER_TYPE wei_val = wei[in_idx]; +# elif PRELOAD_WEIGHTS_LINE + FILTER_TYPE wei_val = wei[(FILTER_SIZE_X - k_x - 1)]; +# else + const uint in_idx = (FILTER_SIZE_Z - k_z - 1) * FILTER_Z_PITCH + (FILTER_SIZE_Y - k_y - 1) * FILTER_Y_PITCH + (FILTER_SIZE_X - k_x - 1); + FILTER_TYPE wei_val = DT_FILTER_BLOCK_READ(weights, filter_offset + in_idx * FEATURE_SLICE_SIZE); +# endif + unroll_for (uint x_block = 0; x_block < X_BLOCK_SIZE; x_block++) { + const int input_offset_x = input_x + k_x + x_block; + const bool zero_x = (input_offset_x >= INPUT0_SIZE_X * STRIDE_SIZE_X) || (input_offset_x < 0) || ((input_offset_x % STRIDE_SIZE_X) != 0); + if (!zero_x) { + src_val = input_line[(x_block + k_x) / STRIDE_SIZE_X]; + GET_VEC_ELEM(dst, x_block) += src_val * wei_val; + } // if !zero_x + } // for X_BLOCK_SIZE + } // for FILTER_SIZE_X + } // if !zero_y && !zero_z + } // for FILTER_SIZE_Y + } // for FILTER_SIZE_Z +#else unroll_for (uint x_block = 0; x_block < X_BLOCK_SIZE; x_block++) { unroll_for (uint k_z = 0; k_z < FILTER_SIZE_Z; k_z++) { const int input_offset_z = input_z + k_z; @@ -106,24 +210,41 @@ KERNEL(deconvolution_gpu_b_fs_zyx_fsv16_dw)( unroll_for (uint k_x = 0; k_x < FILTER_SIZE_X; k_x++) { const int input_offset_x = input_x + k_x + x_block; const bool zero_x = (input_offset_x >= INPUT0_SIZE_X * STRIDE_SIZE_X) || (input_offset_x < 0) || ((input_offset_x % STRIDE_SIZE_X) != 0); - const uint in_idx = k_z * FILTER_Z_PITCH + k_y * FILTER_Y_PITCH + k_x; + const uint in_idx = (FILTER_SIZE_Z - k_z - 1) * FILTER_Z_PITCH + (FILTER_SIZE_Y - k_y - 1) * FILTER_Y_PITCH + (FILTER_SIZE_X - k_x - 1); if (!zero_z && !zero_y && !zero_x) { uint fixed_input_offset_x = (uint)input_offset_x / STRIDE_SIZE_X; uint fixed_input_offset_y = (uint)input_offset_y / STRIDE_SIZE_Y; uint fixed_input_offset_z = (uint)input_offset_z / STRIDE_SIZE_Z; - src_val = UNIT_BLOCK_READ(input, input_offset + - fixed_input_offset_z * input_z_pitch + - fixed_input_offset_y * input_y_pitch + - fixed_input_offset_x * input_x_pitch); - DST_VAR = mad(src_val, wei[in_idx], DST_VAR); - } - } - } - } - } + src_val = DT_INPUT_BLOCK_READ(input, input_offset + + fixed_input_offset_z * input_z_pitch + + fixed_input_offset_y * input_y_pitch + + fixed_input_offset_x * input_x_pitch); +# if PRELOAD_WEIGHTS + FILTER_TYPE wei_val = wei[in_idx]; +# else + FILTER_TYPE wei_val = DT_FILTER_BLOCK_READ(weights, filter_offset + in_idx * FEATURE_SLICE_SIZE); +# endif + GET_VEC_ELEM(dst, x_block) += src_val * wei_val; + } // if !zero_z && !zero_y && !zero_x + } // for FILTER_SIZE_X + } // for FILTER_SIZE_Y + } // for FILTER_SIZE_Z + } // for X_BLOCK_SIZE +#endif - dst = ACTIVATION(dst, ACTIVATION_PARAMS); + ACTIVATION_BLOCK_TYPE dequantized = TO_ACTIVATION_BLOCK_TYPE(dst); +#if BIAS_TERM && !ACCUMULATOR_IS_FP + dequantized += TO_ACTIVATION_TYPE(DT_BIAS_BLOCK_READ(bias, f_block * FEATURE_SLICE_SIZE)); +#endif + + OUTPUT_BLOCK_TYPE result; +#if HAS_FUSED_OPS + FUSED_OPS; + result = FUSED_OPS_RESULT; +#else + result = TO_OUTPUT_BLOCK_TYPE(ACTIVATION(dequantized, ACTIVATION_PARAMS)); +#endif const uint output_x_pitch = FEATURE_SLICE_SIZE; const uint output_y_pitch = output_x_pitch * (OUTPUT_PAD_BEFORE_SIZE_X + OUTPUT_SIZE_X + OUTPUT_PAD_AFTER_SIZE_X); @@ -144,15 +265,32 @@ KERNEL(deconvolution_gpu_b_fs_zyx_fsv16_dw)( if ((f_block + 1) * FEATURE_SLICE_SIZE >= OUTPUT_FEATURE_NUM) { unroll_for (uint x_block = 0; x_block < X_BLOCK_SIZE; x_block++) { + if (OUTPUT_SIZE_X % X_BLOCK_SIZE != 0 && x + X_BLOCK_SIZE >= OUTPUT_SIZE_X && x_block >= OUTPUT_SIZE_X % X_BLOCK_SIZE) + break; if (f_block * FEATURE_SLICE_SIZE + sglid < OUTPUT_FEATURE_NUM) - output[output_offset + (x + x_block) * output_x_pitch + sglid] = DST_VAR; + output[output_offset + (x + x_block) * output_x_pitch + sglid] = GET_VEC_ELEM(result, x_block); } } else #endif // OUTPUT_LEFTOVERS +#if OUTPUT_SIZE_X % X_BLOCK_SIZE != 0 + if (x + X_BLOCK_SIZE >= OUTPUT_SIZE_X) { + FUNC_CALL(store_output)(output, output_offset + x * output_x_pitch, OUTPUT_SIZE_X % X_BLOCK_SIZE, (OUTPUT_TYPE *)&result); + } else +#endif { - unroll_for (uint x_block = 0; x_block < X_BLOCK_SIZE; x_block++) { - UNIT_BLOCK_WRITE(output, output_offset + (x + x_block) * output_x_pitch, DST_VAR); - } + FUNC_CALL(store_output)(output, output_offset + x * output_x_pitch, X_BLOCK_SIZE, (OUTPUT_TYPE *)&result); } } + +#undef unroll_for +#undef FEATURE_SLICE_SIZE + +#undef GET_VEC_ELEM + +#undef ACCUMULATOR_BLOCK_TYPE +#undef ACTIVATION_BLOCK_TYPE +#undef OUTPUT_BLOCK_TYPE + +#undef TO_ACTIVATION_BLOCK_TYPE +#undef TO_OUTPUT_BLOCK_TYPE diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl index d34ef721694..c36cb5a07ce 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_bfyx_opt.cl @@ -24,13 +24,13 @@ KERNEL(deconvolution_gpu_bfyx_opt)( #if BIAS_TERM const __global BIAS_TYPE* bias, #endif - uint split_idx -#if FUSED_ELTWISE - , const __global UNIT_TYPE* fuse_input +#if HAS_FUSED_OPS_DECLS + FUSED_OPS_DECLS, #endif - ) + uint split_idx + ) { - UNIT_TYPE result = UNIT_VAL_ZERO; + ACCUMULATOR_TYPE acc = ACCUMULATOR_VAL_ZERO; const uint b_f = get_global_id(2); const uint batch_offset = b_f / OUTPUT_FEATURE_NUM; @@ -93,7 +93,7 @@ KERNEL(deconvolution_gpu_bfyx_opt)( uint filter_idx = filter_offset + of*FILTER_IFM_PITCH + (FILTER_SIZE_Y - i - 1)*FILTER_Y_PITCH + (FILTER_SIZE_X - j - 1)*FILTER_X_PITCH; for (uint h = 0; h < FILTER_OFM_NUM; h++) { - result = fma(input[input_idx], filter[filter_idx], result); + acc += TO_ACCUMULATOR_TYPE(input[input_idx]) * TO_ACCUMULATOR_TYPE(filter[filter_idx]); filter_idx += FILTER_OFM_PITCH; input_idx += INPUT0_FEATURE_PITCH; } @@ -101,7 +101,7 @@ KERNEL(deconvolution_gpu_bfyx_opt)( uint filter_idx = filter_offset + of*FILTER_OFM_PITCH + (FILTER_SIZE_Y - i - 1)*FILTER_Y_PITCH + (FILTER_SIZE_X - j - 1)*FILTER_X_PITCH; for (uint h = 0; h < FILTER_IFM_NUM; h++) { - result = fma(input[input_idx], filter[filter_idx], result); + acc += TO_ACCUMULATOR_TYPE(input[input_idx]) * TO_ACCUMULATOR_TYPE(filter[filter_idx]); filter_idx += FILTER_IFM_PITCH; input_idx += INPUT0_FEATURE_PITCH; } @@ -110,20 +110,21 @@ KERNEL(deconvolution_gpu_bfyx_opt)( } } } + + ACTIVATION_TYPE result = TO_ACTIVATION_TYPE(acc); #if BIAS_TERM result += bias[ofm_offset]; #endif + result = ACTIVATION(result, ACTIVATION_PARAMS); + const uint out_split_offset = g * OUTPUT_FEATURE_PITCH * FILTER_OFM_NUM; const uint dst_index = OUTPUT_OFFSET + out_split_offset + batch_offset*OUTPUT_BATCH_PITCH + of*OUTPUT_FEATURE_PITCH + id_y*OUTPUT_Y_PITCH + id_x*OUTPUT_X_PITCH; -#if FUSED_ELTWISE - const uint fused_index = INPUT1_OFFSET + g * INPUT1_FEATURE_PITCH * FILTER_OFM_NUM + batch_offset*INPUT1_BATCH_PITCH + of*INPUT1_FEATURE_PITCH + id_y*INPUT1_Y_PITCH + id_x*INPUT1_X_PITCH; -#if !GRADIENT - output[dst_index] = ACTIVATION(result + fuse_input[fused_index], ACTIVATION_PARAMS); + +#if HAS_FUSED_OPS + FUSED_OPS; + output[dst_index] = FUSED_OPS_RESULT; #else - output[dst_index] = result + fuse_input[fused_index]; -#endif -#else - output[dst_index] = ACTIVATION(result, ACTIVATION_PARAMS); + output[dst_index] = TO_OUTPUT_TYPE(result); #endif } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_along_f_tile_bfx.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_along_f_tile_bfx.cl new file mode 100644 index 00000000000..7bc15bd0237 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_along_f_tile_bfx.cl @@ -0,0 +1,297 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/fetch.cl" +#include "include/imad.cl" +#include "include/data_types.cl" + +#include "deconvolution_gpu_imad_common.cl" + +DECLARE_LOAD_CONTINOUS_4(load_input_ui, uint) +DECLARE_LOAD_CONTINOUS_4(load_weights_ui, uint) + +#if OUTPUT_TYPE_SIZE == 1 +DECLARE_STORE_BLOCK_16(store_output, OUTPUT_TYPE) +#elif OUTPUT_TYPE_SIZE == 2 +DECLARE_STORE_BLOCK_8(store_output, OUTPUT_TYPE) +#else +DECLARE_STORE_BLOCK_4(store_output, OUTPUT_TYPE) +#endif + +#define FILTER_TYPE4 MAKE_VECTOR_TYPE(FILTER_TYPE, 4) +#define INPUT_TYPE4 MAKE_VECTOR_TYPE(INPUT0_TYPE, 4) + +#define AS_FILTER_TYPE4 CAT(as_, FILTER_TYPE4) +#define AS_INPUT_TYPE4 CAT(as_, INPUT_TYPE4) + +#define WEIGHTS_GET_INDEX(g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(FILTER, g, o, i, z, y, x, (SIMD * TILE_OFM), TILE_IFM) +#define WEIGHTS_TILE_IFM_PITCH (TILE_IFM * SIMD * TILE_OFM) +#define WEIGHTS_IN_TILE_OFM_PITCH (TILE_IFM * SIMD) + +__attribute__((reqd_work_group_size(1, SIMD, 1))) +__attribute__((intel_reqd_sub_group_size(SIMD))) +KERNEL(deconvolution_gpu_imad_ref)( + const __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* restrict output, + const __global FILTER_TYPE* weights, +#if BIAS_TERM + const __global BIAS_TYPE* bias, +#endif +#if HAS_FUSED_OPS_DECLS + FUSED_OPS_DECLS, +#endif + uint split_idx +) { + const __global uint* input_ui = (const __global uint*)input; + const __global uint* weights_ui = (const __global uint*)weights; + + uint out_b = get_global_id(2) * TILE_B; + uint out_fg = get_group_id(1) * SIMD * TILE_OFM; + uint out_f = out_fg + get_sub_group_local_id(); + uint out_x = (uint)get_global_id(0) * TILE_X % ALIGN(OUTPUT_SIZE_X, TILE_X); +#if OUTPUT_DIMS <= 4 + uint out_y = (uint)get_global_id(0) / CEIL_DIV(OUTPUT_SIZE_X, TILE_X); + uint out_z = 0; +#elif OUTPUT_DIMS == 5 + uint out_y = (uint)get_global_id(0) / CEIL_DIV(OUTPUT_SIZE_X, TILE_X) % OUTPUT_SIZE_Y; + uint out_z = (uint)get_global_id(0) / (CEIL_DIV(OUTPUT_SIZE_X, TILE_X) * OUTPUT_SIZE_Y); +#endif + const uint sglid = get_sub_group_local_id(); + +#if GROUPED + uint group = out_fg / FILTER_OFM_NUM; + uint ofm = out_fg % FILTER_OFM_NUM + sglid; +#else + uint group = 0; + uint ofm = out_f; +#endif + uint if_start = group * FILTER_IFM_NUM; + + int in_x_start = (int)out_x + (PADDING_SIZE_X - FILTER_SIZE_X + 1); + int in_y_start = (int)out_y + (PADDING_SIZE_Y - FILTER_SIZE_Y + 1); + int in_z_start = (int)out_z + (PADDING_SIZE_Z - FILTER_SIZE_Z + 1); + + uint fy_start = 0; + uint fy_end = FILTER_SIZE_Y; + uint fy_inc = STRIDE_SIZE_Y; + if (in_y_start < 0) + fy_start = -in_y_start; + else if (in_y_start % STRIDE_SIZE_Y != 0) + fy_start = STRIDE_SIZE_Y - in_y_start % STRIDE_SIZE_Y; + if (in_y_start + FILTER_SIZE_Y - 1 >= INPUT0_SIZE_Y * STRIDE_SIZE_Y) + fy_end = INPUT0_SIZE_Y * STRIDE_SIZE_Y - in_y_start; + + uint fz_start = 0; + uint fz_end = FILTER_SIZE_Z; + uint fz_inc = STRIDE_SIZE_Z; + if (in_z_start < 0) + fz_start = -in_z_start; + else if (in_z_start % STRIDE_SIZE_Z != 0) + fz_start = STRIDE_SIZE_Z - in_z_start % STRIDE_SIZE_Z; + if (in_z_start + FILTER_SIZE_Z - 1 >= INPUT0_SIZE_Z * STRIDE_SIZE_Z) + fz_end = INPUT0_SIZE_Z * STRIDE_SIZE_Z - in_z_start; + + ACCUMULATOR_TYPE acc[TILE_B][TILE_OFM][TILE_X] = { }; + uint in[TILE_B][TILE_IFM / 4]; + uint wei[TILE_OFM][TILE_IFM / 4]; + + for (uint fz = fz_start; fz < fz_end; fz += fz_inc) { + int in_z = in_z_start + fz; + uint fixed_in_z = in_z / STRIDE_SIZE_Z; + + for (uint fy = fy_start; fy < fy_end; fy += fy_inc) { + int in_y = in_y_start + fy; + uint fixed_in_y = in_y / STRIDE_SIZE_Y; + + for (uint fx = 0; fx < FILTER_SIZE_X; fx += 1) { + int in_x = in_x_start + fx + ((TILE_X == SIMD || sglid < TILE_X) ? sglid : 0); + bool zero_x = false; + zero_x |= in_x < 0; + zero_x |= in_x >= INPUT0_SIZE_X * STRIDE_SIZE_X; + zero_x |= in_x % STRIDE_SIZE_X != 0; + in_x = max(in_x, 0); + in_x = min(in_x, INPUT0_SIZE_X * STRIDE_SIZE_X); + uint fixed_in_x = in_x / STRIDE_SIZE_X; + + uint weights_offset = WEIGHTS_GET_INDEX(group, ofm, 0, FILTER_SIZE_Z - fz - 1, FILTER_SIZE_Y - fy - 1, FILTER_SIZE_X - fx - 1) / 4; + +#if INPUT_VALID_TILE_IFM_PITCH +# if OUTPUT_DIMS <= 4 + uint input_offset = INPUT0_GET_INDEX(out_b, if_start, fixed_in_y, fixed_in_x) / 4; +# elif OUTPUT_DIMS == 5 + uint input_offset = INPUT0_GET_INDEX(out_b, if_start, fixed_in_z, fixed_in_y, fixed_in_x) / 4; +# endif +#endif + + for (uint fi = 0; fi < FILTER_IFM_NUM; fi += TILE_IFM) { + // Load weights [TILE_OFM, TILE_IFM, 1, 1] + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { + uint weights_idx = weights_offset + of * WEIGHTS_IN_TILE_OFM_PITCH / 4; + FUNC_CALL(load_weights_ui)(weights_ui, weights_idx, TILE_IFM / 4, wei[of]); + } + weights_offset += WEIGHTS_TILE_IFM_PITCH / 4; + + // Load input [TILE_B, TILE_IFM, 1, 1] +#if !INPUT_VALID_TILE_IFM_PITCH +# if OUTPUT_DIMS <= 4 + uint input_offset = INPUT0_GET_INDEX(out_b, if_start + fi, fixed_in_y, fixed_in_x) / 4; +# elif OUTPUT_DIMS == 5 + uint input_offset = INPUT0_GET_INDEX(out_b, if_start + fi, fixed_in_z, fixed_in_y, fixed_in_x) / 4; +# endif +#endif + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + uint input_idx = input_offset + ob * INPUT_IN_TILE_B_PITCH / 4; + FUNC_CALL(load_input_ui)(input_ui, input_idx, TILE_IFM / 4, in[ob]); + } +#if INPUT_VALID_TILE_IFM_PITCH + input_offset += INPUT_TILE_IFM_PITCH / 4; +#endif + if (zero_x) { + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint ifp = 0; ifp < TILE_IFM / 4; ++ifp) { + in[ob][ifp] = 0; + } + } + } + + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { + __attribute__((opencl_unroll_hint)) + for (uint tx = 0; tx < TILE_X; ++tx) { + __attribute__((opencl_unroll_hint)) + for (uint imad_it = 0; imad_it < TILE_IFM / 4; ++imad_it) { + uint in_val = intel_sub_group_shuffle(in[ob][imad_it], tx); + acc[ob][of][tx] = IMAD(acc[ob][of][tx], AS_INPUT_TYPE4(in_val), AS_FILTER_TYPE4(wei[of][imad_it])); + } + } + } + } + } + } + } + } + + ACTIVATION_TYPE dequantized[TILE_B][TILE_OFM][TILE_X]; + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { + __attribute__((opencl_unroll_hint)) + for (uint tx = 0; tx < TILE_X; ++tx) { + dequantized[ob][of][tx] = TO_ACTIVATION_TYPE(acc[ob][of][tx]); + } + } + } + +#if BIAS_TERM + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { + BIAS_TYPE bias_val = bias[out_f + of * SIMD]; + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint tx = 0; tx < TILE_X; ++tx) { + dequantized[ob][of][tx] += TO_ACTIVATION_TYPE(bias_val); + } + } + } +#endif + + OUTPUT_TYPE result[TILE_B][TILE_OFM][TILE_X]; + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { +#if FUSED_OPS_CAN_USE_PRELOAD + FUSED_OPS_PRELOAD; +#endif + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint tx = 0; tx < TILE_X; ++tx) { +#if HAS_FUSED_OPS +# if FUSED_OPS_CAN_USE_PRELOAD + FUSED_OPS_CALC; +# else + FUSED_OPS; +# endif + result[ob][of][tx] = FUSED_OPS_RESULT; +#else + result[ob][of][tx] = TO_OUTPUT_TYPE(dequantized[ob][of][tx]); +#endif + } + } + } + + bool leftovers_x = OUTPUT_SIZE_X % TILE_X != 0 && out_x + TILE_X >= OUTPUT_SIZE_X; + bool leftovers_f = OUTPUT_FEATURE_NUM % SIMD != 0 && out_f + SIMD >= OUTPUT_FEATURE_NUM; + +#if OUTPUT_NAIVE_STORE + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { + __attribute__((opencl_unroll_hint)) + for (uint tx = 0; tx < TILE_X; ++tx) { + if ((leftovers_x && tx >= OUTPUT_SIZE_X % TILE_X) || + (leftovers_f && out_f + of * SIMD >= OUTPUT_FEATURE_NUM)) + break; +#if OUTPUT_DIMS <= 4 + uint output_idx = OUTPUT_GET_INDEX(out_b + ob, out_f + of * SIMD, out_y, out_x + tx); +#elif OUTPUT_DIMS == 5 + uint output_idx = OUTPUT_GET_INDEX(out_b + ob, out_f + of * SIMD, out_z, out_y, out_x + tx); +#endif + output[output_idx] = result[ob][of][tx]; + } + } + } +#elif OUTPUT_BLOCK_X_STORE + __attribute__((opencl_unroll_hint)) + for (uint ob = 0; ob < TILE_B; ++ob) { + __attribute__((opencl_unroll_hint)) + for (uint of = 0; of < TILE_OFM; ++of) { +#if OUTPUT_DIMS <= 4 + uint output_idx = OUTPUT_GET_INDEX(out_b + ob, out_fg + of * SIMD, out_y, out_x); +#elif OUTPUT_DIMS == 5 + uint output_idx = OUTPUT_GET_INDEX(out_b + ob, out_fg + of * SIMD, out_z, out_y, out_x); +#endif + if (!leftovers_x && !leftovers_f) { + FUNC_CALL(store_output)(output, output_idx, TILE_X, result[ob][of]); + } else if (!leftovers_f) { + FUNC_CALL(store_output)(output, output_idx, OUTPUT_SIZE_X % TILE_X, result[ob][of]); + } else { + __attribute__((opencl_unroll_hint)) + for (uint tx = 0; tx < TILE_X; ++tx) { + if (out_f + of * SIMD < OUTPUT_FEATURE_NUM && out_x + tx < OUTPUT_SIZE_X) { + output[output_idx + sglid + tx * SIMD] = result[ob][of][tx]; + } + } + } + } + } +#endif +} + +#undef FILTER_TYPE4 +#undef INPUT_TYPE4 +#undef AS_FILTER_TYPE4 +#undef AS_INPUT_TYPE4 + +#undef WEIGHTS_GET_INDEX +#undef WEIGHTS_TILE_IFM_PITCH +#undef WEIGHTS_IN_TILE_OFM_PITCH diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_common.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_common.cl new file mode 100644 index 00000000000..902b7be23f9 --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_common.cl @@ -0,0 +1,245 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/data_types.cl" + +#define CEIL_DIV(a, b) (((a) + ((b) - 1)) / (b)) +#define ALIGN(a, b) (CEIL_DIV(a, b) * (b)) + +#define VEC_TO_ARR_1(var, arr, idx) \ + arr[idx] = var +#define VEC_TO_ARR_2(vec, arr, idx) \ + VEC_TO_ARR_1((vec).lo, arr, idx); \ + VEC_TO_ARR_1((vec).hi, arr, (idx) + 1) +#define VEC_TO_ARR_4(vec, arr, idx) \ + VEC_TO_ARR_2((vec).lo, arr, idx); \ + VEC_TO_ARR_2((vec).hi, arr, (idx) + 2) +#define VEC_TO_ARR_8(vec, arr, idx) \ + VEC_TO_ARR_4((vec).lo, arr, idx); \ + VEC_TO_ARR_4((vec).hi, arr, (idx) + 4) +#define VEC_TO_ARR_16(vec, arr, idx) \ + VEC_TO_ARR_8((vec).lo, arr, idx); \ + VEC_TO_ARR_8((vec).hi, arr, (idx) + 8) + +#define ARR_TO_VEC_1(arr, var, idx) \ + var = arr[idx] +#define ARR_TO_VEC_2(arr, vec, idx) \ + ARR_TO_VEC_1(arr, (vec).lo, idx); \ + ARR_TO_VEC_1(arr, (vec).hi, (idx) + 1) +#define ARR_TO_VEC_4(arr, vec, idx) \ + ARR_TO_VEC_2(arr, (vec).lo, idx); \ + ARR_TO_VEC_2(arr, (vec).hi, (idx) + 2) +#define ARR_TO_VEC_8(arr, vec, idx) \ + ARR_TO_VEC_4(arr, (vec).lo, idx); \ + ARR_TO_VEC_4(arr, (vec).hi, (idx) + 4) +#define ARR_TO_VEC_16(arr, vec, idx) \ + ARR_TO_VEC_8(arr, (vec).lo, idx); \ + ARR_TO_VEC_8(arr, (vec).hi, (idx) + 8) + +#define DECLARE_LOAD_CONTINOUS_16(name, type) \ +inline void FUNC(name)(const __global type* src, uint offset, uint size, type* dst) { \ + uint i = 0; \ + for (; i + 16 <= size; i += 16) { \ + MAKE_VECTOR_TYPE(type, 16) tmp = ((const __global MAKE_VECTOR_TYPE(type, 16)*)(src + offset + i))[0]; \ + VEC_TO_ARR_16(tmp, dst, i); \ + } \ + if (size % 16 >= 8) { \ + MAKE_VECTOR_TYPE(type, 8) tmp = ((const __global MAKE_VECTOR_TYPE(type, 8)*)(src + offset + i))[0]; \ + VEC_TO_ARR_8(tmp, dst, i); \ + i += 8; \ + } \ + if (size % 8 >= 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp = ((const __global MAKE_VECTOR_TYPE(type, 4)*)(src + offset + i))[0]; \ + VEC_TO_ARR_4(tmp, dst, i); \ + i += 4; \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp = ((const __global MAKE_VECTOR_TYPE(type, 2)*)(src + offset + i))[0]; \ + VEC_TO_ARR_2(tmp, dst, i); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + dst[i] = src[offset + i]; \ + } \ +} + +#define DECLARE_LOAD_CONTINOUS_4(name, type) \ +inline void FUNC(name)(const __global type* src, uint offset, uint size, type* dst) { \ + uint i = 0; \ + for (; i + 4 <= size; i += 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp = ((const __global MAKE_VECTOR_TYPE(type, 4)*)(src + offset + i))[0]; \ + VEC_TO_ARR_4(tmp, dst, i); \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp = ((const __global MAKE_VECTOR_TYPE(type, 2)*)(src + offset + i))[0]; \ + VEC_TO_ARR_2(tmp, dst, i); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + dst[i] = src[offset + i]; \ + } \ +} + +#define DECLARE_STORE_BLOCK_16(name, type) \ +inline void FUNC(name)(__global type* dst, uint offset, uint size, type* src) { \ + uint i = 0; \ + const uint sg_size = get_max_sub_group_size(); \ + for (; i + 16 <= size; i += 16) { \ + MAKE_VECTOR_TYPE(type, 16) tmp; \ + ARR_TO_VEC_16(src, tmp, i); \ + BLOCK_WRITEN(type, 16, dst, offset + i * sg_size, tmp); \ + } \ + if (size % 16 >= 8) { \ + MAKE_VECTOR_TYPE(type, 8) tmp; \ + ARR_TO_VEC_8(src, tmp, i); \ + BLOCK_WRITEN(type, 8, dst, offset + i * sg_size, tmp); \ + i += 8; \ + } \ + if (size % 8 >= 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp; \ + ARR_TO_VEC_4(src, tmp, i); \ + BLOCK_WRITEN(type, 4, dst, offset + i * sg_size, tmp); \ + i += 4; \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp; \ + ARR_TO_VEC_2(src, tmp, i); \ + BLOCK_WRITEN(type, 2, dst, offset + i * sg_size, tmp); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + type tmp = src[i]; \ + BLOCK_WRITEN(type, 1, dst, offset + i * sg_size, tmp); \ + } \ +} + +#define DECLARE_STORE_BLOCK_8(name, type) \ +inline void FUNC(name)(__global type* dst, uint offset, uint size, type* src) { \ + uint i = 0; \ + const uint sg_size = get_max_sub_group_size(); \ + for (; i + 8 <= size; i += 8) { \ + MAKE_VECTOR_TYPE(type, 8) tmp; \ + ARR_TO_VEC_8(src, tmp, i); \ + BLOCK_WRITEN(type, 8, dst, offset + i * sg_size, tmp); \ + } \ + if (size % 8 >= 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp; \ + ARR_TO_VEC_4(src, tmp, i); \ + BLOCK_WRITEN(type, 4, dst, offset + i * sg_size, tmp); \ + i += 4; \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp; \ + ARR_TO_VEC_2(src, tmp, i); \ + BLOCK_WRITEN(type, 2, dst, offset + i * sg_size, tmp); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + type tmp = src[i]; \ + BLOCK_WRITEN(type, 1, dst, offset + i * sg_size, tmp); \ + } \ +} + +#define DECLARE_STORE_BLOCK_4(name, type) \ +inline void FUNC(name)(__global type* dst, uint offset, uint size, type* src) { \ + uint i = 0; \ + const uint sg_size = get_max_sub_group_size(); \ + for (; i + 4 <= size; i += 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp; \ + ARR_TO_VEC_4(src, tmp, i); \ + BLOCK_WRITEN(type, 4, dst, offset + i * sg_size, tmp); \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp; \ + ARR_TO_VEC_2(src, tmp, i); \ + BLOCK_WRITEN(type, 2, dst, offset + i * sg_size, tmp); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + type tmp = src[i]; \ + BLOCK_WRITEN(type, 1, dst, offset + i * sg_size, tmp); \ + } \ +} + +#define DECLARE_READ_BLOCK_16(name, type) \ +inline void FUNC(name)(const __global type* src, uint offset, uint size, type* dst) { \ + uint i = 0; \ + const uint sg_size = get_max_sub_group_size(); \ + for (; i + 16 <= size; i += 16) { \ + MAKE_VECTOR_TYPE(type, 16) tmp = BLOCK_READN(type, 16, src, offset + i * sg_size); \ + VEC_TO_ARR_16(tmp, dst, i); \ + } \ + if (size % 16 >= 8) { \ + MAKE_VECTOR_TYPE(type, 8) tmp = BLOCK_READN(type, 8, src, offset + i * sg_size); \ + VEC_TO_ARR_8(tmp, dst, i); \ + i += 8; \ + } \ + if (size % 8 >= 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp = BLOCK_READN(type, 4, src, offset + i * sg_size); \ + VEC_TO_ARR_4(tmp, dst, i); \ + i += 4; \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp = BLOCK_READN(type, 2, src, offset + i * sg_size); \ + VEC_TO_ARR_2(tmp, dst, i); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + type tmp = BLOCK_READN(type, 1, src, offset + i * sg_size); \ + dst[i] = tmp; \ + } \ +} + +#define DECLARE_READ_BLOCK_8(name, type) \ +inline void FUNC(name)(const __global type* src, uint offset, uint size, type* dst) { \ + uint i = 0; \ + const uint sg_size = get_max_sub_group_size(); \ + for (; i + 8 <= size; i += 8) { \ + MAKE_VECTOR_TYPE(type, 8) tmp = BLOCK_READN(type, 8, src, offset + i * sg_size); \ + VEC_TO_ARR_8(tmp, dst, i); \ + } \ + if (size % 8 >= 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp = BLOCK_READN(type, 4, src, offset + i * sg_size); \ + VEC_TO_ARR_4(tmp, dst, i); \ + i += 4; \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp = BLOCK_READN(type, 2, src, offset + i * sg_size); \ + VEC_TO_ARR_2(tmp, dst, i); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + type tmp = BLOCK_READN(type, 1, src, offset + i * sg_size); \ + dst[i] = tmp; \ + } \ +} + +#define DECLARE_READ_BLOCK_4(name, type) \ +inline void FUNC(name)(const __global type* src, uint offset, uint size, type* dst) { \ + uint i = 0; \ + const uint sg_size = get_max_sub_group_size(); \ + for (; i + 4 <= size; i += 4) { \ + MAKE_VECTOR_TYPE(type, 4) tmp = BLOCK_READN(type, 4, src, offset + i * sg_size); \ + VEC_TO_ARR_4(tmp, dst, i); \ + } \ + if (size % 4 >= 2) { \ + MAKE_VECTOR_TYPE(type, 2) tmp = BLOCK_READN(type, 2, src, offset + i * sg_size); \ + VEC_TO_ARR_2(tmp, dst, i); \ + i += 2; \ + } \ + if (size % 2 == 1) { \ + type tmp = BLOCK_READN(type, 1, src, offset + i * sg_size); \ + dst[i] = tmp; \ + } \ +} diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_ref.cl new file mode 100644 index 00000000000..29f8865bedf --- /dev/null +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_imad_ref.cl @@ -0,0 +1,179 @@ +// Copyright (c) 2020 Intel Corporation +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "include/fetch.cl" +#include "include/imad.cl" +#include "include/data_types.cl" + +#include "deconvolution_gpu_imad_common.cl" + +DECLARE_LOAD_CONTINOUS_4(load_input_ui, uint) +DECLARE_LOAD_CONTINOUS_4(load_weights_ui, uint) + +#define FILTER_TYPE4 MAKE_VECTOR_TYPE(FILTER_TYPE, 4) +#define INPUT_TYPE4 MAKE_VECTOR_TYPE(INPUT0_TYPE, 4) + +#define AS_FILTER_TYPE4 CAT(as_, FILTER_TYPE4) +#define AS_INPUT_TYPE4 CAT(as_, INPUT_TYPE4) + +#define WEIGHTS_GET_INDEX(g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV32_ISV4_INDEX(FILTER, g, o, i, z, y, x) + +KERNEL(deconvolution_gpu_imad_ref)( + const __global INPUT0_TYPE* input, + __global OUTPUT_TYPE* restrict output, + const __global FILTER_TYPE* weights, +#if BIAS_TERM + const __global BIAS_TYPE* bias, +#endif +#if HAS_FUSED_OPS_DECLS + FUSED_OPS_DECLS, +#endif + uint split_idx +) { + const __global uint* input_ui = (const __global uint*)input; + const __global uint* weights_ui = (const __global uint*)weights; + + uint out_b = get_global_id(2); + uint out_f = get_global_id(0); + uint out_x = (uint)get_global_id(1) % OUTPUT_SIZE_X; +#if OUTPUT_DIMS <= 4 + uint out_y = (uint)get_global_id(1) / OUTPUT_SIZE_X; + uint out_z = 0; +#elif OUTPUT_DIMS == 5 + uint out_y = (uint)get_global_id(1) / OUTPUT_SIZE_X % OUTPUT_SIZE_Y; + uint out_z = (uint)get_global_id(1) / (OUTPUT_SIZE_X * OUTPUT_SIZE_Y); +#endif + +#if GROUPED + uint group = out_f / FILTER_OFM_NUM; + uint ofm = out_f % FILTER_OFM_NUM; +#else + uint group = 0; + uint ofm = out_f; +#endif + uint if_start = group * FILTER_IFM_NUM; + + int in_x_start = (int)out_x + (PADDING_SIZE_X - FILTER_SIZE_X + 1); + int in_y_start = (int)out_y + (PADDING_SIZE_Y - FILTER_SIZE_Y + 1); + int in_z_start = (int)out_z + (PADDING_SIZE_Z - FILTER_SIZE_Z + 1); + + uint fx_start = 0; + uint fx_end = FILTER_SIZE_X; + uint fx_inc = STRIDE_SIZE_X; + if (in_x_start < 0) + fx_start = -in_x_start; + else if (in_x_start % STRIDE_SIZE_X != 0) + fx_start = STRIDE_SIZE_X - in_x_start % STRIDE_SIZE_X; + if (in_x_start + FILTER_SIZE_X - 1 >= INPUT0_SIZE_X * STRIDE_SIZE_X) + fx_end = INPUT0_SIZE_X * STRIDE_SIZE_X - in_x_start; + + uint fy_start = 0; + uint fy_end = FILTER_SIZE_Y; + uint fy_inc = STRIDE_SIZE_Y; + if (in_y_start < 0) + fy_start = -in_y_start; + else if (in_y_start % STRIDE_SIZE_Y != 0) + fy_start = STRIDE_SIZE_Y - in_y_start % STRIDE_SIZE_Y; + if (in_y_start + FILTER_SIZE_Y - 1 >= INPUT0_SIZE_Y * STRIDE_SIZE_Y) + fy_end = INPUT0_SIZE_Y * STRIDE_SIZE_Y - in_y_start; + + uint fz_start = 0; + uint fz_end = FILTER_SIZE_Z; + uint fz_inc = STRIDE_SIZE_Z; + if (in_z_start < 0) + fz_start = -in_z_start; + else if (in_z_start % STRIDE_SIZE_Z != 0) + fz_start = STRIDE_SIZE_Z - in_z_start % STRIDE_SIZE_Z; + if (in_z_start + FILTER_SIZE_Z - 1 >= INPUT0_SIZE_Z * STRIDE_SIZE_Z) + fz_end = INPUT0_SIZE_Z * STRIDE_SIZE_Z - in_z_start; + + ACCUMULATOR_TYPE acc = 0; + uint in[TILE_IFM / 4]; + uint wei[TILE_IFM / 4]; + + for (uint fz = fz_start; fz < fz_end; fz += fz_inc) { + int in_z = in_z_start + fz; + uint fixed_in_z = in_z / STRIDE_SIZE_Z; + + for (uint fy = fy_start; fy < fy_end; fy += fy_inc) { + int in_y = in_y_start + fy; + uint fixed_in_y = in_y / STRIDE_SIZE_Y; + + for (uint fx = fx_start; fx < fx_end; fx += fx_inc) { + int in_x = in_x_start + fx; + uint fixed_in_x = in_x / STRIDE_SIZE_X; + + for (uint fi = 0; fi < FILTER_IFM_NUM; fi += TILE_IFM) { + // Load weights [1, TILE_IFM, 1, 1] + uint weights_idx = WEIGHTS_GET_INDEX(group, ofm, fi, FILTER_SIZE_Z - fz - 1, FILTER_SIZE_Y - fy - 1, FILTER_SIZE_X - fx - 1); + FUNC_CALL(load_weights_ui)(weights_ui, weights_idx / 4, TILE_IFM / 4, wei); + + // Load input [1, TILE_IFM, 1, 1] +#if FILTER_GROUPS_NUM == 1 || FILTER_IFM_NUM % TILE_IFM == 0 +# if OUTPUT_DIMS <= 4 + uint input_idx = INPUT0_GET_INDEX(out_b, fi + if_start, fixed_in_y, fixed_in_x); +# elif OUTPUT_DIMS == 5 + uint input_idx = INPUT0_GET_INDEX(out_b, fi + if_start, fixed_in_z, fixed_in_y, fixed_in_x); +# endif + FUNC_CALL(load_input_ui)(input_ui, input_idx / 4, TILE_IFM / 4, in); +#else + for (uint tifm = 0; tifm < TILE_IFM; ++tifm) { +# if OUTPUT_DIMS <= 4 + uint input_idx = INPUT0_GET_INDEX(out_b, fi + if_start + tifm, fixed_in_y, fixed_in_x); +# elif OUTPUT_DIMS == 5 + uint input_idx = INPUT0_GET_INDEX(out_b, fi + if_start + tifm, fixed_in_z, fixed_in_y, fixed_in_x); +# endif + ((INPUT0_TYPE*)(in))[tifm] = input[input_idx]; + } +#endif + + __attribute__((opencl_unroll_hint)) + for (uint imad_it = 0; imad_it < TILE_IFM / 4; ++imad_it) { + acc = IMAD(acc, AS_INPUT_TYPE4(in[imad_it]), AS_FILTER_TYPE4(wei[imad_it])); + } + } + } + } + } + + ACTIVATION_TYPE dequantized; + dequantized = TO_ACTIVATION_TYPE(acc); + +#if BIAS_TERM + BIAS_TYPE bias_val = bias[out_f]; + dequantized += TO_ACTIVATION_TYPE(bias_val); +#endif + + OUTPUT_TYPE result; +#if HAS_FUSED_OPS + FUSED_OPS; + result = FUSED_OPS_RESULT; +#else + result = TO_OUTPUT_TYPE(dequantized); +#endif + +#if OUTPUT_DIMS <= 4 + uint output_idx = OUTPUT_GET_INDEX(out_b, out_f, out_y, out_x); +#elif OUTPUT_DIMS == 5 + uint output_idx = OUTPUT_GET_INDEX(out_b, out_f, out_z, out_y, out_x); +#endif + output[output_idx] = result; +} + +#undef FILTER_TYPE4 +#undef INPUT_TYPE4 +#undef AS_FILTER_TYPE4 +#undef AS_INPUT_TYPE4 + +#undef WEIGHTS_GET_INDEX diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl index 1929806cfd9..f7fd8ee408a 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/deconvolution_gpu_ref.cl @@ -21,13 +21,13 @@ KERNEL(deconvolution_gpu_yxfb_ref)( #if BIAS_TERM const __global BIAS_TYPE* bias, #endif - uint split_idx -#if FUSED_ELTWISE - , const __global UNIT_TYPE* fuse_input +#if HAS_FUSED_OPS_DECLS + FUSED_OPS_DECLS, #endif - ) + uint split_idx + ) { - UNIT_TYPE result = UNIT_VAL_ZERO; + ACCUMULATOR_TYPE acc = ACCUMULATOR_VAL_ZERO; #if DIM_ORDER_XYBF == 1 const uint out_x = get_global_id(0); @@ -99,42 +99,47 @@ KERNEL(deconvolution_gpu_yxfb_ref)( uint fixed_input_offset_x = (uint)input_offset_x / STRIDE_SIZE_X; uint fixed_input_offset_y = (uint)input_offset_y / STRIDE_SIZE_Y; uint fixed_input_offset_z = (uint)input_offset_z / STRIDE_SIZE_Z; -#if OUTPUT_LAYOUT_B_FS_ZYX_FSV16 || OUTPUT_LAYOUT_B_FS_YX_FSV16 || OUTPUT_LAYOUT_BS_FS_ZYX_BSV16_FSV16 + uint input_idx; -#else - uint input_idx = input_offset + (uint)fixed_input_offset_x*INPUT0_X_PITCH + (uint)fixed_input_offset_y*INPUT0_Y_PITCH + (uint)fixed_input_offset_z*INPUT0_Z_PITCH; +#if INPUT0_SIMPLE + input_idx = input_offset + (uint)fixed_input_offset_x*INPUT0_X_PITCH + (uint)fixed_input_offset_y*INPUT0_Y_PITCH + (uint)fixed_input_offset_z*INPUT0_Z_PITCH; #endif + #if GRADIENT uint filter_idx = filter_offset + of*FILTER_IFM_PITCH + (FILTER_SIZE_Z - k - 1)*FILTER_Z_PITCH + (FILTER_SIZE_Y - i - 1)*FILTER_Y_PITCH + (FILTER_SIZE_X - j - 1)*FILTER_X_PITCH; - for (uint h = 0; h < FILTER_OFM_NUM; h++) - { -#if INPUT0_LAYOUT_B_FS_ZYX_FSV16 || INPUT0_LAYOUT_BS_FS_ZYX_BSV16_FSV16 - input_idx = INPUT0_GET_INDEX(batch_offset, h + g*FILTER_IFM_NUM, fixed_input_offset_z, fixed_input_offset_y, fixed_input_offset_x); -#elif INPUT0_LAYOUT_BS_FS_YX_FSV16 + for (uint h = 0; h < FILTER_OFM_NUM; h++) { +#if !INPUT0_SIMPLE +# if INPUT0_DIMS <= 4 input_idx = INPUT0_GET_INDEX(batch_offset, h + g*FILTER_IFM_NUM, fixed_input_offset_y, fixed_input_offset_x); +# elif INPUT0_DIMS == 5 + input_idx = INPUT0_GET_INDEX(batch_offset, h + g*FILTER_IFM_NUM, fixed_input_offset_z, fixed_input_offset_y, fixed_input_offset_x); +# endif #endif - result = fma(input[input_idx], filter[filter_idx], result); + + acc += TO_ACCUMULATOR_TYPE(input[input_idx]) * TO_ACCUMULATOR_TYPE(filter[filter_idx]); filter_idx += FILTER_OFM_PITCH; -#if !INPUT0_LAYOUT_B_FS_ZYX_FSV16 && !INPUT0_LAYOUT_BS_FS_ZYX_BSV16_FSV16 && !INPUT0_LAYOUT_B_FS_YX_FSV16 +#if INPUT0_SIMPLE input_idx += INPUT0_FEATURE_PITCH; #endif } -#else +#else // GRADIENT uint filter_idx = filter_offset + of*FILTER_OFM_PITCH + (FILTER_SIZE_Z - k - 1)*FILTER_Z_PITCH + (FILTER_SIZE_Y - i - 1)*FILTER_Y_PITCH + (FILTER_SIZE_X - j - 1)*FILTER_X_PITCH; - for (uint h = 0; h < FILTER_IFM_NUM; h++) - { -#if OUTPUT_LAYOUT_B_FS_ZYX_FSV16 || OUTPUT_LAYOUT_BS_FS_ZYX_BSV16_FSV16 - input_idx = INPUT0_GET_INDEX(batch_offset, h + g*FILTER_IFM_NUM, fixed_input_offset_z, fixed_input_offset_y, fixed_input_offset_x); -#elif OUTPUT_LAYOUT_B_FS_YX_FSV16 + for (uint h = 0; h < FILTER_IFM_NUM; h++) { +#if !INPUT0_SIMPLE +# if INPUT0_DIMS <= 4 input_idx = INPUT0_GET_INDEX(batch_offset, h + g*FILTER_IFM_NUM, fixed_input_offset_y, fixed_input_offset_x); +# elif INPUT0_DIMS == 5 + input_idx = INPUT0_GET_INDEX(batch_offset, h + g*FILTER_IFM_NUM, fixed_input_offset_z, fixed_input_offset_y, fixed_input_offset_x); +# endif #endif - result = fma(input[input_idx], filter[filter_idx], result); + + acc += TO_ACCUMULATOR_TYPE(input[input_idx]) * TO_ACCUMULATOR_TYPE(filter[filter_idx]); filter_idx += FILTER_IFM_PITCH; -#if !OUTPUT_LAYOUT_B_FS_ZYX_FSV16 && !OUTPUT_LAYOUT_B_FS_YX_FSV16 && !OUTPUT_LAYOUT_BS_FS_ZYX_BSV16_FSV16 +#if INPUT0_SIMPLE input_idx += INPUT0_FEATURE_PITCH; #endif } -#endif +#endif // GRADIENT } } } @@ -142,35 +147,27 @@ KERNEL(deconvolution_gpu_yxfb_ref)( } } + ACTIVATION_TYPE pre_activation = TO_ACTIVATION_TYPE(acc); #if BIAS_TERM - result += bias[ofm_offset]; + pre_activation += TO_ACTIVATION_TYPE(bias[ofm_offset]); #endif - const uint out_split_offset = g * OUTPUT_FEATURE_PITCH * FILTER_OFM_NUM; -#if OUTPUT_LAYOUT_B_FS_ZYX_FSV16 || OUTPUT_LAYOUT_BS_FS_ZYX_BSV16_FSV16 - const uint dst_index = OUTPUT_OFFSET + OUTPUT_GET_INDEX(batch_offset, g * FILTER_OFM_NUM + of, out_z, out_y, out_x); -#elif OUTPUT_LAYOUT_B_FS_YX_FSV16 - const uint dst_index = OUTPUT_OFFSET + OUTPUT_GET_INDEX(batch_offset, g * FILTER_OFM_NUM + of, out_y, out_x); + ACTIVATION_TYPE post_activation = ACTIVATION(pre_activation, ACTIVATION_PARAMS); + + OUTPUT_TYPE result; +#if HAS_FUSED_OPS + FUSED_OPS; + result = FUSED_OPS_RESULT; #else - const uint dst_index = OUTPUT_OFFSET + out_split_offset + batch_offset*OUTPUT_BATCH_PITCH + of*OUTPUT_FEATURE_PITCH + out_z*OUTPUT_Z_PITCH + out_y*OUTPUT_Y_PITCH + out_x*OUTPUT_X_PITCH; -#endif -#if FUSED_ELTWISE -#if OUTPUT_LAYOUT_B_FS_ZYX_FSV16 || OUTPUT_LAYOUT_BS_FS_ZYX_BSV16_FSV16 - const uint fused_index = INPUT1_OFFSET + INPUT1_GET_INDEX(batch_offset, g * FILTER_OFM_NUM + of, out_z, out_y, out_x); -#elif OUTPUT_LAYOUT_B_FS_YX_FSV16 - const uint fused_index = INPUT1_OFFSET + INPUT1_GET_INDEX(batch_offset, g * FILTER_OFM_NUM + of, out_y, out_x); -#else - const uint fused_index = INPUT1_OFFSET + split_idx * INPUT1_FEATURE_PITCH * FILTER_OFM_NUM + batch_offset*INPUT1_BATCH_PITCH + of*INPUT1_FEATURE_PITCH + out_z*INPUT1_Z_PITCH + out_y*INPUT1_Y_PITCH + out_x*INPUT1_X_PITCH; -#endif -#if !GRADIENT - output[dst_index] = ACTIVATION(result + fuse_input[fused_index], ACTIVATION_PARAMS); -#else - output[dst_index] = result + fuse_input[fused_index]; + result = TO_OUTPUT_TYPE(post_activation); #endif +#if OUTPUT_DIMS <= 4 + const uint dst_index = OUTPUT_GET_INDEX(batch_offset, g * FILTER_OFM_NUM + of, out_y, out_x); +#elif OUTPUT_DIMS == 5 + const uint dst_index = OUTPUT_GET_INDEX(batch_offset, g * FILTER_OFM_NUM + of, out_z, out_y, out_x); #else - output[dst_index] = ACTIVATION(result, ACTIVATION_PARAMS); +# error deconvolution_gpu_ref.cl - Unsupported number of output dimensions. #endif + output[dst_index] = result; } - -#undef ACTIVATION diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gen9_common_conv_bwd_data.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gen9_common_conv_bwd_data.cl index 30e1ebf3164..12935b052f8 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gen9_common_conv_bwd_data.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/gen9_common_conv_bwd_data.cl @@ -15,6 +15,8 @@ *******************************************************************************/ #include "ocl_types.h" +#include "include/fetch.cl" +#include "include/data_types.cl" #if ID > 1 #define CASE_3D 1 @@ -30,12 +32,16 @@ __attribute__((intel_reqd_sub_group_size(SUB_GROUP_SIZE))) // attr:no-format #endif KERNEL(gen9_common_conv_bwd_data_kernel)( const __global DATA_T *diff_dst, - __global DATA_T *diff_src, + __global DATA_T * restrict diff_src, const __global DATA_T *wei, #if WITH_BIAS const __global DATA_T *bias, #endif - uint split_idx) +#if HAS_FUSED_OPS_DECLS + FUSED_OPS_DECLS, +#endif + uint split_idx + ) { const int input_offset = (INPUT0_PAD_BEFORE_FEATURE_NUM / OC_BLOCK) * OD_FULL * OH_FULL * OW_FULL * OC_BLOCK * MB_BLOCK + (INPUT0_PAD_BEFORE_SIZE_Z) * OH_FULL * OW_FULL * OC_BLOCK * MB_BLOCK + @@ -235,6 +241,17 @@ KERNEL(gen9_common_conv_bwd_data_kernel)( blockC00 = ACTIVATION(blockC00, ACTIVATION_PARAMS); blockC01 = ACTIVATION(blockC01, ACTIVATION_PARAMS); +#if HAS_FUSED_OPS + { + FUSED_OPS_BLOCK_C00; + blockC00 = FUSED_OPS_RESULT_BLOCK_C00; + } + { + FUSED_OPS_BLOCK_C01; + blockC01 = FUSED_OPS_RESULT_BLOCK_C01; + } +#endif + SAVE_SRC_DIFF(blockC00, src_write0, 0); SAVE_SRC_DIFF(blockC01, src_write0, 8); @@ -426,6 +443,10 @@ KERNEL(gen9_common_conv_bwd_data_kernel)( for (int i = 0; i < IW_BLOCK; i++) { blockC00[i] = ACTIVATION(blockC00[i], ACTIVATION_PARAMS); if (iw + i >= IW) continue; +#if HAS_FUSED_OPS + FUSED_OPS_BLOCK_CI; + blockC00[i] = FUSED_OPS_RESULT_BLOCK_CI; +#endif BLOCK_WRITE((__global BLOCK_DATA_T *)(&(src_write0)[i * IC_BLOCK]), AS_BLOCK_DATA_T(blockC00[i])); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl index 485d24ba340..f51dcb7a395 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/fetch.cl @@ -118,18 +118,17 @@ inline uint FUNC(get_b_fs_yx_fsv_index)(uint b, uint f, uint y, uint x, uint f_pad_before, uint f_pad_after, uint y_pad_before, uint y_pad_after, uint x_pad_before, uint x_pad_after, uint alignment) { - const uint fs = f / alignment; - const uint fsv = f % alignment; + const uint feature = f + f_pad_before; + const uint fs = feature / alignment; + const uint fsv = feature % alignment; const uint x_pitch = alignment; const uint y_pitch = x_pitch * (x_pad_before + x_size + x_pad_after); const uint total_f_size = f_pad_before + f_size + f_pad_after; const uint fs_pitch = y_pitch * (y_pad_before + y_size + y_pad_after); const uint b_pitch = fs_pitch * ((total_f_size + alignment - 1) / alignment); - const uint fs_pad_before = f_pad_before / alignment; - const uint output_offset = b * b_pitch + - (fs + fs_pad_before) * fs_pitch + + fs * fs_pitch + (y_pad_before + y) * y_pitch + (x_pad_before + x) * x_pitch + fsv; @@ -142,7 +141,7 @@ inline uint FUNC(get_b_fs_yx_fsv_index_safe)(uint b, uint f, uint y, uint x, uint f_pad_before, uint f_pad_after, uint y_pad_before, uint y_pad_after, uint x_pad_before, uint x_pad_after, uint alignment) { - const uint f_mod = f % f_size; + const uint f_mod = f_pad_before + (f % f_size); const uint fs = f_mod / alignment; const uint fsv = f_mod % alignment; const uint x_pitch = alignment; @@ -151,10 +150,8 @@ inline uint FUNC(get_b_fs_yx_fsv_index_safe)(uint b, uint f, uint y, uint x, const uint fs_pitch = y_pitch * (y_pad_before + y_size + y_pad_after); const uint b_pitch = fs_pitch * ((total_f_size + alignment - 1) / alignment); - const uint fs_pad_before = f_pad_before / alignment; - const uint output_offset = b * b_pitch + - (fs_pad_before + fs) * fs_pitch + + fs * fs_pitch + (y_pad_before + (y % y_size)) * y_pitch + (x_pad_before + (x % x_size)) * x_pitch + fsv; @@ -1103,8 +1100,9 @@ inline uint FUNC(get_b_fs_zyx_fsv_index)(uint b, uint f, uint z, uint y, uint x uint x_pad_before, uint x_pad_after, uint alignment) { - const uint fs = f / alignment; - const uint fsv = f % alignment; + const uint feature = f + f_pad_before; + const uint fs = feature / alignment; + const uint fsv = feature % alignment; const uint x_pitch = alignment; const uint y_pitch = x_pitch * (x_pad_before + x_size + x_pad_after); const uint z_pitch = y_pitch * (y_pad_before + y_size + y_pad_after); @@ -1112,10 +1110,8 @@ inline uint FUNC(get_b_fs_zyx_fsv_index)(uint b, uint f, uint z, uint y, uint x const uint total_f_size = f_pad_before + f_size + f_pad_after; const uint b_pitch = fs_pitch * ((total_f_size + alignment - 1) / alignment); - const uint fs_pad_before = f_pad_before / alignment; - const uint output_offset = b * b_pitch + - (fs_pad_before + fs) * fs_pitch + + fs * fs_pitch + (z_pad_before + z) * z_pitch + (y_pad_before + y) * y_pitch + (x_pad_before + x) * x_pitch @@ -1131,7 +1127,7 @@ inline uint FUNC(get_b_fs_zyx_fsv_index_safe)(uint b, uint f, uint z, uint y, u uint y_pad_before, uint y_pad_after, uint x_pad_before, uint x_pad_after, uint alignment) { - const uint f_mod = f % f_size; + const uint f_mod = f_pad_before + (f % f_size); const uint fs = f_mod / alignment; const uint fsv = f_mod % alignment; const uint x_pitch = alignment; @@ -1141,10 +1137,8 @@ inline uint FUNC(get_b_fs_zyx_fsv_index_safe)(uint b, uint f, uint z, uint y, u const uint total_f_size = f_pad_before + f_size + f_pad_after; const uint b_pitch = fs_pitch * ((total_f_size + alignment - 1) / alignment); - const uint fs_pad_before = f_pad_before / alignment; - const uint output_offset = b * b_pitch + - (fs_pad_before + fs) * fs_pitch + + fs * fs_pitch + (z_pad_before + (z % z_size)) * z_pitch + (y_pad_before + (y % y_size)) * y_pitch + (x_pad_before + (x % x_size)) * x_pitch @@ -1160,7 +1154,7 @@ inline uint FUNC(get_bs_fs_zyx_bsv_fsv_index_safe)(uint b, uint f, uint z, uint uint y_pad_before, uint y_pad_after, uint x_pad_before, uint x_pad_after, uint alignmentF, uint alignmentB) { const uint b_mod = b % b_size; - const uint f_mod = f % f_size; + const uint f_mod = f_pad_before + (f % f_size); const uint fs = f_mod / alignmentF; const uint fsv = f_mod % alignmentF; const uint bs = b_mod / alignmentB; @@ -1172,10 +1166,8 @@ inline uint FUNC(get_bs_fs_zyx_bsv_fsv_index_safe)(uint b, uint f, uint z, uint const uint fs_pitch = z_pitch * (z_pad_before + z_size + z_pad_after); const uint b_pitch = fs_pitch * ((total_f_size + alignmentF - 1) / alignmentF); - const uint fs_pad_before = f_pad_before / alignmentF; - const uint output_offset = (bs * b_pitch) + (bsv * alignmentF) + - (fs_pad_before + fs) * fs_pitch + + fs * fs_pitch + (z_pad_before + (z % z_size)) * z_pitch + (y_pad_before + (y % y_size)) * y_pitch + (x_pad_before + (x % x_size)) * x_pitch @@ -1191,8 +1183,9 @@ inline uint FUNC(get_bs_fs_zyx_bsv16_fsv16_index)(uint b, uint f, uint z, uint uint y_pad_before, uint y_pad_after, uint x_pad_before, uint x_pad_after) { const uint alignment = 16; - const uint fs = f / alignment; - const uint fsv = f % alignment; + const uint feature = f + f_pad_before; + const uint fs = feature / alignment; + const uint fsv = feature % alignment; const uint bs = b / alignment; const uint bsv = b % alignment; @@ -1204,10 +1197,8 @@ inline uint FUNC(get_bs_fs_zyx_bsv16_fsv16_index)(uint b, uint f, uint z, uint const uint total_f_size = f_pad_before + f_size + f_pad_after; const uint bs_pitch = fs_pitch * ((total_f_size + alignment - 1) / alignment); - const uint fs_pad_before = f_pad_before / alignment; - const uint output_offset = bs * bs_pitch + - (fs_pad_before + fs) * fs_pitch + + fs * fs_pitch + (z_pad_before + z) * z_pitch + (y_pad_before + y) * y_pitch + (x_pad_before + x) * x_pitch + @@ -1510,6 +1501,56 @@ inline uint FUNC(get_gs_oi_yxs_gsv_yxsv4_index)(uint g, uint o, uint i, uint y, ((o) / (sub_group_size))*CAT(prefix, _OFM_PITCH) \ ) +inline uint FUNC(get_g_os_zyx_is_osv_isv_index)(uint g, uint o, uint i, uint z, uint y, uint x, + uint g_size, uint o_size, uint i_size, uint z_size, uint y_size, uint x_size, + uint osv, uint isv) { + uint is_size = (i_size + isv - 1) / isv; + uint os_size = (o_size + osv - 1) / osv; + + uint isv_index = i % isv; + uint osv_index = o % osv; + uint is_index = i / isv; + uint os_index = o / osv; + + uint isv_pitch = 1; + uint osv_pitch = isv_pitch * isv; + uint is_pitch = osv_pitch * osv; + uint x_pitch = is_pitch * is_size; + uint y_pitch = x_pitch * x_size; + uint z_pitch = y_pitch * y_size; + uint os_pitch = z_pitch * z_size; + uint g_pitch = os_pitch * os_size; + + uint index = 0; + index += isv_index * isv_pitch; + index += osv_index * osv_pitch; + index += is_index * is_pitch; + index += x * x_pitch; + index += y * y_pitch; + index += z * z_pitch; + index += os_index * os_pitch; + index += g * g_pitch; + return index; +} + +#define GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, osv, isv) \ + FUNC_CALL(get_g_os_zyx_is_osv_isv_index)( \ + g, o, i, z, y, x, \ + CAT(tensor, _GROUPS_NUM), \ + CAT(tensor, _OFM_NUM), \ + CAT(tensor, _IFM_NUM), \ + CAT(tensor, _SIZE_Z), \ + CAT(tensor, _SIZE_Y), \ + CAT(tensor, _SIZE_X), \ + osv, isv) + +#define GET_FILTER_G_OS_ZYX_IS_OSV16_ISV4_INDEX(tensor, g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, 16, 4) +#define GET_FILTER_G_OS_ZYX_IS_OSV16_ISV16_INDEX(tensor, g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, 16, 16) +#define GET_FILTER_G_OS_ZYX_IS_OSV16_ISV32_INDEX(tensor, g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, 16, 32) +#define GET_FILTER_G_OS_ZYX_IS_OSV32_ISV4_INDEX(tensor, g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, 32, 4) +#define GET_FILTER_G_OS_ZYX_IS_OSV32_ISV16_INDEX(tensor, g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, 32, 16) +#define GET_FILTER_G_OS_ZYX_IS_OSV32_ISV32_INDEX(tensor, g, o, i, z, y, x) GET_FILTER_G_OS_ZYX_IS_OSV_ISV_INDEX(tensor, g, o, i, z, y, x, 32, 32) + #define DECLARE_SAMPLER const sampler_t imageSampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST #if FP16_UNIT_USED diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/reshape_dims.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/reshape_dims.cl index 8fa7b51b46c..4d53bc3d264 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/reshape_dims.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/include/reshape_dims.cl @@ -125,15 +125,16 @@ inline uint8 FUNC(reshape_6_to_4)(uint o, uint i, uint w, uint z, uint y, uint x return (uint8)(0, dst_b, dst_f, 0, 0, dst_y, dst_x, 0); } -inline uint8 FUNC(reshape_grouped_to_simple)(uint g, uint o, uint i, uint z, uint y, uint x, uint src_size_groups, uint dst_size_ofm) +inline uint8 FUNC(reshape_grouped)(uint g, uint o, uint i, uint z, uint y, uint x, uint src_size_ofm, uint dst_size_ofm) { - const uint ofm_per_group = dst_size_ofm / src_size_groups; - const uint dst_ofm = g * ofm_per_group + (o % ofm_per_group); + const uint flat_ofm = g * src_size_ofm + o; + const uint dst_ofm = flat_ofm % dst_size_ofm; + const uint dst_g = flat_ofm / dst_size_ofm; const uint dst_ifm = i; const uint dst_z = z; const uint dst_y = y; const uint dst_x = x; - return (uint8)(0, dst_ofm, dst_ifm, 0, dst_z, dst_y, dst_x, 0); + return (uint8)(dst_g, dst_ofm, dst_ifm, 0, dst_z, dst_y, dst_x, 0); } inline uint8 FUNC(reshape_dims)( @@ -178,11 +179,15 @@ inline uint8 FUNC(reshape_dims_with_groups)( { if (src_dims == 5 && dst_dims == 4) // goiyx -> oiyx { - return FUNC_CALL(reshape_grouped_to_simple)(g, o, i, 0, y, x, src_size_groups, dst_size_ofm); + return FUNC_CALL(reshape_grouped)(g, o, i, 0, y, x, src_size_ofm, dst_size_ofm); } - else if (src_dims == 6 && dst_dims == 5) // goizyx -> oizyx + else if (src_dims == 6 && dst_dims == 5) // goizyx -> oizyx or goizyx -> goiyx { - return FUNC_CALL(reshape_grouped_to_simple)(g, o, i, z, y, x, src_size_groups, dst_size_ofm); + return FUNC_CALL(reshape_grouped)(g, o, i, z, y, x, src_size_ofm, dst_size_ofm); + } + else if (src_dims == 6 && dst_dims == 4) // goizyx -> oiyx + { + return FUNC_CALL(reshape_grouped)(g, o, i, 0, y, x, src_size_ofm, dst_size_ofm); } return (uint8)(g, o, i, w, z, y, x, 0); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl index c5a130db22a..5bdb29ecde9 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cl_kernels/reorder_weights.cl @@ -97,6 +97,18 @@ inline uint FUNC(get_input_index)(uint g, uint o, uint i, uint z, uint y, uint x return GET_FILTER_GS_OI_YXS_GSV16_YXSV4_INDEX(INPUT0, g, o, i, y, x); #elif defined INPUT0_LAYOUT_GS_OI_YXS_GSV32_YXSV4 return GET_FILTER_GS_OI_YXS_GSV32_YXSV4_INDEX(INPUT0, g, o, i, y, x); +#elif defined INPUT0_LAYOUT_G_OS_ZYX_IS_OSV16_ISV4 + return GET_FILTER_G_OS_ZYX_IS_OSV16_ISV4_INDEX(INPUT0, g, o, i, z, y, x); +#elif defined INPUT0_LAYOUT_G_OS_ZYX_IS_OSV16_ISV16 + return GET_FILTER_G_OS_ZYX_IS_OSV16_ISV16_INDEX(INPUT0, g, o, i, z, y, x); +#elif defined INPUT0_LAYOUT_G_OS_ZYX_IS_OSV16_ISV32 + return GET_FILTER_G_OS_ZYX_IS_OSV16_ISV32_INDEX(INPUT0, g, o, i, z, y, x); +#elif defined INPUT0_LAYOUT_G_OS_ZYX_IS_OSV32_ISV4 + return GET_FILTER_G_OS_ZYX_IS_OSV32_ISV4_INDEX(INPUT0, g, o, i, z, y, x); +#elif defined INPUT0_LAYOUT_G_OS_ZYX_IS_OSV32_ISV16 + return GET_FILTER_G_OS_ZYX_IS_OSV32_ISV16_INDEX(INPUT0, g, o, i, z, y, x); +#elif defined INPUT0_LAYOUT_G_OS_ZYX_IS_OSV32_ISV32 + return GET_FILTER_G_OS_ZYX_IS_OSV32_ISV32_INDEX(INPUT0, g, o, i, z, y, x); #else #error reorder_weights.cl: input format - not supported #endif @@ -214,6 +226,18 @@ inline uint FUNC(get_output_index)(uint g, uint o, uint i, uint z, uint y, uint return GET_FILTER_GS_OI_YXS_GSV32_YXSV4_INDEX(OUTPUT, g, o, i, y, x); #elif defined OUTPUT_LAYOUT_G_OS_IS_YX_OSV16_ISV4 return GET_FILTER_G_OS_IS_YX_OSV16_ISV4_INDEX(OUTPUT, g, o, i, y, x); +#elif defined OUTPUT_LAYOUT_G_OS_ZYX_IS_OSV16_ISV4 + return GET_FILTER_G_OS_ZYX_IS_OSV16_ISV4_INDEX(OUTPUT, g, o, i, z, y, x); +#elif defined OUTPUT_LAYOUT_G_OS_ZYX_IS_OSV16_ISV16 + return GET_FILTER_G_OS_ZYX_IS_OSV16_ISV16_INDEX(OUTPUT, g, o, i, z, y, x); +#elif defined OUTPUT_LAYOUT_G_OS_ZYX_IS_OSV16_ISV32 + return GET_FILTER_G_OS_ZYX_IS_OSV16_ISV32_INDEX(OUTPUT, g, o, i, z, y, x); +#elif defined OUTPUT_LAYOUT_G_OS_ZYX_IS_OSV32_ISV4 + return GET_FILTER_G_OS_ZYX_IS_OSV32_ISV4_INDEX(OUTPUT, g, o, i, z, y, x); +#elif defined OUTPUT_LAYOUT_G_OS_ZYX_IS_OSV32_ISV16 + return GET_FILTER_G_OS_ZYX_IS_OSV32_ISV16_INDEX(OUTPUT, g, o, i, z, y, x); +#elif defined OUTPUT_LAYOUT_G_OS_ZYX_IS_OSV32_ISV32 + return GET_FILTER_G_OS_ZYX_IS_OSV32_ISV32_INDEX(OUTPUT, g, o, i, z, y, x); #else #error reorder_weights.cl: output format - not supported #endif @@ -240,35 +264,26 @@ KERNEL (reorder_weights)(const __global INPUT0_TYPE* input, __global OUTPUT_TYPE #if OUTPUT_GROUPS_NUM > 1 const unsigned g = (uint)get_global_id(0) / OUTPUT_OFM_NUM; const unsigned o = (uint)get_global_id(0) % OUTPUT_OFM_NUM; - const unsigned i = (uint)get_global_id(1); -#if OUTPUT_DIMS == 5 - const unsigned z = 0; - const unsigned y = (uint)get_global_id(2) / OUTPUT_SIZE_X; - const unsigned x = (uint)get_global_id(2) % OUTPUT_SIZE_X; -#elif OUTPUT_DIMS == 6 - const unsigned zyx = get_global_id(2); - const unsigned x = zyx % INPUT0_SIZE_X; - const unsigned y = (zyx / INPUT0_SIZE_X) % INPUT0_SIZE_Y; - const unsigned z = (zyx / INPUT0_SIZE_X) / INPUT0_SIZE_Y; -#endif #else - const unsigned o = (uint)get_global_id(0); - const unsigned i = (uint)get_global_id(1); const unsigned g = 0; -#if OUTPUT_DIMS == 2 - const unsigned z = 0; - const unsigned y = 0; - const unsigned x = 0; -#elif OUTPUT_DIMS == 4 - const unsigned z = 0; - const unsigned y = (uint)get_global_id(2) / INPUT0_SIZE_X; - const unsigned x = (uint)get_global_id(2) % INPUT0_SIZE_X; -#elif OUTPUT_DIMS == 5 - const unsigned zyx = get_global_id(2); - const unsigned x = zyx % INPUT0_SIZE_X; - const unsigned y = (zyx / INPUT0_SIZE_X) % INPUT0_SIZE_Y; - const unsigned z = (zyx / INPUT0_SIZE_X) / INPUT0_SIZE_Y; + const unsigned o = (uint)get_global_id(0); #endif + + const unsigned i = (uint)get_global_id(1); + +#if OUTPUT_DIMS == 2 || (OUTPUT_DIMS == 3 && OUTPUT_GROUPED) + const unsigned x = 0; + const unsigned y = 0; + const unsigned z = 0; +#elif OUTPUT_DIMS == 4 || (OUTPUT_DIMS == 5 && OUTPUT_GROUPED) + const unsigned x = (uint)get_global_id(2) % OUTPUT_SIZE_X; + const unsigned y = (uint)get_global_id(2) / OUTPUT_SIZE_X; + const unsigned z = 0; +#elif OUTPUT_DIMS == 5 || (OUTPUT_DIMS == 6 && OUTPUT_GROUPED) + const unsigned zyx = get_global_id(2); + const unsigned x = zyx % OUTPUT_SIZE_X; + const unsigned y = (zyx / OUTPUT_SIZE_X) % OUTPUT_SIZE_Y; + const unsigned z = (zyx / OUTPUT_SIZE_X) / OUTPUT_SIZE_Y; #endif #if OUTPUT_GROUPS_NUM > 1 // Add grouped macro instead this check @@ -276,6 +291,14 @@ KERNEL (reorder_weights)(const __global INPUT0_TYPE* input, __global OUTPUT_TYPE #else uint8 ir = RESHAPE_WEIGHT_DIMS(OUTPUT, INPUT0, o, i, 0, z, y, x); #endif - output[FUNC_CALL(get_output_index)(g, o, i, z, y, x)] = TO_OUTPUT_TYPE(input[FUNC_CALL(get_input_index)(ir[0],ir[1],ir[2],ir[4],ir[5],ir[6])]); + + uint input_idx = FUNC_CALL(get_input_index)(ir[0],ir[1],ir[2],ir[4],ir[5],ir[6]); +#if !REORDER_ROTATE + uint output_idx = FUNC_CALL(get_output_index)(g, o, i, z, y, x); +#else + uint output_idx = FUNC_CALL(get_output_index)(g, o, i, OUTPUT_SIZE_Z - z - 1, OUTPUT_SIZE_Y - y - 1, OUTPUT_SIZE_X - x - 1); +#endif + + output[output_idx] = TO_OUTPUT_TYPE(input[input_idx]); } #endif diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp index e6a3470e8ad..a1ad008b8cb 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.cpp @@ -445,8 +445,8 @@ JitDefinitions DataTensorJitConstant::GetDefinitions() const { definitions.push_back({ safe_index_func_name, safe_index_func_val }); definitions.push_back({ index_func_name, index_func_val }); } else { - definitions.push_back({ safe_index_func_name, "f" }); - definitions.push_back({ index_func_name, "f" }); + definitions.push_back({ safe_index_func_name, "(f)" }); + definitions.push_back({ index_func_name, "(f)" }); } } else { definitions.push_back({ safe_index_func_name, safe_index_func_val }); @@ -1171,10 +1171,15 @@ JitConstants FusedOpsCodeGenerator::MakeOpJitConstants(const FusedOpsConfigurati auto in_lo = p->per_tensor_input_range ? Broadcast(std::to_string(p->in_lo), desc.tensors[0].GetDType(), vec_size) : GetInputVarName(0); auto in_hi = p->per_tensor_input_range ? Broadcast(std::to_string(p->in_hi), desc.tensors[0].GetDType(), vec_size) : GetInputVarName(1); - op_decls += "\\\n\t" + tmp_type + " " + tmp_var + " = min(max(" + in_lo + ", " + in_converted + "), " + in_hi + ");"; + if (p->has_clamp) { + op_decls += "\\\n\t" + tmp_type + " " + tmp_var + " = min(max(" + in_lo + ", " + in_converted + "), " + in_hi + ");"; + } else { + op_decls += "\\\n\t" + tmp_type + " " + tmp_var + " = " + in_converted + ";"; + } op_decls += "\\\n\t" + tmp_var + " = " + tmp_var + "*" + pre_scale + ";"; if (p->has_pre_shift) op_decls += "\\\n\t" + tmp_var + " = " + tmp_var + " + " + pre_shift + ";"; + op_decls += "\\\n\t" + tmp_var + " = round(" + tmp_var + ");"; bool need_round = (p->has_post_scale || p->has_post_shift) && @@ -1254,9 +1259,9 @@ std::string FusedOpsCodeGenerator::GetIdx(size_t input_id, idx_desc idx, bool sh } if (should_be_safe) { - return GetInputTensorName(input_id) + "_GET_INDEX_SAFE(" + idx_order +")"; + return GetInputTensorName(input_id) + "_GET_INDEX_SAFE(" + idx_order + ")"; } else { - return GetInputTensorName(input_id) + "_GET_INDEX(" + idx_order +")"; + return GetInputTensorName(input_id) + "_GET_INDEX(" + idx_order + ")"; } } @@ -1349,11 +1354,10 @@ std::string FusedOpsCodeGenerator::GetInputVarName(size_t input_id) const { } std::string FusedOpsCodeGenerator::GetOutputVarName(std::string input_var) const { - static int i = 0; std::replace(input_var.begin(), input_var.end(), '[', '_'); std::replace(input_var.begin(), input_var.end(), ']', '_'); std::replace(input_var.begin(), input_var.end(), ' ', '_'); - return input_var + "_" + std::to_string(i++); + return input_var + "_out"; } std::string FusedOpsCodeGenerator::GetType(Datatype dt, size_t vec_size) const { @@ -1396,7 +1400,7 @@ std::vector FusedOpsCodeGenerator::GetRequiredInputs() const { auto p = std::dynamic_pointer_cast(desc.op_params); if (p) { std::vector res = {}; - if (!p->per_tensor_input_range) { + if (!p->per_tensor_input_range && p->has_clamp) { res.push_back(0); res.push_back(1); } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h index 89dad7dc015..8814b35547e 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/jitter.h @@ -306,7 +306,8 @@ public: std::string y; std::string x; size_t dims; - explicit idx_desc(std::vector idx, DataTensor t) : b("0"), f("0"), z("0"), y("0"), x("0"), dims(0) { + explicit idx_desc(std::vector idx, DataTensor t) + : b("0"), f("0"), z("0"), y("0"), x("0"), dims(0) { dims = idx.size(); switch (dims) { case 1: f = idx[0]; break; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp index f0859bfaf1f..106ec8b3fa9 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.cpp @@ -43,7 +43,8 @@ static WeightsType DataTypeToWeightsType(Datatype t) { static WeightsFormatSupportType CheckWeights(const weight_bias_params& newParams, WeightsType reqType, WeightsLayout reqLayouts, - const ParamsKey& paramsKey) { + const ParamsKey& paramsKey, + bool rotate) { // validate if weights type is image and if device supports requested sizes if (Tensor::IsImageType(reqLayouts)) { if (!CheckImageSize(newParams, reqLayouts)) @@ -59,8 +60,9 @@ static WeightsFormatSupportType CheckWeights(const weight_bias_params& newParams } reorderNeeded |= tensor.GetLayout() != reqLayouts; + reorderNeeded |= rotate; - if (reorderNeeded && !pitchesDifferFromLS) { + if (reorderNeeded && !pitchesDifferFromLS && !rotate) { reorderNeeded = !((reqLayouts == WeightsLayout::io && tensor.GetLayout() == WeightsLayout::iyxo) || (reqLayouts == WeightsLayout::oi && tensor.GetLayout() == WeightsLayout::oiyx)); } @@ -104,11 +106,12 @@ bool UpdateWeightsParams(weight_bias_params& newParams, WeightsLayout reqLayout, WeightsReorderParams& weightsReorderParams, const ParamsKey& paramsKey, - size_t groups) { + size_t groups, + bool rotate) { const auto& optParams = static_cast(options); const auto inType = DataTypeToWeightsType(newParams.inputs[0].GetDType()); const auto dtype = paramsKey.isEnabledDifferentInputWeightsTypes() ? newParams.weights.GetDType() : inType; - switch (CheckWeights(newParams, inType, reqLayout, paramsKey)) { + switch (CheckWeights(newParams, inType, reqLayout, paramsKey, rotate)) { case SUPPORTED: return true; case UNSUPPORTED: @@ -124,6 +127,7 @@ bool UpdateWeightsParams(weight_bias_params& newParams, r_params.layerID = newParams.layerID + "_reorder_"; r_params.input = newParams.weights; r_params.output = newParams.weights.TransformIgnorePadding(reqLayout, dtype, groups, false); + r_params.rotate_180 = rotate; r_params.engineInfo = newParams.engineInfo; reorder_optional_params op; @@ -132,7 +136,8 @@ bool UpdateWeightsParams(weight_bias_params& newParams, if (kernels_data.empty()) { throw std::runtime_error("No suitable kernel found for weights reorder from " + toString(r_params.input.GetLayout()) + " to " + - toString(r_params.output.GetLayout())); + toString(r_params.output.GetLayout()) + + (rotate ? " with rotate" : "")); } weightsReorderParams.engine = WeightsReorderParams::Engine::GPU; @@ -228,13 +233,15 @@ bool CheckInputsOutputNoPitchSameDims(const base_params& params) { if (params.inputs.size()) { no_pitch_same_dims = !params.inputs[0].PitchesDifferFromLogicalDims(); - if (params.inputs[0].GetLayout() == DataLayout::b_fs_yx_fsv16 && params.inputs[0].Feature().v % 16 != 0) + if ((params.inputs[0].GetLayout() == DataLayout::b_fs_yx_fsv16 && params.inputs[0].Feature().v % 16 != 0) || + (params.inputs[0].GetLayout() == DataLayout::b_fs_zyx_fsv16 && params.inputs[0].Feature().v % 16 != 0)) return false; for (size_t i = 1; i < params.inputs.size(); i++) { no_pitch_same_dims = no_pitch_same_dims && (params.inputs[0] == params.inputs[i]); - if (params.inputs[i].GetLayout() == DataLayout::b_fs_yx_fsv16 && params.inputs[i].Feature().v % 16 != 0) + if ((params.inputs[i].GetLayout() == DataLayout::b_fs_yx_fsv16 && params.inputs[i].Feature().v % 16 != 0) || + (params.inputs[0].GetLayout() == DataLayout::b_fs_zyx_fsv16 && params.inputs[0].Feature().v % 16 != 0)) return false; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h index 22f077a54a2..9d1880c2063 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/common/kernel_selector_utils.h @@ -30,7 +30,8 @@ bool UpdateWeightsParams(weight_bias_params& newParams, WeightsLayout layout, WeightsReorderParams& weightsReorderParams, const ParamsKey& paramsKey = ParamsKey(), - size_t groups = 1); + size_t groups = 1, + bool rotate = false); JitConstants GetTensorFriendlyWorkGroupsJit(const DataTensor& t); std::vector GetTensorFriendlyWorkGroups(const DataTensor& t); std::vector GetOptimalLocalWorkGroupSizes(std::vector gws, const EngineInfo& info); diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.cpp index 3c35eea802b..322842f2e1d 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.cpp @@ -64,8 +64,9 @@ JitConstants KernelBase::MakeBaseParamsJitConstants(const base_params& params) c jit.AddConstant(MakeJitConstant("INPUT" + toCodeString(i), params.inputs[i])); } +#if !NDEBUG jit.AddConstant(MakeJitConstant("LayerID", params.layerID)); - +#endif return jit; } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h index 3d6abfd34ea..ae2c23a7eb3 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_base.h @@ -50,6 +50,7 @@ public: virtual const std::string GetName() const { return kernelName; } static const primitive_db& get_db() { return db; } + static void ResetCounter() { counter = 0; } protected: static const primitive_db db; diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp index be16f4bef5e..04d72b396ee 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_common.cpp @@ -370,6 +370,12 @@ std::string toString(WeightsLayout layout) { case WeightsLayout::gs_oi_yxs_gsv32_yxsv4: return "GS_OI_YXS_GSV32_YXSV4"; case WeightsLayout::g_os_is_yx_isv16_osv16: return "G_OS_IS_YX_ISV16_OSV16"; case WeightsLayout::g_os_is_yx_osv16_isv4: return "G_OS_IS_YX_OSV16_ISV4"; + case WeightsLayout::g_os_zyx_is_osv16_isv4: return "G_OS_ZYX_IS_OSV16_ISV4"; + case WeightsLayout::g_os_zyx_is_osv16_isv16: return "G_OS_ZYX_IS_OSV16_ISV16"; + case WeightsLayout::g_os_zyx_is_osv16_isv32: return "G_OS_ZYX_IS_OSV16_ISV32"; + case WeightsLayout::g_os_zyx_is_osv32_isv4: return "G_OS_ZYX_IS_OSV32_ISV4"; + case WeightsLayout::g_os_zyx_is_osv32_isv16: return "G_OS_ZYX_IS_OSV32_ISV16"; + case WeightsLayout::g_os_zyx_is_osv32_isv32: return "G_OS_ZYX_IS_OSV32_ISV32"; default: throw std::invalid_argument("Failed to convert WeightsLayout " + std::to_string(layout) + " to string"); } } diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h index 9ac8306dfdc..f5484896c01 100644 --- a/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h +++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/kernel_selector_params.h @@ -143,7 +143,6 @@ public: uint32_t split : 1; uint32_t dilation : 1; uint32_t depthwise_separable_opt : 1; - uint32_t transposed : 1; uint32_t local : 1; uint32_t grouped : 1; uint32_t deformable : 1; @@ -186,6 +185,7 @@ public: } resample; struct reorder_t { uint32_t winograd : 1; + uint32_t rotate : 1; } reorder; struct eltwise_t { uint32_t stride : 1; @@ -317,7 +317,6 @@ public: void EnableDepthwiseSeparableOpt() { key.restrict.val.dedicated.conv.depthwise_separable_opt = 1; } void EnableLocalConvolution() { key.restrict.val.dedicated.conv.local = 1; } void EnableGroupedConvolution() { key.restrict.val.dedicated.conv.grouped = 1; } - void EnableTranspose() { key.restrict.val.dedicated.conv.transposed = 1; } void EnableInt8Quantization() { key.restrict.val.quantization = 1; } void EnableOutputCalibration() { key.restrict.val.output_calibration = 1; } void EnableDeformableMode() { key.restrict.val.dedicated.conv.deformable = 1; } @@ -339,6 +338,7 @@ public: void EnableQuantizeScaleShiftOpt() { key.restrict.val.dedicated.quantize.scale_shift_opt = 1; } void EnableWinogradReorder() { key.restrict.val.dedicated.reorder.winograd = 1; } + void EnableRotateReorder() { key.restrict.val.dedicated.reorder.rotate = 1; } void EnableSoftmaxDim(SoftmaxDim d); void EnableConcatAxis(ConcatAxis a); void EnableReampleType(ResampleType a); diff --git a/inference-engine/thirdparty/clDNN/src/activation.cpp b/inference-engine/thirdparty/clDNN/src/activation.cpp index b7b0bbcd2e6..621a2d64865 100644 --- a/inference-engine/thirdparty/clDNN/src/activation.cpp +++ b/inference-engine/thirdparty/clDNN/src/activation.cpp @@ -46,6 +46,10 @@ layout activation_inst::calc_output_layout(activation_node const& node) { CLDNN_ERROR_MESSAGE(node.id(), "Requested activation is not supported for integer type."); } + if (node.has_fused_primitives()) { + input_node_layout.data_type = node.get_fused_output_layout().data_type; + } + return input_node_layout; } diff --git a/inference-engine/thirdparty/clDNN/src/deconvolution.cpp b/inference-engine/thirdparty/clDNN/src/deconvolution.cpp index fa64e155db3..d74aafc3c8e 100644 --- a/inference-engine/thirdparty/clDNN/src/deconvolution.cpp +++ b/inference-engine/thirdparty/clDNN/src/deconvolution.cpp @@ -36,6 +36,15 @@ layout deconvolution_inst::calc_output_layout(deconvolution_node const& node) { auto input_layout = node.input().get_output_layout(); auto weights_layout = node.weights(0).get_output_layout(); // weights are stored after inputs + auto data_type = input_layout.data_type; + if ((input_layout.data_type == data_types::i8 || input_layout.data_type == data_types::u8) && !node.has_fused_primitives()) { + data_type = data_types::f32; + } + + if (node.has_fused_primitives()) { + data_type = node.get_fused_output_layout().data_type; + } + auto input_offset = desc->input_offset; auto strd = desc->stride; auto group = desc->groups; @@ -71,7 +80,7 @@ layout deconvolution_inst::calc_output_layout(deconvolution_node const& node) { desc->output_size.spatial[0], desc->output_size.spatial[1], desc->output_size.spatial[2]); - return {input_layout.data_type, input_layout.format, output_size}; + return {data_type, input_layout.format, output_size}; } // compute output_dim <= stride * (input_size - 1) + kernel_size + 2 * input_offset; @@ -98,7 +107,7 @@ layout deconvolution_inst::calc_output_layout(deconvolution_node const& node) { tensor output_size(input_layout.size.batch[0], number_of_features, x, y, z); - return {input_layout.data_type, input_layout.format, output_size}; + return {data_type, input_layout.format, output_size}; } std::string deconvolution_inst::to_string(deconvolution_node const& node) { diff --git a/inference-engine/thirdparty/clDNN/src/gpu/crop_gpu.cpp b/inference-engine/thirdparty/clDNN/src/gpu/crop_gpu.cpp index 9a3e1e10bd5..3df324804d6 100644 --- a/inference-engine/thirdparty/clDNN/src/gpu/crop_gpu.cpp +++ b/inference-engine/thirdparty/clDNN/src/gpu/crop_gpu.cpp @@ -106,6 +106,9 @@ attach_crop_gpu::attach_crop_gpu() { implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::b_fs_zyx_fsv16), val_fw); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::b_fs_zyx_fsv16), val_fw); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f32, format::bs_fs_yx_bsv16_fsv16), val_fw); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f16, format::bs_fs_yx_bsv16_fsv16), val_fw); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f32, format::bs_fs_zyx_bsv16_fsv16), val_fw); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f16, format::bs_fs_zyx_bsv16_fsv16), val_fw); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i64, format::bs_fs_zyx_bsv16_fsv16), val_fw); diff --git a/inference-engine/thirdparty/clDNN/src/gpu/deconvolution_gpu.cpp b/inference-engine/thirdparty/clDNN/src/gpu/deconvolution_gpu.cpp index b2412ce648d..5b137d9138b 100644 --- a/inference-engine/thirdparty/clDNN/src/gpu/deconvolution_gpu.cpp +++ b/inference-engine/thirdparty/clDNN/src/gpu/deconvolution_gpu.cpp @@ -32,7 +32,7 @@ struct deconvolution_gpu : typed_primitive_gpu_impl { protected: // TODO: share it with convolution and fully connected - bool validate_impl(const typed_primitive_inst& instance) const override { + bool validate_impl(const typed_primitive_inst&) const override { bool res = true; CLDNN_ERROR_NOT_EQUAL(_outer.id(), @@ -41,23 +41,6 @@ protected: "padding mode", 0.0f, "Unknown padding mode in deconvolution."); - // Check whether all memory elements use the same unit type (FP16 or FP32). - auto input_count = instance.inputs_memory_count(); - auto input_data_type = 0 == input_count ? - instance.node.input().get_output_layout().data_type : - instance.input_memory().get_layout().data_type; - CLDNN_ERROR_DATA_TYPES_MISMATCH(_outer.id(), - "Input memory", - input_data_type, - "output memory", - instance.output_memory().get_layout().data_type, - ""); - CLDNN_ERROR_DATA_TYPES_MISMATCH(_outer.id(), - "Input memory", - input_data_type, - "filter memory", - instance.weights_memory(0).get_layout().data_type, - ""); return res; } @@ -65,15 +48,10 @@ protected: kernel::kernel_arguments_data get_arguments(typed_primitive_inst& instance, int32_t split) const override { kernel::kernel_arguments_data args = parent::get_arguments(instance, split); - auto* desc = static_cast(instance.desc().get()); - int dep_size = static_cast((desc->weights.size() + desc->bias.size() + 1)); args.weights = (memory_impl::cptr) &instance.weights_memory(split); args.bias = (memory_impl::cptr) (instance.bias_term() ? &instance.bias_memory(split) : nullptr); - if (static_cast(instance.dependencies().size()) > dep_size) - args.inputs.emplace_back(&instance.dep_memory(dep_size)); - return args; } @@ -86,27 +64,6 @@ public: const auto& primitive = arg.get_primitive(); const auto& weights_layout = arg.weights(0).get_output_layout(); - switch (weights_layout.fused_format()) { - // FP32 (float) - case fuse(data_types::f32, format::goiyx): - case fuse(data_types::f32, format::yxio): - case fuse(data_types::f32, format::gyxio): - case fuse(data_types::f32, format::goizyx): - case fuse(data_types::f16, format::goiyx): - case fuse(data_types::f16, format::yxio): - case fuse(data_types::f16, format::gyxio): - case fuse(data_types::f16, format::goizyx): - case fuse(data_types::f32, format::oiyx): - case fuse(data_types::f32, format::yxfb): - case fuse(data_types::f32, format::oizyx): - case fuse(data_types::f16, format::oiyx): - case fuse(data_types::f16, format::yxfb): - case fuse(data_types::f16, format::oizyx): - break; - default: - throw std::runtime_error("deconvolution weights format unsupported"); - } - const auto& weights_size = weights_layout.size; const auto& split = primitive->split(); @@ -146,11 +103,6 @@ public: deconv_params.gradient = primitive->gradient(); - if (arg.get_dependencies().size() > primitive->weights.size() + primitive->bias.size() + 1) { - deconv_params.fused_eltwise = true; - deconv_params.inputs.push_back(convert_data_tensor(arg.fused_sum().get_output_layout())); - } - auto& kernel_selector = kernel_selector::deconvolution_kernel_selector::Instance(); auto best_kernels = kernel_selector.GetBestKernels(deconv_params, deconv_optional_params); @@ -179,6 +131,8 @@ attach_deconvolution_gpu::attach_deconvolution_gpu() { deconvolution_gpu::create); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f32, format::b_fs_yx_fsv16), deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f32, format::bs_fs_yx_bsv16_fsv16), + deconvolution_gpu::create); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f16, format::yxfb), deconvolution_gpu::create); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f16, format::bfyx), @@ -195,6 +149,30 @@ attach_deconvolution_gpu::attach_deconvolution_gpu() { deconvolution_gpu::create); implementation_map::add(std::make_tuple(engine_types::ocl, data_types::f16, format::byxf), deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::bfyx), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::bfyx), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::bfzyx), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::bfzyx), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::b_fs_yx_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::b_fs_yx_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::b_fs_zyx_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::b_fs_zyx_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::bs_fs_yx_bsv16_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::bs_fs_yx_bsv16_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::i8, format::bs_fs_zyx_bsv16_fsv16), + deconvolution_gpu::create); + implementation_map::add(std::make_tuple(engine_types::ocl, data_types::u8, format::bs_fs_zyx_bsv16_fsv16), + deconvolution_gpu::create); } } // namespace detail diff --git a/inference-engine/thirdparty/clDNN/src/gpu/device_info.cpp b/inference-engine/thirdparty/clDNN/src/gpu/device_info.cpp index d4072dc4ae9..1402e05d551 100644 --- a/inference-engine/thirdparty/clDNN/src/gpu/device_info.cpp +++ b/inference-engine/thirdparty/clDNN/src/gpu/device_info.cpp @@ -61,7 +61,7 @@ device_info_internal::device_info_internal(const cl::Device& device) { supports_subgroups_short = extensions.find("cl_intel_subgroups_short") != std::string::npos; - supports_imad = true; + supports_imad = dev_name.find("Gen12") != std::string::npos; supports_immad = false; dev_type = static_cast(device.getInfo()); diff --git a/inference-engine/thirdparty/clDNN/src/gpu/primitive_gpu_base.h b/inference-engine/thirdparty/clDNN/src/gpu/primitive_gpu_base.h index b9898a7ea33..11a76b62ca7 100644 --- a/inference-engine/thirdparty/clDNN/src/gpu/primitive_gpu_base.h +++ b/inference-engine/thirdparty/clDNN/src/gpu/primitive_gpu_base.h @@ -53,6 +53,11 @@ struct typed_primitive_gpu_impl : public typed_primitive_impl { _outer(arg), _device_info(arg.get_program().get_engine().get_context()->get_device_info()), _kernel_data(kd) { + // weights reorder params got copied to parent, clear in _kernel_data to release shared ptr + _kernel_data.weightsReorderParams.engine = kernel_selector::generic_kernel_params::Engine::NONE; + _kernel_data.weightsReorderParams.cpuKernel = nullptr; + _kernel_data.weightsReorderParams.clKernel = nullptr; + _kernels.reserve(kd.kernels.size()); for (size_t i = 0; i < kd.kernels.size(); ++i) { gpu::kernel kernel(_outer.get_program().get_engine().get_context(), diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/post_optimize_weights.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/post_optimize_weights.cpp index 8e19ddd48f8..d9c90f85280 100644 --- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/post_optimize_weights.cpp +++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/post_optimize_weights.cpp @@ -48,13 +48,15 @@ post_optimize_weights::weights_bias_offset post_optimize_weights::get_weights_bi template void post_optimize_weights::optimize_weights(T& node, program_impl& p) { auto offsets = get_weights_bias_offset(node); + auto* impl = node.get_selected_impl().get(); + auto output_layout = node.get_output_layout(); + auto& weights_reorder_params = impl->_weights_reorder_params; + for (auto i = offsets.weights_offset; i < offsets.bias_offset; i++) { auto& weights_node = node.get_dependency(i); - auto* impl = node.get_selected_impl().get(); - auto output_layout = node.get_output_layout(); auto weights_layout = weights_node.get_output_layout(); - auto reorders = _rf.get_weights_reorder(weights_node.id(), weights_layout, impl->_weights_reorder_params); + auto reorders = _rf.get_weights_reorder(weights_node.id(), weights_layout, weights_reorder_params); for (auto& reorder : reorders) { // insert new generic_layer node to topology @@ -64,9 +66,15 @@ void post_optimize_weights::optimize_weights(T& node, program_impl& p) { g_node.get_output_layout(false); g_node.selected_impl = g_node.type()->choose_impl(p.get_engine(), g_node); } - // set the old output layout and do not invalidate users as change of weights will not affect output layout - node.set_output_layout(output_layout, false); } + + // Reset weights reorder params to not keep source code pointer + weights_reorder_params.engine = kernel_selector::generic_kernel_params::Engine::NONE; + weights_reorder_params.clKernel = nullptr; + weights_reorder_params.cpuKernel = nullptr; + + // set the old output layout and do not invalidate users as change of weights will not affect output layout + node.set_output_layout(output_layout, false); } void post_optimize_weights::run(program_impl& p) { diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp index 59ca9135d08..9e182726ee5 100644 --- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp +++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp @@ -41,8 +41,10 @@ void pre_replace_deconv::run(program_impl& p) { if (!p.get_options().get()->enabled()) continue; + auto& deconv_node = node->as(); + auto& weights_node = deconv_node.weights(); auto deconv_prim = node->as().typed_desc(); - tensor filter_size = { 1, 1, 1, 1, 1 }; + tensor filter_size = weights_node.get_output_layout().size; auto weights = deconv_prim->weights; std::vector weights_vec; @@ -52,28 +54,33 @@ void pre_replace_deconv::run(program_impl& p) { for (auto& weights_id : weights_vec) { auto weights_iter = p.nodes_map.find(weights_id); if (weights_iter == p.nodes_map.end()) continue; - - auto weights_node_ptr = weights_iter->second; - // get filter spatial sizes for input offset adjustment, perform this only once as all filters should - // have same size - if (weights_id == weights_vec[0]) - filter_size = weights_node_ptr->get_output_layout().size; } // limit optimization to stride = 1 - if (deconv_prim->stride.spatial[0] == 1 && deconv_prim->stride.spatial[1] == 1 && !deconv_prim->gradient()) { + bool unit_stride = std::all_of(deconv_prim->stride.spatial.begin(), + deconv_prim->stride.spatial.end(), + [](tensor::value_type v) { return v == 1; }); + if (unit_stride && !deconv_prim->gradient()) { primitive_id deconv_id = node->id(); auto& input_node = node->get_dependency(0); + auto groups = deconv_node.get_groups(); - // disable for 5D - if (cldnn::format::dimension(input_node.get_output_layout().format) == 5) - continue; + bool perform_opt = false; + // fp16 and fp32 bfyx implementation supports transposed convolution + perform_opt |= cldnn::format::dimension(input_node.get_output_layout().format) == 4 && + (input_node.get_output_layout().data_type == data_types::f32 || input_node.get_output_layout().data_type == data_types::f16) && + !((_lo.get_optimization_attributes().b_fs_yx_fsv16_network || input_node.get_output_layout().format == format::b_fs_yx_fsv16) && + _lo.is_format_optimized(node->as(), format::b_fs_yx_fsv16)); + // int8/uint8 input + perform_opt |= (input_node.get_output_layout().data_type == data_types::i8 || input_node.get_output_layout().data_type == data_types::u8) && + // imad convolution kernel limitation for groups + (groups == 1 || weights_node.get_output_layout().size.feature[0] % 4 == 0 || + groups == static_cast(input_node.get_output_layout().size.feature[0])) && + // no uint8/int8 3D convolution support + input_node.get_output_layout().format.dimension() == 4; - // Disable for blocked formats - if ((_lo.get_optimization_attributes().b_fs_yx_fsv16_network || input_node.get_output_layout().format == format::b_fs_yx_fsv16) && - _lo.is_format_optimized(node->as(), format::b_fs_yx_fsv16)) { + if (!perform_opt) continue; - } primitive_id input_id = deconv_prim->input[0]; @@ -110,27 +117,35 @@ void pre_replace_deconv::run(program_impl& p) { } } auto rename_id = deconv_id + "_tmp"; + auto was_output = node->is_output(); + if (was_output) { + node->set_output(false); + auto& outputs = p.get_outputs(); + outputs.erase(std::remove(outputs.begin(), outputs.end(), node.get()), outputs.end()); + } p.rename(*node, rename_id); // create convolution primitive if (biases.size() != 0) { auto conv_prim = std::make_shared(deconv_id, - input_id, - weights_vec, - bias_vec, - stride, - input_offset, - tensor{ 1, 1, 1, 1 }, - output_padding); + input_id, + weights_vec, + bias_vec, + groups, + stride, + input_offset, + tensor{ 1, 1, 1, 1 }, + output_padding); p.get_or_create(conv_prim); } else { auto conv_prim = std::make_shared(deconv_id, - input_id, - weights_vec, - stride, - input_offset, - tensor{ 1, 1, 1, 1 }, - output_padding); + input_id, + weights_vec, + groups, + stride, + input_offset, + tensor{ 1, 1, 1, 1 }, + output_padding); p.get_or_create(conv_prim); } @@ -170,8 +185,10 @@ void pre_replace_deconv::run(program_impl& p) { p.nodes_map.erase(rename_id); } - update_processing_order = true; - + if (was_output) { + conv_node->set_output(true); + p.get_outputs().push_back(conv_node); + } p.mark_if_data_flow(*conv_node); conv_node->recalc_output_layout(true); diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp index 66d1ce4e562..b48728b1f3d 100644 --- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp +++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_primitive_fusing.cpp @@ -60,7 +60,7 @@ void prepare_primitive_fusing::run(program_impl& p) { fuse_sigmoid_mul_to_swish(p); fuse_simple_primitives(p); fuse_activations(p); - fuse_skip_layers(p); + optimize_fused_ops(p); } void prepare_primitive_fusing::fuse_sigmoid_mul_to_swish(program_impl &p) { @@ -230,45 +230,6 @@ void prepare_primitive_fusing::fuse_activations(program_impl &p) { } } -void prepare_primitive_fusing::fuse_skip_layers(program_impl& p) { - // This loop tries fusing eltwise (sum) with deconvolution - auto itr = p.get_processing_order().begin(); - while (itr != p.get_processing_order().end()) { - auto node_itr = itr++; - auto& node = (*node_itr); - - program_helpers::do_for_types(*node, [&p](eltwise_node& node) { - if (node.get_primitive()->mode != eltwise_mode::sum || node.inputs_count() != 2) - return; - - // both inputs should be deconvolutions - if (!(node.input(0).is_type() && node.input(1).is_type())) { - return; - } - - auto& to_fuse_with = node.input(0); - int to_fuse_index = 1; - - // remove dependencies and users of elwtise that is going to be extracted - p.add_connection(node.input(to_fuse_index), to_fuse_with); - p.remove_connection(node.input(to_fuse_index), node); - - p.get_processing_order().erase(&to_fuse_with); - p.get_processing_order().insert(&node, &to_fuse_with); - - if (!node.get_fused_activations_funcs().empty()) { - for (size_t i = 0; i < node.get_fused_activations_funcs().size(); i++) { - to_fuse_with.add_fused_activation(node.get_fused_activations_funcs()[i], - node.get_fused_activations_params()[i]); - } - } - to_fuse_with.set_output_padding(node.get_output_layout().data_padding); - - p.extract_and_remove(node); - }); - } -} - void prepare_primitive_fusing::fuse_simple_primitives(program_impl &p) { bool recalc_processing_order = false; @@ -389,6 +350,8 @@ void prepare_primitive_fusing::fuse_simple_primitives(program_impl &p) { should_fuse |= input_data.is_type(); + should_fuse |= input_data.is_type(); + if (!should_fuse) return; @@ -424,6 +387,8 @@ void prepare_primitive_fusing::fuse_simple_primitives(program_impl &p) { should_fuse |= input_data.is_type() && mvn_supports_fusings(input_data.as()); + should_fuse |= input_data.is_type(); + if (!should_fuse) return; @@ -488,6 +453,14 @@ void prepare_primitive_fusing::fuse_simple_primitives(program_impl &p) { should_fuse |= input_data.is_type() && mvn_supports_fusings(input_data.as()) && quantize_node.get_scale_shift_opt(); + should_fuse |= input_data.is_type() && quantize_node.get_scale_shift_opt(); + + should_fuse |= input_data.is_type() && quantize_node.get_scale_shift_opt() && + // fp16/fp32 optimized kernels don't support chaning data type + (input_data.get_dependency(0).get_output_layout().data_type == data_types::u8 || + input_data.get_dependency(0).get_output_layout().data_type == data_types::i8 || + input_data.get_output_layout().data_type == out_layout.data_type); + if (!should_fuse) return; @@ -507,10 +480,12 @@ void prepare_primitive_fusing::fuse_simple_primitives(program_impl &p) { auto parent2 = parents[1]; bool can_fuse_parent1 = (parent1->is_type() && conv_supports_fusings(parent1->as())) || - (parent1->is_type() && mvn_supports_fusings(parent1->as())); + (parent1->is_type() && mvn_supports_fusings(parent1->as())) || + (parent1->is_type()); bool can_fuse_parent2 = (parent2->is_type() && conv_supports_fusings(parent2->as())) || - (parent2->is_type() && mvn_supports_fusings(parent2->as())); + (parent2->is_type() && mvn_supports_fusings(parent2->as())) || + (parent2->is_type()); std::vector can_fuse_parents = { can_fuse_parent1, can_fuse_parent2 }; @@ -577,6 +552,49 @@ void prepare_primitive_fusing::fuse_simple_primitives(program_impl &p) { p.get_processing_order().calc_processing_order(p); } +void prepare_primitive_fusing::optimize_fused_ops(program_impl& p) { + auto itr = p.get_processing_order().begin(); + while (itr != p.get_processing_order().end()) { + auto node_itr = itr++; + auto& node = (*node_itr); + + if (!node->has_fused_primitives()) + continue; + + // TODO: try more optimizations: + // 1. clamp optimization + // 2. fuse conv bias to quantize shift + auto& fused_prims = node->get_fused_primitives(); + + // Drop relu if the next fused op is quantize with u8 output and no in_shift + auto fp_itr = fused_prims.begin(); + while (fp_itr != fused_prims.end()) { + auto curr_itr = fp_itr++; + if (fp_itr == fused_prims.end()) + break; + + auto& fp = *curr_itr; + auto& fp_next = *fp_itr; + + if (fp.node->is_type() && fp_next.node->is_type()) { + auto& activation_node = fp.node->as(); + auto& quantize_node = fp_next.node->as(); + bool can_skip = activation_node.get_primitive()->activation_function == activation_func::relu && + activation_node.get_primitive()->additional_params.a == 0.0f && + fp.deps.empty() && + (quantize_node.get_output_layout().data_type == data_types::u8 || + quantize_node.get_output_layout().data_type == data_types::i8) && + quantize_node.get_scale_shift_opt() && + !quantize_node.get_need_pre_shift(); + + if (can_skip) { + fused_prims.erase(curr_itr); + } + } + } + } +} + void prepare_conv_eltw_fusing::fuse_conv_depth_to_space(program_impl& p, program_node* node) { // make sure this convolution have only 1 user and it's depth_to_space // make sure convolution is not an output diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp index bb81b6dadcf..5eb670fd294 100644 --- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp +++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/prepare_quantization.cpp @@ -145,9 +145,12 @@ void prepare_quantization::prepare_scale_shift_opt(program_impl &p) { return offset; }; + bool has_negative_scales = false; bool need_post_scale = false; bool need_post_shift = false; bool need_pre_shift = false; + auto out_dt = quantize_node.get_output_layout().data_type; + bool need_clamp = levels != 256 || (out_dt != data_types::u8 && out_dt != data_types::i8); bool per_tensor_in_scale = true; bool per_tensor_in_shift = true; bool per_tensor_in_range = true; @@ -193,6 +196,9 @@ void prepare_quantization::prepare_scale_shift_opt(program_impl &p) { if (data_output_shift[s_offset] != 0.0f) { need_post_shift = true; } + if (data_input_scale[s_offset] < 0.0f) { + has_negative_scales = true; + } } } } @@ -255,6 +261,9 @@ void prepare_quantization::prepare_scale_shift_opt(program_impl &p) { if (half_to_float(data_output_shift[s_offset]) != 0.0f) { need_post_shift = true; } + if (half_to_float(data_input_scale[s_offset]) < 0.0f) { + has_negative_scales = true; + } } } } @@ -288,6 +297,10 @@ void prepare_quantization::prepare_scale_shift_opt(program_impl &p) { throw std::runtime_error("prepare_quantization: Unsupported precision of quantize output values"); } + if (has_negative_scales) { + return; + } + layout dummy_layout(data_types::f32, format::bfyx, tensor(1, 1, 1, 1)); float zero = 0.f; auto in_scale_prim = std::make_shared(quantize_node.id() + "_in_scale", memory::attach(dummy_layout, &zero, 1)); @@ -348,6 +361,10 @@ void prepare_quantization::prepare_scale_shift_opt(program_impl &p) { quantize_node.set_input_shift_val(in_shift_val); } + if (need_clamp) { + quantize_node.set_need_clamp(); + } + if (per_tensor_in_range) { quantize_node.set_per_tensor_input_range(); quantize_node.set_input_lo_val(in_lo_val); diff --git a/inference-engine/thirdparty/clDNN/src/include/pass_manager.h b/inference-engine/thirdparty/clDNN/src/include/pass_manager.h index 34f8c96cfa6..46ea00903a6 100644 --- a/inference-engine/thirdparty/clDNN/src/include/pass_manager.h +++ b/inference-engine/thirdparty/clDNN/src/include/pass_manager.h @@ -200,8 +200,8 @@ private: void fuse_sigmoid_mul_to_swish(program_impl &p); void fuse_reorders(program_impl& p); void fuse_activations(program_impl& p); - void fuse_skip_layers(program_impl& p); void fuse_simple_primitives(program_impl &p); + void optimize_fused_ops(program_impl &p); layout_optimizer& _lo; }; diff --git a/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h b/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h index 6896c984433..d43f2803178 100644 --- a/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h +++ b/inference-engine/thirdparty/clDNN/src/include/primitive_inst.h @@ -59,7 +59,7 @@ struct primitive_impl { virtual bool validate(const primitive_inst& instance) const = 0; std::string get_kernel_name() const { return _kernel_name; } // TODO: added a derived class for weights reordering (maybe for all static data reordering) - const kernel_selector::weights_reorder_params _weights_reorder_params; + kernel_selector::weights_reorder_params _weights_reorder_params; // class typed_primitive_gpu_impl override this with return false; virtual bool is_cpu() const { return true; } diff --git a/inference-engine/thirdparty/clDNN/src/include/program_node.h b/inference-engine/thirdparty/clDNN/src/include/program_node.h index c3a420422d5..63130afc94f 100644 --- a/inference-engine/thirdparty/clDNN/src/include/program_node.h +++ b/inference-engine/thirdparty/clDNN/src/include/program_node.h @@ -297,6 +297,7 @@ public: } const std::vector& get_fused_primitives() const { return fused_prims; } + std::vector& get_fused_primitives() { return fused_prims; } size_t get_fused_inputs_count() const { size_t count = 0; diff --git a/inference-engine/thirdparty/clDNN/src/include/quantize_inst.h b/inference-engine/thirdparty/clDNN/src/include/quantize_inst.h index 8af76142a39..6e05a4fa3e1 100644 --- a/inference-engine/thirdparty/clDNN/src/include/quantize_inst.h +++ b/inference-engine/thirdparty/clDNN/src/include/quantize_inst.h @@ -35,10 +35,12 @@ public: program_node& input(size_t index = 0) const { return get_dependency(index); } size_t inputs_count() const { return get_dependencies().size(); } bool get_scale_shift_opt() const { return scale_shift_opt; } + bool get_need_pre_shift() { return need_pre_shift; } void set_scale_shift_opt() { scale_shift_opt = true; } void set_need_post_scale() { need_post_scale = true; } void set_need_post_shift() { need_post_shift = true; } void set_need_pre_shift() { need_pre_shift = true; } + void set_need_clamp() { need_clamp = true; } void set_per_tensor_input_scale() { per_tensor_input_scale = true; } void set_per_tensor_input_shift() { per_tensor_input_shift = true; } void set_per_tensor_input_range() { per_tensor_input_range = true; } @@ -57,6 +59,7 @@ public: need_post_scale, need_post_shift, need_pre_shift, + need_clamp, per_tensor_input_range, per_tensor_input_scale, per_tensor_input_shift, @@ -79,6 +82,7 @@ private: bool need_post_scale = false; bool need_post_shift = false; bool need_pre_shift = false; + bool need_clamp = false; bool per_tensor_input_range = false; bool per_tensor_input_scale = false; diff --git a/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h b/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h index f4e5157e1ad..f2d835373f2 100644 --- a/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h +++ b/inference-engine/thirdparty/clDNN/src/include/to_string_utils.h @@ -205,6 +205,18 @@ inline std::string fmt_to_str(format fmt) { return "g_os_is_zyx_isv16_osv16"; case format::g_os_is_yx_osv16_isv4: return "g_os_is_yx_osv16_isv4"; + case format::g_os_zyx_is_osv16_isv4: + return "g_os_zyx_is_osv16_isv4"; + case format::g_os_zyx_is_osv16_isv16: + return "g_os_zyx_is_osv16_isv16"; + case format::g_os_zyx_is_osv16_isv32: + return "g_os_zyx_is_osv16_isv32"; + case format::g_os_zyx_is_osv32_isv4: + return "g_os_zyx_is_osv32_isv4"; + case format::g_os_zyx_is_osv32_isv16: + return "g_os_zyx_is_osv32_isv16"; + case format::g_os_zyx_is_osv32_isv32: + return "g_os_zyx_is_osv32_isv32"; default: return "unknown (" + std::to_string(fmt.value) + ")"; } diff --git a/inference-engine/thirdparty/clDNN/src/kernel_selector_helper.cpp b/inference-engine/thirdparty/clDNN/src/kernel_selector_helper.cpp index a10f9262568..84fd606aab1 100644 --- a/inference-engine/thirdparty/clDNN/src/kernel_selector_helper.cpp +++ b/inference-engine/thirdparty/clDNN/src/kernel_selector_helper.cpp @@ -308,6 +308,8 @@ kernel_selector::weights_layout to_weights_layout(format f) { return kernel_selector::weights_layout::g_os_iyx_osv32; case format::gs_oiyx_gsv16: return kernel_selector::weights_layout::gs_oiyx_gsv16; + case format::gs_oizyx_gsv16: + return kernel_selector::weights_layout::gs_oizyx_gsv16; case format::gs_oiyx_gsv32: return kernel_selector::weights_layout::gs_oiyx_gsv32; case format::gyxio: @@ -324,6 +326,18 @@ kernel_selector::weights_layout to_weights_layout(format f) { return kernel_selector::weights_layout::g_os_is_zyx_isv16_osv16; case format::g_os_is_yx_osv16_isv4: return kernel_selector::weights_layout::g_os_is_yx_osv16_isv4; + case format::g_os_zyx_is_osv16_isv4: + return kernel_selector::weights_layout::g_os_zyx_is_osv16_isv4; + case format::g_os_zyx_is_osv16_isv16: + return kernel_selector::weights_layout::g_os_zyx_is_osv16_isv16; + case format::g_os_zyx_is_osv16_isv32: + return kernel_selector::weights_layout::g_os_zyx_is_osv16_isv32; + case format::g_os_zyx_is_osv32_isv4: + return kernel_selector::weights_layout::g_os_zyx_is_osv32_isv4; + case format::g_os_zyx_is_osv32_isv16: + return kernel_selector::weights_layout::g_os_zyx_is_osv32_isv16; + case format::g_os_zyx_is_osv32_isv32: + return kernel_selector::weights_layout::g_os_zyx_is_osv32_isv32; default: throw std::invalid_argument("Unable to convert tensor layout " + fmt_to_str(f) + " to weights layout"); } @@ -417,6 +431,8 @@ cldnn::format::type from_weights_layout(kernel_selector::weights_layout l) { return cldnn::format::g_os_iyx_osv32; case kernel_selector::weights_layout::gs_oiyx_gsv16: return cldnn::format::gs_oiyx_gsv16; + case kernel_selector::weights_layout::gs_oizyx_gsv16: + return cldnn::format::gs_oizyx_gsv16; case kernel_selector::weights_layout::gs_oiyx_gsv32: return cldnn::format::gs_oiyx_gsv32; case kernel_selector::weights_layout::gyxio: @@ -433,6 +449,18 @@ cldnn::format::type from_weights_layout(kernel_selector::weights_layout l) { return cldnn::format::g_os_is_zyx_isv16_osv16; case kernel_selector::weights_layout::os_is_yx_osv16_isv4: return cldnn::format::g_os_is_yx_osv16_isv4; + case kernel_selector::weights_layout::g_os_zyx_is_osv16_isv4: + return cldnn::format::g_os_zyx_is_osv16_isv4; + case kernel_selector::weights_layout::g_os_zyx_is_osv16_isv16: + return cldnn::format::g_os_zyx_is_osv16_isv16; + case kernel_selector::weights_layout::g_os_zyx_is_osv16_isv32: + return cldnn::format::g_os_zyx_is_osv16_isv32; + case kernel_selector::weights_layout::g_os_zyx_is_osv32_isv4: + return cldnn::format::g_os_zyx_is_osv32_isv4; + case kernel_selector::weights_layout::g_os_zyx_is_osv32_isv16: + return cldnn::format::g_os_zyx_is_osv32_isv16; + case kernel_selector::weights_layout::g_os_zyx_is_osv32_isv32: + return cldnn::format::g_os_zyx_is_osv32_isv32; default: return cldnn::format::bfyx; } @@ -487,6 +515,14 @@ kernel_selector::data_tensor convert_data_tensor(const layout& l, uint32_t split new_vals[3] = align_to(vals[3], 4); new_vals[2] = align_to(vals[2], 8); } + if (ks_layout == kernel_selector::Tensor::bs_fs_yx_bsv16_fsv16) { + new_vals[0] = align_to(vals[0], 16); + new_vals[1] = align_to(vals[1], 16); + } + if (ks_layout == kernel_selector::Tensor::bs_fs_zyx_bsv16_fsv16) { + new_vals[0] = align_to(vals[0], 16); + new_vals[1] = align_to(vals[1], 16); + } for (size_t i = 0; i < vec.size(); i++) { const size_t tensor_index = vec.size() - 1 - i; @@ -505,6 +541,15 @@ kernel_selector::data_tensor convert_data_tensor(const layout& l, uint32_t split pitch *= (reserved_in_mem_count + lp + up); } + if (ks_layout == kernel_selector::Tensor::bs_fs_yx_bsv16_fsv16) { + vec[2].pitch = (vec[0].v * vec[1].v) * 16; + vec[3].pitch = vec[2].pitch * vec[2].v; + } + if (ks_layout == kernel_selector::Tensor::bs_fs_zyx_bsv16_fsv16) { + vec[3].pitch = (vec[0].v * vec[1].v * vec[2].v) * 16; + vec[4].pitch = vec[3].pitch * vec[3].v; + } + const int feature_index = kernel_selector::DataTensor::Channelndex(ks_layout, kernel_selector::Tensor::DataChannelName::FEATURE); vec[feature_index].v /= split; diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp index bf9ce7f43cc..02d5dfc4649 100644 --- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp +++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp @@ -425,7 +425,7 @@ bool layout_optimizer::convolution_b_fs_zyx_fsv16_opt(layout const &input_layout (input_layout.data_type == data_types::f32 || input_layout.data_type == data_types::f16) && ((input_layout.size.feature[0] / conv->split()) % 16 == 0 || input_layout.size.feature[0] == 3) && weights_layout.data_type == input_layout.data_type && - (weights_layout.size.batch[0] % 16 == 0 || weights_layout.size.batch[0] % 8 == 0) && + (weights_layout.size.batch[0] % 16 == 0 || (weights_layout.size.batch[0] == 8 && conv->groups > 1)) && conv->dilation == tensor(1)) return true; return false; @@ -477,6 +477,12 @@ bool layout_optimizer::deconvolution_b_fs_zyx_fsv16_opt(layout const &input_layo (input_layout.data_type == data_types::f32 || input_layout.data_type == data_types::f16) && deconv->split() == 1) return true; + + if (input_layout.format.dimension() == 5 && + (input_layout.data_type == data_types::i8 || input_layout.data_type == data_types::u8) && + deconv->split() == 1) + return true; + return false; } @@ -489,6 +495,12 @@ bool layout_optimizer::deconvolution_b_fs_yx_fsv16_opt(layout const &input_layou deconv->split() == 1 && (deconv->groups == 1 || (static_cast(deconv->groups) == weights_layout.size.group[0]))) return true; + + if (input_layout.format.dimension() == 4 && + (input_layout.data_type == data_types::i8 || input_layout.data_type == data_types::u8) && + deconv->split() == 1) + return true; + return false; } diff --git a/inference-engine/thirdparty/clDNN/src/program.cpp b/inference-engine/thirdparty/clDNN/src/program.cpp index 4d2c0497ce1..bade80bc005 100644 --- a/inference-engine/thirdparty/clDNN/src/program.cpp +++ b/inference-engine/thirdparty/clDNN/src/program.cpp @@ -62,6 +62,8 @@ #include "gpu/ocl_toolkit.h" +#include "kernel_base.h" + #include #include #include @@ -95,6 +97,7 @@ program_impl::program_impl(engine_impl& engine_ref, : engine(&engine_ref), options(options), processing_order() { + kernel_selector::KernelBase::ResetCounter(); set_options(); pm = std::unique_ptr(new pass_manager(*this)); prepare_nodes(topology); diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/activation_grad_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/activation_grad_gpu_test.cpp deleted file mode 100644 index 263714495ff..00000000000 --- a/inference-engine/thirdparty/clDNN/tests/test_cases/activation_grad_gpu_test.cpp +++ /dev/null @@ -1,231 +0,0 @@ -/* -// Copyright (c) 2016 Intel Corporation -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -*/ - -/////////////////////////////////////////////////////////////////////////////////////////////////// - -#include -#include -#include -#include "api/memory.hpp" -#include -#include "api/activation_grad.hpp" -#include -#include -#include -#include -#include "test_utils/test_utils.h" -#include "test_utils/float16.h" - -using namespace cldnn; -using namespace tests; - -TEST(activation_grad_f16_fw_gpu, basic_bfyx_all_functions) -{ - // Input: - // 1 -2 -3 4 5 - // 2 2 3 4 -6 - // 3 -3 3 5 1 - // 1 1 1 -1 1 - // - // a: 0.5, b: 2.5 - // - - const auto& engine = get_test_engine(); - - auto input_grad = memory::allocate(engine, { data_types::f16, format::bfyx,{ 1, 1, 5, 4 } }); - auto input = memory::allocate(engine, { data_types::f16, format::bfyx,{ 1, 1, 5, 4 } }); - auto input_params = memory::allocate(engine, { data_types::f16, format::bfyx,{ 1, 1, 2, 1 } }); - set_values(input_grad, - { FLOAT16(1.0f), FLOAT16(-2.0f),FLOAT16(-3.0f), FLOAT16(4.0f), FLOAT16(5.0f), - FLOAT16(2.0f), FLOAT16(2.0f), FLOAT16(3.0f), FLOAT16(4.0f), FLOAT16(-6.0f), - FLOAT16(3.0f), FLOAT16(-3.0f),FLOAT16(3.0f), FLOAT16(5.0f), FLOAT16(1.0f), - FLOAT16(1.0f), FLOAT16(1.0f), FLOAT16(1.0f), FLOAT16(-1.0f), FLOAT16(1.0f) }); - - set_values(input, - { FLOAT16(12.0f), FLOAT16(-22.0f), FLOAT16(-32.0f), FLOAT16(42.0f), FLOAT16(52.0f), - FLOAT16(22.0f),FLOAT16(22.0f), FLOAT16(32.0f), FLOAT16(42.0f), FLOAT16(-62.0f), - FLOAT16(32.0f), FLOAT16(-32.0f), FLOAT16(32.0f), FLOAT16(52.0f), FLOAT16(12.0f), - FLOAT16(12.0f), FLOAT16(12.0f), FLOAT16(12.0f), FLOAT16(-12.0f), FLOAT16(12.0f) }); - - std::vector funcs = { - activation_grad_func::none, - activation_grad_func::relu, - activation_grad_func::relu_negative_slope, - }; - - activation_additional_params params = { 0.5f, 2.5f }; - set_values(input_params, { FLOAT16(params.a), FLOAT16(params.b) }); - - for (uint8_t i = 0; i < 2; i++) - { - for (auto func : funcs) - { - topology topology(input_layout("input_grad", input_grad.get_layout())); - topology.add(data("input", input)); - - if (i == 0) - { - topology.add(activation_grad("activation_grad", "input_grad", "input", func, params)); - } - else - { - topology.add(data("input_params", input_params)); - topology.add(activation_grad("activation_grad", "input_grad", "input", "input_params", func)); - } - - network network(engine, topology); - network.set_input_data("input_grad", input_grad); - auto outputs = network.execute(); - EXPECT_EQ(outputs.size(), size_t(1)); - EXPECT_EQ(outputs.begin()->first, "activation_grad"); - - auto output_memory = outputs.at("activation_grad").get_memory(); - auto output_layout = output_memory.get_layout(); - auto output_ptr = output_memory.pointer(); - auto input_grad_ptr = input_grad.pointer(); - auto input_ptr = input.pointer(); - - int y_size = output_layout.size.spatial[1]; - int x_size = output_layout.size.spatial[0]; - int f_size = output_layout.size.feature[0]; - int b_size = output_layout.size.batch[0]; - EXPECT_EQ(output_layout.format, format::bfyx); - EXPECT_EQ(y_size, 4); - EXPECT_EQ(x_size, 5); - EXPECT_EQ(f_size, 1); - EXPECT_EQ(b_size, 1); - - std::vector out; - - for (size_t i = 0; i < output_layout.get_linear_size(); ++i) - { - switch (func) - { - case activation_grad_func::none: - EXPECT_FLOAT_EQ(float16_to_float32(input_grad_ptr[i]), float16_to_float32(output_ptr[i])); - break; - case activation_grad_func::relu: - EXPECT_FLOAT_EQ(float16_to_float32(input_grad_ptr[i]) * (float16_to_float32(input_ptr[i]) > 0), float16_to_float32(output_ptr[i])); - break; - case activation_grad_func::relu_negative_slope: - EXPECT_FLOAT_EQ(float16_to_float32(input_grad_ptr[i]) * ((float16_to_float32(input_ptr[i]) > 0) + params.a * (float16_to_float32(input_ptr[i]) <= 0)), float16_to_float32(output_ptr[i])); - break; - default: - break; - } - } - } - } -} - -TEST(activation_grad_f32_fw_gpu, basic_bfyx_all_functions) -{ - // Input: - // 1 -2 -3 4 5 - // 2 2 3 4 -6 - // 3 -3 3 5 1 - // 1 1 1 -1 1 - // - // a: 0.5, b: 2.5 - // - - const auto& engine = get_test_engine(); - - auto input_grad = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 5, 4 } }); - auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 5, 4 } }); - auto input_params = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 1 } }); - set_values(input_grad, - { 1.0f, -2.0f, -3.0f, 4.0f, 5.0f, - 2.0f, 2.0f, 3.0f, 4.0f, -6.0f, - 3.0f, -3.0f, 3.0f, 5.0f, 1.0f, - 1.0f, 1.0f, 1.0f, -1.0f, 1.0f }); - - set_values(input, - { 12.0f, -22.0f, -32.0f, 42.0f, 52.0f, - 22.0f, 22.0f, 32.0f, 42.0f, -62.0f, - 32.0f, -32.0f, 32.0f, 52.0f, 12.0f, - 12.0f, 12.0f, 12.0f, -12.0f, 12.0f }); - - std::vector funcs = { - activation_grad_func::none, - activation_grad_func::relu, - activation_grad_func::relu_negative_slope, - }; - - activation_additional_params params = { 0.5f, 2.5f }; - set_values(input_params, { params.a, params.b }); - - for (uint8_t i = 0; i < 2; i++) - { - for (auto func : funcs) - { - topology topology(input_layout("input_grad", input_grad.get_layout())); - topology.add(data("input", input)); - - if (i == 0) - { - topology.add(activation_grad("activation_grad", "input_grad", "input", func, params)); - } - else - { - topology.add(data("input_params", input_params)); - topology.add(activation_grad("activation_grad", "input_grad", "input", "input_params", func)); - } - - network network(engine, topology); - network.set_input_data("input_grad", input_grad); - auto outputs = network.execute(); - EXPECT_EQ(outputs.size(), size_t(1)); - EXPECT_EQ(outputs.begin()->first, "activation_grad"); - - auto output_memory = outputs.at("activation_grad").get_memory(); - auto output_layout = output_memory.get_layout(); - auto output_ptr = output_memory.pointer(); - auto input_grad_ptr = input_grad.pointer(); - auto input_ptr = input.pointer(); - - int y_size = output_layout.size.spatial[1]; - int x_size = output_layout.size.spatial[0]; - int f_size = output_layout.size.feature[0]; - int b_size = output_layout.size.batch[0]; - EXPECT_EQ(output_layout.format, format::bfyx); - EXPECT_EQ(y_size, 4); - EXPECT_EQ(x_size, 5); - EXPECT_EQ(f_size, 1); - EXPECT_EQ(b_size, 1); - - std::vector out; - - for (size_t i = 0; i < output_layout.get_linear_size(); ++i) - { - switch (func) - { - case activation_grad_func::none: - EXPECT_FLOAT_EQ(input_grad_ptr[i], output_ptr[i]); - break; - case activation_grad_func::relu: - EXPECT_FLOAT_EQ(input_grad_ptr[i] * (input_ptr[i] > 0), output_ptr[i]); - break; - case activation_grad_func::relu_negative_slope: - EXPECT_FLOAT_EQ(input_grad_ptr[i] * ((input_ptr[i] > 0) + params.a * (input_ptr[i] <= 0)), output_ptr[i]); - break; - default: - break; - } - } - } - } -} diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/concatenation_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/concatenation_gpu_test.cpp index 9678e61fa92..37c57a6f032 100644 --- a/inference-engine/thirdparty/clDNN/tests/test_cases/concatenation_gpu_test.cpp +++ b/inference-engine/thirdparty/clDNN/tests/test_cases/concatenation_gpu_test.cpp @@ -182,6 +182,280 @@ TEST(concat_gpu, mixed_input_types_5d) { } } +TEST(concat_gpu, i8_optimization_with_pool) { + const auto& engine = get_test_engine(); + + auto input0 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 1, 8, 3}}); + auto input1 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 1, 8, 3}}); + + + set_values(input0, { 11, 12, 13, + 14, 12, 12, + 13, -14, 13, + 13, -13, 15, + 16, -16, -13, + -14, 12, 11, + 16, -14, -13, + 18, -13, -15, }); + set_values(input1, { 11, 12, 13, + 15, 12, 12, + 13, 14, 12, + 13, 13, 15, + 12, 14, 13, + 14, 17, 18, + 13, 14, 11, + 13, 13, 15 }); + + + VF output_vec = {13, 13, 13, 13, 15, 15, + 16, 15, 16, 14, 13, 14, + 13, 14, 13, 18, 16, 18, + 16, 15, 16, 15, 18, 14, + 18, 14, -13, 15}; + + layout reorder_layout(data_types::i8, format::yxfb, {7, 2, 2, 1}); + topology topology(input_layout("input0", input0.get_layout()), + input_layout("input1", input1.get_layout()), + pooling("pool0", "input0", pooling_mode::max, {1, 1, 2, 2}, {1, 1, 1, 1}), + pooling("pool1", "input1", pooling_mode::max, {1, 1, 2, 2}, {1, 1, 1, 1}), + concatenation("concat", + {"pool0", "pool1"}, + concatenation::concatenation_axis::along_f, + data_types::i8, + padding{{0, 0, 0, 0}, 0}), + reorder("reorder", "concat", reorder_layout)); + cldnn::build_options options; + options.set_option(cldnn::build_option::optimize_data(true)); + network network(engine, topology, options); + network.set_input_data("input0", input0); + network.set_input_data("input1", input1); + auto outputs = network.execute(); + + EXPECT_EQ(outputs.size(), size_t(1)); + EXPECT_EQ(outputs.begin()->first, "reorder"); + + auto output_memory = outputs.at("reorder").get_memory(); + auto output_layout = output_memory.get_layout(); + auto output_ptr = output_memory.pointer(); + + int y_size = output_layout.size.spatial[0]; + int x_size = output_layout.size.spatial[1]; + int f_size = output_layout.size.feature[0]; + int b_size = output_layout.size.batch[0]; + EXPECT_EQ(output_layout.format, format::yxfb); + EXPECT_EQ(y_size, 7); + EXPECT_EQ(x_size, 2); + EXPECT_EQ(f_size, 2); + EXPECT_EQ(b_size, 1); + + for (size_t x = 0; x < output_layout.count(); ++x) { + EXPECT_EQ(output_vec[x], output_ptr[x]); + } +} + +TEST(concat_gpu, i8_optimization_with_conv) { + // Filter : 3x2x3 + // Stride : 2x1 + // Input1 : 4x5 + // Input2 : 4x5 + // Input3 : 4x5 + // Concat output : 3x4x5 + // Conv input : 3x4x5 + // Output : 2x3 + // + // Input0: + // 1 2 3 -4 5 + // 2 2 3 4 -6 + // -3 3 3 5 1 + // -1 1 1 1 -1 + // Input1: + // 5 5 3 -4 5 + // 2 -2 5 4 6 + // 6 1 3 5 1 + // 1 2 -3 -4 5 + // Input2: + // -2 1 3 2 -5 + // 1 2 -2 4 2 + // 3 5 3 -3 1 + // 5 4 3 2 1 + // + // Filter: + // 1 2 1 1 2 1 1 2 1 + // 2 1 2 2 1 2 2 1 2 + // + // Output: + // 53 54 30 + // 52 47 37 + const auto& engine = get_test_engine(); + + auto input0 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 1, 5, 4}}); + auto input1 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 1, 5, 4}}); + auto input2 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 1, 5, 4}}); + auto weights = memory::allocate(engine, { data_types::i8, format::bfyx, { 1, 3, 3, 2 } }); + + set_values(weights, { 1, 2, 1, + 2, 1, 2, 1, 2, 1, + 2, 1, 2, 1, 2, 1, + 2, 1, 2 }); + + set_values(input0, { 1, 2, 3, -4, 5, + 2, 2, 3, 4, -6, + -3, 3, 3, 5, 1, + -1, 1, 1, 1, -1 }); + set_values(input1, { 5, 5, 3, -4, 5, + 2, -2, 5, 4, 6, + 6, 1, 3, 5, 1, + 1, 2, -3, -4, 5 }); + set_values(input2, { -2, 1, 3, 2, -5, + 1, 2, -2, 4, 2, + 3, 5, 3, -3, 1, + 5, 4, 3, 2, 1 }); + + VF output_vec = { 53, 54, 30, 52, 47, 37 }; + + + layout reorder_layout(data_types::i8, format::bfyx, {1, 1, 2, 3}); + topology topology(input_layout("input0", input0.get_layout()), + input_layout("input1", input1.get_layout()), + input_layout("input2", input2.get_layout()), + concatenation("concat", + {"input0", "input1", "input2"}, + concatenation::concatenation_axis::along_f, + data_types::i8, + padding{{0, 0, 0, 0}, 0}), + data("weights", weights), + convolution("conv", "concat", { "weights" }, { 1,1,1,2 }), + reorder("output", "conv", reorder_layout)); + cldnn::build_options options; + options.set_option(cldnn::build_option::optimize_data(true)); + network network(engine, topology, options); + network.set_input_data("input0", input0); + network.set_input_data("input1", input1); + network.set_input_data("input2", input2); + auto outputs = network.execute(); + + EXPECT_EQ(outputs.size(), size_t(1)); + EXPECT_EQ(outputs.begin()->first, "output"); + + auto output_memory = outputs.at("output").get_memory(); + auto output_layout = output_memory.get_layout(); + auto output_ptr = output_memory.pointer(); + + int y_size = output_layout.size.spatial[1]; + int x_size = output_layout.size.spatial[0]; + int f_size = output_layout.size.feature[0]; + int b_size = output_layout.size.batch[0]; + EXPECT_EQ(output_layout.format, format::bfyx); + EXPECT_EQ(y_size, 2); + EXPECT_EQ(x_size, 3); + EXPECT_EQ(f_size, 1); + EXPECT_EQ(b_size, 1); + + for (size_t x = 0; x < output_layout.count(); ++x) { + EXPECT_EQ(output_vec[x], output_ptr[x]); + } +} + +TEST(concat_gpu, i8_optimization_with_pool_conv) { + // Filter : 32x2x1 + // Input offset : 0x0x-1x0 + // Stride : 1x1 + // Input0 : 16x3x2 + // Input1 : 16x3x2 + // Output : 1x1x3 + // + // Input0: + // -3 6 0 2 -1 -1 6 0 5 4 1 6 2 4 0 5 + // -2 -1 1 0 2 3 3 3 6 2 4 7 3 6 7 -1 + // 7 7 5 -3 1 -1 5 4 0 3 -2 6 2 5 2 4 + // 5 -1 3 6 2 0 -3 -1 0 3 0 -1 1 6 1 6 + // 5 -2 2 -1 5 6 3 4 1 0 6 6 7 2 6 3 + // 6 7 -1 5 5 6 -1 0 -1 5 5 2 3 -1 -3 4 + // + // Input1: + // 4 -2 0 0 6 2 0 4 6 4 4 4 -3 -1 4 -3 + // 1 0 -1 5 -1 1 4 2 7 7 0 2 3 4 -1 3 + // 7 7 2 -3 -1 5 -2 2 6 -3 0 7 0 3 3 3 + // -1 0 -2 -2 7 -3 -3 -1 5 0 3 4 0 -1 2 5 + // 2 -1 2 -3 0 -3 -3 2 4 3 3 5 5 7 5 1 + // 2 2 -3 6 6 7 1 -1 -2 5 1 -1 4 5 -3 -2 + // + // Filters: + // -1, 2, -2, 2, -2, 1, 1, 0, -1, 1, 2, -2, 2, 1, -2, 0, + // 0, -2, -2, -2, -2, -1, 2, 1, 2, -1, -1, 0, 2, -2, -2, 1, + // 0, -2, 0, 1, -2, -1, -2, 0, -1, -1, -2, 1, -2, 0, 1, 2, + // 2, 2, 2, -2, 0, 2, 1, -2, -1, -1, 0, -2, 2, -1, 2, -1 + // + // Output: + // -14, -35, -10 + + const auto& engine = get_test_engine(); + + auto input0 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 16, 3, 2}}); + auto input1 = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 16, 3, 2}}); + auto weights = memory::allocate(engine, {data_types::i8, format::bfyx, {1, 32, 2, 1}}); + + set_values(weights, {-1, 2, -2, 2, -2, 1, 1, 0, -1, 1, 2, -2, 2, 1, -2, 0, 0, -2, -2, -2, -2, -1, 2, 1, 2, -1, -1, 0, 2, -2, -2, 1, + 0, -2, 0, 1, -2, -1, -2, 0, -1, -1, -2, 1, -2, 0, 1, 2, 2, 2, 2, -2, 0, 2, 1, -2, -1, -1, 0, -2, 2, -1, 2, -1}); + + set_values(input0, {-3, 6, 0, 2, -1, -1, 6, 0, 5, 4, 1, 6, 2, 4, 0, 5, + -2, -1, 1, 0, 2, 3, 3, 3, 6, 2, 4, 7, 3, 6, 7, -1, + 7, 7, 5, -3, 1, -1, 5, 4, 0, 3, -2, 6, 2, 5, 2, 4, + 5, -1, 3, 6, 2, 0, -3, -1, 0, 3, 0, -1, 1, 6, 1, 6, + 5, -2, 2, -1, 5, 6, 3, 4, 1, 0, 6, 6, 7, 2, 6, 3, + 6, 7, -1, 5, 5, 6, -1, 0, -1, 5, 5, 2, 3, -1, -3, 4 }); + + set_values(input1, { 4, -2, 0, 0, 6, 2, 0, 4, 6, 4, 4, 4, -3, -1, 4, -3, + 1, 0, -1, 5, -1, 1, 4, 2, 7, 7, 0, 2, 3, 4, -1, 3, + 7, 7, 2, -3, -1, 5, -2, 2, 6, -3, 0, 7, 0, 3, 3, 3, + -1, 0, -2, -2, 7, -3, -3, -1, 5, 0, 3, 4, 0, -1, 2, 5, + 2, -1, 2, -3, 0, -3, -3, 2, 4, 3, 3, 5, 5, 7, 5, 1, + 2, 2, -3, 6, 6, 7, 1, -1, -2, 5, 1, -1, 4, 5, -3, -2}); + + VF output_vec = { -14, -35, -10 }; + + layout reorder_layout(data_types::i8, format::bfyx, {1, 1, 3, 1}); + topology topology(input_layout("input0", input0.get_layout()), + input_layout("input1", input1.get_layout()), + pooling("pool0", "input0", pooling_mode::max, {1, 1, 2, 2}, {1, 1, 1, 1}), + pooling("pool1", "input1", pooling_mode::max, {1, 1, 2, 2}, {1, 1, 1, 1}), + concatenation("concat", + {"pool0", "pool1"}, + concatenation::concatenation_axis::along_f, + data_types::i8, + padding{{0, 0, 0, 0}, 0}), + data("weights", weights), + convolution("conv", "concat", {"weights"}, {1, 1, 1, 1}, {0, 0, -1, 0}), + reorder("output", "conv", reorder_layout) ); + cldnn::build_options options; + options.set_option(cldnn::build_option::optimize_data(true)); + network network(engine, topology, options); + network.set_input_data("input0", input0); + network.set_input_data("input1", input1); + auto outputs = network.execute(); + + EXPECT_EQ(outputs.size(), size_t(1)); + EXPECT_EQ(outputs.begin()->first, "output"); + + auto output_memory = outputs.at("output").get_memory(); + auto output_layout = output_memory.get_layout(); + auto output_ptr = output_memory.pointer(); + + int y_size = output_layout.size.spatial[0]; + int x_size = output_layout.size.spatial[1]; + int f_size = output_layout.size.feature[0]; + int b_size = output_layout.size.batch[0]; + EXPECT_EQ(output_layout.format, format::bfyx); + EXPECT_EQ(y_size, 3); + EXPECT_EQ(x_size, 1); + EXPECT_EQ(f_size, 1); + EXPECT_EQ(b_size, 1); + + for (size_t x = 0; x < output_layout.count(); ++x) { + EXPECT_EQ(output_vec[x], output_ptr[x]); + } +} + using TestParamType_concat = ::testing::tuple, // 1 - Inputs Features Sizes size_t, // 2 - Input Y Size diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/crop_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/crop_gpu_test.cpp index eda32d662bd..50ab7f31397 100644 --- a/inference-engine/thirdparty/clDNN/tests/test_cases/crop_gpu_test.cpp +++ b/inference-engine/thirdparty/clDNN/tests/test_cases/crop_gpu_test.cpp @@ -19,6 +19,7 @@ #include "api/memory.hpp" #include #include "api/crop.hpp" +#include #include #include #include @@ -794,3 +795,84 @@ TEST(crop_gpu, basic_in3x1x2x2x1_crop_all_bfzyx) { } } } + +// batch size, input feature, crop out feature, (in_out format, crop format) +using crop_test_params = std::tuple>; + +class crop_gpu : public ::testing::TestWithParam {}; + +TEST_P(crop_gpu, pad_test) { + auto p = GetParam(); + + const auto& engine = get_test_engine(); + + auto batch_num = std::get<0>(p); + auto feature_num = std::get<1>(p); + auto x_size = 1; + auto y_size = 1; + auto z_size = 1; + + auto crop_batch_num = batch_num; + auto crop_feature_num_1 = std::get<2>(p); + auto crop_x_size = 1; + auto crop_y_size = 1; + auto crop_z_size = 1; + auto feature_offset_1 = feature_num - crop_feature_num_1; + + auto in_out_format = std::get<3>(p).first; + auto crop_format = std::get<3>(p).second; + + auto input = memory::allocate(engine, { data_types::f32, in_out_format, { tensor(spatial(x_size, y_size, z_size), feature(feature_num), batch(batch_num)) } }); + + topology topology; + topology.add(input_layout("input", input.get_layout())); + topology.add(reorder("reorder", "input", crop_format, data_types::f32)); + topology.add(crop("crop1", "reorder", tensor(batch(crop_batch_num), spatial(crop_x_size, crop_y_size, crop_z_size), feature(crop_feature_num_1)), { tensor(feature(feature_offset_1), spatial(0,0,0), batch(0)) })); + topology.add(reorder("out", "crop1", in_out_format, data_types::f32)); + + std::vector input_vec; + std::vector res; + std::vector input_data; + std::vector res_data; + for (size_t i = 0; i < feature_num; i++) { + input_data.push_back(static_cast(i)); + } + for (size_t i = 0; i < crop_feature_num_1; i++) { + res_data.push_back(input_data[feature_offset_1 + i]); + } + for (size_t i = 0; i < batch_num; i++) { + input_vec.insert(input_vec.end(), input_data.begin(), input_data.end()); + res.insert(res.end(), res_data.begin(), res_data.end()); + } + set_values(input, input_vec); + build_options bo; + bo.set_option(build_option::optimize_data(true)); + + network network(engine, topology, bo); + network.set_input_data("input", input); + auto outputs = network.execute(); + + auto output = outputs.at("out").get_memory(); + auto output_ptr = output.pointer(); + + for (size_t i = 0; i < res.size(); i++) + EXPECT_EQ(output_ptr[i], res[i]); +} + +static std::vector> formats = { + std::make_pair(format::bfyx, format::b_fs_yx_fsv16), + std::make_pair(format::bfzyx, format::b_fs_zyx_fsv16), + std::make_pair(format::bfyx, format::bs_fs_yx_bsv16_fsv16), + std::make_pair(format::bfzyx, format::bs_fs_zyx_bsv16_fsv16), + }; +static std::vector batches = {1, 8, 16, 17}; +static std::vector in_features = {18, 24, 32}; +static std::vector crop_features = {4, 8, 12, 17}; + +INSTANTIATE_TEST_CASE_P(crop_test, crop_gpu, + ::testing::Combine( + ::testing::ValuesIn(batches), + ::testing::ValuesIn(in_features), + ::testing::ValuesIn(crop_features), + ::testing::ValuesIn(formats) + ), ); diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/deconvolution_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/deconvolution_gpu_test.cpp index 3a1210e08ea..289d9c4c648 100644 --- a/inference-engine/thirdparty/clDNN/tests/test_cases/deconvolution_gpu_test.cpp +++ b/inference-engine/thirdparty/clDNN/tests/test_cases/deconvolution_gpu_test.cpp @@ -28,10 +28,116 @@ #include "test_utils/test_utils.h" #include "test_utils/float16.h" #include "api/reorder.hpp" +#include "src/include/to_string_utils.h" + +namespace cldnn { +template<> struct type_to_data_type { static const data_types value = data_types::f16; }; +} using namespace cldnn; using namespace tests; +template +struct deconvolution_traits { + using accumulator_type = InputT; +}; + +template <> +struct deconvolution_traits { + using accumulator_type = int; +}; + +template <> +struct deconvolution_traits { + using accumulator_type = int; +}; + +template <> +struct deconvolution_traits { + using accumulator_type = float; +}; + +template +T kahan_summation(std::vector &input) { + T sum = 0; + T c = 0; + for (T x : input) { + T y = x - c; + T t = sum + y; + c = (t - sum) - y; + sum = t; + } + return sum; +} + +template ::accumulator_type> +VVVF reference_deconvolution( + const VVVVF& input, // fyx dimensions order + const VVVVF& weights, + float bias, + tensor stride, + tensor offset, + size_t input_f_start +) { + auto ifm = weights.size(); + auto filter_z = static_cast(weights[0].size()); + auto filter_y = static_cast(weights[0][0].size()); + auto filter_x = static_cast(weights[0][0][0].size()); + + auto in_z = static_cast(input[0].size()); + auto in_y = static_cast(input[0][0].size()); + auto in_x = static_cast(input[0][0][0].size()); + + auto stride_x = stride.spatial[0]; + auto stride_y = stride.spatial[1]; + auto stride_z = stride.spatial[2]; + + auto offset_x = offset.spatial[0]; + auto offset_y = offset.spatial[1]; + auto offset_z = offset.spatial[2]; + + int out_x = 2 * offset_x + (in_x - 1) * stride_x + filter_x; + int out_y = 2 * offset_y + (in_y - 1) * stride_y + filter_y; + int out_z = 2 * offset_z + (in_z - 1) * stride_z + filter_z; + VVVF output(static_cast(out_z), VVF(static_cast(out_y), VF(static_cast(out_x)))); + + for (int oz = 0; oz < out_z; ++oz) { + for (int oy = 0; oy < out_y; ++oy) { + for (int ox = 0; ox < out_x; ++ox) { + VF values; + for (int fz = 0; fz < filter_z; ++fz) { + int iz = oz - filter_z + 1 - offset_z + fz; + if (iz < 0 || iz >= in_z * stride_z || iz % stride_z != 0) + continue; + iz = iz / stride_z; + + for (int fy = 0; fy < filter_y; ++fy) { + int iy = oy - filter_y + 1 - offset_y + fy; + if (iy < 0 || iy >= in_y * stride_y || iy % stride_y != 0) + continue; + iy = iy / stride_y; + + for (int fx = 0; fx < filter_x; ++fx) { + int ix = ox - filter_x + 1 - offset_x + fx; + if (ix < 0 || ix >= in_x * stride_x || ix % stride_x != 0) + continue; + ix = ix / stride_x; + + for (size_t ifi = 0; ifi < ifm; ++ifi) { + auto in_val = input[input_f_start + ifi][iz][iy][ix]; + auto wei_val = weights[ifi][filter_z - fz - 1][filter_y - fy - 1][filter_x - fx - 1]; + values.push_back(static_cast(in_val) * static_cast(wei_val)); + } + } + } + } + output[oz][oy][ox] = static_cast(kahan_summation(values)) + static_cast(bias); + } + } + } + return output; +} + TEST(deconvolution_f32_fw_gpu, basic_wsiz2x2_in2x2x1x1_nopad) { // Filter : 2x2 // Input : 2x2 @@ -2257,3 +2363,500 @@ TEST(deconvolution_f16_fw_gpu, basic_wsiz2x2_in1x2x2x2_fs_b_yx_fsv32_stride1_pad EXPECT_FLOAT_EQ(expected_output_vec[i], output_ptr[i]) << " index=" << i; } } + +struct deconvolution_random_test_params { + data_types input_type; + format::type input_format; + tensor input_size; + data_types weights_type; + format::type weights_format; + tensor weights_size; + tensor strides; + tensor input_offset; + bool with_bias; + data_types output_type; + cldnn::implementation_desc deconv_desc; + + static std::string print_params(const testing::TestParamInfo& param_info) { + auto& param = param_info.param; + auto to_string_neg = [](int v) { + if (v >= 0) { + return std::to_string(v); + } else { + return "m" + std::to_string(-v); + } + }; + + auto print_tensor = [&](const tensor& size) { + return to_string_neg(size.batch[0]) + "x" + + to_string_neg(size.feature[0]) + "x" + + to_string_neg(size.spatial[0]) + "x" + + to_string_neg(size.spatial[1]) + "x" + + to_string_neg(size.spatial[2]); + }; + + // construct a readable name + return "in_" + dt_to_str(param.input_type) + + "_" + fmt_to_str(param.input_format) + + "_" + print_tensor(param.input_size) + + "_wei_" + dt_to_str(param.weights_type) + + "_" + fmt_to_str(param.weights_format) + + "_" + print_tensor(param.weights_size) + + (param.with_bias ? "_bias" : "") + + "_s_" + print_tensor(param.strides) + + "_off_" + print_tensor(param.input_offset) + + "_out_" + dt_to_str(param.output_type) + + (!param.deconv_desc.kernel_name.empty() ? "_kernel_" + param.deconv_desc.kernel_name : "") + + (param.deconv_desc.output_format != format::any ? "_fmt_" + fmt_to_str(param.deconv_desc.output_format) : ""); + } +}; + +template +struct typed_comparator { + static ::testing::AssertionResult compare(const char* lhs_expr, const char* rhs_expr, T ref, T val) { + return ::testing::internal::EqHelper::Compare(lhs_expr, rhs_expr, ref, val); + } +}; + +template <> +struct typed_comparator { + static ::testing::AssertionResult compare(const char* lhs_expr, const char* rhs_expr, float ref, float val) { + return ::testing::internal::CmpHelperFloatingPointEQ(lhs_expr, rhs_expr, ref, val); + } +}; + +template <> +struct typed_comparator { + static ::testing::AssertionResult compare(const char* lhs_expr, const char* rhs_expr, FLOAT16 ref, FLOAT16 val) { + double abs_error = std::abs(0.05 * (double)ref); + return ::testing::internal::DoubleNearPredFormat(lhs_expr, rhs_expr, "5 percent", (double)ref, (double)val, abs_error); + } +}; + +template +struct type_test_ranges { + static constexpr int min = -1; + static constexpr int max = 1; +}; + +template <> +struct type_test_ranges { + static constexpr int min = 0; + static constexpr int max = 255; +}; + +template <> +struct type_test_ranges { + static constexpr int min = -127; + static constexpr int max = 127; +}; + +#define TYPED_ASSERT_EQ(ref, val) \ + ASSERT_PRED_FORMAT2(typed_comparator::compare, ref, val) + +#define TYPED_EXPECT_EQ(ref, val) \ + EXPECT_PRED_FORMAT2(typed_comparator::compare, ref, val) + +template +class deconvolution_random_test_base { +public: + template + void set_memory(cldnn::memory& mem, const VVVVVF& data) { + auto ptr = mem.pointer(); + + auto b = data.size(); + auto f = data[0].size(); + auto z = data[0][0].size(); + auto y = data[0][0][0].size(); + auto x = data[0][0][0][0].size(); + + for (size_t bi = 0; bi < b; ++bi) { + for (size_t fi = 0; fi < f; ++fi) { + for (size_t zi = 0; zi < z; ++zi) { + for (size_t yi = 0; yi < y; ++yi) { + for (size_t xi = 0; xi < x; ++xi) { + auto coords = cldnn::tensor(batch(bi), feature(fi), spatial(xi, yi, zi, 0)); + auto offset = mem.get_layout().get_linear_offset(coords); + ptr[offset] = data[bi][fi][zi][yi][xi]; + } + } + } + } + } + } + + template + void set_memory_weights(cldnn::memory& mem, const VVVVVVF& data) { + auto ptr = mem.pointer(); + + auto g = data.size(); + auto b = data[0].size(); + auto f = data[0][0].size(); + auto z = data[0][0][0].size(); + auto y = data[0][0][0][0].size(); + auto x = data[0][0][0][0][0].size(); + + for (size_t gi = 0; gi < g; ++gi) { + for (size_t bi = 0; bi < b; ++bi) { + for (size_t fi = 0; fi < f; ++fi) { + for (size_t zi = 0; zi < z; ++zi) { + for (size_t yi = 0; yi < y; ++yi) { + for (size_t xi = 0; xi < x; ++xi) { + auto coords = cldnn::tensor(group(gi), batch(bi), feature(fi), spatial(xi, yi, zi, 0)); + auto offset = mem.get_layout().get_linear_offset(coords); + ptr[offset] = data[gi][bi][fi][zi][yi][xi]; + } + } + } + } + } + } + } + + template + VVVVVF generate_random(cldnn::tensor size) { + return generate_random_5d( + size.batch[0], + size.feature[0], + size.spatial[2], + size.spatial[1], + size.spatial[0], + type_test_ranges::min, + type_test_ranges::max); + } + + template + VVVVVVF generate_random_weights(cldnn::tensor size) { + return generate_random_6d( + size.group[0], + size.batch[0], + size.feature[0], + size.spatial[2], + size.spatial[1], + size.spatial[0], + type_test_ranges::min, + type_test_ranges::max); + } + + void run(cldnn::engine eng, const deconvolution_random_test_params& params, cldnn::build_options build_opts) { + uint32_t groups = params.weights_size.group[0]; + size_t ifm = params.weights_size.feature[0]; + size_t ofm = params.weights_size.batch[0]; + + auto input_data = generate_random(params.input_size); + auto weights_data = generate_random_weights(params.weights_size); + + auto in_layout = cldnn::layout(cldnn::type_to_data_type::value, params.input_format, params.input_size); + auto wei_layout = cldnn::layout(cldnn::type_to_data_type::value, params.weights_format, params.weights_size); + + auto wei_mem = cldnn::memory::allocate(eng, wei_layout); + auto in_mem = cldnn::memory::allocate(eng, in_layout); + + this->set_memory_weights(wei_mem, weights_data); + this->set_memory(in_mem, input_data); + + auto topo = cldnn::topology( + cldnn::input_layout("input", in_layout), + cldnn::data("weights", wei_mem) + ); + + VF bias_data; + + if (params.with_bias) { + auto bias_size = cldnn::tensor(feature(params.weights_size.batch[0] * params.weights_size.group[0])); + auto bias_lay = cldnn::layout(cldnn::type_to_data_type::value, cldnn::format::bfyx, bias_size); + auto bias_mem = cldnn::memory::allocate(eng, bias_lay); + bias_data = generate_random_1d(bias_lay.size.feature[0], -1, 1); + set_values(bias_mem, bias_data); + topo.add(cldnn::data("bias", bias_mem)); + topo.add(cldnn::deconvolution("deconv", "input", { "weights" }, { "bias" }, groups, params.strides, params.input_offset)); + } else { + topo.add(cldnn::deconvolution("deconv", "input", { "weights" }, groups, params.strides, params.input_offset)); + } + + if (!params.deconv_desc.kernel_name.empty() || params.deconv_desc.output_format != cldnn::format::any) { + build_opts.set_option(cldnn::build_option::force_implementations({ { "deconv", params.deconv_desc } })); + } + + auto net = cldnn::network(eng, topo, build_opts); + net.set_input_data("input", in_mem); + + auto result = net.execute(); + + std::string kernel; + for (auto i : net.get_primitives_info()) { + if (i.original_id == "deconv") + kernel = i.kernel_id; + } + + auto out_mem = result.at("deconv").get_memory(); + + // Compare results + { + auto ptr = out_mem.pointer(); + + auto b = static_cast(out_mem.get_layout().size.batch[0]); + auto of = static_cast(out_mem.get_layout().size.feature[0]); + + for (size_t bi = 0; bi < b; ++bi) { + for (size_t fi = 0; fi < of; ++fi) { + size_t group = fi / ofm; + auto reference = reference_deconvolution( + input_data[bi], + weights_data[group][fi % ofm], + bias_data.empty() ? 0.f : static_cast(bias_data[fi]), + params.strides, + params.input_offset, + group * ifm); + + ASSERT_EQ(reference.size(), out_mem.get_layout().size.spatial[2]); + ASSERT_EQ(reference[0].size(), out_mem.get_layout().size.spatial[1]); + ASSERT_EQ(reference[0][0].size(), out_mem.get_layout().size.spatial[0]); + + for (size_t zi = 0; zi < reference.size(); zi++) { + for (size_t yi = 0; yi < reference[0].size(); yi++) { + for (size_t xi = 0; xi < reference[0][0].size(); xi++) { + auto ref_val = reference[zi][yi][xi]; + auto out_coords = cldnn::tensor(batch(bi), feature(fi), spatial(xi, yi, zi, 0)); + auto out_offset = out_mem.get_layout().get_linear_offset(out_coords); + auto out_val = ptr[out_offset]; + TYPED_ASSERT_EQ(ref_val, out_val) + << "at b=" << bi << ", f=" << fi << ", z=" << zi << ", y=" << yi << ", x=" << xi << std::endl + << " kernel: " << kernel; + } + } + } + } + } + } + } +}; + +#undef TYPED_ASSERT_EQ +#undef TYPED_EXPECT_EQ + +class deconvolution_random_test : public testing::TestWithParam { +protected: + void SetUp() override { + eng = get_test_engine(); + build_opts.set_option(cldnn::build_option::optimize_data(true)); + } + + void run() { + auto params = GetParam(); + switch (params.input_type) { + case data_types::f32: + run_typed_in(); + break; + case data_types::f16: + run_typed_in(); + break; + case data_types::i8: + run_typed_in(); + break; + case data_types::u8: + run_typed_in(); + break; + default: + break; + } + } + + cldnn::engine eng; + cldnn::build_options build_opts; + +private: + template + void run_typed() { + auto& params = GetParam(); + deconvolution_random_test_base test; + test.run(eng, params, build_opts); + } + + template + void run_typed_in_wei() { + auto& params = GetParam(); + switch (params.output_type) { + case data_types::f32: + run_typed(); + break; + case data_types::f16: + run_typed(); + break; + default: + break; + } + } + + template + void run_typed_in() { + auto& params = GetParam(); + switch (params.weights_type) { + case data_types::f32: + run_typed_in_wei(); + break; + case data_types::f16: + run_typed_in_wei(); + break; + case data_types::i8: + run_typed_in_wei(); + break; + case data_types::u8: + run_typed_in_wei(); + break; + default: + break; + } + } +}; + +class deconvolution_random_test_params_generator : public std::vector { +public: + using self = deconvolution_random_test_params_generator; + self& add(const deconvolution_random_test_params& params) { + push_back(params); + return *this; + } + + self& add_smoke_2d(data_types in_dt, data_types wei_dt, data_types out_dt, format::type in_fmt, format::type out_fmt) { + std::vector batches = { 1, 2 }; + for (auto b : batches) { + // 1x1 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7}, wei_dt, format::oiyx, {15, 15, 1, 1}, tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7}, wei_dt, format::oiyx, {15, 15, 1, 1}, {1, 1, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + // 3x3 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7}, wei_dt, format::oiyx, {15, 15, 3, 3}, tensor(1), {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7}, wei_dt, format::oiyx, {15, 15, 3, 3}, {1, 1, 2, 2}, {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Grouped + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7}, wei_dt, format::goiyx, tensor(group(2), batch(16), feature(4), spatial(1, 1)), tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7}, wei_dt, format::goiyx, tensor(group(2), batch(16), feature(4), spatial(1, 1)), {1, 1, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7}, wei_dt, format::goiyx, tensor(group(2), batch(16), feature(4), spatial(3, 3)), tensor(1), {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7}, wei_dt, format::goiyx, tensor(group(2), batch(16), feature(4), spatial(3, 3)), {1, 1, 2, 2}, {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Depthwise + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7}, wei_dt, format::goiyx, tensor(group(16), spatial(1, 1)), tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7}, wei_dt, format::goiyx, tensor(group(16), spatial(1, 1)), {1, 1, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7}, wei_dt, format::goiyx, tensor(group(16), spatial(3, 3)), tensor(1), {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7}, wei_dt, format::goiyx, tensor(group(16), spatial(3, 3)), {1, 1, 2, 2}, {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + + } + return *this; + } + + self& add_smoke_3d(data_types in_dt, data_types wei_dt, data_types out_dt, format::type in_fmt, format::type out_fmt) { + std::vector batches = { 1, 2 }; + for (auto b : batches) { + // 1x1 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7, 7}, wei_dt, format::oizyx, {15, 15, 1, 1, 1}, tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7, 7}, wei_dt, format::oizyx, {15, 15, 1, 1, 1}, {1, 1, 2, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + // 3x3 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7, 7}, wei_dt, format::oizyx, {15, 15, 3, 3, 3}, tensor(1), {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 15, 7, 7, 7}, wei_dt, format::oizyx, {15, 15, 3, 3, 3}, {1, 1, 2, 2, 2}, {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Grouped + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(2), batch(16), feature(4), spatial(1, 1, 1)), tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(2), batch(16), feature(4), spatial(1, 1, 1)), {1, 1, 2, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(2), batch(16), feature(4), spatial(3, 3, 3)), tensor(1), {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 8, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(2), batch(16), feature(4), spatial(3, 3, 3)), {1, 1, 2, 2, 2}, {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Depthwise + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(16), spatial(1, 1, 1)), tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(16), spatial(1, 1, 1)), {1, 1, 2, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(16), spatial(3, 3, 3)), tensor(1), {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 16, 7, 7, 7}, wei_dt, format::goizyx, tensor(group(16), spatial(3, 3, 3)), {1, 1, 2, 2, 2}, {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + } + return *this; + } + + self& add_extra_2d(data_types in_dt, data_types wei_dt, data_types out_dt, format::type in_fmt, format::type out_fmt) { + std::vector batches = { 1, 2, 16 }; + for (auto b : batches) { + // 1x1 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17}, wei_dt, format::oiyx, {41, 31, 1, 1}, tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17}, wei_dt, format::oiyx, {41, 31, 1, 1}, {1, 1, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + // 3x3 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17}, wei_dt, format::oiyx, {41, 31, 3, 3}, tensor(1), {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17}, wei_dt, format::oiyx, {41, 31, 3, 3}, {1, 1, 2, 2}, {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Asymmetric weights + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17}, wei_dt, format::oiyx, {41, 31, 3, 2}, tensor(1), {0, 0, 0, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17}, wei_dt, format::oiyx, {41, 31, 3, 2}, {1, 1, 2, 2}, {0, 0, 0, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Uneven groups + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17}, wei_dt, format::goiyx, tensor(group(3), batch(7), feature(9), spatial(1, 1)), tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17}, wei_dt, format::goiyx, tensor(group(3), batch(7), feature(9), spatial(1, 1)), {1, 1, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17}, wei_dt, format::goiyx, tensor(group(3), batch(7), feature(9), spatial(3, 3)), tensor(1), {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17}, wei_dt, format::goiyx, tensor(group(3), batch(7), feature(9), spatial(3, 3)), {1, 1, 2, 2}, {0, 0, -1, -1, 0}, true, out_dt, implementation_desc{out_fmt, ""} }); + } + return *this; + } + + self& add_extra_3d(data_types in_dt, data_types wei_dt, data_types out_dt, format::type in_fmt, format::type out_fmt) { + std::vector batches = { 1, 2, 16 }; + for (auto b : batches) { + // 1x1 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17, 11}, wei_dt, format::oizyx, {41, 31, 1, 1, 1}, tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17, 11}, wei_dt, format::oizyx, {41, 31, 1, 1, 1}, {1, 1, 2, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + // 3x3 + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17, 11}, wei_dt, format::oizyx, {41, 31, 3, 3, 3}, tensor(1), {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17, 11}, wei_dt, format::oizyx, {41, 31, 3, 3, 3}, {1, 1, 2, 2, 2}, {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Asymmetric weights + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17, 11}, wei_dt, format::oizyx, {41, 31, 3, 2, 4}, tensor(1), {0, 0, 0, -1, -2}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 31, 19, 17, 11}, wei_dt, format::oizyx, {41, 31, 3, 2, 4}, {1, 1, 2, 2, 2}, {0, 0, 0, -1, -2}, true, out_dt, implementation_desc{out_fmt, ""} }); + // Uneven groups + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17, 11}, wei_dt, format::goizyx, tensor(group(3), batch(7), feature(9), spatial(1, 1, 1)), tensor(1), tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17, 11}, wei_dt, format::goizyx, tensor(group(3), batch(7), feature(9), spatial(1, 1, 1)), {1, 1, 2, 2, 2}, tensor(0), true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17, 11}, wei_dt, format::goizyx, tensor(group(3), batch(7), feature(9), spatial(3, 3, 3)), tensor(1), {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + push_back(deconvolution_random_test_params{ in_dt, in_fmt, {b, 27, 19, 17, 11}, wei_dt, format::goizyx, tensor(group(3), batch(7), feature(9), spatial(3, 3, 3)), {1, 1, 2, 2, 2}, {0, 0, -1, -1, -1}, true, out_dt, implementation_desc{out_fmt, ""} }); + } + return *this; + } + + self& add_all_2d(data_types in_dt, data_types wei_dt, data_types out_dt, format::type in_fmt, format::type out_fmt) { + return add_smoke_2d(in_dt, wei_dt, out_dt, in_fmt, out_fmt) + .add_extra_2d(in_dt, wei_dt, out_dt, in_fmt, out_fmt); + } + + self& add_all_3d(data_types in_dt, data_types wei_dt, data_types out_dt, format::type in_fmt, format::type out_fmt) { + return add_smoke_3d(in_dt, wei_dt, out_dt, in_fmt, out_fmt) + .add_extra_3d(in_dt, wei_dt, out_dt, in_fmt, out_fmt); + } +}; + +TEST_P(deconvolution_random_test, basic) { + run(); +} + +INSTANTIATE_TEST_CASE_P(smoke, deconvolution_random_test, testing::ValuesIn( + deconvolution_random_test_params_generator() + .add_smoke_2d(data_types::f32, data_types::f32, data_types::f32, format::bfyx, format::any) + .add_smoke_3d(data_types::f32, data_types::f32, data_types::f32, format::bfzyx, format::any) + .add_smoke_2d(data_types::f32, data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_smoke_3d(data_types::f32, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) + + .add_smoke_2d(data_types::f16, data_types::f16, data_types::f16, format::bfyx, format::any) + .add_smoke_3d(data_types::f16, data_types::f16, data_types::f16, format::bfzyx, format::any) + .add_smoke_2d(data_types::f16, data_types::f16, data_types::f16, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_smoke_3d(data_types::f16, data_types::f16, data_types::f16, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) + + .add_smoke_2d(data_types::i8, data_types::i8, data_types::f32, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_smoke_3d(data_types::i8, data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) +), deconvolution_random_test_params::print_params); + +INSTANTIATE_TEST_CASE_P(DISABLED_extended, deconvolution_random_test, testing::ValuesIn( + deconvolution_random_test_params_generator() + .add_extra_2d(data_types::f32, data_types::f32, data_types::f32, format::bfyx, format::any) + .add_extra_3d(data_types::f32, data_types::f32, data_types::f32, format::bfzyx, format::any) + .add_extra_2d(data_types::f32, data_types::f32, data_types::f32, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_extra_3d(data_types::f32, data_types::f32, data_types::f32, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) + + .add_extra_2d(data_types::f16, data_types::f16, data_types::f16, format::bfyx, format::any) + .add_extra_3d(data_types::f16, data_types::f16, data_types::f16, format::bfzyx, format::any) + .add_extra_2d(data_types::f16, data_types::f16, data_types::f16, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_extra_3d(data_types::f16, data_types::f16, data_types::f16, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) + + .add_extra_2d(data_types::i8, data_types::i8, data_types::f32, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_all_2d(data_types::u8, data_types::i8, data_types::f32, format::b_fs_yx_fsv16, format::b_fs_yx_fsv16) + .add_extra_3d(data_types::i8, data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) + .add_all_3d(data_types::u8, data_types::i8, data_types::f32, format::b_fs_zyx_fsv16, format::b_fs_zyx_fsv16) + + .add_all_2d(data_types::i8, data_types::i8, data_types::f32, format::bs_fs_yx_bsv16_fsv16, format::bs_fs_yx_bsv16_fsv16) + .add_all_2d(data_types::u8, data_types::i8, data_types::f32, format::bs_fs_yx_bsv16_fsv16, format::bs_fs_yx_bsv16_fsv16) + .add_all_3d(data_types::i8, data_types::i8, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, format::bs_fs_zyx_bsv16_fsv16) + .add_all_3d(data_types::u8, data_types::i8, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, format::bs_fs_zyx_bsv16_fsv16) +), deconvolution_random_test_params::print_params); diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp index a90428e1805..31dc29d04b3 100644 --- a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp +++ b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp @@ -31,6 +31,7 @@ #include "api/data.hpp" #include "api/resample.hpp" #include "api/mvn.hpp" +#include "api/deconvolution.hpp" #include "test_utils/test_utils.h" @@ -126,6 +127,17 @@ public: size_t reorders_count_fused = get_reorders_count(fused); size_t reorders_count_not_fused = get_reorders_count(not_fused); + std::stringstream description; + description << std::endl << "not fused: " << std::endl; + for (auto i : not_fused.get_primitives_info()) { + description << " " << i.original_id << " " << i.kernel_id << std::endl; + } + description << "fused: " << std::endl; + for (auto i : fused.get_primitives_info()) { + description << " " << i.original_id << " " << i.kernel_id << std::endl; + } + SCOPED_TRACE(description.str()); + // Subtract reorders count to handle execution in different layouts when input/output reorders can be added in the graph ASSERT_EQ(fused.get_executed_primitives().size() - reorders_count_fused, p.expected_fused_primitives); ASSERT_EQ(not_fused.get_executed_primitives().size() - reorders_count_not_fused, p.expected_not_fused_primitives); @@ -350,6 +362,7 @@ public: #define CASE_CONV_FP32_10 {32, 16, 4, 5, 4}, {32, 32, 4, 5, 4}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::bfzyx, data_types::f32, format::bfzyx #define CASE_CONV_FP32_11 {1, 32, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx #define CASE_CONV_FP32_12 {1, 16, 4, 5, 4}, {1, 16, 2, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx +#define CASE_CONV_FP32_13 {1, 16, 18, 5, 4}, {1, 16, 16, 3, 2}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 2, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::os_is_zyx_isv16_osv16, data_types::f32, format::bfzyx #define CASE_CONV_FP16_1 {1, 15, 4, 5}, {1, 30, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::f16, format::bfyx, data_types::f16, format::bfyx #define CASE_CONV_FP16_2 {1, 16, 4, 5}, {1, 32, 2, 3}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::os_is_yx_isv16_osv16, data_types::f16, format::bfyx @@ -654,6 +667,7 @@ INSTANTIATE_TEST_CASE_P(fusings_gpu, conv_fp32_eltwise_b_fs_zyx_fsv16, bc_test_params{CASE_CONV_FP32_9, 2, 3}, bc_test_params{CASE_CONV_FP32_11, 2, 3}, bc_test_params{CASE_CONV_FP32_12, 2, 3}, + // bc_test_params{CASE_CONV_FP32_13, 2, 3}, - leads to mvn_scale_activation_quantize_i8_eltwise_fp32_quantize_i8.basic/11 test failure bc_test_params{CASE_CONV_FP16_6, 2, 3}, bc_test_params{CASE_CONV_FP16_7, 2, 3}, @@ -1498,6 +1512,67 @@ INSTANTIATE_TEST_CASE_P(fusings_gpu, conv_int8_scale_quantize_i8, bc_test_params{CASE_CONV3D_S8S8_4, 2, 4}, }), ); +class conv_int8_relu_quantize : public ConvFusingTest {}; +TEST_P(conv_int8_relu_quantize, i8) { + auto p = GetParam(); + create_topologies(input_layout("input", get_input_layout(p)), + data("weights", get_mem(get_weights_layout(p))), + data("bias", get_mem(get_bias_layout(p))), + data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)), + data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out_lo", get_mem(get_single_element_layout(p), -127)), + data("out_hi", get_mem(get_single_element_layout(p), 127)), + convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation), + activation("relu", "conv_prim", activation_func::relu), + quantize("quantize", "relu", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::i8), + reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32) + ); + // Output elements are in range [-127, 127] + // 1.0f difference is allowed, since quantize can return different values in ref and scale_shift kernels + // due to big error of division (in ref kernel). + tolerance = 1.0f; + execute(p); +} + +TEST_P(conv_int8_relu_quantize, u8) { + auto p = GetParam(); + create_topologies(input_layout("input", get_input_layout(p)), + data("weights", get_mem(get_weights_layout(p))), + data("bias", get_mem(get_bias_layout(p))), + data("in_lo", get_mem(get_per_channel_layout(p), 0)), + data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out_lo", get_mem(get_single_element_layout(p), 0)), + data("out_hi", get_mem(get_single_element_layout(p), 255)), + convolution("conv_prim", "input", {"weights"}, {"bias"}, p.groups, p.stride, p.pad, p.dilation), + activation("relu", "conv_prim", activation_func::relu), + quantize("quantize", "relu", "in_lo", "in_hi", "out_lo", "out_hi", 256, data_types::u8), + reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32) + ); + tolerance = 1.0f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P(fusings_gpu, conv_int8_relu_quantize, + ::testing::ValuesIn(std::vector{ + bc_test_params{CASE_CONV_U8S8_1, 2, 4}, + bc_test_params{CASE_CONV_U8S8_2, 2, 4}, + bc_test_params{CASE_CONV_U8S8_3, 2, 4}, + bc_test_params{CASE_CONV_U8S8_4, 2, 4}, + bc_test_params{CASE_CONV_S8S8_1, 2, 4}, + bc_test_params{CASE_CONV_S8S8_2, 2, 4}, + bc_test_params{CASE_CONV_S8S8_3, 2, 4}, + bc_test_params{CASE_CONV_S8S8_4, 2, 4}, + + bc_test_params{CASE_CONV3D_U8S8_1, 2, 4}, + bc_test_params{CASE_CONV3D_U8S8_2, 2, 4}, + bc_test_params{CASE_CONV3D_U8S8_3, 2, 4}, + bc_test_params{CASE_CONV3D_U8S8_4, 2, 4}, + bc_test_params{CASE_CONV3D_S8S8_1, 2, 4}, + bc_test_params{CASE_CONV3D_S8S8_2, 2, 4}, + bc_test_params{CASE_CONV3D_S8S8_3, 2, 4}, + bc_test_params{CASE_CONV3D_S8S8_4, 2, 4}, + }), ); + class conv_int8_scale_activation_quantize_i8 : public ConvFusingTest {}; TEST_P(conv_int8_scale_activation_quantize_i8, basic) { auto p = GetParam(); @@ -2842,3 +2917,650 @@ INSTANTIATE_TEST_CASE_P(fusings_gpu, lrn_test_params{CASE_LRN_FP16_5, 2, 4, lrn_norm_region_across_channel, "lrn_gpu_across_channel_multiple_features"}, }), ); + +/* ----------------------------------------------------------------------------------------------------- */ +/* -------------------------------- Activation cases --------------------------------------------------- */ +/* ----------------------------------------------------------------------------------------------------- */ +struct activation_test_params { + tensor input_size; + data_types input_type; + format input_format; + activation_func activation_function; + activation_additional_params additional_params; + data_types default_type; + format default_format; + size_t expected_fused_primitives; + size_t expected_not_fused_primitives; +}; + +#define CASE_ACTIVATION_F32_1 {1, 16, 8, 8}, data_types::f32, format::bfyx, activation_func::hyperbolic_tan, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F32_2 {1, 16, 8, 8}, data_types::f32, format::bfyx, activation_func::hard_sigmoid, {1.f, 1.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F32_3 {1, 16, 8, 8}, data_types::f32, format::bfyx, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F32_4 {1, 16, 8, 8}, data_types::f32, format::b_fs_yx_fsv16, activation_func::hyperbolic_tan, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F32_5 {1, 16, 8, 8}, data_types::f32, format::b_fs_yx_fsv16, activation_func::hard_sigmoid, {1.f, 1.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F32_6 {1, 16, 8, 8}, data_types::f32, format::b_fs_yx_fsv16, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_F32_1 {1, 16, 8, 8, 8}, data_types::f32, format::bfzyx, activation_func::hyperbolic_tan, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_F32_2 {1, 16, 8, 8, 8}, data_types::f32, format::bfzyx, activation_func::hard_sigmoid, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_F32_3 {1, 16, 8, 8, 8}, data_types::f32, format::bfzyx, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F16_1 {1, 16, 8, 8}, data_types::f16, format::bfyx, activation_func::hyperbolic_tan, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F16_2 {1, 16, 8, 8}, data_types::f16, format::bfyx, activation_func::hard_sigmoid, {1.f, 1.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F16_3 {1, 16, 8, 8}, data_types::f16, format::bfyx, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F16_4 {1, 16, 8, 8}, data_types::f16, format::b_fs_yx_fsv16, activation_func::hyperbolic_tan, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F16_5 {1, 16, 8, 8}, data_types::f16, format::b_fs_yx_fsv16, activation_func::hard_sigmoid, {1.f, 1.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_F16_6 {1, 16, 8, 8}, data_types::f16, format::b_fs_yx_fsv16, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_F16_1 {1, 16, 8, 8, 8}, data_types::f16, format::bfzyx, activation_func::hyperbolic_tan, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_F16_2 {1, 16, 8, 8, 8}, data_types::f16, format::bfzyx, activation_func::hard_sigmoid, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_F16_3 {1, 16, 8, 8, 8}, data_types::f16, format::bfzyx, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_U8_1 {1, 16, 8, 8}, data_types::u8, format::bfyx, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_U8_2 {1, 16, 8, 8}, data_types::u8, format::b_fs_yx_fsv16, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx +#define CASE_ACTIVATION_3D_U8_1 {1, 16, 8, 8, 8}, data_types::u8, format::bfzyx, activation_func::relu, {0.f, 0.f}, data_types::f32, format::bfyx + +class ActivationFusingTest : public ::BaseFusingTest { +public: + void execute(activation_test_params& p) { + auto input_prim = get_mem(get_input_layout(p)); + + network network_not_fused(this->engine, this->topology_non_fused, bo_not_fused); + network network_fused(this->engine, this->topology_fused, bo_fused); + + network_fused.set_input_data("input", input_prim); + network_not_fused.set_input_data("input", input_prim); + + compare(network_not_fused, network_fused, p); + } + + layout get_input_layout(activation_test_params& p) { return layout{p.input_type, p.input_format, p.input_size}; } + + layout get_per_channel_layout(activation_test_params& p) { + return layout{p.default_type, p.default_format, tensor{1, p.input_size.feature[0], 1, 1}}; + } + activation_func get_activation_function(activation_test_params& p) { return p.activation_function; } + activation_additional_params get_activation_additional_params(activation_test_params& p) { return p.additional_params; } + format get_input_format(activation_test_params &p) { return p.input_format; } +}; + +class activation_quantize_i8 : public ActivationFusingTest {}; +TEST_P(activation_quantize_i8, basic) { + auto p = GetParam(); + create_topologies(input_layout("input", get_input_layout(p)), + activation("act", "input", get_activation_function(p), get_activation_additional_params(p)), + data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)), + data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out_low", get_mem(get_single_element_layout(p), -127, 0)), + data("out_high", get_mem(get_single_element_layout(p), 0, 127)), + quantize("quant", "act", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8), + reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32)); + + tolerance = 1.f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P( + fusings_gpu, + activation_quantize_i8, + ::testing::ValuesIn(std::vector{ + activation_test_params{CASE_ACTIVATION_F32_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_3, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_4, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_5, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_6, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F32_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F32_3, 2, 3} + }), ); + +INSTANTIATE_TEST_CASE_P( + DISABLED_fusings_gpu, + activation_quantize_i8, + ::testing::ValuesIn(std::vector{ + // fp16 cases + activation_test_params{CASE_ACTIVATION_F16_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_3, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_4, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_5, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_6, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F16_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F16_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F16_3, 2, 3}, + + // u8 cases + activation_test_params{CASE_ACTIVATION_U8_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_U8_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_U8_1, 2, 3} + }), ); + +class activation_opt_quantize_i8 : public ActivationFusingTest {}; +TEST_P(activation_opt_quantize_i8, basic) { + auto p = GetParam(); + implementation_desc activation_impl = {get_input_format(p), "activation_opt"}; + this->bo_fused.set_option(build_option::force_implementations({{"act", activation_impl}})); + + create_topologies(input_layout("input", get_input_layout(p)), + activation("act", "input", get_activation_function(p), get_activation_additional_params(p)), + data("in_low", get_mem(get_per_channel_layout(p), min_random, 0)), + data("in_high", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out_low", get_mem(get_single_element_layout(p), -127, 0)), + data("out_high", get_mem(get_single_element_layout(p), 0, 127)), + quantize("quant", "act", "in_low", "in_high", "out_low", "out_high", 255, data_types::i8), + reorder("reorder_bfyx", "quant", format::bfyx, data_types::f32)); + + tolerance = 1.f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P( + fusings_gpu, + activation_opt_quantize_i8, + ::testing::ValuesIn(std::vector{ + activation_test_params{CASE_ACTIVATION_F32_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_F32_3, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F32_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F32_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F32_3, 2, 3} + }), ); + +INSTANTIATE_TEST_CASE_P( + DISABLED_fusings_gpu, + activation_opt_quantize_i8, + ::testing::ValuesIn(std::vector{ + // fp16 cases + activation_test_params{CASE_ACTIVATION_F16_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_F16_3, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F16_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F16_2, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_F16_3, 2, 3}, + + // u8 cases + activation_test_params{CASE_ACTIVATION_U8_1, 2, 3}, + activation_test_params{CASE_ACTIVATION_3D_U8_1, 2, 3} + }), ); + +/* ----------------------------------------------------------------------------------------------------- */ +/* --------------------------------------- Deconvolution cases ----------------------------------------- */ +/* ----------------------------------------------------------------------------------------------------- */ +using deconv_test_params = bc_test_params; + +// in_shape; out_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; default_type; default_format; +#define CASE_DECONV_FP32_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_osv16_isv16, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_osv16_isv16, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::gs_oiyx_gsv16, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfyx, data_types::f32, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_osv16_isv16, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::is_os_yx_osv16_isv16, data_types::f32, format::bfyx +#define CASE_DECONV_FP32_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::f32, format::b_fs_yx_fsv16, data_types::f32, format::gs_oiyx_gsv16, data_types::f32, format::bfyx + +#define CASE_DECONV_FP16_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::f16, format::oiyx, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_osv16_isv16, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_osv16_isv16, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::gs_oiyx_gsv16, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfyx, data_types::f16, format::oiyx, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_osv16_isv16, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::is_os_yx_osv16_isv16, data_types::f16, format::bfyx +#define CASE_DECONV_FP16_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::f16, format::b_fs_yx_fsv16, data_types::f16, format::gs_oiyx_gsv16, data_types::f16, format::bfyx + +#define CASE_DECONV_S8S8_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::goiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_S8S8_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::i8, format::b_fs_yx_fsv16, data_types::i8, format::goiyx, data_types::f32, format::bfyx + +#define CASE_DECONV_U8S8_1 {1, 15, 4, 5}, {1, 30, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_2 {1, 16, 4, 5}, {1, 32, 6, 7}, {1, 1, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_3 {1, 16, 4, 5}, {1, 32, 4, 5}, {1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_4 {1, 32, 4, 5}, {1, 32, 4, 5}, {1, 1, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::goiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_5 {1, 15, 4, 5}, {1, 30, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfyx, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_6 {1, 16, 4, 5}, {1, 32, 9, 11}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_7 {1, 16, 4, 5}, {1, 32, 7, 9}, {1, 1, 1, 1}, tensor{1, 1, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::oiyx, data_types::f32, format::bfyx +#define CASE_DECONV_U8S8_8 {1, 32, 4, 5}, {1, 32, 7, 9}, {1, 1, 3, 3}, tensor{1, 1, 2, 2}, tensor{0, 0, -1, -1, 0, 0}, tensor{1}, 32, data_types::u8, format::b_fs_yx_fsv16, data_types::i8, format::goiyx, data_types::f32, format::bfyx + +// 3D +// in_shape; out_shape; kernel; stride; pad; dilation; groups; data_type; input_format; weights_type; weights_format; default_type; default_format; +#define CASE_DECONV_FP32_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_osv16_isv16, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_osv16_isv16, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::gs_oizyx_gsv16, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::bfzyx, data_types::f32, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_osv16_isv16, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::is_os_zyx_osv16_isv16, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::f32, format::b_fs_zyx_fsv16, data_types::f32, format::gs_oizyx_gsv16, data_types::f32, format::bfzyx +#define CASE_DECONV_FP32_3D_9 {16, 16, 4, 5, 3}, {16, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f32, format::bs_fs_zyx_bsv16_fsv16, data_types::f32, format::is_os_zyx_osv16_isv16, data_types::f32, format::bfzyx + +#define CASE_DECONV_FP16_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfzyx, data_types::f16, format::oizyx, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_osv16_isv16, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_osv16_isv16, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::gs_oizyx_gsv16, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::bfzyx, data_types::f16, format::oizyx, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_osv16_isv16, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::is_os_zyx_osv16_isv16, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::f16, format::b_fs_zyx_fsv16, data_types::f16, format::gs_oizyx_gsv16, data_types::f16, format::bfzyx +#define CASE_DECONV_FP16_3D_9 {16, 16, 4, 5, 3}, {16, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::f16, format::bs_fs_zyx_bsv16_fsv16, data_types::f16, format::is_os_zyx_osv16_isv16, data_types::f16, format::bfzyx + +#define CASE_DECONV_S8S8_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::goizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_S8S8_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::i8, format::b_fs_zyx_fsv16, data_types::i8, format::goizyx, data_types::f32, format::bfzyx + +#define CASE_DECONV_U8S8_3D_1 {1, 15, 4, 5, 3}, {1, 30, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_2 {1, 16, 4, 5, 3}, {1, 32, 6, 7, 5}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_3 {1, 16, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 1, 1, 1}, tensor{1}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_4 {1, 32, 4, 5, 3}, {1, 32, 4, 5, 3}, {1, 1, 3, 3, 3}, tensor{1}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::goizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_5 {1, 15, 4, 5, 3}, {1, 30, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::bfzyx, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_6 {1, 16, 4, 5, 3}, {1, 32, 9, 11, 7}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_7 {1, 16, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 1, 1, 1}, tensor{1, 1, 2, 2, 2}, tensor{0}, tensor{1}, 1, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::oizyx, data_types::f32, format::bfzyx +#define CASE_DECONV_U8S8_3D_8 {1, 32, 4, 5, 3}, {1, 32, 7, 9, 5}, {1, 1, 3, 3, 3}, tensor{1, 1, 2, 2, 2}, tensor{0, 0, -1, -1, -1}, tensor{1}, 32, data_types::u8, format::b_fs_zyx_fsv16, data_types::i8, format::goizyx, data_types::f32, format::bfzyx + +class DeconvolutionFusingTest : public ::WeightsPrimitiveFusingTest {}; + +class deconv_actv : public DeconvolutionFusingTest {}; +TEST_P(deconv_actv, basic) { + auto p = GetParam(); + create_topologies( + input_layout("input", get_input_layout(p)), + data("weights", get_mem(get_weights_layout(p))), + deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad), + activation("act", "deconv", activation_func::relu), + reorder("out", "act", p.default_format, data_types::f32) + ); + // Need much higher tolerance because of deconvolution -> convolution optimization + tolerance = 1.f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P(fusings_gpu, deconv_actv, + ::testing::ValuesIn(std::vector{ + deconv_test_params{ CASE_DECONV_FP32_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_8, 2, 3 }, + + deconv_test_params{ CASE_DECONV_FP16_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_8, 2, 3 }, + + deconv_test_params{ CASE_DECONV_U8S8_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_8, 2, 3 }, + + deconv_test_params{ CASE_DECONV_S8S8_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_8, 2, 3 }, + + deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP32_3D_9, 2, 3 }, + + deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 3 }, + deconv_test_params{ CASE_DECONV_FP16_3D_9, 2, 3 }, + + deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 3 }, + + deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 3 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 3 }, +}), ); + +class deconv_actv_eltw_actv : public DeconvolutionFusingTest {}; +TEST_P(deconv_actv_eltw_actv, basic) { + auto p = GetParam(); + create_topologies( + input_layout("input", get_input_layout(p)), + data("weights", get_mem(get_weights_layout(p))), + data("eltw_data", get_mem(get_output_layout(p))), + deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad), + activation("act1", "deconv", activation_func::relu), + eltwise("eltw", {"act1", "eltw_data"}, eltwise_mode::sum), + activation("act2", "eltw", activation_func::relu), + reorder("out", "act2", p.default_format, data_types::f32) + ); + // Need much higher tolerance because of deconvolution -> convolution optimization + tolerance = 1.f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P(fusings_gpu, deconv_actv_eltw_actv, + ::testing::ValuesIn(std::vector{ + // Some fusings disabled under deconvolution -> convolution optimization + deconv_test_params{ CASE_DECONV_FP32_1, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_FP16_1, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_U8S8_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_S8S8_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_FP32_3D_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_8, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_9, 2, 5 }, + + deconv_test_params{ CASE_DECONV_FP16_3D_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_8, 2, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_9, 2, 5 }, + + deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 5 }, +}), ); + +class deconv_scale_actv_quant_i8 : public DeconvolutionFusingTest {}; +TEST_P(deconv_scale_actv_quant_i8, basic) { + auto p = GetParam(); + create_topologies( + input_layout("input", get_input_layout(p)), + data("weights", get_mem(get_weights_layout(p))), + data("scale_data", get_mem(get_per_channel_layout(p), 1.f/p.kernel.count())), + data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)), + data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out_lo", get_mem(get_single_element_layout(p), -127)), + data("out_hi", get_mem(get_single_element_layout(p), 127)), + deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad), + scale("scale", "deconv", "scale_data"), + activation("actv", "scale", activation_func::softsign), + quantize("quant", "actv", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8), + reorder("out", "quant", p.default_format, data_types::f32) + ); + + tolerance = 1.f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P(fusings_gpu, deconv_scale_actv_quant_i8, + ::testing::ValuesIn(std::vector{ + // Some fusings disabled under deconvolution -> convolution optimization + // Quantize fusing disabled for fp16/fp32 for performance reasons + // deconv_test_params{ CASE_DECONV_FP32_1, 4, 5 }, FIXME Failure due to activation + quantization fusing + deconv_test_params{ CASE_DECONV_FP32_2, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_4, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_5, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_6, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_7, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_8, 3, 5 }, + + deconv_test_params{ CASE_DECONV_FP16_1, 4, 5 }, + deconv_test_params{ CASE_DECONV_FP16_2, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_4, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_5, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_6, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_7, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_8, 3, 5 }, + + deconv_test_params{ CASE_DECONV_U8S8_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_S8S8_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_FP32_3D_1, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_2, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_3, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_4, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_5, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_6, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_7, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP32_3D_8, 3, 5 }, + // FIXME no quantize implementation for bs_fs_yx_bsv16_fsv16 format AND add_required_reorders pass completely ruins data types + // add_required_reorders pass tries to reorder everything to output type if no format exists, this ruins fp32 -> int8 quantize + // deconv_test_params{ CASE_DECONV_FP32_3D_9, 3, 5 }, + + deconv_test_params{ CASE_DECONV_FP16_3D_1, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_2, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_3, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_4, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_5, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_6, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_7, 3, 5 }, + deconv_test_params{ CASE_DECONV_FP16_3D_8, 3, 5 }, + // deconv_test_params{ CASE_DECONV_FP16_3D_9, 3, 5 }, + + deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 5 }, + + deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 5 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 5 }, +}), ); + +class deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8 : public DeconvolutionFusingTest {}; +TEST_P(deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8, basic) { + auto p = GetParam(); + create_topologies( + input_layout("input", get_input_layout(p)), + data("weights", get_mem(get_weights_layout(p))), + data("scale1_data", get_mem(get_per_channel_layout(p), 1.f / p.kernel.count())), + data("in1_lo", get_mem(get_per_channel_layout(p), min_random, 0)), + data("in1_hi", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out1_lo", get_mem(get_single_element_layout(p), 0)), + data("out1_hi", get_mem(get_single_element_layout(p), 255)), + data("eltw_data", get_mem(layout(p.default_type, p.input_format, p.out_shape))), + data("scale2_data", get_mem(get_per_channel_layout(p), 1.f / p.kernel.count())), + data("in2_lo", get_mem(get_per_channel_layout(p), min_random, 0)), + data("in2_hi", get_mem(get_per_channel_layout(p), 1, max_random)), + data("out2_lo", get_mem(get_single_element_layout(p), -127)), + data("out2_hi", get_mem(get_single_element_layout(p), 127)), + deconvolution("deconv", "input", { "weights" }, p.groups, p.stride, p.pad), + scale("scale1", "deconv", "scale1_data"), + activation("actv1", "scale1", activation_func::relu), + quantize("quant1", "actv1", "in1_lo", "in1_hi", "out1_lo", "out1_hi", 256, data_types::u8), + eltwise("eltw", {"quant1", "eltw_data"}, eltwise_mode::sum, p.default_type), + scale("scale2", "eltw", "scale2_data"), + activation("actv2", "scale2", activation_func::relu), + quantize("quant2", "actv2", "in2_lo", "in2_hi", "out2_lo", "out2_hi", 255, data_types::i8), + reorder("out", "quant2", p.default_format, data_types::f32) + ); + + tolerance = 1.f; + execute(p); +} + +INSTANTIATE_TEST_CASE_P(fusings_gpu, deconv_scale_actv_quant_u8_eltw_scale_actv_quant_i8, + ::testing::ValuesIn(std::vector{ + // Some fusings disabled under deconvolution -> convolution optimization + // Quantize fusing disabled for fp16/fp32 for performance reasons + // deconv_test_params{ CASE_DECONV_FP32_1, 7, 9 }, FIXME Failure due to activation + quantization fusing + deconv_test_params{ CASE_DECONV_FP32_2, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_4, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_5, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_6, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_7, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_8, 6, 9 }, + + deconv_test_params{ CASE_DECONV_FP16_1, 7, 9 }, + deconv_test_params{ CASE_DECONV_FP16_2, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_4, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_5, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_6, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_7, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_8, 6, 9 }, + + deconv_test_params{ CASE_DECONV_U8S8_1, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_2, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_4, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_5, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_6, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_7, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_8, 2, 9 }, + + deconv_test_params{ CASE_DECONV_S8S8_1, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_2, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_4, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_5, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_6, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_7, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_8, 2, 9 }, + + // deconv_test_params{ CASE_DECONV_FP32_3D_1, 6, 9 }, FIXME Failure due to activation + quantization fusing + deconv_test_params{ CASE_DECONV_FP32_3D_2, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3D_3, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3D_4, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3D_5, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3D_6, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3D_7, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP32_3D_8, 6, 9 }, + // deconv_test_params{ CASE_DECONV_FP32_3D_9, 6, 9 }, + + deconv_test_params{ CASE_DECONV_FP16_3D_1, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_2, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_3, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_4, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_5, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_6, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_7, 6, 9 }, + deconv_test_params{ CASE_DECONV_FP16_3D_8, 6, 9 }, + // deconv_test_params{ CASE_DECONV_FP16_3D_9, 6, 9 }, + + deconv_test_params{ CASE_DECONV_U8S8_3D_1, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_2, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_3, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_4, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_5, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_6, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_7, 2, 9 }, + deconv_test_params{ CASE_DECONV_U8S8_3D_8, 2, 9 }, + + deconv_test_params{ CASE_DECONV_S8S8_3D_1, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_2, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_3, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_4, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_5, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_6, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_7, 2, 9 }, + deconv_test_params{ CASE_DECONV_S8S8_3D_8, 2, 9 }, +}), ); diff --git a/inference-engine/thirdparty/clDNN/tests/test_utils/test_utils.h b/inference-engine/thirdparty/clDNN/tests/test_utils/test_utils.h index 230ded1c8fb..5ab013510ca 100644 --- a/inference-engine/thirdparty/clDNN/tests/test_utils/test_utils.h +++ b/inference-engine/thirdparty/clDNN/tests/test_utils/test_utils.h @@ -76,6 +76,8 @@ template using VVVVF = std::vector>; // batch of 3d feature maps template using VVVVVF = std::vector>; // split of bfyx filters +template +using VVVVVVF = std::vector>; // split of bfyx filters template inline VF flatten_4d(cldnn::format input_format, VVVVF &data) { @@ -173,6 +175,14 @@ std::vector>>>> generate_rand return v; } +template +VVVVVVF generate_random_6d(size_t a, size_t b, size_t c, size_t d, size_t e, size_t f, int min, int max, int k = 8) { + VVVVVVF v(a); + for (size_t i = 0; i < a; ++i) + v[i] = generate_random_5d(b, c, d, e, f, min, max, k); + return v; +} + template void set_value(const cldnn::pointer& ptr, uint32_t index, T value) { ptr[index] = value; } template T get_value(const cldnn::pointer& ptr, uint32_t index) { return ptr[index]; } diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.cpp index 963f7b37902..c39e52517d6 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.cpp @@ -44,7 +44,9 @@ gemm_bf16_convolution_fwd_t::pp_ker_t::pp_ker_t( , do_sum_(false) , max_data_reg_idx_(31), max_unroll_(12), compute_reg_step_(1) , data_reg_base_idx_(0) - , bf16_emu_(nullptr), eltwise_injector_(nullptr) + , bf16_emu_(nullptr) + , attr_(pd->attr()) + , jit_eltwise_injectors_(0) { using namespace types; using namespace Xbyak; @@ -55,14 +57,20 @@ gemm_bf16_convolution_fwd_t::pp_ker_t::pp_ker_t( const auto dst_md = memory_desc_wrapper(pd->dst_pd()); dst_os_stride_ = dst_md.blk_off(0, 0, 0, 1); - + bool do_depthwise_ = false; auto &post_ops = pd->attr()->post_ops_; - const int eltwise_ind = post_ops.find(primitive_kind::eltwise); - do_eltwise_ = eltwise_ind != -1; - if (do_eltwise_) - eltwise_injector_ = new jit_uni_eltwise_injector_f32( - this, post_ops.entry_[eltwise_ind].eltwise, true, - reserved_eltwise_gpr, reserved_eltwise_maskr); + for (int i = 0; i < post_ops.len_; i++) { + auto& post_op = post_ops.entry_[i]; + if (post_op.is_eltwise()) { + jit_eltwise_injectors_.push_back(new jit_uni_eltwise_injector_f32(this, + post_op.eltwise.alg, + post_op.eltwise.alpha, + post_op.eltwise.beta, + true, reserved_eltwise_gpr, reserved_eltwise_maskr)); + } else if (post_op.is_depthwise()) { + do_depthwise_ = true; + } + } do_sum_ = dst_data_type != data_type::f32 && post_ops.contain(primitive_kind::sum, 0); @@ -75,6 +83,9 @@ gemm_bf16_convolution_fwd_t::pp_ker_t::pp_ker_t( if (do_bias_) vreg_bias = Zmm(data_reg_base_idx_++); + if (do_depthwise_) + vreg_dw = Zmm(data_reg_base_idx_++); + vlen_ = cpu_isa_traits::vlen / sizeof(float); if (!mayiuse(avx512_core_bf16)) { @@ -110,6 +121,8 @@ void gemm_bf16_convolution_fwd_t::pp_ker_t::generate() mov(reg_len, ptr[reg_param + PARAM_OFF(spatial_length)]); mov(reg_oc_iter, ptr[reg_param + PARAM_OFF(oc_work)]); + mov(reg_oc_offset, ptr[reg_param + PARAM_OFF(oc_offset)]); + if (do_sum_) vbroadcastss(vreg_sum_scale, ptr[reg_param + PARAM_OFF(sum_scale)]); #undef PARAM_OFF @@ -153,8 +166,38 @@ void gemm_bf16_convolution_fwd_t::pp_ker_t::generate() vfmadd231ps(vreg_dst(idx), vreg_prev_dst(idx), vreg_sum_scale); } - if (do_eltwise_) - eltwise_injector_->compute_vector(vreg_dst_idx(idx)); + int eltwise_inj_idx = 0; + const auto& p = attr_->post_ops_; + for (int i = 0; i < p.len_; i++) { + auto& post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + jit_eltwise_injectors_[eltwise_inj_idx]->compute_vector(vreg_dst_idx(idx)); + eltwise_inj_idx++; + } else if (post_op.is_depthwise()) { + mov(reg_dw, reinterpret_cast(post_op.depthwise.weights_data)); + lea(reg_dw, ptr[reg_dw + reg_oc_offset]); + + switch (post_op.depthwise.alg) { + case alg_kind::depthwise_scale_shift: { + vbroadcastss(vreg_dw, ptr[reg_dw]); + vmulps(vreg_dst(idx), vreg_dst(idx), vreg_dw); + mov(reg_dw, reinterpret_cast(post_op.depthwise.biases_data)); + lea(reg_dw, ptr[reg_dw + reg_oc_offset]); + vbroadcastss(vreg_dw, ptr[reg_dw]); + vaddps(vreg_dst(idx), vreg_dst(idx), vreg_dw); + break; + } + case alg_kind::depthwise_prelu: { + vpxord(vreg_dw, vreg_dw, vreg_dw); + vcmpps(kmask, vreg_dst(idx), vreg_dw, _cmp_lt_os); + vbroadcastss(vreg_dw, ptr[reg_dw]); + vmulps(vreg_dst(idx) | kmask, vreg_dst(idx), vreg_dw); + break; + } + default: assert(!"unsupported depthwise algorithm"); + } + } + } if (dst_data_type == data_type::bf16) { // TODO: implement store by zmm registers for bf16 @@ -230,6 +273,8 @@ void gemm_bf16_convolution_fwd_t::pp_ker_t::generate() if (do_bias_) add(reg_bias, sizeof(acc_data_t)); + add(reg_oc_offset, sizeof(acc_data_t)); + dec(reg_oc_iter); jnz(oc_loop, T_NEAR); // oc_loop end @@ -237,17 +282,17 @@ void gemm_bf16_convolution_fwd_t::pp_ker_t::generate() postamble(); - if (do_eltwise_) - eltwise_injector_->prepare_table(); + for (auto& inj : jit_eltwise_injectors_) + inj->prepare_table(); ker_ = getCode(); } template void gemm_bf16_convolution_fwd_t::pp_ker_t::operator () - (dst_data_t *dst, const acc_data_t *acc, const acc_data_t *bias, + (dst_data_t *dst, const acc_data_t *acc, const acc_data_t *bias, size_t g_offset, float sum_scale, size_t dst_stride_in_elements, - size_t acc_stride_in_elements, size_t len, bool do_parallel) + size_t acc_stride_in_elements, size_t len, bool do_parallel, const post_ops_t& p) { assert(ker_); if (len == 0) @@ -260,7 +305,7 @@ void gemm_bf16_convolution_fwd_t::pp_ker_t::operator () ker_args args; args.acc = acc + start_oc * acc_stride_in_elements; args.dst = dst + start_oc * dst_stride_in_elements; - args.bias = bias + start_oc; + args.bias = bias + start_oc + g_offset; args.sum_scale = sum_scale; args.dst_stride_in_bytes = dst_stride_in_elements * sizeof(dst_data_t); @@ -268,6 +313,8 @@ void gemm_bf16_convolution_fwd_t::pp_ker_t::operator () acc_stride_in_elements * sizeof(acc_data_t); args.spatial_length = len; args.oc_work = end_oc - start_oc; + args.oc_offset = (start_oc + g_offset) * sizeof(acc_data_t); + ker_(&args); } }); @@ -287,6 +334,8 @@ void gemm_bf16_convolution_fwd_t::execute_forward() const { : nullptr; const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_; + auto src_offset = this->pd()->src_pd()->desc()->layout_desc.blocking.offset_padding; + auto dst_offset = this->pd()->dst_pd()->desc()->layout_desc.blocking.offset_padding; float *bias = nullptr; if (pd()->desc()->bias_desc.data_type == data_type::bf16) { @@ -337,9 +386,9 @@ void gemm_bf16_convolution_fwd_t::execute_forward() const { for (size_t iwork = start; iwork < end; ++iwork) { int oh = ohb * jcp.oh_block; int ow = owb * jcp.ow_block; - const src_data_t *_src = src + (n * jcp.ngroups + g) * src_step; + const src_data_t *_src = src + (n * jcp.ngroups + g) * src_step + src_offset; const wei_data_t *_weights = weights + g * weights_g_size; - dst_data_t *_dst_im = dst + (n * jcp.ngroups + g) * dst_step; + dst_data_t *_dst_im = dst + (n * jcp.ngroups + g) * dst_step + dst_offset; const int h_step = nstl::min(jcp.oh_block, jcp.oh - oh); const int w_step = nstl::min(jcp.ow_block, jcp.ow - ow); if (jcp.im2col_sz) { @@ -369,8 +418,8 @@ void gemm_bf16_convolution_fwd_t::execute_forward() const { if (this->pd()->is_postprocess_required()) { size_t acc_str = LDC; size_t dst_str = M; - (*pp_ker_)(dst_local, _acc, bias + g * jcp.oc, - sum_scale, dst_str, acc_str, m, jcp.nthr == 1); + (*pp_ker_)(dst_local, _acc, bias, g * jcp.oc, + sum_scale, dst_str, acc_str, m, jcp.nthr == 1, post_ops); } nd_iterator_step(g, jcp.ngroups, n, jcp.mb, od, jcp.od, ohb, nb_oh, diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.hpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.hpp index 23839c1cc08..ef17f5d9e84 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.hpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_convolution.hpp @@ -119,17 +119,25 @@ struct gemm_bf16_convolution_fwd_t: public cpu_primitive_t { } virtual bool is_gemm_conv_format() const { - auto const &po = this->attr()->post_ops_; - auto is_eltwise = [&](int idx) - { return po.entry_[idx].is_eltwise(); }; - auto is_sum = [&](int idx) { return po.entry_[idx].is_sum(); }; + auto const &p = this->attr()->post_ops_; + auto all_post_ops_supported = [&]() { + bool ok = true; - switch (po.len_) { - case 0: return true; // no post_ops - case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise - case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise - default: return false; - } + for (int i = 0; i < p.len_; i++) { + ok = ok && utils::one_of(p.entry_[i].kind, primitive_kind::sum, primitive_kind::eltwise, primitive_kind::depthwise); + } + return ok; + }; + + auto contain = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind) != -1; }; + auto position = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind); }; + auto count = [&](mkldnn::impl::primitive_kind_t kind) { return p.count(kind); }; + + return all_post_ops_supported() && + count(primitive_kind::sum) <= 1 && + IMPLICATION(contain(primitive_kind::sum), position(primitive_kind::sum) == 0); + + return false; } }; @@ -174,12 +182,14 @@ private: ~pp_ker_t() { delete bf16_emu_; - delete eltwise_injector_; + for (auto inj : jit_eltwise_injectors_) + delete inj; + jit_eltwise_injectors_.clear(); } void operator()(dst_data_t *dst, const acc_data_t *acc, - const acc_data_t *bias, float sum_scale, - size_t dst_str, size_t acc_str, size_t len, bool do_parallel); + const acc_data_t *bias, size_t g_offset, float sum_scale, + size_t dst_str, size_t acc_str, size_t len, bool do_parallel, const post_ops_t& p); size_t dst_os_stride_; @@ -193,6 +203,7 @@ private: size_t acc_stride_in_bytes; size_t spatial_length; size_t oc_work; + size_t oc_offset; }; enum { @@ -215,10 +226,16 @@ private: Xbyak::Reg64 reg_dst_str = r13; Xbyak::Reg64 reg_acc_str = r14; + using Vmm = typename cpu_isa_traits::Vmm; + Xbyak::Reg64 reg_oc_offset = r10; + Xbyak::Reg64 reg_dw = r9; + Xbyak::Opmask kmask = k7; + post_ops_t post_ops_; + Xbyak::Reg64 reserved_eltwise_gpr = r10; Xbyak::Opmask reserved_eltwise_maskr = k2; - Xbyak::Zmm vreg_sum_scale, vreg_bias; + Xbyak::Zmm vreg_sum_scale, vreg_bias, vreg_dw; Xbyak::Zmm bf16_emu_reserv_1 = Xbyak::Zmm(27); Xbyak::Zmm bf16_emu_reserv_2 = Xbyak::Zmm(28); @@ -237,7 +254,8 @@ private: int data_reg_base_idx_; size_t vlen_; bf16_emulation_t *bf16_emu_; - jit_uni_eltwise_injector_f32 *eltwise_injector_; + const primitive_attr_t* attr_; + nstl::vector*> jit_eltwise_injectors_; void generate(); int vreg_dst_idx(int iter) { diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_inner_product.hpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_inner_product.hpp index 0e131d01244..ee4675a539d 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_inner_product.hpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/gemm_bf16_inner_product.hpp @@ -59,9 +59,7 @@ struct gemm_bf16_inner_product_fwd_t: public cpu_primitive_t { && IMPLICATION(this->with_bias(), one_of( desc()->bias_desc.data_type, data_type::f32, data_type::bf16)) - && attr()->post_ops_.len_ <= 1 - && IMPLICATION(attr()->post_ops_.len_ == 1, - attr()->post_ops_.entry_[0].is_eltwise()) + && is_supported_post_ops() && dense_gemm_consitency_check(src_pd(), weights_pd(), dst_pd()); if (!ok) return status::unimplemented; @@ -73,6 +71,21 @@ struct gemm_bf16_inner_product_fwd_t: public cpu_primitive_t { return status::success; } + virtual bool is_supported_post_ops() const { + const auto& p = this->attr()->post_ops_; + + auto all_post_ops_supported = [&]() { + bool ok = true; + + for (int i = 0; i < p.len_; i++) { + ok = ok && utils::one_of(p.entry_[i].kind, primitive_kind::eltwise, primitive_kind::depthwise); + } + return ok; + }; + + return all_post_ops_supported(); + } + bool dst_is_acc_; private: @@ -92,10 +105,9 @@ struct gemm_bf16_inner_product_fwd_t: public cpu_primitive_t { , pp_kernel_(nullptr) { bool has_bias = pd()->with_bias(), - has_eltwise = pd()->attr()->post_ops_.len_ == 1, - has_scale = !pd()->attr()->output_scales_.has_default_values(); - postops_in_ip_ = false - || !pd()->dst_is_acc_ || has_bias || has_eltwise || has_scale; + has_post_ops = pd()->attr()->post_ops_.len_ > 0, + has_scale = !pd()->attr()->output_scales_.has_default_values(); + postops_in_ip_ = has_bias || has_post_ops || has_scale; if (postops_in_ip_) { if (mayiuse(avx512_core_bf16)) { pp_kernel_ = new inner_product_utils::jit_pp_kernel_t(apd); diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp index cc5c504d7bd..f88636a708c 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp @@ -589,7 +589,7 @@ status_t jit_avx2_1x1_conv_kernel_f32::init_conf(jit_1x1_conv_conf_t &jcp, if (post_op.is_eltwise()) { if (post_op.eltwise.alg != alg_kind::eltwise_relu) return status::unimplemented; - } else if (post_op.is_depthwise()) { + } else if (post_op.is_depthwise() || post_op.is_quantization()) { return status::unimplemented; } } diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp index f2529f0ed9f..472f07fb31d 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp @@ -598,7 +598,7 @@ status_t jit_avx2_conv_fwd_kernel_f32::init_conf(jit_conv_conf_t &jcp, if (post_op.is_eltwise()) { if (post_op.eltwise.alg != alg_kind::eltwise_relu) return status::unimplemented; - } else if (post_op.is_depthwise()) { + } else if (post_op.is_depthwise() || post_op.is_quantization()) { return status::unimplemented; } } @@ -1054,7 +1054,7 @@ status_t jit_avx2_conv_bwd_data_kernel_f32::init_conf(jit_conv_conf_t &jcp, if (!mayiuse(avx2)) { for (int i = 0; i < p.len_; i++) { auto &post_op = p.entry_[i]; - if (post_op.is_depthwise()) { + if (post_op.is_depthwise() || post_op.is_quantization()) { return status::unimplemented; } } diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.cpp index ee1b9273df9..eecaa2e355d 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.cpp @@ -102,7 +102,7 @@ void jit_avx512_core_bf16_1x1_conv_kernel::reduce_loop(int load_loop_blk, }; #endif auto vreg_accum = [=](int i_load, int i_ur) { - int idx = i_ur * load_loop_blk + i_load; + int idx = i_ur + i_load * ur; assert(idx < 31); return Zmm(idx); }; @@ -213,8 +213,36 @@ void jit_avx512_core_bf16_1x1_conv_kernel::reduce_loop(int load_loop_blk, } } /* Eltwise post-op */ - if (jcp.with_eltwise) - eltwise_injector_->compute_vector_range(0, ur * load_loop_blk); + int eltwise_inj_idx = 0; + int depthwise_inj_idx = 0; + const auto& p = attr_.post_ops_; + + for (int i = 0; i < p.len_; i++) { + auto& post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + eltwise_injectors[eltwise_inj_idx]->compute_vector_range(0, ur * load_loop_blk); + eltwise_inj_idx++; + } else if (post_op.is_depthwise()) { + mov(reg_d_weights, reinterpret_cast(post_op.depthwise.weights_data)); + mov(reg_d_bias, reinterpret_cast(post_op.depthwise.biases_data)); + + add(reg_d_weights, reg_oc_off); + add(reg_d_bias, reg_oc_off); + + for (int j = 0; j < load_loop_blk; ++j) { + int start_idx = vreg_accum(j, 0).getIdx(); + int end_idx = start_idx + ur; + + depthwise_injectors[depthwise_inj_idx]->compute_vector_range( + start_idx, end_idx, reg_d_weights, reg_d_bias); + + add(reg_d_weights, jcp.oc_block * sizeof(float)); + add(reg_d_bias, jcp.oc_block * sizeof(float)); + } + + depthwise_inj_idx++; + } + } }; auto store_output = [=](bool output_is_aligned) { @@ -434,6 +462,24 @@ void jit_avx512_core_bf16_1x1_conv_kernel::reduce_loop(int load_loop_blk, void jit_avx512_core_bf16_1x1_conv_kernel::generate() { + const auto& p = attr_.post_ops_; + for (int i = 0; i < p.len_; i++) { + auto& post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + eltwise_injectors.push_back(new jit_uni_eltwise_injector_f32( + this, + post_op.eltwise.alg, + post_op.eltwise.alpha, + post_op.eltwise.beta + )); + } else if (post_op.is_depthwise()) { + depthwise_injectors.push_back(new jit_uni_depthwise_injector_f32( + this, + post_op.depthwise.alg + )); + } + } + preamble(); mov(reg_bcast_data, ptr[param1 + GET_OFF(bcast_data)]); @@ -454,6 +500,7 @@ void jit_avx512_core_bf16_1x1_conv_kernel::generate() mov(reg_output_stride, ptr[param1 + GET_OFF(output_stride)]); } + mov(reg_oc_off, ptr[param1 + GET_OFF(oc_off)]); auto load_loop_body = [=](int load_loop_blk) { bcast_loop(load_loop_blk); add(reg_load_data, load_loop_blk * jcp.load_loop_load_step); @@ -479,6 +526,7 @@ void jit_avx512_core_bf16_1x1_conv_kernel::generate() assert(!"invalid prop_kind"); } sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step); + add(reg_oc_off, load_loop_blk * jcp.oc_block * jcp.typesize_out); }; const int simd_w = 16; @@ -542,8 +590,8 @@ void jit_avx512_core_bf16_1x1_conv_kernel::generate() postamble(); - if (jcp.with_eltwise) - eltwise_injector_->prepare_table(); + for (auto& inj : eltwise_injectors) + inj->prepare_table(); if (jcp.prop_kind == backward_weights) { const uint16_t dst_prm_array[32] = @@ -561,15 +609,21 @@ bool jit_avx512_core_bf16_1x1_conv_kernel::post_ops_ok( jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) { const auto &p = attr.post_ops_; - auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); }; - auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); }; + auto all_post_ops_supported = [&]() { + bool ok = true; - switch (p.len_) { - case 0: return true; // no post_ops - case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise - case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise - default: return false; - } + for (int i = 0; i < p.len_; i++) { + ok = ok && utils::one_of(p.entry_[i].kind, primitive_kind::sum, primitive_kind::eltwise, primitive_kind::depthwise); + } + return ok; + }; + auto contain = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind) != -1; }; + auto position = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind); }; + auto count = [&](mkldnn::impl::primitive_kind_t kind) { return p.count(kind); }; + + return all_post_ops_supported() && + count(primitive_kind::sum) <= 1 && + IMPLICATION(contain(primitive_kind::sum), position(primitive_kind::sum) == 0); return false; } diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.hpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.hpp index 7d681265135..3aebab823e8 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.hpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_conv_kernel.hpp @@ -22,6 +22,8 @@ #include "jit_primitive_conf.hpp" #include "jit_uni_eltwise.hpp" #include "jit_avx512_core_bf16cvt.hpp" +#include "jit_uni_depthwise.hpp" + //#define BF16_CONV_1x1_BWD_W_JIT_KER_USES_PERMW_TRANSPOSITION @@ -36,13 +38,8 @@ struct jit_avx512_core_bf16_1x1_conv_kernel : public jit_generator { const primitive_attr_t &attr) : jit_generator(nullptr, ker_code_size), jcp(ajcp), attr_(attr) - , eltwise_injector_(nullptr) , bf16_emu_(nullptr) { - if (jcp.with_eltwise) - eltwise_injector_ = new jit_uni_eltwise_injector_f32( - this, jcp.eltwise); - if (!mayiuse(avx512_core_bf16)) bf16_emu_ = new bf16_emulation_t(this, bf16_emu_reserv_1, bf16_emu_reserv_2, @@ -54,7 +51,13 @@ struct jit_avx512_core_bf16_1x1_conv_kernel : public jit_generator { } ~jit_avx512_core_bf16_1x1_conv_kernel() { - delete eltwise_injector_; + for (auto inj : eltwise_injectors) + delete inj; + eltwise_injectors.clear(); + + for (auto inj : depthwise_injectors) + delete inj; + depthwise_injectors.clear(); delete bf16_emu_; } @@ -98,7 +101,7 @@ struct jit_avx512_core_bf16_1x1_conv_kernel : public jit_generator { reg64_t reg_load_loop_work = rsi; reg64_t reg_reduce_loop_work = r11; reg64_t bcast_loop_iter = rdx; - reg64_t reduce_loop_iter = abi_param1; + reg64_t reduce_loop_iter = r13; reg64_t reg_reduce_pos_flag = rax; reg64_t reg_output_stride = r13; reg64_t reg_bias_data = r12; @@ -123,8 +126,12 @@ struct jit_avx512_core_bf16_1x1_conv_kernel : public jit_generator { Xbyak::Zmm zmm_bias = Xbyak::Zmm(31); Xbyak::Label dst_prm_table; + reg64_t reg_oc_off = abi_param1; + reg64_t reg_d_weights = imm_addr64; + reg64_t reg_d_bias = r13; - jit_uni_eltwise_injector_f32 *eltwise_injector_; + nstl::vector*> eltwise_injectors; + nstl::vector*> depthwise_injectors; int bcast_loop_work_offt = 0; #ifdef BF16_CONV_1x1_BWD_W_JIT_KER_USES_PERMW_TRANSPOSITION diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_convolution.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_convolution.cpp index a88c76e2aac..741c46864d6 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_convolution.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_1x1_convolution.cpp @@ -193,6 +193,7 @@ const { } else p.bcast_data = src + data_blk_off(src_d, n, _icb, ih, iw); + p.oc_off = _ocb * jcp.oc_block * sizeof(dst_data_t); kernel_->jit_ker(&p); }; diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.cpp index a1a6b5fdc26..cff073b97dc 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.cpp @@ -119,14 +119,39 @@ void jit_avx512_core_bf16_fwd_kernel::store_output(int ur_w) } } - if (jcp.with_eltwise) { - if (ur_w == jcp.ur_w) { - eltwise_injector_->compute_vector_range(0, - jcp.nb_oc_blocking * jcp.ur_w); - } else { - for (int k = 0; k < jcp.nb_oc_blocking; k++) - eltwise_injector_->compute_vector_range(k * jcp.ur_w, - k * jcp.ur_w + ur_w); + int eltwise_inj_idx = 0; + int depthwise_inj_idx = 0; + const auto &p = attr_.post_ops_; + + for (int i = 0; i < p.len_; i++) { + auto& post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + if (ur_w == jcp.ur_w) { + eltwise_injectors[eltwise_inj_idx]->compute_vector_range(0, + jcp.nb_oc_blocking * jcp.ur_w); + } else { + for (int k = 0; k < jcp.nb_oc_blocking; k++) + eltwise_injectors[eltwise_inj_idx]->compute_vector_range(k * jcp.ur_w, + k * jcp.ur_w + ur_w); + } + + eltwise_inj_idx++; + } else if (post_op.is_depthwise()) { + mov(reg_d_weights, reinterpret_cast(post_op.depthwise.weights_data)); + mov(reg_d_bias, reinterpret_cast(post_op.depthwise.biases_data)); + + add(reg_d_weights, ptr[this->param1 + GET_OFF(oc_off)]); + add(reg_d_bias, ptr[this->param1 + GET_OFF(oc_off)]); + + for (int k = 0; k < jcp.nb_oc_blocking; k++) { + depthwise_injectors[depthwise_inj_idx]->compute_vector_range( + k*jcp.ur_w, k*jcp.ur_w + ur_w, reg_d_weights, reg_d_bias); + + add(reg_d_weights, jcp.oc_block * sizeof(float)); + add(reg_d_bias, jcp.oc_block * sizeof(float)); + } + + depthwise_inj_idx++; } } @@ -312,6 +337,24 @@ void jit_avx512_core_bf16_fwd_kernel::compute_loop( void jit_avx512_core_bf16_fwd_kernel::generate() { + const auto &p = attr_.post_ops_; + for (int i = 0; i < p.len_; i++) { + auto &post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + eltwise_injectors.push_back(new jit_uni_eltwise_injector_f32( + this, + post_op.eltwise.alg, + post_op.eltwise.alpha, + post_op.eltwise.beta + )); + } else if (post_op.is_depthwise()) { + depthwise_injectors.push_back(new jit_uni_depthwise_injector_f32( + this, + post_op.depthwise.alg + )); + } + } + int iw = jcp.iw; int ow = jcp.ow; int ow_block = jcp.ow_block; @@ -500,23 +543,29 @@ void jit_avx512_core_bf16_fwd_kernel::generate() } postamble(); - if (jcp.with_eltwise) - eltwise_injector_->prepare_table(); + for (auto& inj : eltwise_injectors) + inj->prepare_table(); } bool jit_avx512_core_bf16_fwd_kernel::post_ops_ok( jit_conv_conf_t &jcp, const primitive_attr_t &attr) { const auto &p = attr.post_ops_; - auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); }; - auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); }; + auto all_post_ops_supported = [&]() { + bool ok = true; - switch (p.len_) { - case 0: return true; // no post_ops - case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise - case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise - default: return false; - } + for (int i = 0; i < p.len_; i++) { + ok = ok && utils::one_of(p.entry_[i].kind, primitive_kind::sum, primitive_kind::eltwise, primitive_kind::depthwise); + } + return ok; + }; + auto contain = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind) != -1; }; + auto position = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind); }; + auto count = [&](mkldnn::impl::primitive_kind_t kind) { return p.count(kind); }; + + return all_post_ops_supported() && + count(primitive_kind::sum) <= 1 && + IMPLICATION(contain(primitive_kind::sum), position(primitive_kind::sum) == 0); return false; } diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.hpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.hpp index 51298bf519b..d8a7258bc11 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.hpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_conv_kernel.hpp @@ -25,6 +25,7 @@ #include "jit_primitive_conf.hpp" #include "jit_uni_eltwise.hpp" #include "jit_avx512_core_bf16cvt.hpp" +#include "jit_uni_depthwise.hpp" //#define BF16_CONV_BWD_W_JIT_KER_USES_PERMW_TRANSPOSITION //#define BF16_CONV_BWD_W_DOES_NOT_USE_BARRIERS @@ -40,12 +41,8 @@ struct jit_avx512_core_bf16_fwd_kernel : public jit_generator { jit_generator(nullptr, ker_code_size), jcp(ajcp), attr_(attr), - eltwise_injector_(nullptr), bf16_emu_(nullptr) { - if (jcp.with_eltwise) - eltwise_injector_ = new jit_uni_eltwise_injector_f32( - this, jcp.eltwise); if (!mayiuse(avx512_core_bf16)) bf16_emu_ = new bf16_emulation_t(this, bf16_emu_reserv_1, bf16_emu_reserv_2, @@ -58,7 +55,13 @@ struct jit_avx512_core_bf16_fwd_kernel : public jit_generator { ~jit_avx512_core_bf16_fwd_kernel() { delete bf16_emu_; - delete eltwise_injector_; + for (auto inj : eltwise_injectors) + delete inj; + eltwise_injectors.clear(); + + for (auto inj : depthwise_injectors) + delete inj; + depthwise_injectors.clear(); } DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_bf16_fwd_kernel) @@ -144,7 +147,12 @@ private: Xbyak::Zmm bf16_emu_reserv_5 = Xbyak::Zmm(29); Xbyak::Zmm bf16_emu_reserv_6 = Xbyak::Zmm(30); - jit_uni_eltwise_injector_f32 *eltwise_injector_; + reg64_t reg_d_weights = imm_addr64; + reg64_t reg_d_bias = reg_kj; + + nstl::vector*> eltwise_injectors; + nstl::vector*> depthwise_injectors; + bf16_emulation_t *bf16_emu_; inline void prepare_output(int ur_w); diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_convolution.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_convolution.cpp index 6ab2965bd46..ccd1d86cc65 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_convolution.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_convolution.cpp @@ -117,6 +117,7 @@ void _jit_avx512_core_bf16_convolution_fwd_t::execute_forward_1d() auto src_w = src + src_d.blk_off(n, g_icb, iw_s); auto wht_w = weights + wht_blk_off(weights_d, g, ocb); + par_conv.oc_off = g_oc * sizeof(dst_data_t); par_conv.src = src_w; par_conv.dst = dst_w; par_conv.filt = wht_w; @@ -219,6 +220,7 @@ void _jit_avx512_core_bf16_convolution_fwd_t::execute_forward_2d() auto aux_src = src_w + i_t_overflow * dilate_h * src_h_stride; auto aux_wht = wht_w + i_t_overflow * wht_h_stride; + par_conv.oc_off = g_oc * sizeof(dst_data_t); par_conv.src = aux_src; par_conv.dst = dst_w; par_conv.filt = aux_wht; @@ -337,6 +339,7 @@ void _jit_avx512_core_bf16_convolution_fwd_t::execute_forward_3d() auto aux_src = src_w + i_t_overflow * dilate_h * src_h_stride; auto aux_wht = wht_w + i_t_overflow * wht_h_stride; + par_conv.oc_off = g_oc * sizeof(dst_data_t); par_conv.src = aux_src; par_conv.dst = dst_w; par_conv.filt = aux_wht; diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.cpp index 9925f8c2c85..cec8c088c96 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.cpp @@ -164,11 +164,40 @@ void jit_avx512_dw_conv_fwd_kernel_bf16::apply_filter_unrolled( L(iter_exit_label); } -void jit_avx512_dw_conv_fwd_kernel_bf16::apply_activation( +void jit_avx512_dw_conv_fwd_kernel_bf16::apply_postprocess( int ur_ch_blocks, int ur_w) { - if (this->jcp.with_eltwise) { - eltwise_injector_->compute_vector_range( - acc_idx_start, ur_w * ur_ch_blocks + acc_idx_start); + int eltwise_inj_idx = 0; + int depthwise_inj_idx = 0; + const auto& p = attr_.post_ops_; + + for (int i = 0; i < p.len_; i++) { + auto& post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + int start_idx = get_acc_reg(0).getIdx(); + int end_idx = get_acc_reg(ur_w * ur_ch_blocks).getIdx(); + + eltwise_injectors[eltwise_inj_idx]->compute_vector_range(start_idx, end_idx); + eltwise_inj_idx++; + } else if (post_op.is_depthwise()) { + mov(reg_d_weights, reinterpret_cast(post_op.depthwise.weights_data)); + mov(reg_d_bias, reinterpret_cast(post_op.depthwise.biases_data)); + + add(reg_d_weights, ptr[this->param1 + GET_OFF(oc_off)]); + add(reg_d_bias, ptr[this->param1 + GET_OFF(oc_off)]); + + for (int ch = 0; ch < ur_ch_blocks; ch++) { + int start_idx = get_acc_reg(ur_w * ch).getIdx(); + int end_idx = get_acc_reg(ur_w * ch + ur_w).getIdx(); + + depthwise_injectors[depthwise_inj_idx]->compute_vector_range( + start_idx, end_idx, reg_d_weights, reg_d_bias); + + add(reg_d_weights, jcp.ch_block * sizeof(float)); + add(reg_d_bias, jcp.ch_block * sizeof(float)); + } + + depthwise_inj_idx++; + } } } @@ -246,7 +275,7 @@ void jit_avx512_dw_conv_fwd_kernel_bf16::loop_ow(int ur_ch_blocks) { load_src(ur_ch_blocks, ur_w); apply_filter_unrolled(ur_ch_blocks, ur_w); - apply_activation(ur_ch_blocks, ur_w); + apply_postprocess(ur_ch_blocks, ur_w); store_dst(ur_ch_blocks, ur_w); add(reg_input, jcp.typesize_in * ur_w * jcp.ch_block * jcp.stride_w); @@ -267,7 +296,7 @@ void jit_avx512_dw_conv_fwd_kernel_bf16::loop_ow(int ur_ch_blocks) { load_src(ur_ch_blocks, ur_w); apply_filter(ur_ch_blocks, ur_w); - apply_activation(ur_ch_blocks, ur_w); + apply_postprocess(ur_ch_blocks, ur_w); store_dst(ur_ch_blocks, ur_w); add(reg_input, jcp.typesize_in * ur_w * jcp.ch_block * jcp.stride_w); @@ -281,6 +310,24 @@ void jit_avx512_dw_conv_fwd_kernel_bf16::loop_ow(int ur_ch_blocks) { } void jit_avx512_dw_conv_fwd_kernel_bf16::generate() { + const auto& p = attr_.post_ops_; + for (int i = 0; i < p.len_; i++) { + auto& post_op = p.entry_[i]; + if (post_op.is_eltwise()) { + eltwise_injectors.push_back(new jit_uni_eltwise_injector_f32( + this, + post_op.eltwise.alg, + post_op.eltwise.alpha, + post_op.eltwise.beta + )); + } else if (post_op.is_depthwise()) { + depthwise_injectors.push_back(new jit_uni_depthwise_injector_f32( + this, + post_op.depthwise.alg + )); + } + } + this->preamble(); mov(reg_input, ptr[this->param1 + GET_OFF(src)]); @@ -316,8 +363,8 @@ void jit_avx512_dw_conv_fwd_kernel_bf16::generate() { this->postamble(); - if (jcp.with_eltwise) - eltwise_injector_->prepare_table(); + for (auto& inj : eltwise_injectors) + inj->prepare_table(); } inline void jit_avx512_dw_conv_bwd_data_kernel_bf16::load_ddst( diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.hpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.hpp index d9cf5766fd5..3395ad22589 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.hpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_avx512_core_bf16_dw_conv_kernel.hpp @@ -23,7 +23,7 @@ #include "jit_generator.hpp" #include "jit_primitive_conf.hpp" #include "jit_uni_eltwise.hpp" - +#include "jit_uni_depthwise.hpp" #include "jit_avx512_core_bf16cvt.hpp" namespace mkldnn { @@ -33,11 +33,8 @@ namespace cpu { struct jit_avx512_dw_conv_fwd_kernel_bf16 : public jit_generator { DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_dw_conv_fwd_kernel_bf16) - jit_avx512_dw_conv_fwd_kernel_bf16(jit_conv_conf_t ajcp, const primitive_attr_t&) - : jcp(ajcp), eltwise_injector_(nullptr), bf16_emu_(nullptr) { - if (jcp.with_eltwise) - eltwise_injector_ = new jit_uni_eltwise_injector_f32( - this, jcp.eltwise); + jit_avx512_dw_conv_fwd_kernel_bf16(jit_conv_conf_t ajcp, const primitive_attr_t& attr) + : jcp(ajcp), attr_(attr), bf16_emu_(nullptr) { if (!mayiuse(avx512_core_bf16)) bf16_emu_ = new bf16_emulation_t(this, bf16_emu_reserv_1, bf16_emu_reserv_2, bf16_emu_reserv_3, bf16_emu_reserv_4, @@ -48,11 +45,19 @@ struct jit_avx512_dw_conv_fwd_kernel_bf16 : public jit_generator { } ~jit_avx512_dw_conv_fwd_kernel_bf16() { - delete eltwise_injector_; + for (auto inj : eltwise_injectors) + delete inj; + eltwise_injectors.clear(); + + for (auto inj : depthwise_injectors) + delete inj; + depthwise_injectors.clear(); + delete bf16_emu_; } jit_conv_conf_t jcp; + const primitive_attr_t& attr_; void (*jit_ker)(jit_conv_call_s *); private: @@ -78,6 +83,8 @@ private: reg64_t reg_ur_w = rbp; reg64_t reg_ch_blocks = aux1_reg_input; reg64_t imm_addr64 = aux1_reg_input; + reg64_t reg_d_weights = imm_addr64; + reg64_t reg_d_bias = iter_kh; Xbyak::Zmm zmm_ker_reg = Xbyak::Zmm(0); Xbyak::Zmm zmm_src_reg = Xbyak::Zmm(1); @@ -99,11 +106,12 @@ private: inline void load_src(int ur_ch_blocks, int ur_w); inline void apply_filter(int ur_ch_blocks, int ur_w); inline void apply_filter_unrolled(int ur_ch_blocks, int ur_w); - inline void apply_activation(int ur_ch_blocks, int ur_w); + inline void apply_postprocess(int ur_ch_blocks, int ur_w); inline void store_dst(int ur_ch_blocks, int ur_w); inline void loop_ow(int ur_ch_blocks); - jit_uni_eltwise_injector_f32 *eltwise_injector_; + nstl::vector*> eltwise_injectors; + nstl::vector*> depthwise_injectors; bf16_emulation_t *bf16_emu_; diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_utils.hpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_utils.hpp index bdc947fe6a2..0319c260c29 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_utils.hpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_utils.hpp @@ -74,34 +74,21 @@ template bool jit_uni_dw_conv_fwd_kernel::post_ops_ok( jit_conv_conf_t &jcp, const primitive_attr_t &attr, bool is_bf16) { const auto &p = attr.post_ops_; + auto all_post_ops_supported = [&]() { + bool ok = true; - if (is_bf16) { - auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); }; - auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); }; - - switch (p.len_) { - case 0: return true; // no post_ops - case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise - case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise - default: return false; + for (int i = 0; i < p.len_; i++) { + ok = ok && utils::one_of(p.entry_[i].kind, primitive_kind::sum, primitive_kind::eltwise, primitive_kind::depthwise); } - } else { - auto all_post_ops_supported = [&]() { - bool ok = true; + return ok; + }; + auto contain = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind) != -1; }; + auto position = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind); }; + auto count = [&](mkldnn::impl::primitive_kind_t kind) { return p.count(kind); }; - for (int i = 0; i < p.len_; i++) { - ok = ok && utils::one_of(p.entry_[i].kind, primitive_kind::sum, primitive_kind::eltwise, primitive_kind::depthwise, primitive_kind::quantization); - } - return ok; - }; - auto contain = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind) != -1; }; - auto position = [&](mkldnn::impl::primitive_kind_t kind) { return p.find(kind); }; - auto count = [&](mkldnn::impl::primitive_kind_t kind) { return p.count(kind); }; - - return all_post_ops_supported() && - count(primitive_kind::sum) <= 1 && - IMPLICATION(contain(primitive_kind::sum), position(primitive_kind::sum) == 0); - } + return all_post_ops_supported() && + count(primitive_kind::sum) <= 1 && + IMPLICATION(contain(primitive_kind::sum), position(primitive_kind::sum) == 0); return false; } diff --git a/inference-engine/thirdparty/mkl-dnn/src/cpu/ref_depthwise.cpp b/inference-engine/thirdparty/mkl-dnn/src/cpu/ref_depthwise.cpp index 28368f088a5..9e9b66a13c6 100644 --- a/inference-engine/thirdparty/mkl-dnn/src/cpu/ref_depthwise.cpp +++ b/inference-engine/thirdparty/mkl-dnn/src/cpu/ref_depthwise.cpp @@ -37,6 +37,32 @@ template inline T prelu_fwd(T s_val, T w_val) { return s_val >= 0 ? s_val : s_val*w_val; } +union float_raw { + float f; + unsigned short i[2]; +}; + +static float bf16tof32(mkldnn_bfloat16_t bf16) { + union float_raw t = { 0 }; + t.i[1] = bf16; + t.i[0] = 0; + return t.f; +} + +static mkldnn_bfloat16_t f32tobf16(float f32) { + union float_raw t = { 0 }; + t.f = f32; + return t.i[1]; +} + +inline mkldnn_bfloat16_t bf16_scale_shift_fwd(mkldnn_bfloat16_t s_val, mkldnn_bfloat16_t w_val, mkldnn_bfloat16_t b_val) { + return f32tobf16(bf16tof32(s_val) * bf16tof32(w_val) + bf16tof32(b_val)); +} + +inline mkldnn_bfloat16_t bf16_prelu_fwd(mkldnn_bfloat16_t s_val, mkldnn_bfloat16_t w_val) { + return s_val >= 0 ? s_val : f32tobf16(bf16tof32(s_val) * bf16tof32(w_val)); +} + ref_depthwise_scalar_fwd_t::ref_depthwise_scalar_fwd_t(const alg_kind_t alg_) : alg(alg_) { using namespace alg_kind; @@ -91,15 +117,24 @@ void ref_depthwise_fwd_t::execute_forward() const { data_t b_val = bias ? bias[bias_d.off(wei_idx)] : (data_t)0; data_t &d_val = dst[data_off]; - switch (alg_kind) { - case depthwise_scale_shift: d_val = scale_shift_fwd(s_val, w_val, b_val); break; - case depthwise_prelu: d_val = prelu_fwd(s_val, w_val); break; - default: assert(!"unknown depthwise alg_kind"); + if (data_type == mkldnn_bf16) { + switch (alg_kind) { + case depthwise_scale_shift: d_val = bf16_scale_shift_fwd(s_val, w_val, b_val); break; + case depthwise_prelu: d_val = bf16_prelu_fwd(s_val, w_val); break; + default: assert(!"unknown depthwise alg_kind"); + } + } else { + switch (alg_kind) { + case depthwise_scale_shift: d_val = scale_shift_fwd(s_val, w_val, b_val); break; + case depthwise_prelu: d_val = prelu_fwd(s_val, w_val); break; + default: assert(!"unknown depthwise alg_kind"); + } } }); } template struct ref_depthwise_fwd_t; +template struct ref_depthwise_fwd_t; } } diff --git a/inference-engine/thirdparty/mkl-dnn/tests/gtests/CMakeLists.txt b/inference-engine/thirdparty/mkl-dnn/tests/gtests/CMakeLists.txt index e850b9fac43..019d137f180 100644 --- a/inference-engine/thirdparty/mkl-dnn/tests/gtests/CMakeLists.txt +++ b/inference-engine/thirdparty/mkl-dnn/tests/gtests/CMakeLists.txt @@ -64,6 +64,7 @@ file(GLOB PRIM_TEST_CASES_SRC test_convolution_forward_x8s8fp.cpp test_convolution_forward_x8s8fp_3d.cpp test_convolution_eltwise_forward_f32.cpp + test_convolution_eltwise_forward_bf16.cpp test_convolution_eltwise_forward_x8s8f32s32.cpp test_convolution_backward_data_f32.cpp test_convolution_backward_data_s16s16s32.cpp @@ -77,6 +78,7 @@ file(GLOB PRIM_TEST_CASES_SRC test_rnn_forward.cpp test_roi_pooling_forward.cpp test_convolution_depthwise_forward_f32.cpp + test_convolution_depthwise_forward_bf16f32.cpp test_convolution_depthwise_forward_x8s8f32s32.cpp test_convolution_dw_conv_f32.cpp test_convolution_dw_conv_u8s8s32.cpp diff --git a/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_bf16f32.cpp b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_bf16f32.cpp new file mode 100644 index 00000000000..50ef454e1ae --- /dev/null +++ b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_bf16f32.cpp @@ -0,0 +1,87 @@ +/******************************************************************************* +* Copyright 2020 Intel Corporation +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*******************************************************************************/ + +#include "mkldnn_test_common.hpp" +#include "gtest/gtest.h" +#include "mkldnn.hpp" +#include "test_convolution_depthwise_forward_common.hpp" + +namespace mkldnn { + +using convolution_test = convolution_depthwise_test; + +TEST_P(convolution_test, TestConvolution) +{ +} + +#define EXPAND_FORMATS(src, weights, bias, dst) \ + { mkldnn::memory::format::src, mkldnn::memory::format::weights, \ + mkldnn::memory::format::bias, mkldnn::memory::format::dst } + +#define FMT_WEIGHTS_BLOCKED16 OIhw8i16o2i +#define FMT_WEIGHTS_BLOCKED16_DW Goihw16g + +#define ENGINE mkldnn::engine::kind::cpu +#define ALGORITHM mkldnn::convolution_direct + +#define CONCAT_WITH_UNDERSCORE_(a,b) a ## _ ## b +#define CONCAT_WITH_UNDERSCORE(a,b) CONCAT_WITH_UNDERSCORE_(a,b) + +#define INST_TEST_CASE_(str, ...) INSTANTIATE_TEST_CASE_P( \ + str, convolution_test, ::testing::Values(__VA_ARGS__)) + +#define INST_TEST_CASE(str, ...) INST_TEST_CASE_( \ + CONCAT_WITH_UNDERSCORE(CONCAT_WITH_UNDERSCORE(Convolution, \ + str), depthwise), __VA_ARGS__) + +#define EXPAND_ARGS(args) args + +#define PARAMS(...) \ + EXPAND_ARGS(PARAMS_CONV(depthwise_scale_shift, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(depthwise_prelu, __VA_ARGS__)) + +#define PARAMS_CONV(alg, src, weights, bias, dst, ...) \ + test_convolution_depthwise_params_t {alg, ENGINE, ALGORITHM, \ + EXPAND_FORMATS(src, weights, bias, dst), /* empty attributes */ {}, \ + {__VA_ARGS__} } + + INST_TEST_CASE(SimpleSmall, + PARAMS(nchw, oihw, x, nchw, 2, 1, 32, 13, 13, 48, 11, 11, 3, 3, 0, 0, 1, 1), + PARAMS(nchw, oihw, x, nchw, 2, 1, 16, 13, 13, 48, 13, 13, 1, 1, 0, 0, 1, 1), + PARAMS(nchw, goihw, x, nchw, 2, 64, 64, 16, 16, 64, 16, 16, 3, 3, 0, 0, 1, 1), + PARAMS(nchw, goihw, x, nchw, 2, 2, 32, 9, 9, 32, 9, 9, 1, 1, 0, 0, 1, 1) + ); + + INST_TEST_CASE(SimpleSmall_Blocked16, + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16, x, nChw16c, + 2, 1, 32, 13, 13, 48, 11, 11, 3, 3, 0, 0, 1, 1), + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16, x, nChw16c, + 2, 1, 16, 13, 13, 48, 13, 13, 1, 1, 0, 0, 1, 1), + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16_DW, x, nChw16c, + 2, 64, 64, 16, 16, 64, 16, 16, 3, 3, 0, 0, 1, 1), + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16_DW, x, nChw16c, + 2, 32, 32, 9, 9, 32, 9, 9, 1, 1, 0, 0, 1, 1), + + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16, x, nChw16c, + 2, 1, 8, 13, 13, 16, 13, 13, 1, 1, 0, 0, 1, 1), + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16, x, nChw16c, + 2, 1, 8, 3, 3, 16, 3, 3, 1, 1, 0, 0, 1, 1), + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16, x, nChw16c, + 2, 1, 16, 13, 13, 48, 13, 13, 1, 1, 0, 0, 1, 1), + PARAMS(nChw16c, FMT_WEIGHTS_BLOCKED16, x, nChw16c, + 2, 1, 16, 13, 13, 48, 13, 13, 3, 3, 0, 0, 1, 1) + ); +} diff --git a/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_common.hpp b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_common.hpp index 730be03b7d2..875df0615ca 100644 --- a/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_common.hpp +++ b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_depthwise_forward_common.hpp @@ -34,6 +34,13 @@ inline typename std::remove_reference::type rnd_up(const T a, const U b) { return div_up(a, b) * b; } +static float bf16tof32(mkldnn_bfloat16_t bf16) { + union float_raw t = { 0 }; + t.i[1] = bf16; + t.i[0] = 0; + return t.f; +} + template void compute_ref_conv_depthwise_fwd(const test_convolution_sizes_t &c, @@ -42,6 +49,7 @@ void compute_ref_conv_depthwise_fwd(const test_convolution_sizes_t &c, const memory &depthwise_weights, const memory &depthwise_bias) { data_t_src *src_data = (data_t_src *)src.get_data_handle(); + memory::data_type data_type_src = data_traits::data_type; data_t_wei *weights_data = (data_t_wei *)weights.get_data_handle(); data_t_dst *bias_data = (data_t_dst *)(w_bias ? bias.get_data_handle() : nullptr); @@ -90,8 +98,13 @@ void compute_ref_conv_depthwise_fwd(const test_convolution_sizes_t &c, + oc * padded_ic_w / c.ng * c.kh * c.kw + ic * c.kh * c.kw + kh * c.kw + kw; - dst_data[didx] += src_data[map_index(src_d, iidx)] + if (data_type_src == mkldnn_bf16) { + dst_data[didx] += bf16tof32(src_data[map_index(src_d, iidx)]) + * bf16tof32(weights_data[map_index(weights_d, widx)]); + } else { + dst_data[didx] += src_data[map_index(src_d, iidx)] * weights_data[map_index(weights_d, widx)]; + } } switch (depthwise_alg) { diff --git a/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_bf16.cpp b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_bf16.cpp new file mode 100644 index 00000000000..3c3008b8797 --- /dev/null +++ b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_bf16.cpp @@ -0,0 +1,89 @@ +/******************************************************************************* +* Copyright 2020 Intel Corporation +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*******************************************************************************/ + +#include "mkldnn_test_common.hpp" +#include "gtest/gtest.h" +#include "math_utils.hpp" +#include "mkldnn.hpp" +#include "test_convolution_eltwise_forward_common.hpp" + +namespace mkldnn { + +using convolution_test = convolution_eltwise_test; + +TEST_P(convolution_test, TestConvolutionEltwise) +{ +} + +#define EXPAND_FORMATS(src, weights, bias, dst) \ + { mkldnn::memory::format::src, mkldnn::memory::format::weights, \ + mkldnn::memory::format::bias, mkldnn::memory::format::dst } + +#define CONCAT_WITH_UNDERSCORE_(a,b) a ## _ ## b +#define CONCAT_WITH_UNDERSCORE(a,b) CONCAT_WITH_UNDERSCORE_(a,b) + +#define INST_TEST_CASE_(str, ...) INSTANTIATE_TEST_SUITE_P( \ + str, convolution_test, ::testing::Values(__VA_ARGS__)) + +#define INST_TEST_CASE(str, ...) INST_TEST_CASE_( \ + CONCAT_WITH_UNDERSCORE(CONCAT_WITH_UNDERSCORE(Convolution, \ + str), eltwise), __VA_ARGS__) + +#define EXPAND_ARGS(args) args + +#define PARAMS(...) \ + EXPAND_ARGS(PARAMS_CONV(eltwise_relu, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_tanh, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_elu, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_square, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_abs, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_sqrt, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_linear, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_bounded_relu, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_soft_relu, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_logistic, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_exp, __VA_ARGS__)), \ + EXPAND_ARGS(PARAMS_CONV(eltwise_swish, __VA_ARGS__)) + +#define ELTWISE_ALPHA 0.5f +#define ELTWISE_BETA 1.5f + +#define PARAMS_CONV(alg, src, weights, bias, dst, ...) \ + test_convolution_eltwise_params_t {alg, mkldnn::engine::kind::cpu, \ + mkldnn::convolution_direct, ELTWISE_ALPHA, ELTWISE_BETA, \ + EXPAND_FORMATS(src, weights, bias, dst), /* empty attributes */ {}, \ + {__VA_ARGS__} } + + INST_TEST_CASE(SimpleSmall, + PARAMS(nchw, oihw, x, nchw, 2, 1, 32, 13, 13, 48, 11, 11, 3, 3, 0, 0, 1, 1), + PARAMS(nchw, oihw, x, nchw, 2, 1, 16, 13, 13, 48, 13, 13, 1, 1, 0, 0, 1, 1), + PARAMS(nchw, goihw, x, nchw, 2, 64, 64, 16, 16, 64, 16, 16, 3, 3, 0, 0, 1, 1), + PARAMS(nchw, goihw, x, nchw, 2, 32, 32, 9, 9, 32, 9, 9, 1, 1, 0, 0, 1, 1) + ); + + INST_TEST_CASE(SimpleSmall_Blocked16, + PARAMS(nChw16c, Goihw16g, x, nChw16c, 1, 48, 48, 20, 20, 48, 20, 20, 3, 3, 1, 1, 1, 1), + PARAMS(nChw16c, OIhw8i16o2i, x, nChw16c, 1, 1, 48, 20, 20, 48, 20, 20, 1, 1, 0, 0, 1, 1), + PARAMS(nChw16c, OIhw8i16o2i, x, nChw16c, 1, 1, 48, 20, 20, 48, 20, 20, 3, 3, 0, 0, 1, 1), + PARAMS(nChw16c, OIhw8i16o2i, x, nChw16c, 2, 1, 32, 32, 32, 32, 32, 32, 3, 3, 0, 0, 1, 1) + ); + + INST_TEST_CASE(SimpleSmall_Blocked16_Tail, + PARAMS(nChw16c, Goihw16g, x, nChw16c, 1, 47, 47, 20, 20, 47, 20, 20, 3, 3, 1, 1, 1, 1), + PARAMS(nChw16c, OIhw8i16o2i, x, nChw16c, 1, 1, 47, 20, 20, 47, 20, 20, 1, 1, 0, 0, 1, 1), + PARAMS(nChw16c, OIhw8i16o2i, x, nChw16c, 2, 1, 32, 32, 32, 32, 32, 32, 3, 3, 0, 0, 1, 1) + ); +} diff --git a/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_common.hpp b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_common.hpp index 6927c796638..5d8cfeed51b 100644 --- a/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_common.hpp +++ b/inference-engine/thirdparty/mkl-dnn/tests/gtests/test_convolution_eltwise_forward_common.hpp @@ -23,6 +23,13 @@ using namespace mkldnn::impl::math; namespace mkldnn { +static float bf16tof32(mkldnn_bfloat16_t bf16) { + union float_raw t = { 0 }; + t.i[1] = bf16; + t.i[0] = 0; + return t.f; +} + template void compute_ref_conv_eltwise_fwd(const test_convolution_sizes_t &c, @@ -31,6 +38,7 @@ void compute_ref_conv_eltwise_fwd(const test_convolution_sizes_t &c, float elt_alpha, float elt_beta) { data_t_src *src_data = (data_t_src *)src.get_data_handle(); + memory::data_type data_type_src = data_traits::data_type; data_t_wei *weights_data = (data_t_wei *)weights.get_data_handle(); data_t_dst *bias_data = (data_t_dst *)(w_bias ? bias.get_data_handle() : nullptr); @@ -75,8 +83,13 @@ void compute_ref_conv_eltwise_fwd(const test_convolution_sizes_t &c, + oc * padded_ic_w / c.ng * c.kh * c.kw + ic * c.kh * c.kw + kh * c.kw + kw; - dst_data[didx] += src_data[map_index(src_d, iidx)] + if (data_type_src == mkldnn_bf16) { + dst_data[didx] += bf16tof32(src_data[map_index(src_d, iidx)]) + * bf16tof32(weights_data[map_index(weights_d, widx)]); + } else { + dst_data[didx] += src_data[map_index(src_d, iidx)] * weights_data[map_index(weights_d, widx)]; + } } auto &d = dst_data[didx]; @@ -92,6 +105,7 @@ void compute_ref_conv_eltwise_fwd(const test_convolution_sizes_t &c, case eltwise_soft_relu: d = soft_relu_fwd(d); break; case eltwise_logistic: d = logistic_fwd(d); break; case eltwise_exp: d = exp_fwd(d); break; + case eltwise_gelu: d = gelu_fwd(d); break; case eltwise_clamp: d = clamp_fwd(d, elt_alpha, elt_beta); break; case eltwise_swish: d = swish_fwd(d, elt_alpha); break; default: assert(!"unknown alg_kind"); diff --git a/inference-engine/thirdparty/mkldnn.cmake b/inference-engine/thirdparty/mkldnn.cmake index baabd04a1d7..9a82e874678 100644 --- a/inference-engine/thirdparty/mkldnn.cmake +++ b/inference-engine/thirdparty/mkldnn.cmake @@ -127,15 +127,15 @@ if(GEMM STREQUAL "OPENBLAS") list(APPEND ${TARGET}_LINKER_LIBS ${BLAS_LIBRARIES}) elseif (GEMM STREQUAL "MKL") ## enable cblas_gemm from mlkml package -if(WIN32 OR APPLE) - detect_mkl("mklml") -else() - if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") - detect_mkl("mklml_intel") + if(WIN32 OR APPLE) + detect_mkl("mklml") else() - detect_mkl("mklml_gnu") + if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel") + detect_mkl("mklml_intel") + else() + detect_mkl("mklml_gnu") + endif() endif() -endif() add_definitions(-DUSE_MKL -DUSE_CBLAS) include_directories(AFTER ${MKLINC}) list(APPEND ${TARGET}_LINKER_LIBS ${MKLLIB}) diff --git a/inference-engine/thirdparty/movidius/CMakeLists.txt b/inference-engine/thirdparty/movidius/CMakeLists.txt index f75dfd724bf..8dacbc13fc9 100644 --- a/inference-engine/thirdparty/movidius/CMakeLists.txt +++ b/inference-engine/thirdparty/movidius/CMakeLists.txt @@ -20,4 +20,7 @@ if (ENABLE_MYRIAD) "${IE_MAIN_SOURCE_DIR}/thirdparty/movidius/mvnc" "${CMAKE_BINARY_DIR}/thirdparty/movidius/mvnc") + if(ENABLE_TESTS) + add_subdirectory(tests) + endif() endif() diff --git a/inference-engine/thirdparty/movidius/XLink/pc/Win/src/win_synchapi.c b/inference-engine/thirdparty/movidius/XLink/pc/Win/src/win_synchapi.c index 26bd365e9e2..a9e09c4f7db 100644 --- a/inference-engine/thirdparty/movidius/XLink/pc/Win/src/win_synchapi.c +++ b/inference-engine/thirdparty/movidius/XLink/pc/Win/src/win_synchapi.c @@ -34,7 +34,12 @@ int pthread_cond_timedwait(pthread_cond_t* __cond, msec = __abstime->tv_sec * 1000 + __abstime->tv_nsec / 1000000; } - return SleepConditionVariableCS(&__cond->_cv, __mutex, (DWORD)msec); + // SleepConditionVariableCS returns bool=true on success. + if (SleepConditionVariableCS(&__cond->_cv, __mutex, (DWORD)msec)) + return 0; + + const int rc = (int)GetLastError(); + return rc == ERROR_TIMEOUT ? ETIMEDOUT : rc; } int pthread_cond_broadcast(pthread_cond_t *__cond) diff --git a/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt b/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt index a0347ba7e1a..9b997a0aa2b 100644 --- a/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt +++ b/inference-engine/thirdparty/movidius/mvnc/CMakeLists.txt @@ -73,3 +73,7 @@ if(NOT WIN32) ${LIBUSB_LIBRARY}) endif() +if(ENABLE_TESTS AND ENABLE_MYRIAD_MVNC_TESTS) + add_subdirectory(tests) +endif() + diff --git a/inference-engine/thirdparty/movidius/mvnc/src/watchdog/watchdog.cpp b/inference-engine/thirdparty/movidius/mvnc/src/watchdog/watchdog.cpp index 778bac714cc..35e0316b2a2 100644 --- a/inference-engine/thirdparty/movidius/mvnc/src/watchdog/watchdog.cpp +++ b/inference-engine/thirdparty/movidius/mvnc/src/watchdog/watchdog.cpp @@ -202,9 +202,7 @@ class WatchdogImpl { using Devices = std::list; Devices watchedDevices; - std::mutex devicesListAcc; - std::atomic generation = {0}; - std::atomic_bool threadRunning; + std::atomic_bool threadRunning {false}; pthread_mutex_t routineLock; pthread_cond_t wakeUpPingThread; @@ -215,6 +213,17 @@ class WatchdogImpl { WatchdogImpl& operator = (const WatchdogImpl&) = delete; WatchdogImpl& operator = (WatchdogImpl&&) = delete; + class AutoScope { + public: + explicit AutoScope(const std::function& func) : _func(func) {} + ~AutoScope() { _func(); } + + AutoScope(const AutoScope&) = delete; + AutoScope& operator=(const AutoScope&) = delete; + private: + std::function _func; + }; + private: WatchdogImpl() { @@ -229,6 +238,10 @@ private: if (rc != 0) { throw std::runtime_error("failed to initialize condition variable attribute. rc: " + std::to_string(rc)); } + AutoScope attrDestroy([&attr]{ + if (pthread_condattr_destroy(&attr) != 0) + mvLog(MVLOG_ERROR, "Failed to destroy condition variable attribute."); + }); rc = pthread_condattr_setclock(&attr, CLOCK_MONOTONIC); if (rc != 0) { @@ -372,14 +385,44 @@ public: } private: + /// @note: We are using here pthread_cond_timedwait as a replacement for condition_variable::wait_for, + /// as libstdc++ has bug not using monotonic clock. When GCC 10.x became minimum supported version, + /// that code could be removed. + void wait_for(const milliseconds sleepInterval) { + struct timespec timeToWait = {0, 0}; + const auto sec = std::chrono::duration_cast(sleepInterval); + +#if (defined(__APPLE__) || defined(_WIN32)) + timeToWait.tv_sec = sec.count(); + timeToWait.tv_nsec = + std::chrono::duration_cast(sleepInterval).count() - + std::chrono::nanoseconds(sec).count(); +#else + clock_gettime(CLOCK_MONOTONIC, &timeToWait); + const auto secondInNanoSeconds = 1000000000L; + const auto nsecSum = std::chrono::duration_cast(sleepInterval).count() - + std::chrono::nanoseconds(sec).count() + timeToWait.tv_nsec; + timeToWait.tv_sec += sec.count() + nsecSum / secondInNanoSeconds; + timeToWait.tv_nsec = nsecSum % secondInNanoSeconds; +#endif // (defined(__APPLE__) || defined(_WIN32)) + +#if defined(__APPLE__) + const auto rc = pthread_cond_timedwait_relative_np(&wakeUpPingThread, &routineLock, &timeToWait); +#else + const auto rc = pthread_cond_timedwait(&wakeUpPingThread, &routineLock, &timeToWait); +#endif // defined(__APPLE__) + if (rc != 0 && rc != ETIMEDOUT) { + throw std::runtime_error("Failed to perform wait in a loop for " + std::to_string(sleepInterval.count()) + " ms. rc: " + std::to_string(rc)); + } + } void watchdog_routine() noexcept { try { mvLog(MVLOG_INFO, "thread started\n"); milliseconds sleepInterval; - struct timespec timeToWait = {0, 0}; + CustomUniqueLock lock {&routineLock}; do { @@ -416,29 +459,11 @@ public: } // TODO: no timer coalescing feature, to minimized thread wakes sleepInterval = std::get<0>(*minInterval)->dueIn(currentTime); + if (sleepInterval.count() <= 0) + continue; + mvLog(MVLOG_DEBUG, "sleep interval = %ld ms\n", sleepInterval.count()); - - auto sec = std::chrono::duration_cast(sleepInterval); - -#if (defined(__APPLE__) || defined(_WIN32)) - timeToWait.tv_sec = sec.count(); - timeToWait.tv_nsec = - std::chrono::duration_cast(sleepInterval).count() - - std::chrono::nanoseconds(sec).count(); -#else - clock_gettime(CLOCK_MONOTONIC, &timeToWait); - timeToWait.tv_sec += sec.count(); - timeToWait.tv_nsec += - std::chrono::duration_cast(sleepInterval).count() - - std::chrono::nanoseconds(sec).count(); -#endif // (defined(__APPLE__) || defined(_WIN32)) - -#if defined(__APPLE__) - pthread_cond_timedwait_relative_np(&wakeUpPingThread, &routineLock, &timeToWait); -#else - pthread_cond_timedwait(&wakeUpPingThread, &routineLock, &timeToWait); -#endif // defined(__APPLE__) - + wait_for(sleepInterval); mvLog(MVLOG_DEBUG, "waiting completed in %ld ms\n", duration_cast(steady_clock::now() - currentTime).count()); diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/CMakeLists.txt b/inference-engine/thirdparty/movidius/mvnc/tests/CMakeLists.txt new file mode 100644 index 00000000000..dafab99eb80 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/CMakeLists.txt @@ -0,0 +1,74 @@ +# Copyright (C) 2018-2020 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# + +set(CMAKE_CXX_STANDARD 11) + +include(${XLINK_DIR}/XLink.cmake) + +set(MVNC_TESTS_COMMON_INCLUDE + "cases" + "helpers" + ) + +file(GLOB MVNC_TESTS_COMMON_SOURCES + "helpers/mvnc_test_helper.cpp" + "cases/mvnc_common_test_cases.cpp") + +set(PRIVATE_INCLUDE + ${IE_MAIN_SOURCE_DIR}/tests_new/gtest/googletest/include + ${IE_MAIN_SOURCE_DIR}/tests_new/gtest/googletest/ + ${XLINK_INCLUDE} + ${XLINK_PLATFORM_INCLUDE} + ${WATCHDOG_INCLUDE}) + +function(add_mvnc_test_target TARGET_NAME TESTS_SOURCES) + add_executable(${TARGET_NAME} ${TESTS_SOURCES} ${MVNC_TESTS_COMMON_SOURCES}) + + target_include_directories(${TARGET_NAME} + PUBLIC + "../include" + PRIVATE + ${MVNC_TESTS_COMMON_INCLUDE} + ${PRIVATE_INCLUDE}) + + if(ENABLE_MYRIAD_NO_BOOT) + target_compile_definitions(${TARGET_NAME} PRIVATE NO_BOOT) + endif() + + target_compile_definitions(${TARGET_NAME} + PRIVATE + __PC__) + + target_link_libraries(${TARGET_NAME} gtest gtest_main mvnc) + add_dependencies(${TARGET_NAME} vpu_copy_firmware) +endfunction() + +################# MvncTests ################### + +if(ENABLE_MYRIAD_NO_BOOT) + set(MVNC_TESTS_SOURCES + mvnc_no_boot_tests.cpp + cases/mvnc_no_boot_test_cases.cpp + ) +else() + set(MVNC_TESTS_SOURCES + mvnc_tests_common.cpp + mvnc_tests_usb.cpp + cases/mvnc_usb_test_cases.cpp) +endif() + +add_mvnc_test_target("MvncTests" "${MVNC_TESTS_SOURCES}") + +################# MvncStressTests ################### + +set(MVNC_STRESS_TESTS_SOURCES + mvnc_stress_tests.cpp + cases/mvnc_stress_test_cases.cpp + ) +add_mvnc_test_target("MvncStressTests" "${MVNC_STRESS_TESTS_SOURCES}") + +################# MvncUtilsTests ################### + +set(MVNC_UTILS_TESTS_SOURCES mvnc_utils_tests.cpp) +add_mvnc_test_target("MvncUtilsTests" "${MVNC_UTILS_TESTS_SOURCES}") diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_common_test_cases.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_common_test_cases.cpp new file mode 100644 index 00000000000..8a710ac98f5 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_common_test_cases.cpp @@ -0,0 +1,122 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "mvnc_common_test_cases.h" + +//------------------------------------------------------------------------------ +// Implementation of class MvncTestsCommon +//------------------------------------------------------------------------------ +MvncTestsCommon::MvncTestsCommon() { +#if !(defined(_WIN32) || defined(_WIN64)) + // On linux we should use custom path to firmware due to another searching mechanism for library + strcpy(firmwarePath, "./lib"); +#else + firmwarePath[0] = 0; +#endif +} + +void MvncTestsCommon::SetUp() { + initialize_usb_boot(); + ASSERT_NO_ERROR(setLogLevel(ncLogLevel)); + availableDevices_ = getAmountOfDevices(); +} + +void MvncTestsCommon::TearDown() { + ncDeviceResetAll(); +} + +int MvncTestsCommon::setLogLevel(const mvLog_t logLevel) { + ncStatus_t status = ncGlobalSetOption(NC_RW_LOG_LEVEL, &logLevel, + sizeof(logLevel)); + if (status != NC_OK) { + fprintf(stderr, + "WARNING: failed to set log level: %d with error: %d\n", + ncLogLevel, status); + return -1; + } + ncLogLevel = logLevel; + return 0; +} + +void MvncTestsCommon::openDevices(const int devicesToBoot, ncDeviceHandle_t **deviceHandlers, + int &amountOfBooted) { + ASSERT_TRUE(deviceHandlers != nullptr); + const int availableDevices = getAmountOfDevices(NC_USB); + if (availableDevices < devicesToBoot) { + GTEST_SKIP_("Not enough devices"); + } + + amountOfBooted = 0; + ncDeviceDescr_t ncDeviceDesc = {}; + ncDeviceDesc.protocol = NC_USB; + ncDeviceDesc.platform = NC_ANY_PLATFORM; + + for (int index = 0; index < devicesToBoot; ++index) { + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandlers[index], ncDeviceDesc, watchdogInterval, firmwarePath)); + ASSERT_TRUE(deviceHandlers[index] != nullptr); + ++amountOfBooted; + } + ASSERT_EQ(amountOfBooted, devicesToBoot) << "Not all devices was loaded"; +} + +void MvncTestsCommon::bootOneDevice(ncDeviceProtocol_t deviceProtocol) { + if (deviceProtocol == NC_PCIE) { + GTEST_FATAL_FAILURE_("Boot doesn't supported for PCIe protocol\n"); + } + ASSERT_NO_ERROR(ncDeviceLoadFirmware(NC_ANY_PLATFORM, firmwarePath)); +} + +//------------------------------------------------------------------------------ +// Implementation of class MvncOpenDevice +//------------------------------------------------------------------------------ +void MvncOpenDevice::SetUp() { + MvncTestsCommon::SetUp(); + + _deviceProtocol = GetParam(); + availableDevices_ = getAmountOfDevices(_deviceProtocol); +} + +//------------------------------------------------------------------------------ +// Implementation of class MvncLoggingTests +//------------------------------------------------------------------------------ +void MvncLoggingTests::SetUp() { + MvncOpenDevice::SetUp(); + + _deviceDesc.protocol = _deviceProtocol; + _deviceDesc.platform = NC_ANY_PLATFORM; + + for (int index = 0; index < availableDevices_; ++index) { + ASSERT_NO_ERROR(ncDeviceOpen(&_deviceHandles[index], _deviceDesc, watchdogInterval, firmwarePath)); + } + + setbuf(stdout, buff); + fprintf(stdout, "[workaround for getting full content from XLink]\n"); +} + +void MvncLoggingTests::TearDown() { + setbuf(stdout, NULL); + for (int index = 0; index < availableDevices_; ++index) { + ASSERT_NO_ERROR(ncDeviceClose(&_deviceHandles[index])); + } +} + +//------------------------------------------------------------------------------ +// Implementation of class MvncGraphAllocations +//------------------------------------------------------------------------------ +void MvncGraphAllocations::SetUp() { + MvncOpenDevice::SetUp(); + + // Load blob + blobLoaded = readBINFile(blobPath, _blob); + if (!blobLoaded) { + std::cout << blobPath << " blob for test not found\n"; + } +} + +void MvncGraphAllocations::TearDown() { + for (int index = 0; index < _bootedDevices; ++index) { + ASSERT_NO_ERROR(ncDeviceClose(&_deviceHandle[index])); + } + _bootedDevices = 0; +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_common_test_cases.h b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_common_test_cases.h new file mode 100644 index 00000000000..f2add94a82d --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_common_test_cases.h @@ -0,0 +1,155 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#if (defined(_WIN32) || defined(_WIN64)) +#include "windows.h" +#endif + +#include +#include + +#include "mvnc.h" +#include "mvnc_ext.h" +#include "XLinkLog.h" +#include "mvnc_test_helper.h" + +//------------------------------------------------------------------------------ +// Helpers +//------------------------------------------------------------------------------ +constexpr std::chrono::seconds operator "" _sec(unsigned long long s) +{ + return std::chrono::seconds(s); +} + +//------------------------------------------------------------------------------ +// class MvncTestsCommon +//------------------------------------------------------------------------------ +class MvncTestsCommon : public ::testing::Test { +public: + char firmwarePath[MAX_PATH] = {}; + mvLog_t ncLogLevel = MVLOG_INFO; + int watchdogInterval = 1000; + int availableDevices_ = 0; + + ~MvncTestsCommon() override = default; + MvncTestsCommon(); +protected: + + void SetUp() override; + void TearDown() override; + +public: + int setLogLevel(const mvLog_t logLevel); + + /** + * @brief Boot and open selected amount of device + * @param[out] amountOfBooted Amount of device which was booted + * @param[out] deviceHandlers Pre-allocated array for handlers + */ + void openDevices(const int devicesToBoot, ncDeviceHandle_t** deviceHandlers, + int& amountOfBooted); + + /** + * @brief Load firmware to device + * @warning Only USB devices is supported + */ + virtual void bootOneDevice(ncDeviceProtocol_t deviceProtocol= NC_USB); +}; + +//------------------------------------------------------------------------------ +// class MvncOpenDevice +//------------------------------------------------------------------------------ +class MvncOpenDevice : public MvncTestsCommon, + public testing::WithParamInterface { +protected: + ncDeviceProtocol_t _deviceProtocol = NC_ANY_PROTOCOL; + + ~MvncOpenDevice() override = default; + void SetUp() override; + +}; + +//------------------------------------------------------------------------------ +// class MvncLoggingTests +//------------------------------------------------------------------------------ +class MvncLoggingTests : public MvncOpenDevice { +public: + char buff[BUFSIZ] = {}; +protected: + ncDeviceHandle_t * _deviceHandles[MAX_DEVICES] = {nullptr}; + ncDeviceDescr_t _deviceDesc = {}; + + void SetUp() override; + void TearDown() override; + ~MvncLoggingTests() override = default; +}; + +//------------------------------------------------------------------------------ +// class MvncGraphAllocations +//------------------------------------------------------------------------------ +/** + * @brief Test transfer data from host to device + * @detail Allocate 2 devices and test some graph allocate cases + * @warning For correct testing should be used blob with size more than 30mb + */ +class MvncGraphAllocations: public MvncOpenDevice { +public: + // Devices + ncDeviceHandle_t * _deviceHandle[MAX_DEVICES] = {nullptr}; + int _bootedDevices = 0; + + // Graphs + ncGraphHandle_t* _graphHandle[MAX_DEVICES] = {nullptr}; + + // Blob + const std::string blobPath = "bvlc_googlenet_fp16.blob"; + std::vector _blob; + bool blobLoaded = false; + +protected: + void SetUp() override; + void TearDown() override; + ~MvncGraphAllocations() override = default; +}; + +//------------------------------------------------------------------------------ +// class MvncCloseDevice +//------------------------------------------------------------------------------ +class MvncCloseDevice : public MvncTestsCommon { +protected: + ~MvncCloseDevice() override = default; +}; + +//------------------------------------------------------------------------------ +// Parametric tests initialization +//------------------------------------------------------------------------------ +static const std::vector myriadProtocols = { + NC_USB, + NC_PCIE +}; + +static const std::vector myriadPlatforms = { + NC_MYRIAD_2, + NC_MYRIAD_X +}; + + +namespace { + /** + * @brief Converter from enum to string + */ + struct PrintToStringParamName { + std::string operator()( + const ::testing::TestParamInfo &info) const { + return ncProtocolToStr(info.param); + } + + std::string operator()( + const ::testing::TestParamInfo &info) const { + return std::string("USB_") + ncPlatformToStr(info.param); + } + }; +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_no_boot_test_cases.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_no_boot_test_cases.cpp new file mode 100644 index 00000000000..adfef69bb77 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_no_boot_test_cases.cpp @@ -0,0 +1,27 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "mvnc_no_boot_test_cases.h" + +//------------------------------------------------------------------------------ +// Implementation of class MvncNoBootTests +//------------------------------------------------------------------------------ +void MvncNoBootTests::bootOneDevice() { + // In case already booted device exist, do nothing + if (getAmountOfBootedDevices() == 0) { + MvncTestsCommon::bootOneDevice(NC_USB); + } +} + +//------------------------------------------------------------------------------ +// Implementation of class MvncNoBootOpenDevice +//------------------------------------------------------------------------------ +void MvncNoBootOpenDevice::SetUp() { + MvncNoBootTests::SetUp(); + available_devices = getAmountOfDevices(NC_USB); + ASSERT_TRUE(available_devices > 0); + + // With NO_BOOT option we should boot device with firmware before trying to open + bootOneDevice(); +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_no_boot_test_cases.h b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_no_boot_test_cases.h new file mode 100644 index 00000000000..d00ef1e2018 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_no_boot_test_cases.h @@ -0,0 +1,36 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "mvnc_common_test_cases.h" + +//------------------------------------------------------------------------------ +// class MvncNoBootTests +//------------------------------------------------------------------------------ +class MvncNoBootTests: public MvncTestsCommon { +public: + void bootOneDevice(); +protected: + ~MvncNoBootTests() override = default; +}; + +//------------------------------------------------------------------------------ +// class MvncNoBootOpenDevice +//------------------------------------------------------------------------------ +class MvncNoBootOpenDevice : public MvncNoBootTests { +public: + int available_devices = 0; +protected: + ~MvncNoBootOpenDevice() override = default; + void SetUp() override; +}; + +//------------------------------------------------------------------------------ +// class MvncNoBootCloseDevice +//------------------------------------------------------------------------------ +class MvncNoBootCloseDevice : public MvncNoBootTests { +protected: + ~MvncNoBootCloseDevice() override = default; +}; diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_stress_test_cases.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_stress_test_cases.cpp new file mode 100644 index 00000000000..fe108597210 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_stress_test_cases.cpp @@ -0,0 +1,25 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "mvnc_stress_test_cases.h" + +//------------------------------------------------------------------------------ +// Implementation of class MvncStressTests +//------------------------------------------------------------------------------ +void MvncStressTests::SetUp() { + MvncTestsCommon::SetUp(); + + _deviceProtocol = GetParam(); + available_devices = getAmountOfDevices(_deviceProtocol); + ASSERT_TRUE(available_devices > 0) << ncProtocolToStr(_deviceProtocol) + << " devices not found"; + ASSERT_NO_ERROR(setLogLevel(MVLOG_WARN)); + +#ifdef NO_BOOT + // In case already booted device exist, do nothing + if (getAmountOfBootedDevices() == 0) { + MvncTestsCommon::bootOneDevice(NC_USB); + } +#endif +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_stress_test_cases.h b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_stress_test_cases.h new file mode 100644 index 00000000000..e536ecc7973 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_stress_test_cases.h @@ -0,0 +1,22 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "mvnc_common_test_cases.h" + +//------------------------------------------------------------------------------ +// class MvncStressTests +//------------------------------------------------------------------------------ +class MvncStressTests : public MvncTestsCommon, + public testing::WithParamInterface{ +public: + int available_devices = 0; + +protected: + ~MvncStressTests() override = default; + void SetUp() override; + + ncDeviceProtocol_t _deviceProtocol = NC_ANY_PROTOCOL; +}; diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_usb_test_cases.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_usb_test_cases.cpp new file mode 100644 index 00000000000..5a105980454 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_usb_test_cases.cpp @@ -0,0 +1,31 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "mvnc_usb_test_cases.h" + +//------------------------------------------------------------------------------ +// Implementation of class MvncOpenUSBDevice +//------------------------------------------------------------------------------ +void MvncOpenUSBDevice::SetUp() { + ncDeviceResetAll(); + MvncTestsCommon::SetUp(); + + availableDevices_ = getAmountOfNotBootedDevices(NC_USB); + + deviceDesc_.protocol = NC_USB; + deviceDesc_.platform = NC_ANY_PLATFORM; +} + +//------------------------------------------------------------------------------ +// Implementation of class MvncDevicePlatform +//------------------------------------------------------------------------------ +void MvncDevicePlatform::SetUp() { + MvncOpenUSBDevice::SetUp(); + + available_myriadX_ = getAmountOfMyriadXDevices(NC_USB); + available_myriad2_ = getAmountOfMyriad2Devices(NC_USB); + + devicePlatform_ = GetParam(); + deviceDesc_.platform = devicePlatform_; +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_usb_test_cases.h b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_usb_test_cases.h new file mode 100644 index 00000000000..73ef6a220a4 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/cases/mvnc_usb_test_cases.h @@ -0,0 +1,43 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "mvnc_common_test_cases.h" + +//------------------------------------------------------------------------------ +// class MvncOpenUSBDevice +//------------------------------------------------------------------------------ +class MvncOpenUSBDevice : public MvncTestsCommon { +public: + ncDeviceHandle_t* deviceHandle_ = nullptr; + ncDeviceDescr_t deviceDesc_ = {}; + + ~MvncOpenUSBDevice() override = default; + +protected: + void SetUp() override; +}; + +//------------------------------------------------------------------------------ +// class MvncCloseUSBDevice +//------------------------------------------------------------------------------ +class MvncCloseUSBDevice : public MvncOpenUSBDevice { +}; + +//------------------------------------------------------------------------------ +// class MvncDevicePlatform +//------------------------------------------------------------------------------ +class MvncDevicePlatform : public MvncOpenUSBDevice, + public testing::WithParamInterface{ +public: + long available_myriadX_ = 0; + long available_myriad2_ = 0; + ncDevicePlatform_t devicePlatform_; + + ~MvncDevicePlatform() override = default; + +protected: + void SetUp() override; +}; diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/helpers/mvnc_test_helper.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/helpers/mvnc_test_helper.cpp new file mode 100644 index 00000000000..ef5c417c3cb --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/helpers/mvnc_test_helper.cpp @@ -0,0 +1,140 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include "mvnc_data.h" +#include "mvnc_test_helper.h" + +//------------------------------------------------------------------------------ +// Implementations of helpers - counters +//------------------------------------------------------------------------------ +int getAmountOfDevices(const ncDeviceProtocol_t deviceProtocol, + const ncDevicePlatform_t devicePlatform, + const XLinkDeviceState_t state) { + deviceDesc_t req_deviceDesc = {}; + req_deviceDesc.protocol = convertProtocolToXlink(deviceProtocol); + req_deviceDesc.platform = convertPlatformToXlink(devicePlatform); + + deviceDesc_t deviceDescArray[NC_MAX_DEVICES] = {}; + unsigned int foundDevices = 0; + XLinkFindAllSuitableDevices( + state, req_deviceDesc, deviceDescArray, NC_MAX_DEVICES, &foundDevices); + + return foundDevices; +} + +long getAmountOfMyriadXDevices(ncDeviceProtocol_t deviceProtocol) { + return getAmountOfDevices(deviceProtocol, NC_MYRIAD_X); +} + +long getAmountOfMyriad2Devices(ncDeviceProtocol_t deviceProtocol) { + return getAmountOfDevices(deviceProtocol, NC_MYRIAD_2); +} + +long getAmountOfBootedDevices(ncDeviceProtocol_t deviceProtocol) { + return getAmountOfDevices(deviceProtocol, NC_ANY_PLATFORM, X_LINK_BOOTED); +} + +long getAmountOfNotBootedDevices(ncDeviceProtocol_t deviceProtocol) { + return getAmountOfDevices(deviceProtocol, NC_ANY_PLATFORM, X_LINK_UNBOOTED); +} + +long getAmountOfPCIeDevices() { + return getAmountOfDevices(NC_PCIE); +} + +long getAmountOfUSBDevices() { + return getAmountOfDevices(NC_USB); +} + +//------------------------------------------------------------------------------ +// Implementations of helpers - get devices +//------------------------------------------------------------------------------ +std::vector getDevicesList(const ncDeviceProtocol_t deviceProtocol, + const ncDevicePlatform_t devicePlatform, + const XLinkDeviceState_t state) { + + deviceDesc_t req_deviceDesc = {}; + req_deviceDesc.protocol = convertProtocolToXlink(deviceProtocol); + req_deviceDesc.platform = convertPlatformToXlink(devicePlatform); + + deviceDesc_t deviceDescArray[NC_MAX_DEVICES] = {}; + unsigned int foundDevices = 0; + XLinkFindAllSuitableDevices( + state, req_deviceDesc, deviceDescArray, NC_MAX_DEVICES, &foundDevices); + + std::vector < std::string > devNames; + for (int i = 0; i < foundDevices; ++i) { + devNames.emplace_back(deviceDescArray[i].name); + } + + return devNames; +} + +//------------------------------------------------------------------------------ +// Implementation of helpers - comparators +//------------------------------------------------------------------------------ +bool isMyriadXUSBDevice(const std::string &deviceName) { + return (deviceName.find(MYRIAD_X_NAME_STR) != std::string::npos); +} + +bool isMyriad2USBDevice(const std::string &deviceName) { + return (deviceName.find(MYRIAD_2_NAME_STR) != std::string::npos); +} + +bool isMyriadPCIeDevice(const std::string &deviceName) { + return deviceName.find(std::string(PCIE_NAME_STR)) != std::string::npos; +} + +bool isMyriadUSBDevice(const std::string &deviceName) { + return (isMyriad2USBDevice(deviceName) + || isMyriadXUSBDevice(deviceName) + || isMyriadBootedUSBDevice(deviceName)); +} + +bool isMyriadBootedUSBDevice(const std::string &deviceName) { + return (!isMyriad2USBDevice(deviceName) && + !isMyriadXUSBDevice(deviceName) && + !isMyriadPCIeDevice(deviceName)); +} + +bool isSameProtocolDevice(const std::string &deviceName, const ncDeviceProtocol_t expectedProtocol) { + switch (expectedProtocol) { + case NC_USB: return isMyriadUSBDevice(deviceName); + case NC_PCIE: return isMyriadPCIeDevice(deviceName); + case NC_ANY_PROTOCOL: + return isMyriadPCIeDevice(deviceName) || isMyriadUSBDevice(deviceName); + default: + std::cout << "Unknown device protocol" << std::endl; + return false; + } +} + +bool +isSamePlatformUSBDevice(const std::string &deviceName, const ncDevicePlatform_t expectedPlatform) { + switch (expectedPlatform) { + case NC_MYRIAD_2: return isMyriad2USBDevice(deviceName); + case NC_MYRIAD_X: return isMyriadXUSBDevice(deviceName); + case NC_ANY_PLATFORM: + return isMyriad2USBDevice(deviceName) || isMyriadXUSBDevice(deviceName); + default: + std::cout << "Unknown device platform" << std::endl; + return false; + } +} + +//------------------------------------------------------------------------------ +// Implementation of helpers - file loader +//------------------------------------------------------------------------------ +bool readBINFile(const std::string &fileName, std::vector &buf) { + std::ifstream file(fileName, std::ios_base::binary | std::ios_base::ate); + if (file.fail()) { + std::cout << "Can't open file!" << std::endl; + return false; + } + buf.resize(static_cast(file.tellg())); + file.seekg(0); + file.read(buf.data(), buf.size()); + return true; +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/helpers/mvnc_test_helper.h b/inference-engine/thirdparty/movidius/mvnc/tests/helpers/mvnc_test_helper.h new file mode 100644 index 00000000000..07c5283150c --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/helpers/mvnc_test_helper.h @@ -0,0 +1,121 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include +#include + +#include "XLink.h" +#include "mvnc.h" + +//------------------------------------------------------------------------------ +// Macroses +//------------------------------------------------------------------------------ +#define ASSERT_NO_ERROR(call) ASSERT_EQ(call, 0) +#define ASSERT_ERROR(call) ASSERT_TRUE(call) + + +//------------------------------------------------------------------------------ +// Defines +//------------------------------------------------------------------------------ +#define MYRIAD_X_NAME_STR "ma2480" +#define MYRIAD_2_NAME_STR "ma2450" + +#if (defined(_WIN32) || defined(_WIN64)) +#define PCIE_NAME_STR "mxlink" +#else +#define PCIE_NAME_STR "mxlk" +#endif + +const int MAX_DEVICES = 32; +const int MAX_DEV_NAME = 20; + +#ifndef MAX_PATH +const int MAX_PATH = 255; +#endif + +//------------------------------------------------------------------------------ +// Usb initialization +//------------------------------------------------------------------------------ +// Without this initialization find device on windows could not work +#if (defined(_WIN32) || defined(_WIN64) ) +extern "C" void initialize_usb_boot(); +#else +#define initialize_usb_boot() +#endif + + +//------------------------------------------------------------------------------ +// Helpers - counters +//------------------------------------------------------------------------------ +/** + * @brief Get amount of all currently connected Myriad devices + * @param[in] deviceProtocol Count only platform specific devices + */ +int getAmountOfDevices(const ncDeviceProtocol_t deviceProtocol = NC_ANY_PROTOCOL, + const ncDevicePlatform_t devicePlatform = NC_ANY_PLATFORM, + const XLinkDeviceState_t state = X_LINK_ANY_STATE); + +long getAmountOfMyriadXDevices(ncDeviceProtocol_t deviceProtocol = NC_ANY_PROTOCOL); + +long getAmountOfMyriad2Devices(ncDeviceProtocol_t deviceProtocol = NC_ANY_PROTOCOL); + +long getAmountOfBootedDevices(ncDeviceProtocol_t deviceProtocol = NC_ANY_PROTOCOL); + +long getAmountOfNotBootedDevices(ncDeviceProtocol_t deviceProtocol = NC_ANY_PROTOCOL); + +long getAmountOfPCIeDevices(); + +long getAmountOfUSBDevices(); + +//------------------------------------------------------------------------------ +// Helpers - get devices +//------------------------------------------------------------------------------ +/** + * @brief Get list of all currently connected Myriad devices + */ +std::vector getDevicesList( + const ncDeviceProtocol_t deviceProtocol = NC_ANY_PROTOCOL, + const ncDevicePlatform_t devicePlatform = NC_ANY_PLATFORM, + const XLinkDeviceState_t state = X_LINK_ANY_STATE); + +//------------------------------------------------------------------------------ +// Helpers - comparators +//------------------------------------------------------------------------------ +bool isMyriadXUSBDevice(const std::string &deviceName); + +bool isMyriad2USBDevice(const std::string &deviceName); + +bool isMyriadPCIeDevice(const std::string& deviceName); + +/** + * @warning The booted USB device will also be counted here. + */ +bool isMyriadUSBDevice(const std::string& deviceName); + +bool isMyriadBootedUSBDevice(const std::string &deviceName); + +/** + * @brief Check that device matches the specified protocol + */ +bool isSameProtocolDevice(const std::string &deviceName, + const ncDeviceProtocol_t expectedProtocol); + +/** +* @brief Check that device matches the specified platform for USB +*/ +bool isSamePlatformUSBDevice(const std::string &deviceName, + const ncDevicePlatform_t expectedPlatform); + +//------------------------------------------------------------------------------ +// Helpers - file loader +//------------------------------------------------------------------------------ +/** + * @brief Read blob + * @param fileName Path to blob from bin directory + * @return True if blob is readed without problem + */ +bool readBINFile(const std::string& fileName, std::vector& buf); diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_no_boot_tests.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_no_boot_tests.cpp new file mode 100644 index 00000000000..41184430650 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_no_boot_tests.cpp @@ -0,0 +1,102 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "mvnc.h" +#include "mvnc_no_boot_test_cases.h" + +//------------------------------------------------------------------------------ +// MvncNoBootOpenDevice Tests +//------------------------------------------------------------------------------ +/** +* @brief Open any device and close it +*/ +TEST_F(MvncNoBootOpenDevice, OpenAndClose) { + ncDeviceHandle_t *deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_USB; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + +/** +* @brief Try to open device twice. DeviceHandle shouldn't be overwritten +*/ +TEST_F(MvncNoBootOpenDevice, OpenTwiceSameHandler) { + ncDeviceHandle_t *deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_USB; + deviceDesc.platform = NC_ANY_PLATFORM; + + char dev_addr_first_open[MAX_DEV_NAME]; + unsigned int data_lenght_first = MAX_DEV_NAME; + + char dev_addr_second_open[MAX_DEV_NAME]; + unsigned int data_lenght_second = MAX_DEV_NAME; + + // First open, get device name + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle, NC_RO_DEVICE_NAME, + dev_addr_first_open, &data_lenght_first)); + + // Second open, get device name + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle, NC_RO_DEVICE_NAME, + dev_addr_second_open, &data_lenght_second)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + // Should be the same device + ASSERT_STREQ(dev_addr_first_open, dev_addr_second_open); +} + + +/** + * @brief Open device twice one run after another. It should check, that link to device closed correctly + * @note Mostly this test important for PCIE and connect to booted option, as in that cases XLinkReset have another behavior + */ +TEST_F(MvncNoBootOpenDevice, OpenDeviceWithOneXLinkInitializion) { + ncDeviceHandle_t *deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_USB; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + + // Second open + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + +} + +//------------------------------------------------------------------------------ +// MvncNoBootCloseDevice Tests +//------------------------------------------------------------------------------ +/** +* @brief Correct closing if handle is empty +*/ +TEST_F(MvncNoBootCloseDevice, EmptyDeviceHandler) { + ncDeviceHandle_t *deviceHandle = nullptr; + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + +/** +* @brief Device, which was booted before open, shouldn't reboot after ncDeviceClose call +*/ +TEST_F(MvncNoBootCloseDevice, AlreadyBootedDeviceWillNotReboot) { + bootOneDevice(); + + ASSERT_EQ(getAmountOfBootedDevices(), 1); + + ncDeviceHandle_t *deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_USB; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + + ASSERT_EQ(getAmountOfBootedDevices(), 1); +} diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_stress_tests.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_stress_tests.cpp new file mode 100644 index 00000000000..1cdaf8b66e2 --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_stress_tests.cpp @@ -0,0 +1,157 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "mvnc.h" +#include "ncPrivateTypes.h" +#include "mvnc_stress_test_cases.h" + +//------------------------------------------------------------------------------ +// MvncStressTests Tests +//------------------------------------------------------------------------------ +/** +* @brief Open and close device for 1001 times +*/ +TEST_P(MvncStressTests, OpenClose1001) { + const int iterations = 1001; + ncDeviceHandle_t *deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + for (int i = 0; i < iterations; ++i) { + printf("Iteration %d of %d\n", i, iterations); + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + deviceHandle = nullptr; + } +} + +/** +* @brief Allocate and deallocate graph on device for 1001 times +*/ +TEST_P(MvncStressTests, AllocateDeallocateGraph1001) { + const int iterations = 1001; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + // Load graph + const std::string blobPath = "bvlc_googlenet_fp16.blob"; + std::vector _blob; + + if (!readBINFile(blobPath, _blob)) GTEST_SKIP_("Blob not found\n"); + + // Open device + ncDeviceHandle_t *deviceHandle = nullptr; + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + + for (int i = 0; i < iterations; ++i) { + printf("Iteration %d of %d\n", i, iterations); + + // Create graph handlers + ncGraphHandle_t* graphHandle = nullptr; + std::string graphName = "graph"; + + ASSERT_NO_ERROR(ncGraphCreate(graphName.c_str(), &graphHandle)); + ASSERT_TRUE(graphHandle != nullptr); + + // Allocate graph + ASSERT_NO_ERROR(ncGraphAllocate(deviceHandle, graphHandle, + _blob.data(), _blob.size(), // Blob + _blob.data(), sizeof(ElfN_Ehdr) + sizeof(blob_header_v2)) ); // Header + + // Destroy graph + ASSERT_NO_ERROR(ncGraphDestroy(&graphHandle)); + } + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + + +/** +* @brief Run the full cycle of inference 101 times. +* It includes opening device, allocating graph and fifos, inference, + * destroying graph and fifos, closing device +*/ +TEST_P(MvncStressTests, FullCycleOfWork101Times) { + const int iterations = 101; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + const std::string blobPath = "bvlc_googlenet_fp16.blob"; + std::vector blob; + if (!readBINFile(blobPath, blob)) GTEST_SKIP_("Blob not found\n"); + + for (int i = 0; i < iterations; i++) { + ncDeviceHandle_t *deviceHandle = nullptr; + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + + ncGraphHandle_t* graphHandle = nullptr; + std::string graphName = "graph"; + ASSERT_NO_ERROR(ncGraphCreate(graphName.c_str(), &graphHandle)); + ASSERT_TRUE(graphHandle != nullptr); + + ASSERT_NO_ERROR(ncGraphAllocate(deviceHandle, graphHandle, + blob.data(), blob.size(), // Blob + blob.data(), sizeof(ElfN_Ehdr) + sizeof(blob_header_v2) )); + + + unsigned int dataLength = sizeof(int); + + int numInputs = 0; + ASSERT_NO_ERROR(ncGraphGetOption(graphHandle, NC_RO_GRAPH_INPUT_COUNT, &numInputs, &dataLength)); + + int numOutputs = 0; + ASSERT_NO_ERROR(ncGraphGetOption(graphHandle, NC_RO_GRAPH_OUTPUT_COUNT, &numOutputs, &dataLength)); + + dataLength = sizeof(ncTensorDescriptor_t); + + ncTensorDescriptor_t inputDesc = {}; + ASSERT_NO_ERROR(ncGraphGetOption(graphHandle, NC_RO_GRAPH_INPUT_TENSOR_DESCRIPTORS, &inputDesc, + &dataLength)); + + + ncTensorDescriptor_t outputDesc = {}; + ASSERT_NO_ERROR(ncGraphGetOption(graphHandle, NC_RO_GRAPH_OUTPUT_TENSOR_DESCRIPTORS, &outputDesc, + &dataLength)); + + unsigned int fifo_elements = 4; + + ncFifoHandle_t *inputFifoHandle = nullptr; + ASSERT_NO_ERROR(ncFifoCreate("input", NC_FIFO_HOST_WO, &inputFifoHandle)); + + ASSERT_NO_ERROR(ncFifoAllocate(inputFifoHandle, deviceHandle, &inputDesc, fifo_elements)); + + ncFifoHandle_t *outputFifoHandle = nullptr; + ASSERT_NO_ERROR(ncFifoCreate("output", NC_FIFO_HOST_RO, &outputFifoHandle)); + + ASSERT_NO_ERROR(ncFifoAllocate(outputFifoHandle, deviceHandle, &outputDesc, fifo_elements)); + + uint8_t *input_data = new uint8_t[inputDesc.totalSize]; + uint8_t *result_data = new uint8_t[outputDesc.totalSize]; + ASSERT_NO_ERROR(ncGraphQueueInferenceWithFifoElem(graphHandle, + inputFifoHandle, outputFifoHandle, + input_data, &inputDesc.totalSize, nullptr)); + + void *userParam = nullptr; + ASSERT_NO_ERROR(ncFifoReadElem(outputFifoHandle, result_data, &outputDesc.totalSize, &userParam)); + + delete[] input_data; + delete[] result_data; + ASSERT_NO_ERROR(ncFifoDestroy(&inputFifoHandle)); + ASSERT_NO_ERROR(ncFifoDestroy(&outputFifoHandle)); + + ASSERT_NO_ERROR(ncGraphDestroy(&graphHandle)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + } + +} + +INSTANTIATE_TEST_CASE_P(MvncTestsCommon, + MvncStressTests, + ::testing::ValuesIn(myriadProtocols), + PrintToStringParamName()); diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_tests_common.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_tests_common.cpp new file mode 100644 index 00000000000..03e95c9845a --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_tests_common.cpp @@ -0,0 +1,534 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include +#include "mvnc.h" +#include "ncPrivateTypes.h" +#include "mvnc_common_test_cases.h" + +//------------------------------------------------------------------------------ +// MvncTestsCommon Tests +// Platform independent tests +//------------------------------------------------------------------------------ +TEST_F(MvncTestsCommon, DoubleCheckOfAvailableDevicesCount) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + const int min_name_size = 2; + + struct ncDeviceDescr_t act_devices[NC_MAX_DEVICES] = {}; + int act_devicesCount = 0; + int exp_devicesCount = getAmountOfNotBootedDevices(); + + ASSERT_NO_ERROR(ncAvailableDevices(act_devices, NC_MAX_DEVICES, &act_devicesCount)); + + ASSERT_TRUE(act_devicesCount); + ASSERT_EQ(act_devicesCount, exp_devicesCount); + + for (int i = 0; i < act_devicesCount; ++i) { + ASSERT_GE(strlen(act_devices[i].name), min_name_size); + } + + for (int j = act_devicesCount; j < NC_MAX_DEVICES; ++j) { + ASSERT_EQ(strlen(act_devices[j].name), 0); + } +} + +TEST_F(MvncTestsCommon, AvailableDevicesSholdReturnErrorIfArrayIsNULL) { + int act_devicesCount = 0; + ASSERT_ERROR(ncAvailableDevices(NULL, NC_MAX_DEVICES, &act_devicesCount)); +} + +TEST_F(MvncTestsCommon, AvailableDevicesSholdReturnErrorIfCountPtrIsNULL) { + struct ncDeviceDescr_t act_devices[NC_MAX_DEVICES] = {}; + ASSERT_ERROR(ncAvailableDevices(act_devices, NC_MAX_DEVICES, NULL)); +} + +TEST_F(MvncTestsCommon, CanGetPCIeAndUSB) { + if (!(getAmountOfUSBDevices() && getAmountOfPCIeDevices())) + GTEST_SKIP_("USB and PCIe not available"); + + struct ncDeviceDescr_t act_devices[NC_MAX_DEVICES] = {}; + int act_devicesCount = 0; + ASSERT_NO_ERROR(ncAvailableDevices(act_devices, NC_MAX_DEVICES, &act_devicesCount)); + + bool usb_device_found = false; + bool pcie_device_found = false; + + for (int i = 0; i < act_devicesCount; ++i) { + if (isMyriadUSBDevice(act_devices[i].name)) { + usb_device_found = true; + } else if (isMyriadPCIeDevice(act_devices[i].name)) { + pcie_device_found = true; + } + } + + EXPECT_TRUE(usb_device_found); + EXPECT_TRUE(pcie_device_found); +} + +TEST_F(MvncTestsCommon, ShouldFailToSetNegativeTimeout) { + ASSERT_ERROR(ncSetDeviceConnectTimeout(-1)); +} + +//------------------------------------------------------------------------------ +// MvncTestsCommon Tests +// PCIe + USB Tests +//------------------------------------------------------------------------------ + +/** + * @brief Test that USB and PCIe works at the same time. USB first + */ +TEST_F(MvncTestsCommon, OpenUSBThenPCIEAndClose) { + if (getAmountOfPCIeDevices() == 0) + GTEST_SKIP() << "PCIe devices not found"; + if (getAmountOfUSBDevices() == 0) + GTEST_SKIP() << "USB devices not found"; + + ncDeviceHandle_t *deviceHandle_USB = nullptr; + ncDeviceHandle_t *deviceHandle_PCIe = nullptr; + std::string actDeviceName; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_USB; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_USB, deviceDesc, watchdogInterval, firmwarePath)); + + actDeviceName = deviceHandle_USB->private_data->dev_addr; + ASSERT_TRUE(actDeviceName.size()); + ASSERT_TRUE(isMyriadUSBDevice(actDeviceName)); + + // Open PCIe device + deviceDesc.protocol = NC_PCIE; + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_PCIe, deviceDesc, watchdogInterval, firmwarePath)); + + actDeviceName = deviceHandle_PCIe->private_data->dev_addr; + ASSERT_TRUE(actDeviceName.size()); + ASSERT_TRUE(isMyriadPCIeDevice(actDeviceName)); + + // Close all + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_PCIe)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_USB)); +} + +/** + * @brief Test that USB and PCIe works at the same time. PCIe first + */ +TEST_F(MvncTestsCommon, OpenPCIEThenUSBAndClose) { + if (getAmountOfPCIeDevices() == 0) + GTEST_SKIP() << "PCIe devices not found"; + if (getAmountOfUSBDevices() == 0) + GTEST_SKIP() << "USB devices not found"; + + ncDeviceHandle_t *deviceHandle_USB = nullptr; + ncDeviceHandle_t *deviceHandle_PCIe = nullptr; + std::string actDeviceName; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_PCIE; + deviceDesc.platform = NC_ANY_PLATFORM; + + // Open PCIe device + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_PCIe, deviceDesc, + watchdogInterval, firmwarePath)); + + actDeviceName = deviceHandle_PCIe->private_data->dev_addr; + ASSERT_TRUE(actDeviceName.size()); + ASSERT_TRUE(isMyriadPCIeDevice(actDeviceName)); + + // Open USB device + deviceDesc.protocol = NC_USB; + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_USB, deviceDesc, + watchdogInterval, firmwarePath)); + + actDeviceName = deviceHandle_USB->private_data->dev_addr; + ASSERT_TRUE(actDeviceName.size()); + ASSERT_TRUE(isMyriadUSBDevice(actDeviceName)); + + + // Close all + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_PCIe)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_USB)); +} + +//------------------------------------------------------------------------------ +// MvncOpenDevice Tests +//------------------------------------------------------------------------------ +/** +* @brief Open any device and close it +*/ +TEST_P(MvncOpenDevice, OpenAndClose) { + if (availableDevices_ == 0) + GTEST_SKIP() << ncProtocolToStr(_deviceProtocol) << " devices not found"; + + ncDeviceHandle_t* deviceHandle = nullptr; + std::string deviceName; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + + ASSERT_TRUE(deviceHandle != nullptr); + ASSERT_TRUE(deviceHandle->private_data != nullptr); + ASSERT_TRUE(deviceHandle->private_data->dev_addr_booted != nullptr); + + deviceName = deviceHandle->private_data->dev_addr_booted; + ASSERT_TRUE(deviceName.size() > 0); + + ASSERT_TRUE(isSameProtocolDevice(deviceName, _deviceProtocol)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + +/** + * @brief Check that all field of deviceHandle would be initialized + */ +TEST_P(MvncOpenDevice, AllHandleFieldsInitialized) { + if (availableDevices_ == 0) + GTEST_SKIP() << ncProtocolToStr(_deviceProtocol) << " devices not found"; + + ncDeviceHandle_t* deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, + watchdogInterval, firmwarePath)); + + ASSERT_TRUE(deviceHandle != nullptr); + + devicePrivate_t * device = deviceHandle->private_data; + ASSERT_TRUE(device != nullptr); + ASSERT_TRUE(device->dev_addr != nullptr); + ASSERT_TRUE(device->dev_addr_booted != nullptr); + ASSERT_TRUE(device->xlink != nullptr); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + +/** +* @brief Try to open device twice. DeviceHandle shouldn't be overwritten +* @details Expected behavior - ncDeviceOpen should warn that deviceHandle + * already has allocated device +*/ +TEST_P(MvncOpenDevice, OpenTwiceSameHandler) { + if (availableDevices_ == 0) + GTEST_SKIP() << ncProtocolToStr(_deviceProtocol) << " devices not found"; + + ncDeviceHandle_t *deviceHandle = nullptr; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + char dev_addr_first_open[MAX_DEV_NAME]; + unsigned int data_lenght_first = MAX_DEV_NAME; + + char dev_addr_second_open[MAX_DEV_NAME]; + unsigned int data_lenght_second = MAX_DEV_NAME; + + // First open, get device name + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle, NC_RO_DEVICE_NAME, + dev_addr_first_open, &data_lenght_first)); + + // Second open, get device name + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle, NC_RO_DEVICE_NAME, + dev_addr_second_open, &data_lenght_second)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + // Should be the same device + ASSERT_STREQ(dev_addr_first_open, dev_addr_second_open); +} + +/** + * @brief Try to open device twice with different handlers. Second open should return error + * @reason #-18548 + */ + // Fixme Test only for one device +TEST_P(MvncOpenDevice, DISABLED_OpenSameDeviceTwiceDifferentHandlers) { + if (availableDevices_ == 0) + GTEST_SKIP() << ncProtocolToStr(_deviceProtocol) << " devices not found"; + + ncDeviceHandle_t *deviceHandle1 = nullptr; + ncDeviceHandle_t *deviceHandle2 = nullptr; + + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle1, deviceDesc, + watchdogInterval, firmwarePath)); + + // Till we don't have multiple device support, this function would try to open same device + ASSERT_ERROR(ncDeviceOpen(&deviceHandle2, deviceDesc, + watchdogInterval, firmwarePath)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle1)); +} + + +/** + * @brief Open device twice one run after another. It should check, that link to device closed correctly + * @note Mostly this test important for PCIe and connect to booted option, as in that cases XLinkReset have another behavior + */ +TEST_P(MvncOpenDevice, OpenTwiceWithOneXLinkInitializion) { + if (availableDevices_ == 0) + GTEST_SKIP() << ncProtocolToStr(_deviceProtocol) << " devices not found"; + + ncDeviceHandle_t *deviceHandle = nullptr; + std::string actDeviceName; + + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = _deviceProtocol; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, + watchdogInterval, firmwarePath)); + + actDeviceName = deviceHandle->private_data->dev_addr; + ASSERT_TRUE(isSameProtocolDevice(actDeviceName, _deviceProtocol)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + + // Second open + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, + watchdogInterval, firmwarePath)); + + actDeviceName = deviceHandle->private_data->dev_addr; + ASSERT_TRUE(isSameProtocolDevice(actDeviceName, _deviceProtocol)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + +//------------------------------------------------------------------------------ +// MvncLoggingTests Tests +//------------------------------------------------------------------------------ +TEST_P(MvncLoggingTests, ShouldNotPrintErrorMessagesIfCanNotOpenDevice) { + if (availableDevices_ == 0) + GTEST_SKIP() << ncProtocolToStr(_deviceProtocol) << " devices not found"; + + setLogLevel(MVLOG_INFO); + ncDeviceHandle_t * deviceHandle = nullptr; + + ASSERT_ERROR(ncDeviceOpen(&deviceHandle, _deviceDesc, watchdogInterval, firmwarePath)); + + std::string content(buff); + for (int i = MVLOG_WARN; i < MVLOG_LAST; i++) { + auto found = content.find(mvLogHeader[i]); + ASSERT_TRUE(found == std::string::npos); + } +} + +//------------------------------------------------------------------------------ +// MvncGraphAllocations Tests +//------------------------------------------------------------------------------ +/** + * @brief Allocate graph for one device + */ +TEST_P(MvncGraphAllocations, DISABLED_OneGraph) { + if (!blobLoaded) GTEST_SKIP_("Blob for test is not loaded\n"); + openDevices(1, _deviceHandle, _bootedDevices); + + // Create graph handlers + std::string graphName = "graph"; + ASSERT_NO_ERROR(ncGraphCreate(graphName.c_str(), &_graphHandle[0])); + ASSERT_TRUE(_graphHandle[0] != nullptr); + + // Allocate graph + ASSERT_NO_ERROR(ncGraphAllocate(_deviceHandle[0], _graphHandle[0], + _blob.data(), _blob.size(), // Blob + _blob.data(), sizeof(ElfN_Ehdr) + sizeof(blob_header_v2) )); // Header +} + +/** + * @brief Allocate graphs for 2 device (serial) + */ +TEST_P(MvncGraphAllocations, DISABLED_AllocateGraphsOn2DevicesSerial) { + if (!blobLoaded) + GTEST_SKIP_("Blob for test is not loaded\n"); + openDevices(2, _deviceHandle, _bootedDevices); + + // Create graphs handlers + for (int index = 0; index < _bootedDevices; ++index) { + std::string graphName = "graph"; + graphName += std::to_string(index); + ASSERT_NO_ERROR(ncGraphCreate(graphName.c_str(), &_graphHandle[index])); + ASSERT_TRUE(_graphHandle[index] != nullptr); + } + + // Allocate graphs in serial mode + ncStatus_t rc[MAX_DEVICES]; + + for (int i = 0; i < _bootedDevices; ++i) { + rc[i] = ncGraphAllocate(_deviceHandle[0], _graphHandle[0], + _blob.data(), _blob.size(), // Blob + _blob.data(), sizeof(ElfN_Ehdr) + sizeof(blob_header_v2) ); // Header + } + + for (int i = 0; i < _bootedDevices; ++i) { + ASSERT_NO_ERROR(rc[i]); + } +} + +/** +* @brief Allocate graphs for 2 device (parallel) +* @detail Open devices and then in parallel threads try to load graphs to it +* The error easy appear, if USBLINK_TRANSFER_SIZE is (1024 * 1024 * 20) +* @warning It's depend on USBLINK_TRANSFER_SIZE constant from UsbLinkPlatform.c file +* @warning Need blob to use this tests +*/ +TEST_P(MvncGraphAllocations, DISABLED_AllocateGraphsOn2DevicesParallel) { + if (!blobLoaded) GTEST_SKIP_("Blob for test is not loaded\n"); + openDevices(2, _deviceHandle, _bootedDevices); + + // Create graphs handlers + for (int index = 0; index < _bootedDevices; ++index) { + std::string graphName = "graph"; + graphName += std::to_string(index); + ASSERT_NO_ERROR(ncGraphCreate(graphName.c_str(), &_graphHandle[index])); + ASSERT_TRUE(_graphHandle[index] != nullptr); + } + + // Allocate graphs in parallel threads + std::thread requests[MAX_DEVICES]; + ncStatus_t rc[MAX_DEVICES]; + for (int i = 0; i < _bootedDevices; ++i) { + requests[i] = std::thread([i, &rc, this]() { + rc[i] = ncGraphAllocate(_deviceHandle[0], _graphHandle[0], + _blob.data(), _blob.size(), // Blob + _blob.data(), sizeof(ElfN_Ehdr) + sizeof(blob_header_v2) ); + }); + } + + for (int i = 0; i < _bootedDevices; ++i) { + requests[i].join(); + ASSERT_NO_ERROR(rc[i]); + } +} + +//------------------------------------------------------------------------------ +// MvncCloseDevice Tests +//------------------------------------------------------------------------------ +/** +* @brief Correct closing if handle is empty +*/ +TEST_F(MvncCloseDevice, EmptyDeviceHandler) { + ncDeviceHandle_t *deviceHandle = nullptr; + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); +} + +/** +* @brief Correct closing if some handler fields is null +*/ +TEST_F(MvncCloseDevice, EmptyFieldsOfDeviceHandle) { + + ncDeviceHandle_t *deviceHandlePtr; + auto dH = std::unique_ptr( + (ncDeviceHandle_t*)calloc(1, sizeof(ncDeviceHandle_t)), std::free); + + auto d = std::unique_ptr<_devicePrivate_t, decltype(std::free)*>( + (_devicePrivate_t*)calloc(1, sizeof(_devicePrivate_t)), std::free); + + if (dH.get() && d.get()) { + dH->private_data = d.get(); + d->dev_addr = nullptr; + d->dev_addr_booted = nullptr; + d->device_mon_stream_id = INVALID_LINK_ID; + d->graph_monitor_stream_id = INVALID_LINK_ID; + d->wd_interval = watchdogInterval; + deviceHandlePtr = dH.get(); + } + + ASSERT_EQ(ncDeviceClose(&deviceHandlePtr), NC_INVALID_PARAMETERS); +} + +//------------------------------------------------------------------------------ +// MvncInference Tests +//------------------------------------------------------------------------------ +using MvncInference = MvncGraphAllocations; + +TEST_P(MvncInference, DISABLED_DoOneIterationOfInference) { + if (!blobLoaded) GTEST_SKIP_("Blob for test is not loaded\n"); + openDevices(1, _deviceHandle, _bootedDevices); + + std::string graphName = "graph"; + ASSERT_NO_ERROR(ncGraphCreate(graphName.c_str(), &_graphHandle[0])); + ASSERT_TRUE(&_graphHandle[0] != nullptr); + + ASSERT_NO_ERROR(ncGraphAllocate(_deviceHandle[0], _graphHandle[0], + _blob.data(), _blob.size(), // Blob + _blob.data(), sizeof(ElfN_Ehdr) + sizeof(blob_header_v2) )); + + + unsigned int dataLength = sizeof(int); + + int numInputs = 0; + ASSERT_NO_ERROR(ncGraphGetOption(_graphHandle[0], NC_RO_GRAPH_INPUT_COUNT, &numInputs, &dataLength)); + + int numOutputs = 0; + ASSERT_NO_ERROR(ncGraphGetOption(_graphHandle[0], NC_RO_GRAPH_OUTPUT_COUNT, &numOutputs, &dataLength)); + + dataLength = sizeof(ncTensorDescriptor_t); + + ncTensorDescriptor_t inputDesc = {}; + ASSERT_NO_ERROR(ncGraphGetOption(_graphHandle[0], NC_RO_GRAPH_INPUT_TENSOR_DESCRIPTORS, &inputDesc, + &dataLength)); + + + ncTensorDescriptor_t outputDesc = {}; + ASSERT_NO_ERROR(ncGraphGetOption(_graphHandle[0], NC_RO_GRAPH_OUTPUT_TENSOR_DESCRIPTORS, &outputDesc, + &dataLength)); + + unsigned int fifo_elements = 4; + + ncFifoHandle_t *inputFifoHandle = nullptr; + ASSERT_NO_ERROR(ncFifoCreate("input", NC_FIFO_HOST_WO, &inputFifoHandle)); + + ASSERT_NO_ERROR(ncFifoAllocate(inputFifoHandle, _deviceHandle[0], &inputDesc, fifo_elements)); + + ncFifoHandle_t *outputFifoHandle = nullptr; + ASSERT_NO_ERROR(ncFifoCreate("output", NC_FIFO_HOST_RO, &outputFifoHandle)); + + ASSERT_NO_ERROR(ncFifoAllocate(outputFifoHandle, _deviceHandle[0], &outputDesc, fifo_elements)); + + uint8_t *input_data = new uint8_t[inputDesc.totalSize]; + uint8_t *result_data = new uint8_t[outputDesc.totalSize]; + ASSERT_NO_ERROR(ncGraphQueueInferenceWithFifoElem(_graphHandle[0], + inputFifoHandle, outputFifoHandle, + input_data, &inputDesc.totalSize, nullptr)); + + void *userParam = nullptr; + ASSERT_NO_ERROR(ncFifoReadElem(outputFifoHandle, result_data, &outputDesc.totalSize, &userParam)); + + delete[] input_data; + delete[] result_data; + ASSERT_NO_ERROR(ncFifoDestroy(&inputFifoHandle)); + ASSERT_NO_ERROR(ncFifoDestroy(&outputFifoHandle)); + + ASSERT_NO_ERROR(ncGraphDestroy(&_graphHandle[0])); + + ASSERT_NO_ERROR(ncDeviceClose(&_deviceHandle[0])); +} + + +INSTANTIATE_TEST_CASE_P(MvncTestsCommon, + MvncOpenDevice, + ::testing::ValuesIn(myriadProtocols), + PrintToStringParamName()); + +INSTANTIATE_TEST_CASE_P(MvncTestsCommon, + MvncLoggingTests, + ::testing::ValuesIn(myriadProtocols), + PrintToStringParamName()); + +INSTANTIATE_TEST_CASE_P(MvncTestsCommon, + MvncGraphAllocations, + ::testing::ValuesIn(myriadProtocols), + PrintToStringParamName()); + +INSTANTIATE_TEST_CASE_P(MvncTestsCommon, + MvncInference, + ::testing::ValuesIn(myriadProtocols), + PrintToStringParamName()); diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_tests_usb.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_tests_usb.cpp new file mode 100644 index 00000000000..a5cdea04cec --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_tests_usb.cpp @@ -0,0 +1,247 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include + +#include "mvnc.h" +#include "mvnc_test_helper.h" +#include "mvnc_usb_test_cases.h" +#include "ncPrivateTypes.h" + +//------------------------------------------------------------------------------ +// MvncOpenUSBDevice Tests +//------------------------------------------------------------------------------ +/** +* @brief Open any device with custom firmware path as ncDeviceOpen argument +*/ + +TEST_F(MvncOpenUSBDevice, ShouldOpenDeviceAfterChangeConnectTimeoutFromZero) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + ncDeviceHandle_t *deviceHandle = nullptr; + std::string actDeviceName; + ncDeviceDescr_t deviceDesc = {}; + deviceDesc.protocol = NC_ANY_PROTOCOL; + deviceDesc.platform = NC_ANY_PLATFORM; + + ASSERT_NO_ERROR(ncSetDeviceConnectTimeout(0)); + ASSERT_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + std::this_thread::sleep_for(3_sec); + ASSERT_NO_ERROR(ncDeviceResetAll()); + + ASSERT_NO_ERROR(ncSetDeviceConnectTimeout(30)); + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle, deviceDesc, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle)); + + ASSERT_NO_ERROR(ncDeviceResetAll()); +} + + +TEST_F(MvncOpenUSBDevice, WithCustomFirmware) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + // Use custom firmware dir path as parameter for ncDeviceOpen + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_)); +} + +/** +* @brief Open all available devices and close them +*/ +TEST_F(MvncOpenUSBDevice, AllAvailableDevices) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + ncDeviceHandle_t * deviceHandles[MAX_DEVICES] = {nullptr}; + + for (int index = 0; index < availableDevices_; ++index) { + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandles[index], deviceDesc_, watchdogInterval, firmwarePath)); + } + for (int index = 0; index < availableDevices_; ++index) { + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandles[index])); + } +} + +/** +* @brief Open all available devices in parallel threads and close them +*/ +TEST_F(MvncOpenUSBDevice, AllAvailableMultiThreads) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + std::thread requests[MAX_DEVICES]; + ncDeviceHandle_t * deviceHandle[MAX_DEVICES] = {nullptr}; + ncStatus_t rc[MAX_DEVICES]; + + for (int i = 0; i < availableDevices_; ++i) { + requests[i] = std::thread([i, &rc, &deviceHandle, this]() { + rc[i] = ncDeviceOpen(&deviceHandle[i], deviceDesc_, watchdogInterval, firmwarePath); + }); + } + + for (int i = 0; i < availableDevices_; ++i) { + requests[i].join(); + ASSERT_NO_ERROR(rc[i]); + } + + for (int i = 0; i < availableDevices_; ++i) { + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle[i])); + } +} + +/** +* @brief Open any device with invalid firmware path +*/ +TEST_F(MvncOpenUSBDevice, WithInvalidFirmwarePath) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + const char invalidPath[MAX_PATH] = "./InvalidPath/"; + + // Use custom firmware dir path as parameter for ncDeviceOpen + ASSERT_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, invalidPath)); + + ASSERT_EQ(deviceHandle_, nullptr); +} + +TEST_F(MvncOpenUSBDevice, OpenAvailableDeviceByName) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + char dev_addr_open[NC_MAX_NAME_SIZE]; + unsigned int data_lenght = NC_MAX_NAME_SIZE; + + auto availableDevices = getDevicesList(); + + ASSERT_TRUE(availableDevices.size()); + strncpy(deviceDesc_.name, availableDevices[0].c_str(), NC_MAX_NAME_SIZE); + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle_, NC_RO_DEVICE_NAME, + dev_addr_open, &data_lenght)); + + ASSERT_TRUE(strncmp(dev_addr_open, deviceDesc_.name, NC_MAX_NAME_SIZE) == 0); + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_)); +} + +TEST_F(MvncOpenUSBDevice, ErrorWhenWrongDeviceName) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + char badName[] = "BadName"; + + strncpy(deviceDesc_.name, badName, NC_MAX_NAME_SIZE); + + auto availableDevices = getDevicesList(); + ASSERT_TRUE(availableDevices.size()); + + ASSERT_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); +} + +TEST_F(MvncOpenUSBDevice, OpenTwiceSameHandlerByName) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + char dev_addr_first_open[MAX_DEV_NAME]; + unsigned int data_lenght_first = MAX_DEV_NAME; + + char dev_addr_second_open[MAX_DEV_NAME]; + unsigned int data_lenght_second = MAX_DEV_NAME; + + auto availableDevices = getDevicesList(); + + ASSERT_TRUE(availableDevices.size()); + strncpy(deviceDesc_.name, availableDevices[0].c_str(), NC_MAX_NAME_SIZE); + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle_, NC_RO_DEVICE_NAME, + dev_addr_first_open, &data_lenght_first)); + + // Second open, get device name + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle_, NC_RO_DEVICE_NAME, + dev_addr_second_open, &data_lenght_second)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_)); + // Should be the same device + ASSERT_STREQ(dev_addr_first_open, dev_addr_second_open); +} + +TEST_F(MvncOpenUSBDevice, CheckErrorWhenPlatformConflictWithName) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + ncDevicePlatform_t wrongPlatform = NC_ANY_PLATFORM; + auto availableDevices = getDevicesList(); + + ASSERT_TRUE(availableDevices.size()); + + if(isMyriadXUSBDevice(availableDevices[0])) { + wrongPlatform = NC_MYRIAD_2; + } else { + wrongPlatform = NC_MYRIAD_X; + } + + strncpy(deviceDesc_.name, availableDevices[0].c_str(), NC_MAX_NAME_SIZE); + deviceDesc_.platform = wrongPlatform; + + ASSERT_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); +} + +//------------------------------------------------------------------------------ +// MvncCloseUSBDevice Tests +//------------------------------------------------------------------------------ +#if (!(defined(_WIN32) || defined(_WIN64))) +TEST_F(MvncCloseUSBDevice, USBDeviceWillBeAvailableRightAfterClosing) { + if (availableDevices_ == 0) + GTEST_SKIP(); + + ASSERT_NO_ERROR(ncDeviceOpen( + &deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); + + ASSERT_TRUE(deviceHandle_); + + deviceDesc_t toFindDeviceDescr = { + .protocol = X_LINK_USB_VSC, + .platform = X_LINK_ANY_PLATFORM + }; + strcpy(deviceDesc_.name, deviceHandle_->private_data->dev_addr); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_)); + + deviceDesc_t foundDevice = {}; + XLinkError_t rc = XLinkFindFirstSuitableDevice( + X_LINK_UNBOOTED, toFindDeviceDescr, &foundDevice); + ASSERT_EQ(X_LINK_SUCCESS, rc); +} +#endif + +//------------------------------------------------------------------------------ +// MvncDevicePlatform Tests +//------------------------------------------------------------------------------ +/** +* @brief Open specified device and close it +*/ +TEST_P(MvncDevicePlatform, OpenAndClose) { + if (available_myriad2_ == 0 || available_myriadX_ == 0) + GTEST_SKIP(); + + ASSERT_NO_ERROR(ncDeviceOpen(&deviceHandle_, deviceDesc_, watchdogInterval, firmwarePath)); + + char deviceName[MAX_DEV_NAME]; + unsigned int size = MAX_DEV_NAME; + ASSERT_NO_ERROR(ncDeviceGetOption(deviceHandle_, NC_RO_DEVICE_NAME, deviceName, &size)); + + EXPECT_TRUE(isSamePlatformUSBDevice(deviceName, devicePlatform_)); + + ASSERT_NO_ERROR(ncDeviceClose(&deviceHandle_)); + +} + +INSTANTIATE_TEST_CASE_P(MvncTestsPlatform, + MvncDevicePlatform, + ::testing::ValuesIn(myriadPlatforms), + PrintToStringParamName()); diff --git a/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_utils_tests.cpp b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_utils_tests.cpp new file mode 100644 index 00000000000..4910c1e4beb --- /dev/null +++ b/inference-engine/thirdparty/movidius/mvnc/tests/mvnc_utils_tests.cpp @@ -0,0 +1,61 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "ncCommPrivate.h" +#include "mvnc_test_helper.h" + +#include +#include + +extern "C" { +#include "XLinkStringUtils.h" +} + +class MvncUtilsTest : public ::testing::Test { +public: + void TearDown() override { + std::remove(mvcmdExpectedPath.c_str()); + } + +protected: + std::string mvcmdExpectedPath = ""; + // FIXME: seems it is not going to work on Windows + const std::string tmpDir = "/tmp"; +}; + +TEST_F(MvncUtilsTest, CanGetSpecialFWIfUniversalIsNotPresent) { + mvcmdExpectedPath = tmpDir + "/usb-ma248x.mvcmd"; + + std::ofstream mvcmd; + mvcmd.open(mvcmdExpectedPath, std::ios::out); + + char mvcmdFilePath[MAX_PATH] = ""; + mv_strcpy(mvcmdFilePath, MAX_PATH, tmpDir.c_str()); + + deviceDesc_t dummyDevDesc2480; + strcpy(dummyDevDesc2480.name, "0-ma2480"); + dummyDevDesc2480.protocol = X_LINK_USB_VSC; + dummyDevDesc2480.platform = X_LINK_MYRIAD_X; + + ASSERT_EQ(NC_OK, getFirmwarePath(mvcmdFilePath, MAX_PATH, dummyDevDesc2480)); + ASSERT_STRCASEEQ(mvcmdExpectedPath.c_str(), mvcmdFilePath); +} + +TEST_F(MvncUtilsTest, CanGetUniversalFWIfItExists) { + mvcmdExpectedPath = tmpDir + "/usb-ma2x8x.mvcmd"; + + std::ofstream mvcmd; + mvcmd.open(mvcmdExpectedPath, std::ios::out); + + char mvcmdFilePath[MAX_PATH] = ""; + mv_strcpy(mvcmdFilePath, MAX_PATH, tmpDir.c_str()); + + deviceDesc_t dummyDevDesc2480; + strcpy(dummyDevDesc2480.name, "0-ma2480"); + dummyDevDesc2480.protocol = X_LINK_USB_VSC; + dummyDevDesc2480.platform = X_LINK_MYRIAD_X; + + ASSERT_EQ(NC_OK, getFirmwarePath(mvcmdFilePath, MAX_PATH, dummyDevDesc2480)); + ASSERT_STRCASEEQ(mvcmdExpectedPath.c_str(), mvcmdFilePath); +} diff --git a/inference-engine/thirdparty/movidius/tests/CMakeLists.txt b/inference-engine/thirdparty/movidius/tests/CMakeLists.txt new file mode 100644 index 00000000000..ba621695e8f --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/CMakeLists.txt @@ -0,0 +1 @@ +add_subdirectory(XLink) diff --git a/inference-engine/thirdparty/movidius/tests/XLink/CMakeLists.txt b/inference-engine/thirdparty/movidius/tests/XLink/CMakeLists.txt new file mode 100644 index 00000000000..69cfcedd1fd --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/CMakeLists.txt @@ -0,0 +1,34 @@ +# Copyright (C) 2018-2020 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# + +set(TARGET_NAME "XLinkTests") +set(CMAKE_CXX_STANDARD 11) + +include(${XLINK_DIR}/XLink.cmake) +file(GLOB_RECURSE XLINK_TESTS_SOURCES "*.cpp") + +add_executable(${TARGET_NAME} ${XLINK_TESTS_SOURCES}) + +target_include_directories(${TARGET_NAME} + PRIVATE + helpers + cases + ${IE_MAIN_SOURCE_DIR}/tests_new/gtest/googletest/include + ${IE_MAIN_SOURCE_DIR}/tests_new/gtest/googletest/ + ${XLINK_INCLUDE} + ${XLINK_PLATFORM_INCLUDE}) + +target_compile_definitions(${TARGET_NAME} + PRIVATE + __PC__) + +target_link_libraries(${TARGET_NAME} + PRIVATE + XLink gtest gtest_main) + +set_target_properties(${TARGET_NAME} PROPERTIES + POSITION_INDEPENDENT_CODE TRUE + COMPILE_PDB_NAME ${TARGET_NAME}) + +add_dependencies(${TARGET_NAME} vpu_copy_firmware) diff --git a/inference-engine/thirdparty/movidius/tests/XLink/XLink_common_tests.cpp b/inference-engine/thirdparty/movidius/tests/XLink/XLink_common_tests.cpp new file mode 100644 index 00000000000..b76c3402ca7 --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/XLink_common_tests.cpp @@ -0,0 +1,432 @@ +// Copyright (C) 2018-2019 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "XLink_common_cases.hpp" + +#include + +//------------------------------------------------------------------------------ +// XLinkNullPtrTests +//------------------------------------------------------------------------------ + +TEST_F(XLinkNullPtrTests, XLinkInitialize) { + ASSERT_EQ(XLinkInitialize(nullptr), X_LINK_ERROR); +} + +TEST_F(XLinkNullPtrTests, XLinkConnect) { + ASSERT_EQ(XLinkConnect(nullptr), X_LINK_ERROR); +} + +TEST_F(XLinkNullPtrTests, XLinkOpenAndCloseStream) { + ASSERT_EQ(XLinkOpenStream(0, nullptr, 0), X_LINK_ERROR); + ASSERT_EQ(XLinkCloseStream(0), X_LINK_ERROR); +} + +TEST_F(XLinkNullPtrTests, XLinkFindDevice) { + ASSERT_EQ(XLinkFindFirstSuitableDevice(X_LINK_ANY_STATE, {}, nullptr), X_LINK_ERROR); + ASSERT_EQ(XLinkFindAllSuitableDevices(X_LINK_ANY_STATE, {}, nullptr, -1, nullptr), + X_LINK_ERROR); +} + +TEST_F(XLinkNullPtrTests, XLinkWriteData) { + ASSERT_EQ(XLinkWriteData(0, nullptr, 0), X_LINK_ERROR); +} + +//------------------------------------------------------------------------------ +// XLinkBootTests +//------------------------------------------------------------------------------ + +TEST_P(XLinkBootTests, StressTestBootToOpenAndCloseDevice) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, _protocol) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t deviceDesc = {}; + deviceDesc_t in_deviceDesc = {}; + + in_deviceDesc.protocol = _protocol; + in_deviceDesc.platform = X_LINK_ANY_PLATFORM; + + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_UNBOOTED, in_deviceDesc, &deviceDesc)); + + std::string firmwarePath; + ASSERT_NO_THROW(firmwarePath = getMyriadFirmwarePath(deviceDesc)); + + for (int i = 0; i < 10; ++i) { + printf("Boot device. Iteration: %d\n", i); + ASSERT_EQ(X_LINK_SUCCESS, XLinkBoot(&deviceDesc, firmwarePath.c_str())); + // FIXME: need to find a way to avoid this sleep + std::this_thread::sleep_for(kBootTimeoutSec); + + // Find booted + deviceDesc_t bootedDeviceDesc = {}; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, in_deviceDesc, &bootedDeviceDesc)); + + XLinkHandler_t handler = {0}; + + connectToDevice(bootedDeviceDesc, &handler); + closeDevice(&handler); + } +} + +//------------------------------------------------------------------------------ +// XLinkConnectUSBTests +//------------------------------------------------------------------------------ + +TEST_P(XLinkConnectTests, ConnectToDevice) { + if (getCountSpecificDevices(X_LINK_UNBOOTED, _protocol) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t deviceDesc = {}; + deviceDesc_t bootedDeviceDesc = {}; + + deviceDesc.protocol = _protocol; + bootDevice(deviceDesc, bootedDeviceDesc); + + XLinkHandler_t handler = {0}; + handler.protocol = bootedDeviceDesc.protocol; + handler.devicePath = bootedDeviceDesc.name; + + ASSERT_EQ(X_LINK_SUCCESS, XLinkConnect(&handler)); + std::this_thread::sleep_for(std::chrono::seconds(2)); + + closeDevice(&handler); +} + +//------------------------------------------------------------------------------ +// XLinkFindFirstSuitableDeviceTests +//------------------------------------------------------------------------------ + +TEST_P(XLinkFindFirstSuitableDevicePlatformTests, ReturnCorrectAvailableDeviceName) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, _protocol, _platform) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t deviceDesc = {}; + deviceDesc_t in_deviceDesc = {}; + in_deviceDesc.protocol = _protocol; + in_deviceDesc.platform = _platform; + + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_UNBOOTED, in_deviceDesc, &deviceDesc)); + ASSERT_TRUE(strlen(deviceDesc.name) > 2); + ASSERT_EQ(deviceDesc.protocol, in_deviceDesc.protocol); + + if(_platform == X_LINK_ANY_PLATFORM) { + EXPECT_NE(deviceDesc.platform, X_LINK_ANY_PLATFORM); + } else { + EXPECT_EQ(deviceDesc.platform, _platform); + } + + if(_protocol != X_LINK_USB_VSC) { + std::string deviceName(deviceDesc.name); + switch (_platform) { + case X_LINK_MYRIAD_2: { + EXPECT_TRUE(deviceName.find(kUSBMyriad2) != std::string::npos); + break; + } + case X_LINK_MYRIAD_X: { + EXPECT_TRUE(deviceName.find(kUSBMyriadX) != std::string::npos); + break; + } + default: + break; + } + } +} + +TEST_P(XLinkFindFirstSuitableDeviceTests, CanFindDeviceByName) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, _protocol) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t deviceDesc = {}; + deviceDesc_t in_deviceDesc = {}; + + in_deviceDesc.protocol = _protocol; + ASSERT_EQ(X_LINK_SUCCESS, XLinkFindFirstSuitableDevice( + X_LINK_ANY_STATE, in_deviceDesc, &deviceDesc)); + + deviceDesc_t deviceRequirementsWithName = {}; + deviceRequirementsWithName.protocol = _protocol; + strcpy(deviceRequirementsWithName.name, deviceDesc.name); + + deviceDesc_t deviceDescSearchByName = {}; + ASSERT_EQ(X_LINK_SUCCESS, XLinkFindFirstSuitableDevice( + X_LINK_ANY_STATE, deviceRequirementsWithName, &deviceDescSearchByName)); + + ASSERT_TRUE(strcmp(deviceDesc.name, deviceDescSearchByName.name) == 0); +} + +/** + * This is temporary test. + * For now it's not clear how to tests multiple device as for now we don't have bench like this + */ +TEST_P(XLinkFindFirstSuitableDeviceTests, OnSecondIndexDeviceWillBeNotFound) { + auto availableDevices = getCountSpecificDevices(X_LINK_ANY_STATE, _protocol); + if (availableDevices != 1) { + GTEST_SKIP(); + } + + deviceDesc_t in_deviceDesc = {}; + in_deviceDesc.protocol = _protocol; + + const int index = 1; + // Find device + deviceDesc_t deviceDesc = {}; + ASSERT_EQ(X_LINK_DEVICE_NOT_FOUND, findDeviceOnIndex( + index, X_LINK_ANY_STATE, in_deviceDesc, &deviceDesc)); +} + +TEST_P(XLinkFindFirstSuitableDeviceTests, ReturnCorrectBootedDeviceName) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, _protocol) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t bootedDevice = {}; + deviceDesc_t deviceDesc = {}; + deviceDesc.protocol = _protocol; + + bootDevice(deviceDesc, bootedDevice); + + deviceDesc_t foundDeviceDesc = {}; + deviceDesc_t in_deviceDesc = {}; + in_deviceDesc.protocol = _protocol; + in_deviceDesc.platform = X_LINK_ANY_PLATFORM; + + EXPECT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, in_deviceDesc, &foundDeviceDesc)); + + EXPECT_TRUE(strcmp(bootedDevice.name, foundDeviceDesc.name) == 0); + EXPECT_EQ(foundDeviceDesc.protocol, _protocol); + + if(_protocol == X_LINK_USB_VSC) { + std::string foundDeviceName(foundDeviceDesc.name); + EXPECT_TRUE(foundDeviceName.find(kUSBMyriad2) == std::string::npos); + EXPECT_TRUE(foundDeviceName.find(kUSBMyriadX) == std::string::npos); + } + + connectAndCloseDevice(bootedDevice); +} + +//------------------------------------------------------------------------------ +// XLinkFindAllSuitableDevicesTests +//------------------------------------------------------------------------------ + +TEST_F(XLinkFindAllSuitableDevicesTests, CanFindMoreThenTwoDeviceAnyState_USB_PCIE) { + if (getCountSpecificDevices(X_LINK_UNBOOTED) < 2) { + GTEST_SKIP(); + } + + deviceDesc_t in_deviceDesc = {}; + deviceDesc_t deviceDescArray[XLINK_MAX_DEVICES] = {{}}; + + in_deviceDesc.protocol = X_LINK_ANY_PROTOCOL; + unsigned int numOfFoundDevices = 0; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindAllSuitableDevices( + X_LINK_ANY_STATE, in_deviceDesc, deviceDescArray, + XLINK_MAX_DEVICES, &numOfFoundDevices)); + + ASSERT_EQ(numOfFoundDevices, getCountSpecificDevices(X_LINK_UNBOOTED)); + ASSERT_EQ(numOfFoundDevices, + getCountSpecificDevices(X_LINK_UNBOOTED, X_LINK_ANY_PROTOCOL, X_LINK_MYRIAD_2) + + getCountSpecificDevices(X_LINK_UNBOOTED, X_LINK_ANY_PROTOCOL, X_LINK_MYRIAD_X)); +} + +TEST_F(XLinkFindAllSuitableDevicesTests, CanFindTwoDeviceDifferentState_USB_PCIE) { + if (getCountSpecificDevices(X_LINK_UNBOOTED) < 2) { + GTEST_SKIP(); + } + + deviceDesc_t in_deviceDesc = {}; + in_deviceDesc.protocol = X_LINK_ANY_PROTOCOL; + + // Find & boot one device + deviceDesc_t firstDeviceDesc = {}; + deviceDesc_t bootedDeviceDesc = {}; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_UNBOOTED, in_deviceDesc, &firstDeviceDesc)); + bootDevice(firstDeviceDesc, bootedDeviceDesc); + + deviceDesc_t deviceDescArray[XLINK_MAX_DEVICES] = {{}}; + unsigned int numOfFoundDevices = 0; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindAllSuitableDevices( + X_LINK_ANY_STATE, in_deviceDesc, deviceDescArray, + XLINK_MAX_DEVICES, &numOfFoundDevices)); + + bool foundBootedDevice = false; + for (int i = 0; i < numOfFoundDevices; ++i) { + if (deviceDescArray[i].platform == X_LINK_ANY_PLATFORM) + foundBootedDevice = true; + } + + EXPECT_GE(numOfFoundDevices, 2); + EXPECT_TRUE(foundBootedDevice); + EXPECT_EQ(numOfFoundDevices, getCountSpecificDevices(X_LINK_UNBOOTED) + + getCountSpecificDevices(X_LINK_BOOTED)); + + connectAndCloseDevice(bootedDeviceDesc); +} + +//------------------------------------------------------------------------------ +// XLinkResetRemoteTests +//------------------------------------------------------------------------------ + +TEST_P(XLinkResetRemoteTests, CanResetRemoteDevice) { + if (getCountSpecificDevices(X_LINK_UNBOOTED, _protocol) == 0) { + GTEST_SKIP(); + } + + XLinkHandler_t handler = {0}; + deviceDesc_t deviceDesc = {}; + deviceDesc_t bootedDeviceDesc = {}; + + deviceDesc.protocol = _protocol; + bootDevice(deviceDesc, bootedDeviceDesc); + connectToDevice(bootedDeviceDesc, &handler); + + // Reset device + ASSERT_EQ(X_LINK_SUCCESS, XLinkResetRemote(handler.linkId)); + std::this_thread::sleep_for(kResetTimeoutSec); + + // Make sure that device is really rebooted + deviceDesc_t foundDeviceDesc = {}; + ASSERT_EQ(X_LINK_DEVICE_NOT_FOUND, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, deviceDesc, &foundDeviceDesc)); +} + +//------------------------------------------------------------------------------ +// XLinkResetAllTests +//------------------------------------------------------------------------------ +TEST_P(XLinkResetAllTests, DISABLED_ResetBootedDevice) { + if (getCountSpecificDevices(X_LINK_UNBOOTED, _protocol) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t deviceDesc = {}; + deviceDesc_t bootedDeviceDesc = {}; + + deviceDesc.protocol = _protocol; + bootDevice(deviceDesc, bootedDeviceDesc); + + // Try to reset device + ASSERT_EQ(X_LINK_SUCCESS, XLinkResetAll()); + std::this_thread::sleep_for(kResetTimeoutSec); + + deviceDesc.protocol = X_LINK_ANY_PROTOCOL; + deviceDesc_t afterResetBootedDescr = {}; + ASSERT_EQ(X_LINK_DEVICE_NOT_FOUND, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, deviceDesc, &afterResetBootedDescr)); +} + +//------------------------------------------------------------------------------ +// XLinkOpenStreamTests +//------------------------------------------------------------------------------ + +TEST_P(XLinkOpenStreamTests, CanOpenAndCloseStream) { + streamId_t stream = XLinkOpenStream(_handlerPtr.get()->linkId, "mySuperStream", 1024); + ASSERT_NE(INVALID_STREAM_ID, stream); + ASSERT_NE(INVALID_STREAM_ID_OUT_OF_MEMORY, stream); + ASSERT_EQ(X_LINK_SUCCESS, XLinkCloseStream(stream)); +} + +// CannotOpenStreamMoreThanMemoryOnDevice +TEST_P(XLinkOpenStreamTests, CannotOpenStreamMoreThanMemoryOnDevice) { + const int _512MB = 512 * 1024 * 1024; + streamId_t stream = XLinkOpenStream(_handlerPtr.get()->linkId, "mySuperStream", _512MB); + ASSERT_EQ(INVALID_STREAM_ID_OUT_OF_MEMORY, stream); +} + +// FIXME: the test doesn't work +// TODO: is it correct behavior, should we accept the same names +TEST_P(XLinkOpenStreamTests, DISABLED_CannotOpenTwoStreamsWithTheSameName) { + const int _1KB = 1 * 1024; + const char streamName[] = "mySuperStream"; + streamId_t stream0 = XLinkOpenStream(_handlerPtr.get()->linkId, streamName, _1KB); + ASSERT_NE(INVALID_STREAM_ID, stream0); + + streamId_t stream1 = XLinkOpenStream(_handlerPtr.get()->linkId, streamName, _1KB); + ASSERT_EQ(INVALID_STREAM_ID, stream1); + + ASSERT_EQ(X_LINK_SUCCESS, XLinkCloseStream(stream0)); +} + +// FIXME: XLinkOpenStream doesn't allocate any memory on device +TEST_P(XLinkOpenStreamTests, DISABLED_CannotOpenStreamsMoreThanMemoryOnDevice) { + const int _256MB = 256 * 1024 * 1024; + streamId_t stream0 = XLinkOpenStream(_handlerPtr.get()->linkId, "mySuperStream0", _256MB); + ASSERT_NE(INVALID_STREAM_ID, stream0); + + streamId_t stream1 = XLinkOpenStream(_handlerPtr.get()->linkId, "mySuperStream1", _256MB); + ASSERT_EQ(INVALID_STREAM_ID, stream1); + + ASSERT_EQ(X_LINK_SUCCESS, XLinkCloseStream(stream0)); + ASSERT_EQ(X_LINK_SUCCESS, XLinkCloseStream(stream1)); +} + +//------------------------------------------------------------------------------ +// Initialization of XLinkCommonTests +//------------------------------------------------------------------------------ + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkBootTests, + Combine(Values(X_LINK_USB_VSC, X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkConnectTests, + Combine(Values(X_LINK_USB_VSC, X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkFindFirstSuitableDevicePlatformTests, + Combine(Values(X_LINK_USB_VSC), + Values(X_LINK_MYRIAD_2, X_LINK_MYRIAD_X, X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommonPCIE, + XLinkFindFirstSuitableDevicePlatformTests, + Combine(Values(X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkFindFirstSuitableDeviceTests, + Combine(Values(X_LINK_USB_VSC, X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkResetAllTests, + Combine(Values(X_LINK_USB_VSC, X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkResetRemoteTests, + Combine(Values(X_LINK_USB_VSC, X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkBootTests::getTestCaseName); + +INSTANTIATE_TEST_CASE_P( + XLinkCommon, + XLinkOpenStreamTests, + Combine(Values(X_LINK_USB_VSC, X_LINK_PCIE), + Values(X_LINK_ANY_PLATFORM)), + XLinkOpenStreamTests::getTestCaseName); + diff --git a/inference-engine/thirdparty/movidius/tests/XLink/XLink_deprecated_tests.cpp b/inference-engine/thirdparty/movidius/tests/XLink/XLink_deprecated_tests.cpp new file mode 100644 index 00000000000..63f5616172c --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/XLink_deprecated_tests.cpp @@ -0,0 +1,47 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "XLink_specific_cases.hpp" +#include "usb_boot.h" +#include + +//------------------------------------------------------------------------------ +// XLinkBootTests +//------------------------------------------------------------------------------ + +TEST_F(XLinkBootUSBTests, CanBootConnectAndResetDevice_deprecated) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, X_LINK_USB_VSC) == 0) { + GTEST_SKIP(); + } + + std::string firmwarePath; + char deviceName[XLINK_MAX_NAME_SIZE] = {0}; + // Find device + ASSERT_EQ(X_LINK_SUCCESS, + XLinkGetDeviceName(0, deviceName, XLINK_MAX_NAME_SIZE)); + ASSERT_NO_THROW(firmwarePath = getMyriadUSBFirmwarePath(deviceName)); + + // Boot it + ASSERT_EQ(X_LINK_SUCCESS, XLinkBootRemote(deviceName, firmwarePath.c_str())); + // FIXME: need to find a way to avoid this sleep + std::this_thread::sleep_for(kBootTimeoutSec); + + // Find booted + char bootedDeviceName[XLINK_MAX_NAME_SIZE] = {0}; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkGetDeviceNameExtended(0, bootedDeviceName, XLINK_MAX_NAME_SIZE, DEFAULT_OPENPID)); + + // Connect to device + XLinkHandler_t handler = {}; + handler.protocol = X_LINK_USB_VSC; + handler.devicePath = bootedDeviceName; + + ASSERT_EQ(X_LINK_SUCCESS, XLinkConnect(&handler)); + std::this_thread::sleep_for(std::chrono::seconds(2)); + + // Reset device + ASSERT_EQ(X_LINK_SUCCESS, XLinkResetRemote(handler.linkId)); + // FIXME: need to find a way to avoid this sleep + std::this_thread::sleep_for(kResetTimeoutSec); +} diff --git a/inference-engine/thirdparty/movidius/tests/XLink/XLink_specific_tests.cpp b/inference-engine/thirdparty/movidius/tests/XLink/XLink_specific_tests.cpp new file mode 100644 index 00000000000..89ac8de02e6 --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/XLink_specific_tests.cpp @@ -0,0 +1,154 @@ +// Copyright (C) 2018-2019 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "cases/XLink_specific_cases.hpp" +#include + +//------------------------------------------------------------------------------ +// XLinkFindFirstSuitableDeviceUSBTests +//------------------------------------------------------------------------------ + +TEST_F(XLinkFindFirstSuitableDeviceUSBTests, CanFindBootedDeviceByName) { + if (getCountSpecificDevices(X_LINK_UNBOOTED, X_LINK_USB_VSC) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t deviceDesc = {}; + deviceDesc_t bootedDeviceDesc = {}; + + bootDevice(deviceDesc, bootedDeviceDesc); + + deviceDesc_t foundDeviceDescr = {}; + EXPECT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_ANY_STATE, bootedDeviceDesc, &foundDeviceDescr)); + + EXPECT_TRUE(strcmp(bootedDeviceDesc.name, foundDeviceDescr.name) == 0); + + connectAndCloseDevice(bootedDeviceDesc); +} + +//------------------------------------------------------------------------------ +// XLinkBootUSBTests +//------------------------------------------------------------------------------ + +TEST_F(XLinkBootUSBTests, DeviceNameChangedAfterBoot) { + if (getCountSpecificDevices(X_LINK_UNBOOTED, X_LINK_USB_VSC) == 0) { + GTEST_SKIP(); + } + + deviceDesc_t unbootedDeviceDescr = {}; + deviceDesc_t in_deviceDesc = {}; + in_deviceDesc.protocol = X_LINK_USB_VSC; + in_deviceDesc.platform = X_LINK_ANY_PLATFORM; + + // Get device name + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_UNBOOTED, in_deviceDesc, &unbootedDeviceDescr)); + std::string firmwarePath; + ASSERT_NO_THROW(firmwarePath = getMyriadFirmwarePath(unbootedDeviceDescr)); + + // Boot device + ASSERT_EQ(X_LINK_SUCCESS, XLinkBoot(&unbootedDeviceDescr, firmwarePath.c_str())); + std::this_thread::sleep_for(kBootTimeoutSec); + + // Booted device appear + deviceDesc_t bootedDeviceDesc = {}; + EXPECT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, in_deviceDesc, &bootedDeviceDesc)); + + // The device is not in unbooted and booted list at the same time + deviceDesc_t foundDeviceDesc = {}; + EXPECT_EQ(X_LINK_DEVICE_NOT_FOUND, + XLinkFindFirstSuitableDevice(X_LINK_UNBOOTED, unbootedDeviceDescr, &foundDeviceDesc)); + + connectAndCloseDevice(bootedDeviceDesc); +} + + +//------------------------------------------------------------------------------ +// XLinkFindPCIEDeviceTests +//------------------------------------------------------------------------------ + +TEST_F(XLinkPCIEDeviceTests, CannotFindSameDeviceTwice) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, X_LINK_PCIE) == 0) + GTEST_SKIP(); + + deviceDesc_t deviceRequirements = {}; + deviceRequirements.protocol = X_LINK_PCIE; + deviceRequirements.platform = X_LINK_ANY_PLATFORM; + + deviceDesc_t deviceDescFirst = {}; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_ANY_STATE, deviceRequirements, &deviceDescFirst)); + + // On index 1 + deviceDesc_t deviceDescSecond = {}; + XLinkError_t rc = findDeviceOnIndex( + 1, X_LINK_ANY_STATE, deviceRequirements, &deviceDescSecond); + + if (rc != X_LINK_DEVICE_NOT_FOUND) { + ASSERT_EQ(rc, X_LINK_SUCCESS); + ASSERT_TRUE(strstr(deviceDescFirst.name, PCIE_NAME_SUBSTR) != nullptr); + ASSERT_TRUE(strstr(deviceDescSecond.name, PCIE_NAME_SUBSTR) != nullptr); + ASSERT_TRUE(strcmp(deviceDescFirst.name, deviceDescSecond.name) != 0); + } +} + +/** + * This is real test for two multi-device case, require two PCIe cards + * Boot second and expect that first will be unbooted, second booted + */ +TEST_F(XLinkPCIEDeviceTests, DISABLED_CanFindFirstDeviceAfterBootSecond) { + if (getCountSpecificDevices(X_LINK_ANY_STATE, X_LINK_PCIE) == 0) + GTEST_SKIP(); + + // TODO Add check that there two devices + deviceDesc_t deviceRequirements = {}; + deviceRequirements.protocol = X_LINK_PCIE; + deviceRequirements.platform = X_LINK_ANY_PLATFORM; + + // Find first device + deviceDesc_t firstDeviceDesc = {}; + ASSERT_EQ(X_LINK_SUCCESS, findDeviceOnIndex( + 0, X_LINK_ANY_STATE, deviceRequirements, &firstDeviceDesc)); + + // Find second device + deviceDesc_t secondDeviceDesc = {}; + ASSERT_EQ(X_LINK_SUCCESS, findDeviceOnIndex( + 1, X_LINK_ANY_STATE, deviceRequirements, &secondDeviceDesc)); + + // Boot second device + std::string firmwarePath; + ASSERT_NO_THROW(firmwarePath = getMyriadFirmwarePath(deviceRequirements)); + + EXPECT_EQ(X_LINK_SUCCESS, XLinkBoot(&secondDeviceDesc, firmwarePath.c_str())); + std::this_thread::sleep_for(kBootTimeoutSec); + + // Check that first still in unbooted state + deviceDesc_t firstDeviceDescAfter = {}; + firstDeviceDescAfter.protocol = X_LINK_PCIE; + firstDeviceDescAfter.platform = X_LINK_ANY_PLATFORM; + + + EXPECT_EQ(X_LINK_SUCCESS, findDeviceOnIndex( + 0, X_LINK_UNBOOTED, firstDeviceDesc, &firstDeviceDescAfter)); + + // Check that second device now in booted state + deviceDesc_t secondDeviceDescAfter = {}; + secondDeviceDescAfter.protocol = X_LINK_PCIE; + secondDeviceDescAfter.platform = X_LINK_ANY_PLATFORM; + + EXPECT_EQ(X_LINK_SUCCESS, findDeviceOnIndex( + 0, X_LINK_BOOTED, secondDeviceDesc, &secondDeviceDescAfter)); + + // TODO Move it to separate function + // Close second device + XLinkHandler_t handler = {0}; + handler.protocol = secondDeviceDesc.protocol; + handler.devicePath = secondDeviceDesc.name; + ASSERT_EQ(X_LINK_SUCCESS, XLinkConnect(&handler)); + + ASSERT_EQ(X_LINK_SUCCESS, XLinkResetRemote(handler.linkId)); + std::this_thread::sleep_for(kResetTimeoutSec); +} diff --git a/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_common_cases.cpp b/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_common_cases.cpp new file mode 100644 index 00000000000..9aaefa56185 --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_common_cases.cpp @@ -0,0 +1,98 @@ +// Copyright (C) 2018-2019 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + + +#include "XLink_common_cases.hpp" + +#include + +static XLinkGlobalHandler_t globalHandler; + +//------------------------------------------------------------------------------ +// Implementation of methods of class XLinkTests +//------------------------------------------------------------------------------ + +void XLinkTests::SetUpTestCase() { + ASSERT_EQ(X_LINK_SUCCESS, XLinkInitialize(&globalHandler)); + + // Deprecated field usage. Begin. + globalHandler.protocol = USB_VSC; + // Deprecated field usage. End. + + // Waiting for initialization + std::this_thread::sleep_for(std::chrono::seconds(1)); +} + +//------------------------------------------------------------------------------ +// Implementation of methods of class XLinkDeviceTestsCommon +//------------------------------------------------------------------------------ + +static std::string protocolToString(XLinkProtocol_t protocol) { + switch (protocol) { + case X_LINK_USB_VSC: + return std::string("USB"); + case X_LINK_PCIE: + return std::string("PCIE"); + default: + return std::string("ANY"); + } +} + +static std::string platformToString(XLinkPlatform_t platform) { + switch (platform) { + case X_LINK_MYRIAD_2: + return std::string("Myriad2"); + case X_LINK_MYRIAD_X: + return std::string("MyriadX"); + default: + return std::string("ANY"); + } +} + +std::string XLinkDeviceTestsCommon::getTestCaseName( + const TestParamInfo& param) { + XLinkProtocol_t protocol = get<0>(param.param); + XLinkPlatform_t platform = get<1>(param.param); + + return "protocol=" + protocolToString(protocol) + + "_platform=" + platformToString(platform); +} + +void XLinkDeviceTestsCommon::SetUp() { + _protocol = get<0>(XLinkDeviceTestsCommonParam::GetParam()); + _platform = get<1>(XLinkDeviceTestsCommonParam::GetParam()); +} + +//------------------------------------------------------------------------------ +// Implementation of methods of class XLinkOpenStreamUSBTests +//------------------------------------------------------------------------------ + +std::string XLinkOpenStreamTests::getTestCaseName( + const TestParamInfo& param) { + const auto name = XLinkDeviceTestsCommon::getTestCaseName(param); + + XLinkProtocol_t protocol = get<0>(param.param); + if (getCountSpecificDevices(X_LINK_UNBOOTED, protocol) == 0) { + return "DISABLED_" + name; + } + + return name; +} + +XLinkOpenStreamTests::XLinkOpenStreamTests() : _handlerPtr(new XLinkHandler_t()) { +} + +void XLinkOpenStreamTests::SetUp() { + XLinkDeviceTestsCommon::SetUp(); + + _deviceDesc.protocol = _protocol; + _deviceDesc.platform = _platform; + + XLinkTestsHelper::bootDevice(_deviceDesc, _bootedDesc); + XLinkTestsHelper::connectToDevice(_bootedDesc, _handlerPtr.get()); +} + +void XLinkOpenStreamTests::TearDown() { + XLinkTestsHelper::closeDevice(_handlerPtr.get()); +} diff --git a/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_common_cases.hpp b/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_common_cases.hpp new file mode 100644 index 00000000000..ee45709615e --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_common_cases.hpp @@ -0,0 +1,110 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + + +#pragma once + +#include "XLink.h" +#include "XLink_tests_helpers.hpp" + +#include "gtest/gtest.h" +#include + +using namespace ::testing; +using XLinkDeviceTestsCommonParam = WithParamInterface>; + +//------------------------------------------------------------------------------ +// class XLinkTests +//------------------------------------------------------------------------------ +class XLinkTests : public ::testing::Test, + protected XLinkTestsHelper { +public: + static void SetUpTestCase(); +}; + +//------------------------------------------------------------------------------ +// class XLinkNullPtrTests +//------------------------------------------------------------------------------ +class XLinkNullPtrTests: public XLinkTests {}; + +//------------------------------------------------------------------------------ +// class XLinkFindAllSuitableDevicesTests +//------------------------------------------------------------------------------ + +class XLinkFindAllSuitableDevicesTests : public XLinkTests {}; + + +//------------------------------------------------------------------------------ +// class XLinkCommonTests +//------------------------------------------------------------------------------ +class XLinkDeviceTestsCommon : public XLinkTests, + public XLinkDeviceTestsCommonParam { +public: + //Operations + static std::string getTestCaseName( + const TestParamInfo& param); + + void SetUp() override; + +protected: + XLinkProtocol_t _protocol; + XLinkPlatform_t _platform; +}; + +//------------------------------------------------------------------------------ +// class XLinkBootUSBTests +//------------------------------------------------------------------------------ + +class XLinkBootTests : public XLinkDeviceTestsCommon {}; + +//------------------------------------------------------------------------------ +// class XLinkConnectTests +//------------------------------------------------------------------------------ +class XLinkConnectTests : public XLinkDeviceTestsCommon {}; + +//------------------------------------------------------------------------------ +// class XLinkFindFirstSuitableDeviceTests +//------------------------------------------------------------------------------ + +class XLinkFindFirstSuitableDeviceTests : public XLinkDeviceTestsCommon {}; + +//------------------------------------------------------------------------------ +// class XLinkFindFirstSuitableBootedDeviceTests +//------------------------------------------------------------------------------ + +class XLinkFindFirstSuitableDevicePlatformTests : public XLinkDeviceTestsCommon {}; + +//------------------------------------------------------------------------------ +// class XLinkResetRemoteTests +//------------------------------------------------------------------------------ +class XLinkResetRemoteTests : public XLinkDeviceTestsCommon {}; + +//------------------------------------------------------------------------------ +// class XLinkResetAllTests +//------------------------------------------------------------------------------ +class XLinkResetAllTests : public XLinkDeviceTestsCommon {}; + + + +//------------------------------------------------------------------------------ +// class XLinkOpenStreamTests +//------------------------------------------------------------------------------ + +class XLinkOpenStreamTests : public XLinkDeviceTestsCommon { +public: + //Operations + static std::string getTestCaseName( + const TestParamInfo& param); + +protected: + XLinkOpenStreamTests(); + + void SetUp() override; + void TearDown() override; + +protected: + std::unique_ptr _handlerPtr; + deviceDesc_t _deviceDesc = {}; + deviceDesc_t _bootedDesc = {}; +}; diff --git a/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_specific_cases.hpp b/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_specific_cases.hpp new file mode 100644 index 00000000000..4638dd39297 --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/cases/XLink_specific_cases.hpp @@ -0,0 +1,25 @@ +// Copyright (C) 2018-2019 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "XLink_common_cases.hpp" + +//------------------------------------------------------------------------------ +// class XLinkBootUSBTests +//------------------------------------------------------------------------------ + +class XLinkBootUSBTests : public XLinkTests {}; + +//------------------------------------------------------------------------------ +// class XLinkFindFirstSuitableDeviceUSBTests +//------------------------------------------------------------------------------ + +class XLinkFindFirstSuitableDeviceUSBTests : public XLinkTests {}; + +//------------------------------------------------------------------------------ +// class XLinkFindPCIEDeviceTests +//------------------------------------------------------------------------------ + +class XLinkPCIEDeviceTests: public XLinkTests {}; diff --git a/inference-engine/thirdparty/movidius/tests/XLink/helpers/XLink_tests_helpers.cpp b/inference-engine/thirdparty/movidius/tests/XLink/helpers/XLink_tests_helpers.cpp new file mode 100644 index 00000000000..2ab2d0168a2 --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/helpers/XLink_tests_helpers.cpp @@ -0,0 +1,133 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "XLink_tests_helpers.hpp" +#include + +//------------------------------------------------------------------------------ +// Implementation of methods of class XLinkTestsHelpersBoot +//------------------------------------------------------------------------------ + +void XLinkTestsHelper::bootDevice(const deviceDesc_t& in_deviceDesc, deviceDesc_t& out_bootedDeviceDesc) { + deviceDesc_t tmp_deviceDesc = {}; + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_UNBOOTED, in_deviceDesc, &tmp_deviceDesc)); + + std::string firmwarePath; + ASSERT_NO_THROW(firmwarePath = getMyriadFirmwarePath(tmp_deviceDesc)); + printf("Would boot (%s) device with firmware (%s) \n", tmp_deviceDesc.name, firmwarePath.c_str()); + + ASSERT_EQ(X_LINK_SUCCESS, XLinkBoot(&tmp_deviceDesc, firmwarePath.c_str())); + // FIXME: need to find a way to avoid this sleep + std::this_thread::sleep_for(kBootTimeoutSec); + + // Check, that device booted + tmp_deviceDesc.platform = X_LINK_ANY_PLATFORM; + memset(tmp_deviceDesc.name, 0, XLINK_MAX_NAME_SIZE); + ASSERT_EQ(X_LINK_SUCCESS, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, tmp_deviceDesc, &out_bootedDeviceDesc)); +} + +void XLinkTestsHelper::connectToDevice(deviceDesc_t& in_bootedDeviceDesc, XLinkHandler_t* out_handler) { + if (!out_handler){ + GTEST_FAIL(); + } + + memset(out_handler, 0, sizeof(XLinkHandler_t)); + out_handler->protocol = in_bootedDeviceDesc.protocol; + out_handler->devicePath = in_bootedDeviceDesc.name; + + ASSERT_EQ(X_LINK_SUCCESS, XLinkConnect(out_handler)); +} + +void XLinkTestsHelper::closeDevice(XLinkHandler_t* handler) { + ASSERT_EQ(X_LINK_SUCCESS, XLinkResetRemote(handler->linkId)); + std::this_thread::sleep_for(kResetTimeoutSec); + + // Make sure that device is closed + deviceDesc_t deviceDesc = {}; + deviceDesc.protocol = handler->protocol; + deviceDesc.platform = X_LINK_ANY_PLATFORM; + strcpy(deviceDesc.name, handler->devicePath); + + deviceDesc_t foundDeviceDesc = {}; + ASSERT_EQ(X_LINK_DEVICE_NOT_FOUND, + XLinkFindFirstSuitableDevice(X_LINK_BOOTED, deviceDesc, &foundDeviceDesc)); +} + +void XLinkTestsHelper::connectAndCloseDevice(deviceDesc_t& in_bootedDeviceDesc) { + XLinkHandler_t handler = {0}; + + connectToDevice(in_bootedDeviceDesc, &handler); + closeDevice(&handler); +} + +std::string XLinkTestsHelper::getMyriadUSBFirmwarePath(const std::string& deviceName) { + if (deviceName.find('-') == std::string::npos) { + throw std::invalid_argument("Invalid device address"); + } + + if (deviceName.find("ma2480") != std::string::npos) { + return FIRMWARE_SUBFOLDER + std::string("usb-ma2x8x.mvcmd"); + } + + return FIRMWARE_SUBFOLDER + std::string("usb-ma2450.mvcmd"); +} + +std::string XLinkTestsHelper::getMyriadFirmwarePath(const deviceDesc_t& in_deviceDesc) { + if(in_deviceDesc.protocol != X_LINK_USB_VSC && + in_deviceDesc.protocol != X_LINK_PCIE) { + throw std::invalid_argument("Device protocol must be specified"); + } + + if(in_deviceDesc.protocol == X_LINK_PCIE) { +#if defined(_WIN32) + return FIRMWARE_SUBFOLDER + std::string("pcie-ma248x.elf"); +#else + return FIRMWARE_SUBFOLDER + std::string("pcie-ma248x.mvcmd"); +#endif + } + + return getMyriadUSBFirmwarePath(in_deviceDesc.name); +} + +XLinkError_t XLinkTestsHelper::findDeviceOnIndex( + const int index, + const XLinkDeviceState_t deviceState, + const deviceDesc_t in_deviceRequirements, + deviceDesc_t *out_foundDevicesPtr) { + + deviceDesc_t deviceDescArray[XLINK_MAX_DEVICES] = {}; + unsigned int foundDevices = 0; + XLinkError_t rc = XLinkFindAllSuitableDevices( + deviceState, in_deviceRequirements, deviceDescArray, XLINK_MAX_DEVICES, &foundDevices); + + if (rc != X_LINK_SUCCESS) { + return rc; + } + + if (foundDevices <= index) { + return X_LINK_DEVICE_NOT_FOUND; + } + + out_foundDevicesPtr->platform = deviceDescArray[index].platform; + out_foundDevicesPtr->protocol = deviceDescArray[index].protocol; + strncpy(out_foundDevicesPtr->name, deviceDescArray[index].name, XLINK_MAX_NAME_SIZE); + return X_LINK_SUCCESS; +} + +int XLinkTestsHelper::getCountSpecificDevices(const XLinkDeviceState_t state, + const XLinkProtocol_t deviceProtocol, + const XLinkPlatform_t devicePlatform) { + deviceDesc_t req_deviceDesc = {}; + req_deviceDesc.protocol = deviceProtocol; + req_deviceDesc.platform = devicePlatform; + + deviceDesc_t deviceDescArray[XLINK_MAX_DEVICES] = {}; + unsigned int foundDevices = 0; + XLinkFindAllSuitableDevices( + state, req_deviceDesc, deviceDescArray, XLINK_MAX_DEVICES, &foundDevices); + + return foundDevices; +} diff --git a/inference-engine/thirdparty/movidius/tests/XLink/helpers/XLink_tests_helpers.hpp b/inference-engine/thirdparty/movidius/tests/XLink/helpers/XLink_tests_helpers.hpp new file mode 100644 index 00000000000..238b9d89719 --- /dev/null +++ b/inference-engine/thirdparty/movidius/tests/XLink/helpers/XLink_tests_helpers.hpp @@ -0,0 +1,71 @@ +// Copyright (C) 2018-2020 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "XLink.h" +#include "XLinkPrivateDefines.h" + +#include +#include +#include + +//------------------------------------------------------------------------------ +// Defines +//------------------------------------------------------------------------------ + +using std_seconds = std::chrono::seconds; + +#if (defined(_WIN32) || defined(_WIN64)) + static constexpr char PCIE_NAME_SUBSTR[] = "mxlink"; + static constexpr char FIRMWARE_SUBFOLDER[] = "./"; +#else + static constexpr char PCIE_NAME_SUBSTR[] = "mxlk"; + static constexpr char FIRMWARE_SUBFOLDER[] = "./lib/"; +#endif + +//------------------------------------------------------------------------------ +// Helpers +//------------------------------------------------------------------------------ + +constexpr std::chrono::seconds operator "" _sec(unsigned long long s) +{ + return std::chrono::seconds(s); +} + +//------------------------------------------------------------------------------ +// class XLinkTestsHelper +//------------------------------------------------------------------------------ + +class XLinkTestsHelper { +public: + const std_seconds kBootTimeoutSec = 2_sec; + const std_seconds kResetTimeoutSec = 5_sec; + + const std::string kUSBMyriadX = "ma2480"; + const std::string kUSBMyriad2 = "ma2450"; + + // Device management + void bootDevice(const deviceDesc_t& in_deviceDesc, deviceDesc_t& out_bootedDeviceDesc); + + void connectToDevice(deviceDesc_t& in_bootedDeviceDesc, XLinkHandler_t* out_handler); + void closeDevice(XLinkHandler_t* handler); + + void connectAndCloseDevice(deviceDesc_t& in_bootedDeviceDesc); + + // Firmware + std::string getMyriadUSBFirmwarePath(const std::string& deviceName); + std::string getMyriadFirmwarePath(const deviceDesc_t& in_deviceDesc); + + // Device searching + XLinkError_t findDeviceOnIndex(const int index, + const XLinkDeviceState_t deviceState, + const deviceDesc_t in_deviceRequirements, + deviceDesc_t *out_foundDevicesPtr); + + + static int getCountSpecificDevices(const XLinkDeviceState_t state = X_LINK_ANY_STATE, + const XLinkProtocol_t deviceProtocol = X_LINK_ANY_PROTOCOL, + const XLinkPlatform_t devicePlatform = X_LINK_ANY_PLATFORM); +}; diff --git a/inference-engine/tools/CMakeLists.txt b/inference-engine/tools/CMakeLists.txt index 844e5419d3a..2af002e5bea 100644 --- a/inference-engine/tools/CMakeLists.txt +++ b/inference-engine/tools/CMakeLists.txt @@ -24,4 +24,6 @@ if(ENABLE_PYTHON) install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/benchmark_tool DESTINATION deployment_tools/tools COMPONENT python_tools) + install(FILES + COMPONENT python_tools) endif() diff --git a/inference-engine/tools/cross_check_tool/README.md b/inference-engine/tools/cross_check_tool/README.md new file mode 100644 index 00000000000..6ec1a873e0d --- /dev/null +++ b/inference-engine/tools/cross_check_tool/README.md @@ -0,0 +1,281 @@ +# Cross Check Tool + +Cross Check Tool is a console application that enables comparing accuracy and performance metrics for two successive +model inferences that are performed on two different supported Intel® devices or with different precisions. +The Cross Check Tool can compare the metrics per layer or all over the model. + +## Running the Cross Check Tool + +Cross Check Tool is distributed as a Python module and there is no need to build it. To run the Cross Check Tool, +execute the `cross_check_tool.py` file with necessary parameters. Please note that the Inference Engine assumes that weights +are in the same folder as the `.xml` file. + +You can get the list of all available options using the `-h` option: + +```sh +$python3 cross_check_tool.py -h + +Cross Check Tool is a console application that enables comparing accuracy and +provides performance metrics + +optional arguments: + -h, --help show this help message and exit + +Model specific arguments: + --input INPUT, -i INPUT + Path to an input image file or multi-input file to + infer. Generates input(s) from normal distribution if + empty + --batch BATCH, -b BATCH + Overrides batch size. Default is inherited from model + --model MODEL, -m MODEL + Path to an .xml file that represents the first IR of + the trained model to infer. + --reference_model REFERENCE_MODEL, -ref_m REFERENCE_MODEL + Path to an .xml file that represents the second IR in + different precision to compare the metrics. + --layers LAYERS, -layers LAYERS + Defines layers to check. Options: all, None - for + output layers check, list of comma-separated layer + names to check. Default value is None. + --mapping MAPPING, -map MAPPING + Model Optimizer provided mapping for --model/-m + --reference_mapping REFERENCE_MAPPING, -ref_map REFERENCE_MAPPING + Model Optimizer provided mapping for + --reference_model/-ref_model + --num_of_iterations NUM_OF_ITERATIONS, -ni NUM_OF_ITERATIONS + Number of iterations to collect all over the net + performance + +Plugin specific arguments: + --plugin_path PLUGIN_PATH, -pp PLUGIN_PATH + Path to a plugin folder. + --device DEVICE, -d DEVICE + The first target device to infer the model specified + with the -m or --model option. CPU, GPU, HDDL or + MYRIAD are acceptable. + --config CONFIG, -conf CONFIG + Path to config file for -d or -device device plugin + --reference_device REFERENCE_DEVICE, -ref_d REFERENCE_DEVICE + The second target device to infer the model and + compare the metrics. CPU, GPU, HDDL or MYRIAD are + acceptable. + --reference_config REFERENCE_CONFIG, -ref_conf REFERENCE_CONFIG + Path to config file for -ref_d or -reference_device + device plugin + -l L Required for MKLDNN (CPU)-targeted custom layers. + Comma separated paths to a shared libraries with the + kernels implementation. + +CCT mode arguments: + --dump Enables blobs statistics dumping + --load LOAD Path to a file to load blobs from + +``` +### Examples + +1. To check per-layer accuracy and performance of inference in FP32 precision on the CPU against the GPU, run: + ```sh + $python3 cross_check_tool.py -i \ + -m \ + -d GPU \ + -ref_d CPU \ + --layers all + ``` + + The output looks as follows: + ```sh + [ INFO ] Cross check with one IR was enabled + [ INFO ] GPU:FP32 vs CPU:FP32 + [ INFO ] The same IR on both devices: + [ INFO ] Statistics will be dumped for X layers: , , ... , + [ INFO ] Layer statistics + Max absolute difference : 1.15204E-03 + Min absolute difference : 0.0 + Max relative difference : 1.15204E+17 + Min relative difference : 0.0 + Min reference value : -1.69513E+03 + Min absolute reference value : 2.71080E-06 + Max reference value : 1.17132E+03 + Max absolute reference value : 1.69513E+03 + Min actual value : -1.69513E+03 + Min absolute actual value : 8.66465E-05 + Max actual value : 1.17132E+03 + Max absolute actual value : 1.69513E+03 + Device: -d GPU -ref_d CPU + Status: OPTIMIZED_OUT OPTIMIZED_OUT + Layer type: Convolution Convolution + Real time, microsec: 0 120 + Number of NAN: 0 0 + Number of INF: 0 0 + Number of ZERO: 0 0 + ... + + ... + + [ INFO ] Overall max absolute difference = 0.00115203857421875 + [ INFO ] Overall min absolute difference = 0.0 + [ INFO ] Overall max relative difference = 1.1520386483093504e+17 + [ INFO ] Overall min relative difference = 0.0 + [ INFO ] Execution successful + ``` + +2. To check the overall accuracy and performance of inference on the CPU in FP32 precision against the + Intel® Movidius™ Myriad™ device in FP16 precision, run: + ```sh + $python3 cross_check_tool.py -i \ + -m \ + -d MYRIAD \ + -ref_m \ + -ref_d CPU + ``` + + The output looks as follows: + ```sh + [ INFO ] Cross check with two IRs was enabled + [ INFO ] GPU:FP16 vs CPU:FP32 + [ INFO ] IR for MYRIAD : + [ INFO ] IR for CPU : + [ INFO ] Statistics will be dumped for 1 layer: + [ INFO ] Layer statistics + Max absolute difference : 2.32944E-02 + Min absolute difference : 3.63002E-13 + Max relative difference : 6.41717E+10 + Min relative difference : 1.0 + Min reference value : 3.63002E-13 + Min absolute reference value : 3.63002E-13 + Max reference value : 7.38138E-01 + Max absolute reference value : 7.38138E-01 + Min actual value : 0.0 + Min absolute actual value : 0.0 + Max actual value : 7.14844E-01 + Max absolute actual value : 7.14844E-01 + Device: -d MYRIAD -ref_d CPU + Status: OPTIMIZED_OUT OPTIMIZED_OUT + Layer type: Reshape Reshape + Real time, microsec: 0 0 + Number of NAN: 0 0 + Number of INF: 0 0 + Number of ZERO: 0 0 + ---------------------------------------------------------------------- + Overall performance, microseconds: 2.79943E+05 6.24670E+04 + ---------------------------------------------------------------------- + [ INFO ] Overall max absolute difference = 0.023294448852539062 + [ INFO ] Overall min absolute difference = 3.630019191052519e-13 + [ INFO ] Overall max relative difference = 64171696128.0 + [ INFO ] Overall min relative difference = 1.0 + [ INFO ] Execution successful + ``` + +3. To dump layer statistics from a specific list of layers, run: + ```sh + $python3 cross_check_tool.py -i \ + -m \ + -d MYRIAD \ + --dump \ + --layers + ``` + + The output looks as follows: + ```sh + [ INFO ] Dump mode was enabled + [ INFO ] layer processing + ... + [ INFO ] layer processing + [ INFO ] Dump file path: + [ INFO ] Execution successful + ``` + + If you do not provide the `-i` key, the Cross Check Tool generates an input from normal distributed noise and saves + it in a multi-input file format with the filename `_input_layers_dump.txt` in the same folder as the Intermediate Representation (IR). + +4. To check the overall accuracy and performance of inference on the CPU in FP32 precision against dumped results, run: + ```sh + $python3 cross_check_tool.py -i \ + -m \ + -d CPU \ + --load \ + --layers all + ``` + + The output looks as follows: + ```sh + [ INFO ] Load mode was enabled + [ INFO ] IR for CPU : + [ INFO ] Loading blob from /localdisk/models/FP16/icv_squeezenet_v1.0.xml_GPU_dump.npz + [ INFO ] Statistics will be dumped for X layers: , , ... , + [ INFO ] Layer statistics + Max absolute difference : 0.0 + Min absolute difference : 0.0 + Max relative difference : 0.0 + Min relative difference : 0.0 + Min reference value : 0.0 + Min absolute reference value : 0.0 + Max reference value : 7.14844E-01 + Max absolute reference value : 7.14844E-01 + Min actual value : 0.0 + Min absolute actual value : 0.0 + Max actual value : 7.14844E-01 + Max absolute actual value : 7.14844E-01 + Device: -d CPU -load GPU + Status: OPTIMIZED_OUT OPTIMIZED_OUT + Layer type: Reshape Reshape + Real time, microsec: 0 0 + Number of NAN: 0 0 + Number of INF: 0 0 + Number of ZERO: 609 699 + + ... + + ... + + [ INFO ] Overall max absolute difference = 0.0 + [ INFO ] Overall min absolute difference = 0.0 + [ INFO ] Overall max relative difference = 0.0 + [ INFO ] Overall min relative difference = 0.0 + [ INFO ] Execution successful + ``` + +### Multi-input and dump file format + +Multi-input and dump file is a numpy compressed `.npz` file with hierarchy: + +```sh +{ + ‘layer_name’: { + ‘blob’: np.array([…]) + ‘pc’: { + ‘device’: ‘device_name’, + ‘real_time’: int_real_time_in_microseconds_from_plugin, + ‘exec_type’: ‘exec_type_from_plugin’, + ‘layer_type’: ‘layer_type_from_plugin’, + ‘status’: ‘status_from_plugin’ + } + }, + ‘another_layer_name’: { + ‘blob’: np.array([…]) + ‘pc’: { + ‘device’: ‘device_name’, + ‘real_time’: int_real_time_in_microseconds_from_plugin, + ‘exec_type’: ‘exec_type_from_plugin’, + ‘layer_type’: ‘layer_type_from_plugin’, + ‘status’: ‘status_from_plugin’ + } + }, + ... +} +``` + +### Configuration file + +There is an option to pass configuration file to plugin by providing +`--config` and/or `--reference_config` keys. + +Configuration file is a text file with content of pairs of keys and values. + +Structure of configuration file: + +```sh +KEY VALUE +ANOTHER_KEY ANOTHER_VALUE,VALUE_1 +``` diff --git a/inference-engine/tools/cross_check_tool/__init__.py b/inference-engine/tools/cross_check_tool/__init__.py new file mode 100644 index 00000000000..79b3c5795b4 --- /dev/null +++ b/inference-engine/tools/cross_check_tool/__init__.py @@ -0,0 +1,15 @@ +""" +Copyright (C) 2018-2020 Intel Corporation + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" diff --git a/inference-engine/tools/cross_check_tool/cross_check_tool.py b/inference-engine/tools/cross_check_tool/cross_check_tool.py new file mode 100644 index 00000000000..1176187275f --- /dev/null +++ b/inference-engine/tools/cross_check_tool/cross_check_tool.py @@ -0,0 +1,297 @@ +# Copyright (C) 2018-2019 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# + +import datetime +import logging as log +import os +import sys + +import numpy as np + +try: + from openvino import inference_engine as ie + from openvino.inference_engine import IENetwork, IECore +except Exception as e: + exception_type = type(e).__name__ + print("The following error happened while importing Python API module:\n[ {} ] {}".format(exception_type, e)) + sys.exit(1) + +from utils import get_config_dictionary, get_layers_list, print_output_layers, input_processing, \ + accuracy_metrics, validate_args, build_parser, set_logger, find_out_cct_mode, print_all_over_the_net_metrics, \ + update_global_accuracy_matrics, blob_counters, performance_metrics, manage_user_outputs_with_mapping, \ + dump_output_file, load_dump, error_handling, print_input_layers, set_verbosity + + +### +# PLUGIN +### + + +@error_handling('plugin of \'{plugin.device}\' device config \'{config}\' loading') +def set_plugin_config(core: IECore, device : str, config: str = None): + core.set_config(get_config_dictionary(config_file=config), device_name=device) + + +@error_handling('\'{cpu_ext}\' cpu extensions loading') +def set_cpu_extensions(core: IECore, cpu_ext: str): + core.add_extension(cpu_ext, "CPU") + + +def get_plugin(device: str, cpu_ext: str = None, config: str = None): + ie = IECore() + # log.info('{} plugin:\n API version ............ {}'.format(device, plugin.version), extra={'no_lvl': True}) + set_plugin_config(core=ie, device=device, config=config) + if cpu_ext and 'CPU' in device: + set_cpu_extensions(core=ie, cpu_ext=cpu_ext) + return ie + + +### +# MODEL +### + + +@error_handling('reading {model} IR model') +def get_net(model: str, core: IECore): + model_xml = model + model_bin = os.path.splitext(model_xml)[0] + ".bin" + net = core.read_network(model=model_xml, weights=model_bin) + return net + + +@error_handling('loading network to plugin of {plugin.device} device') +def get_exec_net(core, net, device): + return core.load_network(network=net, device_name=device) + + +@error_handling('output \'{output}\' addition for network from model \'{model}\'') +def get_net_copy_with_output(model: str, output: str, core: IECore): + net_copy = get_net(model=model, core=core) + if output not in ['None', None]: + net_copy.add_outputs(output) + return net_copy + + +@error_handling('getting model layers info') +def get_model_info(net: IENetwork): + layers = net.layers + precision = layers[list(layers.keys())[0]].out_data[0].precision + return layers, net.inputs, net.outputs, precision + + +### +# INFER +### + + +@error_handling('processing inference on \'{device}\' device') +def get_infer_results(executable_network, inputs: dict): + return executable_network.infer(inputs=inputs) + + +@error_handling('getting performance counts from executable network on \'{device}\' device') +def get_perf_counts(executable_network): + return executable_network.requests[0].get_perf_counts() + + +@error_handling('getting inference results for outputs: \'{output}\'') +def infer(net: IENetwork, core: IECore, device : str, inputs: dict, output: list): + executable_network = get_exec_net(core=core, net=net, device=device) + infer_dict = get_infer_results(executable_network=executable_network, inputs=inputs) + pc = get_perf_counts(executable_network=executable_network) + no_i = 'no_info' + no_info_pc = {'cpu_time': no_i, 'exec_time': no_i, 'layer_type': no_i, 'real_time': no_i, 'status': no_i} + result = {} + for out in output: + if out not in infer_dict: + log.warning("There is no '{}' layer in Inference Engine outputs results".format(out)) + continue + pc = pc[out] if out in pc else no_info_pc + pc['device'] = device + result = {out: [infer_dict[out], pc]} + return result + + +@error_handling('getting inference results for outputs: \'{output}\'') +def overall_accuracy_check(model: str, ref_model: str, out_layers: list, ref_out_layers: list, inputs: dict, + ref_inputs: dict, core: IECore, device: str, ref_core: IECore, ref_device : str, layers: str, + num_of_iterations: int): + global_times, ref_global_times = [], [] + if layers in ['None', None]: + net_copy = get_net_copy_with_output(model=model, output=layers, core=core) + ref_net_copy = get_net_copy_with_output(model=ref_model, output=layers, core=ref_core) + for i in range(num_of_iterations): + t1 = datetime.datetime.now() + infer(net=net_copy, core=core, device=device, inputs=inputs, output=out_layers) + t2 = datetime.datetime.now() + infer(net=ref_net_copy, core=ref_core, device=ref_device, inputs=ref_inputs, output=ref_out_layers) + t3 = datetime.datetime.now() + global_times.append(t2 - t1) + ref_global_times.append(t3 - t2) + return global_times, ref_global_times + + +def one_ir_mode(args): + core = get_plugin(args.device, args.l, args.config) + net = get_net(model=args.model, core=core) + net_layers, net_inputs, net_outputs, precision = get_model_info(net) + log.info('{}:{} vs {}:{}'.format(args.device, precision, args.reference_device, precision)) + log.info('The same IR on both devices: {}'.format(args.model)) + out_layers = get_layers_list(net_layers, net_inputs, net_outputs, args.layers) + print_input_layers(net_inputs) + print_output_layers(out_layers) + ref_core = get_plugin(args.reference_device, args.l, args.reference_config) + global_accuracy = [] + inputs = input_processing(model_path=args.model, net_inputs=net_inputs, input_file=args.input) + global_times, ref_global_times = overall_accuracy_check(model=args.model, ref_model=args.model, + out_layers=out_layers, ref_out_layers=out_layers, + inputs=inputs, ref_inputs=inputs, core=core, + device=args.device, ref_core=ref_core, + ref_device=args.reference_device, layers=args.layers, + num_of_iterations=args.num_of_iterations) + for out_layer in out_layers: + log.info('Layer {} statistics'.format(out_layer)) + net_copy = get_net_copy_with_output(model=args.model, output=out_layer, core=core) + results = infer(net=net_copy, core=core, device=args.device, inputs=inputs, output=[out_layer]) + if out_layer not in results: + continue + out_blob, pc = results[out_layer] + ref_results = infer(net=net_copy, core=ref_core, device=args.reference_device, inputs=inputs, output=[out_layer]) + if out_layer not in ref_results: + continue + ref_out_blob, ref_pc = ref_results[out_layer] + a_m = accuracy_metrics(out_blob=out_blob, ref_out_blob=ref_out_blob) + performance_metrics(pc=pc, ref_pc=ref_pc) + blob_counters(out_blob=out_blob, ref_out_blob=ref_out_blob) + global_accuracy = update_global_accuracy_matrics(global_accuracy=global_accuracy, current_accuracy=a_m) + print_all_over_the_net_metrics(global_times=global_times, ref_global_times=ref_global_times, + global_accuracy=global_accuracy) + + +def two_ir_mode(args): + core = get_plugin(args.device, args.l, args.config) + ref_core = get_plugin(args.reference_device, args.l, args.reference_config) + net = get_net(model=args.model, core=core) + net_layers, net_inputs, net_outputs, precision = get_model_info(net) + ref_net = get_net(model=args.reference_model, core=ref_core) + ref_net_layers, ref_net_inputs, ref_net_outputs, ref_precision = get_model_info(ref_net) + log.info('{}:{} vs {}:{}'.format(args.device, precision, args.reference_device, ref_precision)) + log.info('IR for {} : {}'.format(args.device, args.model)) + log.info('IR for {} : {}'.format(args.reference_device, args.reference_model)) + out_layers = get_layers_list(net_layers, net_inputs, net_outputs, args.layers) + ref_out_layers = get_layers_list(ref_net_layers, ref_net_inputs, ref_net_outputs, args.layers) + print_input_layers(net_inputs) + print_output_layers(out_layers) + layers_map = manage_user_outputs_with_mapping(mapping=args.mapping, reference_mapping=args.reference_mapping, + user_layers=out_layers) + inputs = input_processing(model_path=args.model, net_inputs=net_inputs, input_file=args.input, + layers_map=layers_map) + ref_inputs = input_processing(model_path=args.reference_model, net_inputs=ref_net_inputs, input_file=args.input, + layers_map=layers_map) + global_accuracy = [] + global_times, ref_global_times = overall_accuracy_check(model=args.model, ref_model=args.reference_model, + out_layers=out_layers, ref_out_layers=ref_out_layers, + inputs=inputs, ref_inputs=ref_inputs, plugin=core, + ref_plugin=ref_core, layers=args.layers, + num_of_iterations=args.num_of_iterations) + for out_layer in layers_map: + ref_out_layer = layers_map[out_layer] + if out_layer == ref_out_layer: + log.info('Layer {} statistics'.format(out_layer)) + else: + log.info('Statistics \'{}\' vs \'{}\''.format(out_layer, ref_out_layer)) + net_copy = get_net_copy_with_output(model=args.model, output=out_layer, core=core) + ref_net_copy = get_net_copy_with_output(model=args.reference_model, output=ref_out_layer, core=ref_core) + results = infer(net=net_copy, core=core, device=args.device, inputs=inputs, output=[out_layer]) + if out_layer not in results: + continue + out_blob, pc = results[out_layer] + ref_results = infer(net=ref_net_copy, core=ref_core, device=args.reference_device, inputs=ref_inputs, output=[ref_out_layer]) + ref_out_blob, ref_pc = ref_results[ref_out_layer] + if ref_out_layer not in ref_results: + continue + a_m = accuracy_metrics(out_blob=out_blob, ref_out_blob=ref_out_blob) + performance_metrics(pc=pc, ref_pc=ref_pc) + blob_counters(out_blob=out_blob, ref_out_blob=ref_out_blob) + global_accuracy = update_global_accuracy_matrics(global_accuracy=global_accuracy, current_accuracy=a_m) + print_all_over_the_net_metrics(global_times=global_times, ref_global_times=ref_global_times, + global_accuracy=global_accuracy) + + +def dump_mode(args): + core = get_plugin(args.device, args.l, args.config) + net = get_net(model=args.model, core=core) + out_layers = get_layers_list(net.layers, net.inputs, net.outputs, args.layers) + inputs = input_processing(args.model, net.inputs, args.input) + dump_dict = {} + for out_layer in out_layers: + log.info('Layer {} processing'.format(out_layer)) + net_copy = get_net_copy_with_output(model=args.model, output=out_layer, core=core) + results = infer(net=net_copy, core=core, device=args.device, inputs=inputs, output=[out_layer]) + if out_layer not in results: + continue + out_blob, pc = results[out_layer] + dump_dict[out_layer] = np.array({'blob': out_blob, 'pc': pc}) + dump_output_file(args.model + '_' + args.device + '_dump.npz', dump_dict) + + +def load_mode(args): + core = get_plugin(args.device, args.l, args.config) + log.info('IR for {} : {}'.format(args.device, args.model)) + log.info('Loading blob from {}'.format(args.load)) + net = get_net(model=args.model, core=core) + net_layers, net_inputs, net_outputs, precision = get_model_info(net) + out_layers = get_layers_list(net_layers, net_inputs, net_outputs, args.layers) + print_input_layers(net_inputs) + print_output_layers(out_layers) + layers_map = manage_user_outputs_with_mapping(mapping=args.mapping, reference_mapping=args.reference_mapping, + user_layers=out_layers) + inputs = input_processing(args.model, net_inputs, args.input, layers_map) + global_accuracy = [] + loaded = load_dump(args.load) + for out_layer in layers_map: + ref_out_layer = layers_map[out_layer] + if out_layer == ref_out_layer: + log.info('Layer {} statistics'.format(out_layer)) + else: + log.info('Statistics \'{}\' vs \'{}\''.format(out_layer, ref_out_layer)) + net_copy = get_net_copy_with_output(model=args.model, output=out_layer, core=core) + results = infer(net=net_copy, core=core, device=args.device, inputs=inputs, output=[out_layer]) + if out_layer not in results: + continue + out_blob, pc = results[out_layer] + if ref_out_layer not in loaded: + continue + ref_out_blob = loaded[ref_out_layer]['blob'] + a_m = accuracy_metrics(out_blob=out_blob, ref_out_blob=ref_out_blob) + if 'pc' in loaded[ref_out_layer]: + ref_pc = loaded[ref_out_layer]['pc'] + performance_metrics(pc=pc, ref_pc=ref_pc) + blob_counters(out_blob=out_blob, ref_out_blob=ref_out_blob) + global_accuracy = update_global_accuracy_matrics(global_accuracy=global_accuracy, current_accuracy=a_m) + print_all_over_the_net_metrics(global_accuracy=global_accuracy) + + +def main(args): + log.info('Inference Engine:\n API version ............ {}'.format(ie.__version__), extra={'no_lvl': True}) + set_verbosity(args.verbosity) + mode = find_out_cct_mode(args) + if mode == 1: + log.info('Cross check with one IR was enabled') + one_ir_mode(args) + elif mode == 2: + log.info('Cross check with two IRs was enabled') + two_ir_mode(args) + elif mode == 3: + log.info('Dump mode was enabled') + dump_mode(args) + elif mode == 4: + log.info('Load mode was enabled') + load_mode(args) + log.info("Execution successful") + + +if __name__ == '__main__': + set_logger(log.DEBUG) + main(validate_args(build_parser().parse_args())) diff --git a/inference-engine/tools/cross_check_tool/requirements.txt b/inference-engine/tools/cross_check_tool/requirements.txt new file mode 100644 index 00000000000..fc4586c8b30 --- /dev/null +++ b/inference-engine/tools/cross_check_tool/requirements.txt @@ -0,0 +1,2 @@ +numpy +opencv-python \ No newline at end of file diff --git a/inference-engine/tools/cross_check_tool/utils.py b/inference-engine/tools/cross_check_tool/utils.py new file mode 100644 index 00000000000..06de440e927 --- /dev/null +++ b/inference-engine/tools/cross_check_tool/utils.py @@ -0,0 +1,536 @@ +# Copyright (C) 2018-2019 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 +# + +import argparse +import logging as log +import os +import sys +import traceback +import xml + +try: + import cv2 +except Exception as e: + log.error("Can not import OpenCV Python package.\nPlease install required python packages by running:\n" + "pip3 install -r requirements.txt\n\n Original error message: {}".format(e)) + sys.exit(1) + +try: + import numpy as np +except Exception as e: + log.error("Can not import numpy python package.\nPlease install required python packages by running:\n" + "pip3 install -r requirements.txt\n\n Original error message: {}".format(e)) + sys.exit(1) + +verbosity = False + + +def set_verbosity(flag: bool): + global verbosity + verbosity = flag + + +### +# USER INTERACTION +### + + +class LvlFormatter(log.Formatter): + usual = '[ %(levelname)s ] %(msg)s' + format_dict = { + 'no_lvl': '%(msg)s', + log.DEBUG: '[ %(asctime)s ] [ %(levelname)s ] [ %(module)s:%(lineno)d ] %(msg)s', + log.INFO: usual, log.WARNING: usual, log.ERROR: usual, log.CRITICAL: usual + } + + def __init__(self, lvl, fmt=None): + log.Formatter.__init__(self, fmt) + self.lvl = lvl + + def format(self, record: log.LogRecord): + if self.lvl == 'DEBUG': + self._style._fmt = self.format_dict[log.DEBUG] + else: + self._style._fmt = self.format_dict[record.levelno] + if 'no_lvl' in record.__dict__.keys() and record.__dict__['no_lvl']: + self._style._fmt = self.format_dict['no_lvl'] + return log.Formatter.format(self, record) + + +def set_logger(lvl: str): + logger = log.getLogger() + logger.setLevel(lvl) + handler = log.StreamHandler(sys.stdout) + handler.setFormatter(LvlFormatter(lvl)) + logger.addHandler(handler) + + +def error_handling(desc: str): + """ + Error handler that prints description formatted with keyword arguments in case of exception + :param desc: description for an error + :return: decorator + """ + + def decorator(func): + def try_except_func(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + exception_type = type(e).__name__ + log.error("The following error happened while {}:\n[ {} ] {}".format(desc.format(**kwargs), + exception_type, e)) + global verbosity + if verbosity: + traceback.print_tb(tb=e.__traceback__, file=sys.stdout) + sys.exit(1) + + return try_except_func + + return decorator + + +class ExistingFileAction(argparse.Action): + """ + Expand user home directory paths and convert relative-paths to absolute. + """ + + def __call__(self, parser, namespace, values, option_string=None): + if values is not None: + if not os.path.isfile(values): + log.error("File was not found: {}".format(values)) + sys.exit(1) + setattr(namespace, self.dest, values) + + +class ExistingDirAction(argparse.Action): + """ + Expand user home directory paths and convert relative-paths to absolute. + """ + + def __call__(self, parser, namespace, values, option_string=None): + if values is not None: + if not os.path.isdir(values): + log.error("Directory was not found: {}".format(values)) + sys.exit(1) + setattr(namespace, self.dest, values) + + +def build_parser(): + parser = argparse.ArgumentParser( + prog='Cross Check Tool', + description='Cross Check Tool is a console application that enables comparing accuracy and provides performance' + ' metrics', + usage='\n' + '-' * 62 + + '\nFor cross precision check provide two IRs \n' + '(mapping files may be needed) run:' + '\npython3 cross_check_tool.py \\' + '\n--input path/to/file/describing/input \\' + '\n--model path/to/model/*.xml \\' + '\n--device device_for_model \\' + '\n--reference_model path/to/reference_model/*.xml \\' + '\n--reference_device reference_device_for_model \n' + + '-' * 62 + + '\nFor cross device check with one precision provide one IR run:' + '\npython3 cross_check_tool.py \\' + '\n--input path/to/file/describing/input \\' + '\n--model path/to/model/*.xml \\' + '\n--device device_for_model \\' + '\n--reference_device reference_device_for_model \n' + + '-' * 62 + + '\nFor dumping blob and performance counters run:' + '\npython3 cross_check_tool.py \\' + '\n--input path/to/file/describing/input \\' + '\n--model path/to/model/*.xml \\' + '\n--device device_for_model \\' + '\n--dump\n' + + '-' * 62 + + '\nFor check inference against dumped results run:' + '\npython3 cross_check_tool.py \\' + '\n--input path/to/file/describing/input \\' + '\n--model path/to/model/*.xml \\' + '\n--device device_for_model \\' + '\n--load path/to/dump/file/* \n' + + '-' * 62 + + '\nFor all layers check provide:\n' + '--layers=\'all\' \n' + 'For specific number of layers check provide:\n' + '--layers=\'layer_name,another_layer_name,...,last_layer_name\'\n' + + '-' * 62 + + '\nIf --input is empty CCT generates input(s) from normal\n' + 'distribution and dumps this input to a file\n' + + '-' * 62 + ) + + model = parser.add_argument_group('Model specific arguments') + model.add_argument('--input', '-i', type=str, action=ExistingFileAction, + help='Path to an input image file or multi-input file to infer. Generates input(s) from normal ' + 'distribution if empty') + # model.add_argument('--batch', '-b', type=int, help='Overrides batch size. Default is inherited from model') + model.add_argument('--model', '-m', type=str, action=ExistingFileAction, + help='Path to an .xml file that represents the first IR of the trained model to infer.') + model.add_argument('--reference_model', '-ref_m', type=str, action=ExistingFileAction, + help='Path to an .xml file that represents the second IR to compare the metrics. ' + 'Uses --model if empty') + model.add_argument('--layers', '-layers', type=str, default=None, + help='Defines layers to check. Options: all, None - for output layers check, list of ' + 'comma-separated layer names to check. Default value is None.') + model.add_argument('--mapping', '-map', type=str, action=ExistingFileAction, + help='Model Optimizer provided mapping for --model/-m') + model.add_argument('--reference_mapping', '-ref_map', type=str, action=ExistingFileAction, + help='Model Optimizer provided mapping for --reference_model/-ref_model') + + plugin = parser.add_argument_group('Plugin specific arguments') + plugin.add_argument('--plugin_path', '-pp', type=str, action=ExistingDirAction, help='Path to a plugin folder.') + plugin.add_argument('--device', '-d', type=str, required=True, + help='The first target device to infer the model specified with the -m or --model option. ' + 'CPU, GPU, HDDL or MYRIAD are acceptable.') + plugin.add_argument('--config', '-conf', type=str, action=ExistingFileAction, + help='Path to config file for -d or -device device plugin') + plugin.add_argument('--reference_device', '-ref_d', type=str, + help='The second target device to infer the model and compare the metrics. ' + 'CPU, GPU, HDDL or MYRIAD are acceptable.') + plugin.add_argument('--reference_config', '-ref_conf', type=str, action=ExistingFileAction, + help='Path to config file for -ref_d or -reference_device device plugin') + plugin.add_argument('-l', type=str, action=ExistingFileAction, + help='Required for MKLDNN (CPU)-targeted custom layers. Comma separated paths to a shared' + ' libraries with the kernels implementation.') + + modes = parser.add_argument_group('CCT mode arguments') + # TODO eps? nobody uses it + modes.add_argument('--dump', help='Enables blobs statistics dumping', action='store_true', default=False) + modes.add_argument('--load', type=str, action=ExistingFileAction, help='Path to a file to load blobs from') + model.add_argument('--num_of_iterations', '-ni', type=int, default=50, + help='Number of iterations to collect all over the net performance') + parser.add_argument('-v', '--verbosity', action='store_true', default=False, + help='Increase output verbosity') + return parser + + +@error_handling('validating arguments passed to cross_check_tool.py') +def validate_args(args): + # input check + if args.input is None: + log.info('No input was provided by --input/-i. Generate input from noise') + # model check + if args.model is None and args.reference_model is None: + raise Exception( + "Parameters --model/-m and --reference_model/-ref_m are empty. At least one of them is required") + elif args.model is None and args.reference_model: + args.model = args.reference_model + if args.model == args.reference_model: + args.reference_model = None + if args.model != args.reference_model and args.reference_model is not None and args.mapping is None and \ + args.reference_mapping is None: + log.warning('Check over two different IRs was enabled. In case if layer names in this two IRs differ, ' + 'please provide mapping files with --mapping/-map and --reference_mapping/-ref_map') + # device check + if args.device is None and args.reference_device is None: + raise Exception("Parameters -device/-d and -reference_device/-ref_d are not set. Can not proceed." + "\nFor more details use -h option") + if args.reference_device is None and args.reference_model is None and not args.dump and args.load is None: + raise Exception("Please provide --reference_model/-ref_m to compare executions on different devices." + "\nAnother option is to provide --dump key to dump all execution info on one device." + "\nOr provide --load key to compare execution on device with dumped info" + "\nFor more details use -h option") + if args.device is None: + args.device = args.reference_device + args.reference_device = None + # dump and load check + if args.dump and args.load is not None: + raise Exception("Cross Check Tool does not support both loading and dumping modes to be enabled. " + "Choose one of them and proceed") + if args.model is not None and args.reference_model is not None and args.dump or \ + args.device is not None and args.reference_device is not None and args.dump: + raise Exception("Cross Check Tool does support dumping mode to be enabled only for one model on one device" + "\nFor more details use -h option") + if args.model is not None and args.reference_model is not None and args.load is not None or \ + args.device is not None and args.reference_device is not None and args.load is not None: + raise Exception("Cross Check Tool does support loading mode to be enabled for one model on one device against a" + " dumped file\nFor more details use -h option") + return args + + +def find_out_cct_mode(args): + """ + 1 -- one IR mode + 2 -- two IRs mode + 3 -- dump mode + 4 -- load mode + """ + # dump mode + if args.dump and args.model is not None and args.device is not None and \ + args.reference_model is None and args.reference_device is None: + return 3 + # load mode + if args.load is not None and args.model is not None and args.device is not None and args.reference_device is None: + return 4 + # two IR mode + if args.model is not None and args.reference_model is not None: + return 2 + # one IR mode + if args.model is not None and args.reference_model is None: + return 1 + raise Exception('Unknown Cross Check Tool CLI configuration.\nFor more details use -h option') + + +def print_input_layers(inputs: list): + word = 'inputs' if len(inputs) > 1 else 'input' + log.info('{} {} detected: {}'.format(len(inputs), word, ', '.join(inputs))) + + +def print_output_layers(outputs: list): + layers = 'layers' if len(outputs) > 1 else 'layer' + log.info('Statistics will be dumped for {} {}: {}'.format(len(outputs), layers, ', '.join(outputs))) + + +### +# PLUGIN +### + + +@error_handling('parsing config file for plugin: \'{config_file}\'') +def get_config_dictionary(config_file): + config = {'PERF_COUNT': 'YES'} + if not config_file: + return config + with open(config_file) as f: + config_line = f.readline() + key = config_line.split(config_file)[0] + value = config_line[len(key):].strip() + config[key] = value + return config + + +### +# INPUTS +### + + +def read_multi_input_file(input_file: str, net_inputs: dict): + npz = np.load(input_file, allow_pickle=True) + files = npz.files + dump = {} + for net_input in net_inputs: + if net_input not in files: + raise Exception('Can not find input data for input {} in multi-input file {}.\n' + 'Input data was provided for layers: {}\n' + 'Network inputs: {}'.format(net_input, input_file, ', '.join(files), + ', '.join(net_inputs.keys()))) + if 'blob' in npz[net_input].item(0): + just_blob = npz[net_input].item(0)['blob'] + network_shape = net_inputs[net_input].shape + log.info('Layer {} shape = {}, input blob from multi-input file shape = {}' + ''.format(net_input, network_shape, just_blob.shape)) + try: + reshaped_blob = np.reshape(just_blob, network_shape) + except: + raise Exception('Can not reshape input blob from multi-input file for layer {} to shape {}' + ''.format(net_input, network_shape)) + dump[net_input] = reshaped_blob + else: + raise Exception( + 'Can not find \'blob\' parameter for input {} in input file {}'.format(net_input, input_file)) + return dump + + +@error_handling('reading --input/-i by OpenCV python module. OpenCV version: {}. ' + 'It may happen due to wrong input image format'.format(cv2.__version__)) +def read_image_file(input_file: str, net_inputs: dict): + inputs = dict() + if len(net_inputs) == 1: + image = cv2.imread(input_file) + if image is None: + raise Exception('Can not read input image ' + input_file) + only_layer_name = list(net_inputs.keys())[0] + shape = net_inputs[only_layer_name].shape + if len(shape) != 4: + raise Exception('Can not interpret input shape as image') + n, c, h, w = shape + image = cv2.resize(image, (w, h)) + image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW + image = image.reshape((n, c, h, w)) + inputs[only_layer_name] = image + else: + raise Exception('Multi-input topology detected. Please provide multi-input file to --input key') + return inputs + + +def input_processing(model_path: str, net_inputs: dict, input_file: str, layers_map: dict = None): + inputs = dict() + if input_file is None: + for net_input in net_inputs: + inputs[net_input] = np.clip(np.random.normal(0.5, 0.1, size=net_inputs[net_input].shape), 0, 1) + dump_output_file(model_path + '_random_input_dump.npz', {inp: {'blob': inputs[inp]} for inp in inputs}) + return inputs + try: + inputs = read_multi_input_file(input_file=input_file, net_inputs=net_inputs) + except: + inputs = read_image_file(input_file=input_file, net_inputs=net_inputs) + return inputs + + +def accuracy_metrics(out_blob, ref_out_blob): + if out_blob.size != ref_out_blob.size: + raise Exception('Different number of elements in blobs {} and {}. Can not compare' + ''.format(out_blob.size, ref_out_blob.size)) + abs_diff = np.absolute(out_blob - ref_out_blob) + rel_diff = np.divide(abs_diff, np.min(abs_diff) if np.min(abs_diff) != 0 else 1e-20) + + metrics = [ + ('Max absolute difference', np.max(abs_diff)), + ('Min absolute difference', np.min(abs_diff)), + ('Max relative difference', np.max(rel_diff)), + ('Min relative difference', np.min(rel_diff)), + ('Min reference value', np.min(ref_out_blob)), + ('Min absolute reference value', np.min(np.abs(ref_out_blob))), + ('Max reference value', np.max(ref_out_blob)), + ('Max absolute reference value', np.max(np.abs(ref_out_blob))), + ('Min actual value', np.min(out_blob)), + ('Min absolute actual value', np.min(np.abs(out_blob))), + ('Max actual value', np.max(out_blob)), + ('Max absolute actual value', np.max(np.abs(out_blob))) + ] + + for key, value in metrics: + if len(str(value)) > 5: + log.info('{:>35} : {:.5E}'.format(key, value), extra={'no_lvl': True}) + else: + log.info('{:>35} : {}'.format(key, value), extra={'no_lvl': True}) + return {metric: value for metric, value in metrics} + + +def performance_metrics(pc, ref_pc): + compare = [ + ('Device', '-d ' + pc['device'], '-ref_d ' + ref_pc['device']), + ('Status', pc['status'], ref_pc['status']), + ('Layer type', pc['layer_type'], ref_pc['layer_type']), + ('Real time, microsec', pc['real_time'], ref_pc['real_time']) + ] + + for metric, actual, reference in compare: + log.info('{:>35}: {:>16} {:>16}'.format(metric, actual, reference), extra={'no_lvl': True}) + + +def blob_counters(out_blob, ref_out_blob): + counters = [ + ('Number of NAN', np.sum(np.isnan(out_blob)), np.sum(np.isnan(ref_out_blob))), + ('Number of INF', np.sum(np.isinf(out_blob)), np.sum(np.isinf(ref_out_blob))), + ('Number of ZERO', out_blob.size - np.count_nonzero(out_blob), + ref_out_blob.size - np.count_nonzero(ref_out_blob)) + ] + for metric, actual, reference in counters: + log.info('{:>35}: {:>16} {:>16}'.format(metric, actual, reference), extra={'no_lvl': True}) + + +def update_global_accuracy_matrics(global_accuracy: list, current_accuracy: dict): + metrics = [ + ('Max absolute difference', lambda x, y: max(x, y)), + ('Min absolute difference', lambda x, y: min(x, y)), + ('Max relative difference', lambda x, y: max(x, y)), + ('Min relative difference', lambda x, y: min(x, y))] + for metric, formula in metrics: + global_metric = [item for item in global_accuracy if item[0] == metric] + if len(global_metric) == 1: + g_metric, g_value = global_metric[0] + global_accuracy.remove(global_metric[0]) + global_accuracy.append((metric, formula(g_value, current_accuracy[metric]))) + else: + global_accuracy.append((metric, current_accuracy[metric])) + return global_accuracy + + +def print_all_over_the_net_metrics(global_accuracy: (str, float), global_times: list = None, + ref_global_times: list = None): + if global_times is not None and ref_global_times is not None and len(global_times) and len(ref_global_times): + log.info('-' * 70, extra={'no_lvl': True}) + log.info('{:>35}: {:>16,.5E} {:>16,.5E}'.format( + 'Overall performance, microseconds', global_times[len(global_times) // 2].microseconds, + ref_global_times[len(ref_global_times) // 2].microseconds), extra={'no_lvl': True}) + log.info('-' * 70, extra={'no_lvl': True}) + for metric, value in global_accuracy: + log.info('{} {} = {}'.format('Overall', metric.lower(), value)) + + +### +# MAPPING +### + + +def read_mapping(file_name: str): + # TODO check twice + mapping_dict = {} + xml_tree = xml.etree.ElementTree.parse(file_name) + xml_root = xml_tree.getroot() + for child in xml_root: + fw_info = child.find('.//framework') + ir_info = child.find('.//IR') + if fw_info is None: + continue + if ir_info is None: + continue + framework_name = fw_info.attrib['name'] + ':' + fw_info.attrib['out_port_id'] + ir_name = ir_info.attrib['name'] if ir_info is not None else None + ir_layer_id = int(ir_info.attrib['id']) if ir_info is not None else None + mapping_dict[framework_name] = (ir_name, ir_layer_id) + return mapping_dict + + +def map_layers(mapping_file: str = None, ref_mapping_file: str = None): + if mapping_file is not None and ref_mapping_file is not None: + mapping = read_mapping(mapping_file) + ref_mapping = read_mapping(ref_mapping_file) + mapping = {layer: ref_layer for layer in mapping for ref_layer in ref_mapping if layer == ref_layer} + return mapping + + +def manage_user_outputs_with_mapping(mapping, reference_mapping, user_layers): + if mapping is not None and reference_mapping is not None: + layers_map = map_layers(mapping, reference_mapping) + else: + layers_map = {layer: layer for layer in user_layers} + for layer in user_layers: + if layer not in layers_map: + if mapping is not None and reference_mapping is not None: + log.warning( + 'Can not map layer {} from --model/-m to any layer from --reference_model/-ref_m'.format(layer)) + else: + log.warning('Can not find layer {} in --reference_model/-ref_m model'.format(layer)) + for layer in layers_map: + if layer not in user_layers: + del layers_map[layer] + return layers_map + + +def get_layers_list(all_layers: dict, inputs: dict, outputs: list, layers: str): + if layers is not None and layers != 'None': + if layers == 'all': + return {name: layer for name, layer in all_layers.items() if layer.type not in ['Const']} + else: + user_layers = [layer.strip() for layer in layers.split(',')] + layers_to_check = [] + for user_layer in user_layers: + if user_layer not in all_layers: + raise Exception("Layer {} doesn't exist in the model".format(user_layer)) + if user_layer in inputs: + raise Exception("Layer {} is input layer. Can not proceed".format(user_layer)) + layers_to_check.append(user_layer) + return layers_to_check + else: + return outputs + + +### +# FILES +### + +def dump_output_file(output_file, dump_dict): + np.savez_compressed(output_file, **dump_dict) + log.info('Dump file path: {}'.format(output_file)) + + +def load_dump(file_to_load: str): + npz = np.load(file_to_load, allow_pickle=True) + dump = {file: npz[file].item(0) for file in npz} + return dump diff --git a/inference-engine/tools/package_BOM.txt b/inference-engine/tools/package_BOM.txt new file mode 100644 index 00000000000..a16cdeb53f8 --- /dev/null +++ b/inference-engine/tools/package_BOM.txt @@ -0,0 +1,8 @@ +benchmark_tool/benchmark_app.py +benchmark_tool/requirements.txt +benchmark_tool/README.md +cross_check_tool/__init__.py +cross_check_tool/utils.py +cross_check_tool/requirements.txt +cross_check_tool/README.md +cross_check_tool/cross_check_tool.py \ No newline at end of file diff --git a/model-optimizer/automation/package_BOM.txt b/model-optimizer/automation/package_BOM.txt index d0797b34ba0..9b00a3f29e0 100644 --- a/model-optimizer/automation/package_BOM.txt +++ b/model-optimizer/automation/package_BOM.txt @@ -13,6 +13,7 @@ extensions/back/AvgPool.py extensions/back/blob_normalizer.py extensions/back/compress_quantized_weights.py extensions/back/ConvolutionNormalizer.py +extensions/back/CorrectName.py extensions/back/CropToStridedSlice.py extensions/back/CutMemory.py extensions/back/disable_unsupported_ND_operations.py @@ -133,6 +134,7 @@ extensions/front/FillToBroadcast.py extensions/front/flatten_to_reshape.py extensions/front/freeze_placeholder_value.py extensions/front/GeLUMerger_Erf.py +extensions/front/GeLUMerger_Tanh.py extensions/front/global_pooling_to_reduce.py extensions/front/image_scaler.py extensions/front/input_cut.py @@ -299,6 +301,7 @@ extensions/front/onnx/scatter_ext.py extensions/front/onnx/shape_ext.py extensions/front/onnx/slice_ext.py extensions/front/onnx/softmax_ext.py +extensions/front/onnx/softmaxONNX_to_softmax.py extensions/front/onnx/split_ext.py extensions/front/onnx/squeeze_ext.py extensions/front/onnx/top_k_ext.py @@ -549,6 +552,7 @@ extensions/middle/RNNSequenceNormalizeToIE.py extensions/middle/ScaleInput.py extensions/middle/SharedWeightsDuplication.py extensions/middle/SliceConverter.py +extensions/middle/SliceLikeToStridedSlice.py extensions/middle/space_to_depth.py extensions/middle/sparse_reshape.py extensions/middle/ssd_anchors_to_const.py @@ -566,7 +570,6 @@ extensions/middle/UnsqueezeTileReshapeBlockToInterpolate.py extensions/middle/UpsampleToResample.py extensions/middle/UselessMerge.py extensions/middle/UselessSplitEraser.py -extensions/middle/UselessStridedSlice.py extensions/middle/wights_permute_normalizer.py extensions/ops/__init__.py extensions/ops/accum.py @@ -594,7 +597,6 @@ extensions/ops/elementwise.py extensions/ops/embedding_bag.py extensions/ops/Enter.py extensions/ops/Exit.py -extensions/ops/exp.py extensions/ops/fakequantize.py extensions/ops/gather.py extensions/ops/GatherNd.py @@ -608,7 +610,6 @@ extensions/ops/identity.py extensions/ops/instance_normalization.py extensions/ops/interp.py extensions/ops/interpolate.py -extensions/ops/Log.py extensions/ops/LSTM.py extensions/ops/lstm_cell.py extensions/ops/lstm_sequence.py @@ -656,6 +657,7 @@ extensions/ops/select.py extensions/ops/shufflechannel.py extensions/ops/simplernms.py extensions/ops/size.py +extensions/ops/slice_like.py extensions/ops/space_to_depth.py extensions/ops/sparse_fill_empty_rows.py extensions/ops/sparse_reshape.py @@ -695,6 +697,9 @@ install_prerequisites/install_prerequisites_onnx.bat install_prerequisites/install_prerequisites_onnx.sh install_prerequisites/install_prerequisites_tf.bat install_prerequisites/install_prerequisites_tf.sh +install_prerequisites/protobuf-3.6.1-py3.5-win-amd64.egg +install_prerequisites/protobuf-3.6.1-py3.6-win-amd64.egg +install_prerequisites/protobuf-3.6.1-py3.7-win-amd64.egg mo.py mo/__init__.py mo/back/__init__.py @@ -898,12 +903,14 @@ mo/pipeline/__init__.py mo/pipeline/common.py mo/pipeline/unified.py mo/utils/__init__.py +mo/utils/broadcasting.py mo/utils/class_registration.py mo/utils/cli_parser.py mo/utils/custom_replacement_config.py mo/utils/dsu.py mo/utils/error.py mo/utils/find_inputs.py +mo/utils/get_ov_update_message.py mo/utils/graph.py mo/utils/guess_framework.py mo/utils/import_extensions.py @@ -922,6 +929,7 @@ mo/utils/ir_reader/extenders/fakequantize_extender.py mo/utils/ir_reader/extenders/GRUCell_extender.py mo/utils/ir_reader/extenders/interpolate_extender.py mo/utils/ir_reader/extenders/LSTMCell_extender.py +mo/utils/ir_reader/extenders/non_max_suppression_extender.py mo/utils/ir_reader/extenders/non_zero_extender.py mo/utils/ir_reader/extenders/pad_extender.py mo/utils/ir_reader/extenders/parameter_extender.py @@ -930,6 +938,7 @@ mo/utils/ir_reader/extenders/priorbox_clustered_extender.py mo/utils/ir_reader/extenders/priorbox_extender.py mo/utils/ir_reader/extenders/reorg_yolo_extender.py mo/utils/ir_reader/extenders/RNNCell_extender.py +mo/utils/ir_reader/extenders/shape_of_extender.py mo/utils/ir_reader/extenders/strided_slice_extender.py mo/utils/ir_reader/extenders/tensoriterator_extender.py mo/utils/ir_reader/extenders/topk_extender.py diff --git a/model-optimizer/extensions/back/CorrectName.py b/model-optimizer/extensions/back/CorrectName.py new file mode 100644 index 00000000000..1d1e9c0dd52 --- /dev/null +++ b/model-optimizer/extensions/back/CorrectName.py @@ -0,0 +1,47 @@ +""" + Copyright (C) 2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from mo.graph.graph import Graph +from mo.back.replacement import BackReplacementPattern + + +class RestoreOriginalFrameworkName(BackReplacementPattern): + """ + This transformation corrects names of layers to their framework names. + To perform this correction, framework layer name should be in the attribute 'framework_node_name'. + In some cases, renaming is necessary only if some condition is fulfilled. Such condition should be a some + function in the attribute 'rename_condition'. + + For example, in the transformation SoftmaxONNXFrontReplacer such condition is + lambda n: len(n.graph.get_op_nodes(name=node_name)) == 0 + """ + + enabled = True + + def find_and_replace_pattern(self, graph: Graph): + for node in graph.get_op_nodes(): + if not node.has_valid('framework_node_name'): + continue + + if node.has_valid('rename_condition'): + need_renaming = node['rename_condition'](node) + del node['rename_condition'] + if need_renaming: + node.name = node['framework_node_name'] + else: + node.name = node['framework_node_name'] + + del node['framework_node_name'] diff --git a/model-optimizer/extensions/back/NormalizeToNormalizeL2.py b/model-optimizer/extensions/back/NormalizeToNormalizeL2.py index 3c04307f0e0..97acff24931 100644 --- a/model-optimizer/extensions/back/NormalizeToNormalizeL2.py +++ b/model-optimizer/extensions/back/NormalizeToNormalizeL2.py @@ -18,9 +18,11 @@ import numpy as np from extensions.back.ElementwiseOpsToEltwiseOps import SimpleEltwiseToEltwiseOp from extensions.back.insert_compatibility_l2normalization import CompatibilityL2NormalizationPattern from extensions.ops.elementwise import Mul +from extensions.ops.normalize_l2 import NormalizeL2Op from mo.back.replacement import BackReplacementPattern +from mo.front.common.partial_infer.utils import int64_array +from mo.front.tf.graph_utils import create_op_with_const_inputs from mo.graph.graph import Graph, rename_node -from mo.ops.const import Const class NormalizeToNormalizeL2(BackReplacementPattern): @@ -72,19 +74,14 @@ class NormalizeToNormalizeL2(BackReplacementPattern): mul = Mul(graph, {'name': output_name}).create_node() rename_node(mul, output_name) + if not node.across_spatial: + axes = int64_array([1]) + else: + axes = int64_array(np.arange(start=1, stop=node.in_port(0).data.get_shape().size)) + + normalizel2 = create_op_with_const_inputs(graph, NormalizeL2Op, {1: axes}, {'eps_mode': 'add', 'eps': node.eps}) + node.out_port(0).get_connection().set_source(mul.out_port(0)) - node.out_port(0).connect(mul.in_port(0)) node.in_port(1).get_connection().get_source().connect(mul.in_port(1)) - node.in_port(1).disconnect() - - node['type'] = 'NormalizeL2' - node['eps_mode'] = 'add' - node['force_precision_in_ports'] = {1: 'int64'} - - axes_val = np.array([1]) if not node.across_spatial else \ - np.arange(start=1, stop=node.in_port(0).data.get_shape().size) - axes = Const(graph, {'value': axes_val}).create_node() - node.in_port(1).connect(axes.out_port(0)) - - del node['across_spatial'] - del node['channel_shared'] + normalizel2.out_port(0).connect(mul.in_port(0)) + node.in_port(0).get_connection().set_destination(normalizel2.in_port(0)) diff --git a/model-optimizer/extensions/front/ChangeCastOutputType.py b/model-optimizer/extensions/front/ChangeCastOutputType.py index d633a595970..7dcbeaa0916 100644 --- a/model-optimizer/extensions/front/ChangeCastOutputType.py +++ b/model-optimizer/extensions/front/ChangeCastOutputType.py @@ -26,7 +26,6 @@ from mo.middle.passes.convert_data_type import data_type_str_to_np class ChangeCastOutputType(FrontReplacementSubgraph): """ - Change the Cast to int64 to int32 since not all plugins support int64 data type. Change the Cast to fp64 to fp32 since not all plugins support fp64 data type. Change the Cast to fp32 to fp16 when generating IR for fp16. """ @@ -42,10 +41,6 @@ class ChangeCastOutputType(FrontReplacementSubgraph): def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]): node = match['cast'] - if node.dst_type == np.int64: - log.warning('Change data type from {} to {} for node {}'.format(node.dst_type, np.int32, node.name)) - node.dst_type = np.int32 - if node.dst_type == np.float64: log.warning('Change data type from {} to {} for node {}'.format(node.dst_type, np.float32, node.name)) node.dst_type = np.float32 diff --git a/model-optimizer/extensions/front/GeLUMerger_Erf.py b/model-optimizer/extensions/front/GeLUMerger_Erf.py index 765c431e4d4..a212402439a 100644 --- a/model-optimizer/extensions/front/GeLUMerger_Erf.py +++ b/model-optimizer/extensions/front/GeLUMerger_Erf.py @@ -15,6 +15,7 @@ """ import logging as log + from math import sqrt, fabs from extensions.ops.gelu import GeLUOP @@ -35,50 +36,43 @@ class GeLUMergerErf(FrontReplacementSubgraph): ('div', dict(op='Div')), ('erf', dict(op='Erf')), ('add', dict(op='Add')), + ('mul_param', dict(op='Const')), + ('div_param', dict(op='Const')), + ('add_param', dict(op='Const')), + ], edges=[ ('mul', 'mul0'), ('div', 'erf'), ('erf', 'add'), - ('add', 'mul0') + ('add', 'mul0'), + ('mul_param', 'mul'), + ('div_param', 'div'), + ('add_param', 'add'), ]) def replace_sub_graph(self, graph: Graph, match: dict): # Gaussian Error Linear Unit # f(x) = 0.5 * x * (1 + erf(x / sqrt(2)) - add = match['add'] - mul = match['mul'] div = match['div'] inp_port = div.in_port(0).get_source() inp = inp_port.node log.debug('Found potential Erf-based GeLU pattern after {} with name {}'.format(inp.op, inp.name)) # take the values of the mul, add and div - div_param = None - mul_param = None - add_param = None - if div.in_port(0).get_source().node.soft_get('type') == 'Const': - div_param = div.in_port(0).get_source().node.value - elif div.in_port(1).get_source().node.soft_get('type') == 'Const': - div_param = div.in_port(1).get_source().node.value + div_param = match['div_param'] + add_param = match['add_param'] + mul_param = match['mul_param'] - if mul.in_port(0).get_source().node.soft_get('type') == 'Const': - mul_param = mul.in_port(0).get_source().node.value - elif mul.in_port(1).get_source().node.soft_get('type') == 'Const': - mul_param = mul.in_port(1).get_source().node.value + if add_param.value.size == 1 and mul_param.value.size == 1 and div_param.value.size == 1: + mul_param = match['mul_param'].value.item() + add_param = match['add_param'].value.item() + div_param = match['div_param'].value.item() - if add.in_port(0).get_source().node.soft_get('type') == 'Const': - add_param = add.in_port(0).get_source().node.value - elif add.in_port(1).get_source().node.soft_get('type') == 'Const': - add_param = add.in_port(1).get_source().node.value - - if mul_param is None or div_param is None or add_param is None: - return - - sqrt2 = sqrt(2.0) - # check that the values match the approximation - if fabs(div_param - sqrt2) < 1e-06 and mul_param == 0.5 and add_param == 1.0: - log.debug('Confirmed Erf-based GELU pattern after {} with name {}'.format(inp.op, inp.name)) - gelu = GeLUOP(graph, dict(name=inp.name + '/GELU_')).create_node() - inp_port.connect(gelu.in_port(0)) - match['mul0'].out_port(0).get_connection().set_source(gelu.out_port(0)) + sqrt2 = sqrt(2.0) + # check that the values match the approximation + if fabs(div_param - sqrt2) < 1e-06 and mul_param == 0.5 and add_param == 1.0: + log.debug('Confirmed Erf-based GELU pattern after {} with name {}'.format(inp.op, inp.name)) + gelu = GeLUOP(graph, dict(name=inp.name + '/GELU_')).create_node() + inp_port.connect(gelu.in_port(0)) + match['mul0'].out_port(0).get_connection().set_source(gelu.out_port(0)) diff --git a/model-optimizer/extensions/front/GeLUMerger_Tanh.py b/model-optimizer/extensions/front/GeLUMerger_Tanh.py new file mode 100644 index 00000000000..22ced0a619b --- /dev/null +++ b/model-optimizer/extensions/front/GeLUMerger_Tanh.py @@ -0,0 +1,80 @@ +""" + Copyright (C) 2017-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import logging as log + +from math import sqrt, fabs, pi + +from extensions.ops.gelu import GeLUOP +from mo.front.common.replacement import FrontReplacementSubgraph +from mo.graph.graph import Graph + + +class GeLUMergerTanh(FrontReplacementSubgraph): + enabled = True + graph_condition = [lambda graph: graph.graph['cmd_params'].generate_experimental_IR_V10] + + def pattern(self): + log.info('Enabled GeLU Merger for approximation with TanH') + return dict( + nodes=[ + ('pow', dict(op='Pow')), + ('mul', dict(op='Mul')), + ('mul0', dict(op='Mul')), + ('mul1', dict(op='Mul')), + ('mul2', dict(op='Mul')), + ('tanh', dict(op='Tanh')), + ('add', dict(op='Add')), + ('add0', dict(op='Add')), + ('mul_param', dict(op='Const')), + ('mul0_param', dict(op='Const')), + ('mul1_param', dict(op='Const')), + ], + edges=[ + ('pow', 'mul'), + ('mul', 'add'), + ('add', 'mul0'), + ('mul0', 'tanh'), + ('tanh', 'add0'), + ('add0', 'mul1'), + ('mul1', 'mul2'), + ('mul_param', 'mul'), + ('mul0_param', 'mul0'), + ('mul1_param', 'mul1'), + ]) + + def replace_sub_graph(self, graph: Graph, match: dict): + # Gaussian Error Linear Unit, TanH based approximation: + # 0.5*x*(1 + tanh([sqrt(2/pi)]*[x + 0.044715x3]) + inp_port = match['pow'].in_port(0).get_source() + inp = inp_port.node + log.debug('Found potential TanH-based GeLU pattern after {} with name {}'.format(inp.op, inp.name)) + + # take the values of the mul ops + mul_param = match['mul_param'] + mul0_param = match['mul0_param'] + mul1_param = match['mul1_param'] + if mul0_param.value.size == 1 and mul_param.value.size == 1 and mul1_param.value.size == 1: + mul_param = match['mul_param'].value.item() + mul0_param = match['mul0_param'].value.item() + mul1_param = match['mul1_param'].value.item() + sqrt2pi = sqrt(2.0/pi) + # check that the values match the approximation + if fabs(mul0_param - sqrt2pi) < 1e-06 and fabs(mul_param - 0.044715) < 1e-06 and mul1_param == 0.5: + log.debug('Confirmed TanH-based GELU pattern after {} with name {}'.format(inp.op, inp.name)) + gelu = GeLUOP(graph, dict(name=inp.name + '/GELU_')).create_node() + inp_port.connect(gelu.in_port(0)) + match['mul2'].out_port(0).get_connection().set_source(gelu.out_port(0)) diff --git a/model-optimizer/extensions/front/GeLUMerger_test.py b/model-optimizer/extensions/front/GeLUMerger_test.py new file mode 100644 index 00000000000..6c6b698918b --- /dev/null +++ b/model-optimizer/extensions/front/GeLUMerger_test.py @@ -0,0 +1,125 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import unittest + +import numpy as np +from math import sqrt + +from extensions.front.GeLUMerger_Erf import GeLUMergerErf +from extensions.front.GeLUMerger_Tanh import GeLUMergerTanh +from mo.utils.ir_engine.compare_graphs import compare_graphs +from mo.utils.unittest.graph import build_graph + +nodes_attributes_erf = { + 'inp': {'kind': 'op', 'op': 'AnyOp'}, + 'mul': {'kind': 'op', 'op': 'Mul'}, + 'mul0': {'kind': 'op', 'op': 'Mul'}, + 'div': {'kind': 'op', 'op': 'Div'}, + 'erf': {'kind': 'op', 'op': 'Erf'}, + 'add': {'kind': 'op', 'op': 'Add'}, + 'mul_param': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, + 'div_param': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, + 'add_param': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, + 'out': {'kind': 'op', 'op': 'AnyOp'}, +} + +nodes_attributes_tanh = { + 'inp': {'kind': 'op', 'op': 'AnyOp'}, + 'pow': {'kind': 'op', 'op': 'Pow'}, + 'mul': {'kind': 'op', 'op': 'Mul'}, + 'mul0': {'kind': 'op', 'op': 'Mul'}, + 'mul1': {'kind': 'op', 'op': 'Mul'}, + 'mul2': {'kind': 'op', 'op': 'Mul'}, + 'tanh': {'kind': 'op', 'op': 'Tanh'}, + 'add': {'kind': 'op', 'op': 'Add'}, + 'add0': {'kind': 'op', 'op': 'Add'}, + 'mul_param': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, + 'mul0_param': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, + 'mul1_param': {'kind': 'op', 'type': 'Const', 'op': 'Const'}, + 'out': {'kind': 'op', 'op': 'AnyOp'}, +} + +nodes_attributes_ref = { + 'inp': {'kind': 'op', 'op': 'AnyOp'}, + 'gelu': {'kind': 'op', 'op': 'Gelu'}, + 'out': {'kind': 'op', 'op': 'AnyOp'}, +} + +class TestGeLUMergerReplacement(unittest.TestCase): + def test_GeLUMergerErf_test_1(self): + graph = build_graph(nodes_attributes_erf, + [('inp', 'mul0', {'out': 0}), + ('inp', 'div', {'out': 0}), + ('mul', 'mul0'), + ('div', 'erf'), + ('erf', 'add'), + ('add', 'mul0'), + ('mul_param', 'mul'), + ('div_param', 'div'), + ('add_param', 'add'), + ('mul0', 'out'), + ], + {'mul_param': {'shape': np.array([1]), 'value': np.array(0.5)}, + 'add_param': {'shape': np.array([1]), 'value': np.array(1.0)}, + 'div_param': {'shape': np.array([1]), 'value': np.array(sqrt(2.0))} + }, + nodes_with_edges_only=True) + graph_ref = build_graph(nodes_attributes_ref, + [('inp', 'gelu'), + ('gelu', 'out')], + {}, nodes_with_edges_only=True) + graph.stage = 'front' + + replacer = GeLUMergerErf() + replacer.find_and_replace_pattern(graph) + + (flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True) + self.assertTrue(flag, resp) + + def test_GeLUMergerTanh_test_2(self): + graph = build_graph(nodes_attributes_tanh, + [('inp', 'mul2', {'out': 0}), + ('inp', 'add', {'out': 0}), + ('inp', 'pow', {'out': 0}), + ('pow', 'mul'), + ('mul', 'add'), + ('add', 'mul0'), + ('mul0', 'tanh'), + ('tanh', 'add0'), + ('add0', 'mul1'), + ('mul1', 'mul2'), + ('mul_param', 'mul'), + ('mul0_param', 'mul0'), + ('mul1_param', 'mul1'), + ('mul2', 'out'), + ], + {'mul0_param': {'shape': np.array([1]), 'value': np.array(sqrt(2.0/3.1415926))}, + 'mul1_param': {'shape': np.array([1]), 'value': np.array(0.5)}, + 'mul_param': {'shape': np.array([1]), 'value': np.array(0.044715)} + }, + nodes_with_edges_only=True) + graph_ref = build_graph(nodes_attributes_ref, + [('inp', 'gelu'), + ('gelu', 'out')], + {}, nodes_with_edges_only=True) + graph.stage = 'front' + + replacer = GeLUMergerTanh() + replacer.find_and_replace_pattern(graph) + + (flag, resp) = compare_graphs(graph, graph_ref, 'out', check_op_attrs=True) + self.assertTrue(flag, resp) \ No newline at end of file diff --git a/model-optimizer/extensions/front/Log1p.py b/model-optimizer/extensions/front/Log1p.py index 5bfce9d9e38..c37b7f329fa 100644 --- a/model-optimizer/extensions/front/Log1p.py +++ b/model-optimizer/extensions/front/Log1p.py @@ -15,10 +15,10 @@ """ import numpy as np -from extensions.ops.Log import LogOp +from extensions.ops.activation_ops import Log from extensions.ops.elementwise import Add from mo.front.common.replacement import FrontReplacementOp -from mo.graph.graph import Graph, Node +from mo.graph.graph import Graph, Node, rename_nodes from mo.ops.const import Const @@ -31,18 +31,19 @@ class Log1p(FrontReplacementOp): enabled = True def replace_op(self, graph: Graph, node: Node): + node_name = node.soft_get('name', node.id) const_dtype = np.float32 if node.has_valid('data_type'): const_dtype = node.data_type const = Const(graph, {'value': np.array([1], dtype=const_dtype)}).create_node() add = Add(graph, {'name': node.name + '/Add_'}).create_node() - log = LogOp(graph, {'name': node.name + '/Log_'}).create_node() + log = Log(graph, {'name': node.name + '/Log_'}).create_node() # Connect nodes: input -> Add -> Log const.out_port(0).connect(add.in_port(0)) node.in_port(0).get_connection().set_destination(add.in_port(1)) add.out_port(0).connect(log.in_port(0)) + rename_nodes([(node, node_name + '/delete'), (log, node_name)]) # The "explicit" version of the return value is: [(out_node.id, 0)]) return [log.id] - diff --git a/model-optimizer/extensions/front/LogSoftmax.py b/model-optimizer/extensions/front/LogSoftmax.py index e199ef43d44..4a7e2de28fa 100644 --- a/model-optimizer/extensions/front/LogSoftmax.py +++ b/model-optimizer/extensions/front/LogSoftmax.py @@ -13,15 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. """ -from extensions.ops.Log import LogOp from extensions.ops.ReduceOps import ReduceMax, ReduceSum +from extensions.ops.activation_ops import Exp, Log from extensions.ops.elementwise import Sub -from extensions.ops.exp import ExpOp from mo.front.common.partial_infer.utils import int64_array from mo.front.common.replacement import FrontReplacementOp from mo.front.tf.graph_utils import create_op_with_const_inputs from mo.graph.graph import Graph, Node, rename_nodes -from mo.ops.const import Const class LogSoftmaxFrontReplacer(FrontReplacementOp): @@ -69,12 +67,12 @@ class LogSoftmaxFrontReplacer(FrontReplacementOp): reduce_max_node.out_port(0).connect(first_sub_node.in_port(1)) # Creating of Exp -> ReduceSum -> Log block - exp_node = ExpOp(graph, {'name': node_name + '/Exp_'}).create_node() + exp_node = Exp(graph, {'name': node_name + '/Exp_'}).create_node() reduce_sum_node = create_op_with_const_inputs(graph, ReduceSum, {1: int64_array([node.axis])}, op_attrs={'name': node_name + '/ReduceSum_', 'keep_dims': True}) - log_node = LogOp(graph, {'name': node_name + '/Log_'}).create_node() + log_node = Log(graph, {'name': node_name + '/Log_'}).create_node() first_sub_node.out_port(0).connect(exp_node.in_port(0)) exp_node.out_port(0).connect(reduce_sum_node.in_port(0)) diff --git a/model-optimizer/extensions/front/mxnet/slice_like_ext.py b/model-optimizer/extensions/front/mxnet/slice_like_ext.py index 5d803fe40bd..6074471d88a 100644 --- a/model-optimizer/extensions/front/mxnet/slice_like_ext.py +++ b/model-optimizer/extensions/front/mxnet/slice_like_ext.py @@ -14,9 +14,9 @@ limitations under the License. """ +from extensions.ops.slice_like import SliceLike from mo.front.extractor import FrontExtractorOp from mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs -from mo.ops.crop import Crop class SliceLikeFrontExtractor(FrontExtractorOp): @@ -26,15 +26,11 @@ class SliceLikeFrontExtractor(FrontExtractorOp): @classmethod def extract(cls, node): attrs = get_mxnet_layer_attrs(node.symbol_dict) - axes = attrs.tuple("axes", int, []) - offset = [0 for i in range(0, axes[-1])] + axes = list(attrs.tuple("axes", int, [])) node_attrs = { - 'axis': 1, - 'offset': offset, - 'dim': offset, 'axes': axes } # update the attributes of the node - Crop.update_node_stat(node, node_attrs) + SliceLike.update_node_stat(node, node_attrs) return cls.enabled diff --git a/model-optimizer/extensions/front/mxnet/ssd_anchor_reshape.py b/model-optimizer/extensions/front/mxnet/ssd_anchor_reshape.py index bd986fca7f2..a1f7350f3eb 100644 --- a/model-optimizer/extensions/front/mxnet/ssd_anchor_reshape.py +++ b/model-optimizer/extensions/front/mxnet/ssd_anchor_reshape.py @@ -19,6 +19,7 @@ import numpy as np from extensions.front.mxnet.eltwise_scalar_replacers import MulScalarFrontReplacer from extensions.front.mxnet.ssd_detection_output_replacer import SsdPatternDetectionOutputReplacer from extensions.front.split_normalizer import AttributedSplitToSplit +from extensions.ops.slice_like import SliceLike from mo.front.common.replacement import FrontReplacementSubgraph from mo.graph.graph import Graph, Node from mo.middle.pattern_match import find_pattern_matches @@ -64,7 +65,7 @@ class SsdPatternAnchorReshape(FrontReplacementSubgraph): nodes=[ ('power', dict(op='Mul')), ('anchor', dict(op='Const')), - ('slice_like', dict(op='Crop')), + ('slice_like', dict(op='slice_like')), ('reshape1', dict(op='Reshape')), ('reshape2', dict(op='Reshape')), ('reshape3', dict(op='Reshape')) @@ -91,10 +92,9 @@ class SsdPatternAnchorReshape(FrontReplacementSubgraph): variants = np.array([variants_dict['mul_scalar1x'], variants_dict['mul_scalar1y'], variants_dict['mul_scalar2x'], variants_dict['mul_scalar2y']] * int(const.value.size / 4)).reshape(const.value.shape) - priorbox_variants = Const(graph, dict(value=variants, symbol_dict={'name': const.id + '/priorbox_variants'})).create_node() - variants_slice_like = Crop(graph, dict(axis=slice_like.axis, offset=slice_like.offset, dim=slice_like.dim, axes=slice_like.axes, - symbol_dict={'name': slice_like.id + '/variants_slice_like'})) \ - .create_node() + priorbox_variants = Const(graph, dict(value=variants, name=const.id + '/priorbox_variants')).create_node() + variants_slice_like = SliceLike(graph, dict(axes=slice_like.axes, + name=slice_like.id + '/variants_slice_like')).create_node() variants_slice_like.in_port(0).connect(priorbox_variants.out_port(0)) variants_slice_like.in_port(1).connect(crop_shape.out_port(0)) diff --git a/model-optimizer/extensions/front/mxnet/ssd_detection_output_replacer.py b/model-optimizer/extensions/front/mxnet/ssd_detection_output_replacer.py index 8d0f061e709..0033a4912cb 100644 --- a/model-optimizer/extensions/front/mxnet/ssd_detection_output_replacer.py +++ b/model-optimizer/extensions/front/mxnet/ssd_detection_output_replacer.py @@ -68,7 +68,7 @@ class SsdPatternDetectionOutputReplacer(FrontReplacementSubgraph): nodes=[ ('power', dict(op='Mul')), ('anchor', dict(op='Const')), - ('slice_like', dict(op='Crop')), + ('slice_like', dict(op='slice_like')), ('reshape1', dict(op='Reshape')), ('reshape2', dict(op='Reshape')), ('reshape3', dict(op='Reshape')), diff --git a/model-optimizer/extensions/front/onnx/expand_ext.py b/model-optimizer/extensions/front/onnx/expand_ext.py index 8e14af25cb5..2e337413f7f 100644 --- a/model-optimizer/extensions/front/onnx/expand_ext.py +++ b/model-optimizer/extensions/front/onnx/expand_ext.py @@ -24,5 +24,5 @@ class ExpandExtractor(FrontExtractorOp): @classmethod def extract(cls, node): - Broadcast.update_node_stat(node) + Broadcast.update_node_stat(node, {'mode': 'bidirectional'}) return cls.enabled diff --git a/model-optimizer/extensions/front/onnx/non_max_suppression_ext.py b/model-optimizer/extensions/front/onnx/non_max_suppression_ext.py index 84e2564757c..3471da2eff8 100644 --- a/model-optimizer/extensions/front/onnx/non_max_suppression_ext.py +++ b/model-optimizer/extensions/front/onnx/non_max_suppression_ext.py @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ +import numpy as np from extensions.ops.non_max_suppression import NonMaxSuppression from mo.front.extractor import FrontExtractorOp @@ -28,5 +29,6 @@ class NonMaxSuppressionExtractor(FrontExtractorOp): encoding_map = {0: 'corner', 1: 'center'} center_point_box = onnx_attr(node, 'center_point_box', 'i', default=0) NonMaxSuppression.update_node_stat(node, {'sort_result_descending': 0, + 'output_type': np.int64, 'box_encoding': encoding_map[center_point_box]}) return cls.enabled diff --git a/model-optimizer/extensions/front/onnx/pooling_ext.py b/model-optimizer/extensions/front/onnx/pooling_ext.py index b86fef24862..3e41e664983 100644 --- a/model-optimizer/extensions/front/onnx/pooling_ext.py +++ b/model-optimizer/extensions/front/onnx/pooling_ext.py @@ -89,7 +89,7 @@ def common_onnx_pool_extractor(node): node.soft_get('name', node.id), pads)) # Try to convert slightly incorrect models with insufficient pad parameters - assert pads.size * 2 == kernel_shape.size + assert pads.size == kernel_shape.size pads = np.concatenate([pads, pads]) log.warning('Extended pads to {}'.format(pads)) diff --git a/model-optimizer/extensions/front/onnx/shape_ext.py b/model-optimizer/extensions/front/onnx/shape_ext.py index 1f18cbbc1fb..e32a09a42ae 100644 --- a/model-optimizer/extensions/front/onnx/shape_ext.py +++ b/model-optimizer/extensions/front/onnx/shape_ext.py @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ +import numpy as np from mo.front.extractor import FrontExtractorOp from mo.ops.shape import Shape @@ -24,5 +25,5 @@ class ShapeFrontExtractor(FrontExtractorOp): @classmethod def extract(cls, node): - Shape.update_node_stat(node) + Shape.update_node_stat(node, {'output_type': np.int64}) return cls.enabled diff --git a/model-optimizer/extensions/front/onnx/softmaxONNX_to_softmax.py b/model-optimizer/extensions/front/onnx/softmaxONNX_to_softmax.py new file mode 100644 index 00000000000..45d33bd591d --- /dev/null +++ b/model-optimizer/extensions/front/onnx/softmaxONNX_to_softmax.py @@ -0,0 +1,60 @@ +""" + Copyright (C) 2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +from mo.front.common.replacement import FrontReplacementOp +from mo.graph.graph import Graph, Node, rename_nodes +from mo.ops.flatten import FlattenONNX +from mo.ops.reshape import Reshape +from mo.ops.shape import Shape +from mo.ops.softmax import Softmax + + +class SoftmaxONNXFrontReplacer(FrontReplacementOp): + """ + Replace SoftmaxONNX operation with FlattenONNX -> Softmax -> Reshape subgraph + """ + op = "SoftMaxONNX" + enabled = True + + def run_before(self): + from extensions.front.onnx.flattenONNX_to_reshape import FlattenONNXToReshape + return [FlattenONNXToReshape] + + def replace_op(self, graph: Graph, node: Node): + node_name = node.soft_get('name', node.id) + assert node.has_valid('axis'), 'The node "{}" does not have mandatory attribute "axis"'.format(node_name) + + flatten_node = FlattenONNX(graph, {'name': node_name + '/FlattenONNX_', 'axis': node.axis}).create_node() + shape_node = Shape(graph, {'name': node_name + '/ShapeOf_'}).create_node() + softmax_node = Softmax(graph, {'name': node_name + '/Softmax_', + 'axis': 1, + 'framework_node_name': node_name, + 'rename_condition': lambda n: len(n.graph.get_op_nodes(name=node_name)) == 0 + }).create_node() + reshape_node = Reshape(graph, {}).create_node() + + rename_nodes([(node, node_name + '/delete'), (reshape_node, node_name)]) + + flatten_node.out_port(0).connect(softmax_node.in_port(0)) + softmax_node.out_port(0).connect(reshape_node.in_port(0)) + shape_node.out_port(0).connect(reshape_node.in_port(1)) + + source = node.in_port(0).get_source() + + flatten_node.in_port(0).connect(source) + shape_node.in_port(0).connect(source) + + return [reshape_node.id] diff --git a/model-optimizer/extensions/front/onnx/softmax_ext.py b/model-optimizer/extensions/front/onnx/softmax_ext.py index beb27c47d55..0a3c5245827 100644 --- a/model-optimizer/extensions/front/onnx/softmax_ext.py +++ b/model-optimizer/extensions/front/onnx/softmax_ext.py @@ -16,7 +16,7 @@ from mo.front.extractor import FrontExtractorOp from mo.front.onnx.extractors.utils import onnx_attr -from mo.ops.softmax import LogSoftmaxONNX, Softmax +from mo.ops.softmax import LogSoftmaxONNX, SoftmaxONNX class SoftmaxExtractor(FrontExtractorOp): @@ -26,7 +26,7 @@ class SoftmaxExtractor(FrontExtractorOp): @classmethod def extract(cls, node): axis = onnx_attr(node, 'axis', 'i', default=1) - Softmax.update_node_stat(node, {'axis': axis}) + SoftmaxONNX.update_node_stat(node, {'axis': axis}) return cls.enabled diff --git a/model-optimizer/extensions/front/rank_decomposer.py b/model-optimizer/extensions/front/rank_decomposer.py index 0f582025beb..b7dcb4ee535 100644 --- a/model-optimizer/extensions/front/rank_decomposer.py +++ b/model-optimizer/extensions/front/rank_decomposer.py @@ -17,7 +17,7 @@ from mo.front.common.partial_infer.utils import int64_array from mo.front.common.replacement import FrontReplacementOp from mo.front.tf.graph_utils import create_op_node_with_second_input -from mo.graph.graph import Graph +from mo.graph.graph import Graph, rename_nodes from mo.ops.shape import Shape from mo.ops.squeeze import Squeeze @@ -30,11 +30,16 @@ class RankDecomposer(FrontReplacementOp): node = match['op'] name = node.soft_get('name', node.id) - shape_of = Shape(graph, {'name': name + '/shape_of'}).create_node() - rank_1d = Shape(graph, {'name': name + '/rank_of'}).create_node() - rank_0d = create_op_node_with_second_input(graph, Squeeze, int64_array([0]), {'name': name + '/0d_rank_of'}, - rank_1d) + assert node.has_valid('output_type'), \ + 'Rank node should have `output_type` attribute, but it`s not for node {}'.format(name) + + shape_of = Shape(graph, {'name': name + '/shape_of', 'output_type': node.output_type}).create_node() + rank_1d = Shape(graph, {'name': name + '/rank_of', 'output_type': node.output_type}).create_node() + rank_0d = create_op_node_with_second_input( + graph, Squeeze, int64_array(0), {'name': name + '/0d_rank_of'}, rank_1d) shape_of.out_port(0).connect(rank_1d.in_port(0)) node.out_port(0).get_connection().set_source(rank_0d.out_port(0)) node.in_port(0).get_connection().set_destination(shape_of.in_port(0)) + + rename_nodes([(node, name + '/ToBeDeleted'), (rank_0d, name)]) diff --git a/model-optimizer/extensions/front/rank_decomposer_test.py b/model-optimizer/extensions/front/rank_decomposer_test.py new file mode 100644 index 00000000000..f3f504731d1 --- /dev/null +++ b/model-optimizer/extensions/front/rank_decomposer_test.py @@ -0,0 +1,70 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import unittest + +import numpy as np +from generator import generator, generate + +from extensions.front.rank_decomposer import RankDecomposer +from mo.front.common.partial_infer.utils import int64_array +from mo.utils.ir_engine.compare_graphs import compare_graphs +from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, connect, \ + valued_const_with_data + +nodes = lambda output_type: { + **regular_op_with_empty_data('input', {'type': 'Parameter'}), + **regular_op_with_empty_data('rank', {'op': 'Rank', 'type': None, 'output_type': output_type, 'name': 'my_rank'}), + **result(), + + **regular_op_with_empty_data('shape', {'type': 'ShapeOf', 'output_type': output_type}), + **regular_op_with_empty_data('rank_1D', {'type': 'ShapeOf', 'output_type': output_type}), + **valued_const_with_data('zero', int64_array(0)), + **regular_op_with_empty_data('rank_0D', {'type': 'Squeeze'}), +} + + +@generator +class RankDecomposerTest(unittest.TestCase): + + @generate(np.int32, np.int64) + def test_rank_decomposer(self, output_type): + graph = build_graph(nodes_attrs=nodes(output_type), edges=[ + *connect('input', 'rank'), + *connect('rank', 'output'), + ], nodes_with_edges_only=True) + RankDecomposer().find_and_replace_pattern(graph) + + graph_ref = build_graph(nodes_attrs=nodes(output_type), edges=[ + *connect('input', 'shape'), + *connect('shape', 'rank_1D'), + *connect('rank_1D', '0:rank_0D'), + *connect('zero', '1:rank_0D'), + *connect('rank_0D', 'output'), + ], nodes_with_edges_only=True) + + (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True) + self.assertTrue(flag, resp) + self.assertEqual(graph.get_op_nodes(type='Squeeze')[0]['name'], 'my_rank', + 'Name is not inherited from original node for RankDecomposer') + print(output_type) + + def test_rank_decomposer_assertion(self): + graph = build_graph(nodes_attrs=nodes(None), edges=[ + *connect('input', 'rank'), + *connect('rank', 'output'), + ], nodes_with_edges_only=True) + self.assertRaises(AssertionError, RankDecomposer().find_and_replace_pattern, graph) diff --git a/model-optimizer/extensions/front/tf/SizeReplacer.py b/model-optimizer/extensions/front/tf/SizeReplacer.py index 07be4121b4f..42fefddef85 100644 --- a/model-optimizer/extensions/front/tf/SizeReplacer.py +++ b/model-optimizer/extensions/front/tf/SizeReplacer.py @@ -16,8 +16,8 @@ from extensions.ops.ReduceOps import ReduceProd from mo.front.common.partial_infer.utils import int64_array from mo.front.common.replacement import FrontReplacementOp -from mo.graph.graph import Graph, Node -from mo.ops.const import Const +from mo.front.tf.graph_utils import create_op_node_with_second_input +from mo.graph.graph import Graph, rename_nodes from mo.ops.shape import Shape @@ -28,15 +28,16 @@ class SizeFrontReplacer(FrontReplacementOp): op = "Size" enabled = True - def replace_op(self, graph: Graph, node: Node): - shape = Shape(graph, {'name': node.name + '/Shape/'}).create_node() - reduce_prod = ReduceProd(graph, {'name': shape.name + 'ReduceProd/', 'keep_dims': False}).create_node() - reduce_axis = Const(graph, {'value': int64_array([0])}).create_node() + def replace_sub_graph(self, graph: Graph, match: dict): + node = match['op'] + name = node.soft_get('name', node.id) + assert node.has_valid('output_type'), \ + 'Size node should have `output_type` attribute, but it`s not for node {}'.format(name) - # Connect nodes + shape = Shape(graph, {'name': name + '/Shape/', 'output_type': node.output_type}).create_node() node.in_port(0).get_connection().set_destination(shape.in_port(0)) - reduce_prod.in_port(0).get_connection().set_source(shape.out_port(0)) - reduce_prod.in_port(1).get_connection().set_source(reduce_axis.out_port(0)) + reduce_prod = create_op_node_with_second_input( + graph, ReduceProd, int64_array([0]), {'name': shape.name + 'ReduceProd/', 'keep_dims': False}, shape) + node.out_port(0).get_connection().set_source(reduce_prod.out_port(0)) - # The "explicit" version of the return value is: [(out_node.id, 0)]) - return [reduce_prod.id] + rename_nodes([(node, name + '/ToBeDeleted'), (reduce_prod, name)]) diff --git a/model-optimizer/extensions/front/tf/argmax_ext.py b/model-optimizer/extensions/front/tf/argmax_ext.py index dbe36ab3584..47f531bea51 100644 --- a/model-optimizer/extensions/front/tf/argmax_ext.py +++ b/model-optimizer/extensions/front/tf/argmax_ext.py @@ -13,8 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. """ +import numpy as np + from extensions.ops.argmax import ArgMaxOp from mo.front.extractor import FrontExtractorOp +from mo.front.tf.extractors.utils import tf_dtype_extractor class ArgMaxFrontExtractor(FrontExtractorOp): @@ -24,5 +27,7 @@ class ArgMaxFrontExtractor(FrontExtractorOp): @classmethod def extract(cls, node): ArgMaxOp.update_node_stat(node, {'out_max_val': 0, 'top_k': 1, 'axis': None, - 'dim_attrs': ['axis'], 'keepdims': 0, 'remove_values_output': True}) + 'dim_attrs': ['axis'], 'keepdims': 0, 'remove_values_output': True, + 'output_type': tf_dtype_extractor(node.pb.attr['out_type'].type, np.int64), + }) return cls.enabled diff --git a/model-optimizer/extensions/front/tf/non_max_suppression_ext.py b/model-optimizer/extensions/front/tf/non_max_suppression_ext.py index cfc9170c701..2e3930f0d1c 100644 --- a/model-optimizer/extensions/front/tf/non_max_suppression_ext.py +++ b/model-optimizer/extensions/front/tf/non_max_suppression_ext.py @@ -15,6 +15,8 @@ """ import logging as log +import numpy as np + from extensions.ops.non_max_suppression import NonMaxSuppression from mo.front.extractor import FrontExtractorOp @@ -25,7 +27,7 @@ class NonMaxSuppressionV3Extractor(FrontExtractorOp): @classmethod def extract(cls, node): - attrs = {'sort_result_descending': 1, 'center_point_box': 0} + attrs = {'sort_result_descending': 1, 'center_point_box': 0, 'output_type': np.int32} NonMaxSuppression.update_node_stat(node, attrs) return cls.enabled @@ -40,7 +42,7 @@ class NonMaxSuppressionV4Extractor(FrontExtractorOp): if not pad_to_max_output_size: log.warning('The attribute "pad_to_max_output_size" of node {} is equal to False which is not supported.' 'Forcing it to be equal to True'.format(node.soft_get('name'))) - attrs = {'sort_result_descending': 1, 'box_encoding': 'corner'} + attrs = {'sort_result_descending': 1, 'box_encoding': 'corner', 'output_type': np.int32} NonMaxSuppression.update_node_stat(node, attrs) return cls.enabled @@ -55,6 +57,6 @@ class NonMaxSuppressionV5Extractor(FrontExtractorOp): if not pad_to_max_output_size: log.warning('The attribute "pad_to_max_output_size" of node {} is equal to False which is not supported.' 'Forcing it to be equal to True'.format(node.soft_get('name'))) - attrs = {'sort_result_descending': 1, 'box_encoding': 'corner'} + attrs = {'sort_result_descending': 1, 'box_encoding': 'corner', 'output_type': np.int32} NonMaxSuppression.update_node_stat(node, attrs) return cls.enabled diff --git a/model-optimizer/extensions/front/tf/reshape_related_ext.py b/model-optimizer/extensions/front/tf/reshape_related_ext.py index 22f960a841a..51632d2f962 100644 --- a/model-optimizer/extensions/front/tf/reshape_related_ext.py +++ b/model-optimizer/extensions/front/tf/reshape_related_ext.py @@ -31,7 +31,7 @@ class RankFrontExtractor(FrontExtractorOp): @classmethod def extract(cls, node: Node): - Rank.update_node_stat(node) + Rank.update_node_stat(node, {'output_type': np.int32}) return cls.enabled @@ -51,7 +51,7 @@ class ShapeExtractor(FrontExtractorOp): @classmethod def extract(cls, node: Node): - Shape.update_node_stat(node, {'data_type': tf_dtype_extractor(node.pb.attr['out_type'].type, np.int32)}) + Shape.update_node_stat(node, {'output_type': tf_dtype_extractor(node.pb.attr['out_type'].type, np.int32)}) return cls.enabled @@ -61,7 +61,7 @@ class SizeFrontExtractor(FrontExtractorOp): @classmethod def extract(cls, node): - Size.update_node_stat(node) + Size.update_node_stat(node, {'output_type': tf_dtype_extractor(node.pb.attr['out_type'].type, np.int32)}) return cls.enabled diff --git a/model-optimizer/extensions/front/tf/size_replacer_test.py b/model-optimizer/extensions/front/tf/size_replacer_test.py new file mode 100644 index 00000000000..03daf1a2666 --- /dev/null +++ b/model-optimizer/extensions/front/tf/size_replacer_test.py @@ -0,0 +1,68 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import unittest + +import numpy as np +from generator import generator, generate + +from extensions.front.tf.SizeReplacer import SizeFrontReplacer +from mo.front.common.partial_infer.utils import int64_array +from mo.utils.ir_engine.compare_graphs import compare_graphs +from mo.utils.unittest.graph import build_graph, regular_op_with_empty_data, result, connect, \ + valued_const_with_data + +nodes = lambda output_type: { + **regular_op_with_empty_data('input', {'type': 'Parameter'}), + **regular_op_with_empty_data('size', {'op': 'Size', 'type': None, 'output_type': output_type, 'name': 'my_size'}), + **result(), + + **regular_op_with_empty_data('shape', {'type': 'ShapeOf', 'output_type': output_type}), + **valued_const_with_data('zero', int64_array([0])), + **regular_op_with_empty_data('reduce', {'type': 'ReduceProd', 'keep_dims': False}), +} + + +@generator +class SizeReplacerTest(unittest.TestCase): + + @generate(np.int32, np.int64) + def test_size_replacer(self, output_type): + graph = build_graph(nodes_attrs=nodes(output_type), edges=[ + *connect('input', 'size'), + *connect('size', 'output'), + ], nodes_with_edges_only=True) + SizeFrontReplacer().find_and_replace_pattern(graph) + + graph_ref = build_graph(nodes_attrs=nodes(output_type), edges=[ + *connect('input', 'shape'), + *connect('shape', '0:reduce'), + *connect('zero', '1:reduce'), + *connect('reduce', 'output'), + ], nodes_with_edges_only=True) + + (flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True) + self.assertTrue(flag, resp) + self.assertEqual(graph.get_op_nodes(type='ReduceProd')[0]['name'], 'my_size', + 'Name is not inherited from original node for SizeReplacer') + print(output_type) + + def test_size_replacer_assertion(self): + graph = build_graph(nodes_attrs=nodes(None), edges=[ + *connect('input', 'size'), + *connect('size', 'output'), + ], nodes_with_edges_only=True) + self.assertRaises(AssertionError, SizeFrontReplacer().find_and_replace_pattern, graph) diff --git a/model-optimizer/extensions/front/tf/topk_ext.py b/model-optimizer/extensions/front/tf/topk_ext.py index 72069945727..7c338b8594d 100644 --- a/model-optimizer/extensions/front/tf/topk_ext.py +++ b/model-optimizer/extensions/front/tf/topk_ext.py @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ +import numpy as np from extensions.ops.topk import TopK from mo.front.extractor import FrontExtractorOp @@ -25,7 +26,8 @@ class TopKExtractor(FrontExtractorOp): @classmethod def extract(cls, node): sort = 'value' if node.pb.attr['sorted'] else 'none' - TopK.update_node_stat(node, {'mode': 'max', 'axis': -1, 'sort': sort, 'k' : node.pb.attr['k'].i}) + TopK.update_node_stat(node, {'mode': 'max', 'axis': -1, 'sort': sort, 'k': node.pb.attr['k'].i, + 'index_element_type': np.int32}) return cls.enabled @@ -37,5 +39,5 @@ class TopKV2Extractor(FrontExtractorOp): @classmethod def extract(cls, node): sort = 'value' if node.pb.attr['sorted'] else 'none' - TopK.update_node_stat(node, {'mode': 'max', 'axis': -1, 'sort': sort}) + TopK.update_node_stat(node, {'mode': 'max', 'axis': -1, 'sort': sort, 'index_element_type': np.int32}) return cls.enabled diff --git a/model-optimizer/extensions/middle/AnchorToPriorBox.py b/model-optimizer/extensions/middle/AnchorToPriorBox.py index 05bf1682cf2..30dbf2a181f 100644 --- a/model-optimizer/extensions/middle/AnchorToPriorBox.py +++ b/model-optimizer/extensions/middle/AnchorToPriorBox.py @@ -16,6 +16,7 @@ import numpy as np +from extensions.middle.SliceLikeToStridedSlice import SliceLikeToStridedSlice from mo.graph.graph import Graph from mo.middle.replacement import MiddleReplacementPattern from mo.ops.const import Const @@ -33,12 +34,15 @@ class AnchorToPriorBoxes(MiddleReplacementPattern): from extensions.middle.pass_separator import MiddleStart return [MiddleStart] + def run_before(self): + return [SliceLikeToStridedSlice] + def pattern(self): return dict( nodes=[ ('const', dict(op='Const')), ('const_data', dict(kind='data')), - ('slice_like', dict(op='Crop')), + ('slice_like', dict(op='slice_like')), ('slice_like_out', dict(kind='data')), ('reshape', dict(op='Reshape')), ], diff --git a/model-optimizer/extensions/middle/ArgMaxToTopK.py b/model-optimizer/extensions/middle/ArgMaxToTopK.py index e7dc55e2aa3..2b603b05182 100644 --- a/model-optimizer/extensions/middle/ArgMaxToTopK.py +++ b/model-optimizer/extensions/middle/ArgMaxToTopK.py @@ -40,6 +40,7 @@ class ArgMaxToTopK(MiddleReplacementPattern): def replace_pattern(self, graph: Graph, match: dict): node = match['argmax'] + node_name = node.soft_get('name', node.id) connected_ports = [port for port in node.in_ports().values() if not port.disconnected()] if len(connected_ports) == 2: @@ -47,9 +48,11 @@ class ArgMaxToTopK(MiddleReplacementPattern): else: axis = node.axis - assert axis is not None, 'The "axis" should be defined for node "{}"'.format(node.soft_get('name')) + assert axis is not None, 'The "axis" should be defined for node "{}"'.format(node_name) + assert node.has_and_set('output_type'), 'The data type is not set for node "{}"'.format(node_name) topk_node = TopK(graph, {'axis': axis, 'mode': 'max', 'sort': 'index', - 'remove_values_output': node.has_and_set('remove_values_output')}).create_node() + 'remove_values_output': node.has_and_set('remove_values_output'), + 'index_element_type': node.output_type}).create_node() node.in_port(0).get_connection().set_destination(topk_node.in_port(0)) if node.has_and_set('out_max_val'): # in this mode the ArgMax produces tuples (max_ind, max_value) concat_node = Concat(graph, {'axis': 1, 'name': node.name + '/Concat'}).create_node() diff --git a/model-optimizer/extensions/middle/SliceLikeToStridedSlice.py b/model-optimizer/extensions/middle/SliceLikeToStridedSlice.py new file mode 100644 index 00000000000..c9145a921ad --- /dev/null +++ b/model-optimizer/extensions/middle/SliceLikeToStridedSlice.py @@ -0,0 +1,112 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" +import numpy as np +from typing import Dict + +from mo.front.tf.graph_utils import create_op_with_const_inputs +from mo.graph.graph import Graph, Node, rename_nodes +from mo.middle.replacement import MiddleReplacementPattern +from mo.ops.shape import Shape +from mo.ops.strided_slice import StridedSlice +from mo.utils.shape import get_shape_values_by_range_idxs, new_shape_node_from_shape_nodes, \ + get_shape_and_rank_nodes_by_port + + +class SliceLikeToStridedSlice(MiddleReplacementPattern): + """ + Replace mxnet slice_like operation with StridedSlice in reshapable way. + The begin parameter for StridedSlice is always a zero vector. + The end parameter depends on the slice_like inputs and axes. + + 1. If slice_like inputs has the same ranks, we can use second input shape (shape_like) as the end parameter for + StridedSlice. Axes parameter will form end_mask, that allows to use slice only on the desired axes. + Example: + input_shape = [1, 64, 128, 256], shape_like = [1, 2, 3, 4], axes = [2, 3]. + In that case end = shape_like = [1, 2, 3, 4], but end_mask = [0, 0, 1, 1], so output_shape = [1, 64, 3, 4] + + 2. Axes parameter has the last dimension of the first input shape (in that case shape_like >= input_shape). + Here we can use only a part of shape_like as the end parameter. + Example: + input_shape = [1, 64, 128, 256], shape_like = [1, 2, 3, 4, 5], axes = [2, 3]. + end = shape_like[:4] = [1, 2, 3, 4], end_mask = [0, 0, 1, 1], output_shape = [1, 64, 3, 4] + + 3. Usual case, where we form end parameter by concatenate parts of shape_like and input_shape. + Examples: + input_shape = [1, 64, 128, 256, 512], shape_like = [1, 2, 3, 4], axes = [2, 3]. + end = shape_like[:4] + input_shape[4:] = [1, 2, 3, 4, 512], + end_mask = [0, 0, 1, 1, 0], output_shape = [1, 64, 3, 4, 512] + + input_shape = [1, 64, 128, 256], shape_like = [1, 2, 3, 4, 5], axes = [0, 2]. + end = shape_like[:3] + input_shape[3:] = [1, 2, 3, 256], + end_mask = [1, 0, 1, 0], output_shape = [1, 64, 3, 256] + """ + + enabled = True + graph_condition = [lambda graph: graph.graph['fw'] == 'mxnet'] + + @staticmethod + def pattern(): + return dict( + nodes=[ + ('op', dict(kind='op', op='slice_like')) + ], + edges=[] + ) + + @staticmethod + def replace_pattern(graph: Graph, match: Dict[str, Node]): + node = match['op'] + name = node.soft_get('name', node.id) + input_shape = node.in_port(0).data.get_shape() + second_input_shape = node.in_port(1).data.get_shape() + + begin_mask = np.zeros(len(input_shape), dtype=np.int64) + end_mask = np.zeros(len(input_shape), dtype=np.int64) + + for i in node.axes: + end_mask[i] = np.int64(1) + + new_axis_mask = np.zeros(len(input_shape), dtype=np.int64) + shrink_axis_mask = np.zeros(len(input_shape), dtype=np.int64) + ellipsis_mask = np.zeros(len(input_shape), dtype=np.int64) + + ss = create_op_with_const_inputs(graph, StridedSlice, + port_value_dict={1: np.zeros(len(input_shape), dtype=np.int64)}, + op_attrs={'name': 'StridedSlice', 'begin_mask': begin_mask, + 'end_mask': end_mask, 'new_axis_mask': new_axis_mask, + 'shrink_axis_mask': shrink_axis_mask, + 'ellipsis_mask': ellipsis_mask}) + if input_shape.size == second_input_shape.size: + end = Shape(graph, dict(name=name + '/End')).create_node() + end.in_port(0).connect(node.in_port(1).get_source()) + ss.in_port(2).connect(end.out_port(0)) + else: + shape_like, rank_like = get_shape_and_rank_nodes_by_port(node.in_port(1).get_source()) + end_first_part = get_shape_values_by_range_idxs(shape_like, rank_like, 0, node.axes[-1], include_end=True) + if input_shape.size - 1 == node.axes[-1]: + ss.in_port(2).connect(end_first_part.out_port(0)) + else: + shape, rank = get_shape_and_rank_nodes_by_port(node.in_port(0).get_source()) + end_second_part = get_shape_values_by_range_idxs(shape, rank, node.axes[-1], -1, include_begin=False, + include_end=True) + end = new_shape_node_from_shape_nodes([end_first_part, end_second_part]) + ss.in_port(2).connect(end.out_port(0)) + + node.in_port(0).get_connection().set_destination(ss.in_port(0)) + node.in_port(1).disconnect() + node.out_port(0).get_connection().set_source(ss.out_port(0)) + + rename_nodes([(node, name + '/ShouldBeDeleted'), (ss, name)]) diff --git a/model-optimizer/extensions/middle/SliceLikeToStridedSlice_test.py b/model-optimizer/extensions/middle/SliceLikeToStridedSlice_test.py new file mode 100644 index 00000000000..b8bd16b14a6 --- /dev/null +++ b/model-optimizer/extensions/middle/SliceLikeToStridedSlice_test.py @@ -0,0 +1,260 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" +import unittest + +from extensions.middle.SliceLikeToStridedSlice import SliceLikeToStridedSlice +from mo.front.common.partial_infer.utils import int64_array +from mo.utils.ir_engine.compare_graphs import compare_graphs +from mo.utils.unittest.graph import build_graph + +nodes_attributes = { + 'input': {'kind': 'op', 'op': 'Const'}, + 'input_data': {'kind': 'data'}, + + 'shape_like_input': {'kind': 'op', 'op': 'Const'}, + 'shape_like_input_data': {'kind': 'data'}, + + 'slice_like': {'kind': 'op', 'op': 'slice_like'}, + 'slice_like_data': {'kind': 'data', 'shape': None, 'value': None}, + + 'result': {'kind': 'op', 'op': 'Result'}, + + 'shape': {'kind': 'op', 'op': 'ShapeOf'}, + 'shape_data': {'kind': 'data'}, + 'rank_1_d': {'kind': 'op', 'op': 'ShapeOf'}, + 'rank_1_d_data': {'kind': 'data'}, + 'rank': {'kind': 'op', 'op': 'Squeeze'}, + 'rank_data': {'kind': 'data'}, + 'rank_const': {'kind': 'op', 'op': 'Const'}, + 'rank_const_data': {'kind': 'data'}, + + 'shape_like': {'kind': 'op', 'op': 'ShapeOf'}, + 'shape_like_data': {'kind': 'data'}, + 'rank_like_1_d': {'kind': 'op', 'op': 'ShapeOf'}, + 'rank_like_1_d_data': {'kind': 'data'}, + 'rank_like': {'kind': 'op', 'op': 'Squeeze'}, + 'rank_like_const': {'kind': 'op', 'op': 'Const'}, + 'rank_like_const_data': {'kind': 'data'}, + + 'begin': {'kind': 'op', 'op': 'Const'}, + 'begin_data': {'kind': 'data'}, + 'ss': {'kind': 'op', 'op': 'StridedSlice'}, + + 'start_idx_like': {'kind': 'op', 'op': 'Const'}, + 'start_idx_like_data': {'kind': 'data'}, + 'end_idx_like': {'kind': 'op', 'op': 'Const'}, + 'end_idx_like_data': {'kind': 'data'}, + 'end_idx_like_const': {'kind': 'op', 'op': 'Const'}, + 'end_idx_like_const_data': {'kind': 'data'}, + 'end_idx_like_add': {'kind': 'op', 'op': 'Add'}, + 'end_idx_like_add_data': {'kind': 'data'}, + 'delta_like': {'kind': 'op', 'op': 'Const'}, + 'delta_like_data': {'kind': 'data'}, + 'range_like': {'kind': 'op', 'op': 'Range'}, + 'range_like_data': {'kind': 'data'}, + 'gather_like': {'kind': 'op', 'op': 't_gather'}, + 'gather_like_data': {'kind': 'data'}, + 'gather_like_axis': {'kind': 'op', 'op': 'Const'}, + 'gather_like_axis_data': {'kind': 'data'}, + 'concat': {'kind': 'op', 'op': 'Concat'}, + 'concat_data': {'kind': 'data'}, + + 'start_idx': {'kind': 'op', 'op': 'Const'}, + 'start_idx_data': {'kind': 'data'}, + 'start_idx_const': {'kind': 'op', 'op': 'Const'}, + 'start_idx_const_data': {'kind': 'data'}, + 'start_idx_add': {'kind': 'op', 'op': 'Add'}, + 'start_idx_add_data': {'kind': 'data'}, + 'end_idx': {'kind': 'op', 'op': 'Add'}, + 'end_idx_data': {'kind': 'data'}, + 'end_idx_axis': {'kind': 'op', 'op': 'Const'}, + 'end_idx_axis_data': {'kind': 'data'}, + 'end_idx_const': {'kind': 'op', 'op': 'Const'}, + 'end_idx_const_data': {'kind': 'data'}, + 'end_idx_add': {'kind': 'op', 'op': 'Add'}, + 'end_idx_add_data': {'kind': 'data'}, + 'delta': {'kind': 'op', 'op': 'Const'}, + 'delta_data': {'kind': 'data'}, + 'range': {'kind': 'op', 'op': 'Range'}, + 'range_data': {'kind': 'data'}, + 't_gather': {'kind': 'op', 'op': 't_gather'}, + 'gather_data': {'kind': 'data'}, + 'gather_axis': {'kind': 'op', 'op': 'Const'}, + 'gather_axis_data': {'kind': 'data'} + +} + +edges = [ + ('input', 'input_data'), + ('input_data', 'slice_like', {'in': 0}), + ('shape_like_input', 'shape_like_input_data'), + ('shape_like_input_data', 'slice_like', {'in': 1}), + ('slice_like', 'slice_like_data'), + ('slice_like_data', 'result') +] + +same_input_shapes_dims_edges = [ + ('input', 'input_data'), + ('input_data', 'ss', {'in': 0}), + ('ss', 'slice_like_data'), + ('slice_like_data', 'result'), + ('shape_like_input', 'shape_like_input_data'), + ('shape_like_input_data', 'shape_like'), + ('shape_like', 'shape_like_data'), + ('shape_like_data', 'ss', {'in': 2}), + ('begin', 'begin_data'), + ('begin_data', 'ss', {'in': 1}) +] + +shape_like_sub_graph_edges = [ + ('input', 'input_data'), + ('input_data', 'ss', {'in': 0}), + ('ss', 'slice_like_data'), + ('slice_like_data', 'result'), + ('begin', 'begin_data'), + ('begin_data', 'ss', {'in': 1}), + ('shape_like_input', 'shape_like_input_data'), + ('shape_like_input_data', 'shape_like'), + ('shape_like', 'shape_like_data'), + ('shape_like_data', 'rank_like_1_d'), + ('rank_like_1_d', 'rank_like_1_d_data'), + ('rank_like_1_d_data', 'rank_like', {'in': 0}), + ('rank_like_const', 'rank_like_const_data'), + ('rank_like_const_data', 'rank_like', {'in': 1}), + ('end_idx_like', 'end_idx_like_data'), + ('end_idx_like_const', 'end_idx_like_const_data'), + ('end_idx_like_data', 'end_idx_like_add', {'in': 0}), + ('end_idx_like_const_data', 'end_idx_like_add', {'in': 1}), + ('end_idx_like_add', 'end_idx_like_add_data'), + ('end_idx_like_add_data', 'range_like', {'in': 1}), + ('start_idx_like', 'start_idx_like_data'), + ('start_idx_like_data', 'range_like', {'in': 0}), + ('delta_like', 'delta_like_data'), + ('delta_like_data', 'range_like', {'in': 2}), + ('range_like', 'range_like_data'), + ('range_like_data', 'gather_like', {'in': 1}), + ('shape_like_data', 'gather_like', {'in': 0}), + ('gather_like_axis', 'gather_like_axis_data'), + ('gather_like_axis_data', 'gather_like', {'in': 2}), + ('gather_like', 'gather_like_data') +] + +last_axis_index = shape_like_sub_graph_edges + [('gather_like_data', 'ss', {'in': 2})] + +input_sub_graph_edges = [ + ('input_data', 'shape'), + ('shape', 'shape_data'), + ('shape_data', 'rank_1_d'), + ('rank_1_d', 'rank_1_d_data'), + ('rank_1_d_data', 'rank', {'in': 0}), + ('rank_const', 'rank_const_data'), + ('rank_const_data', 'rank', {'in': 1}), + ('rank', 'rank_data'), + ('rank_data', 'end_idx', {'in': 0}), + ('end_idx_axis', 'end_idx_axis_data'), + ('end_idx_axis_data', 'end_idx', {'in': 1}), + ('end_idx', 'end_idx_data'), + ('end_idx_data', 'end_idx_add', {'in': 0}), + ('end_idx_const', 'end_idx_const_data'), + ('end_idx_const_data', 'end_idx_add', {'in': 1}), + ('start_idx', 'start_idx_data'), + ('start_idx_data', 'start_idx_add', {'in': 0}), + ('start_idx_const', 'start_idx_const_data'), + ('start_idx_const_data', 'start_idx_add', {'in': 1}), + ('end_idx_add', 'end_idx_add_data'), + ('start_idx_add', 'start_idx_add_data'), + ('delta', 'delta_data'), + ('start_idx_add_data', 'range', {'in': 0}), + ('end_idx_add_data', 'range', {'in': 1}), + ('delta_data', 'range', {'in': 2}), + ('range', 'range_data'), + ('range_data', 't_gather', {'in': 1}), + ('shape_data', 't_gather', {'in': 0}), + ('gather_axis', 'gather_axis_data'), + ('gather_axis_data', 't_gather', {'in': 2}), + ('t_gather', 'gather_data'), + ('gather_data', 'concat', {'in': 1}), + ('concat', 'concat_data'), + ('concat_data', 'ss', {'in': 2}), + ('gather_like_data', 'concat', {'in': 0}) +] + +input_part_shape_edges = shape_like_sub_graph_edges + input_sub_graph_edges + + +class SliceLikeToStridedSliceTest(unittest.TestCase): + + def test_1(self): + graph = build_graph( + nodes_attributes, + edges, + update_attributes={ + 'input_data': {'shape': int64_array([1, 224, 224, 3])}, + 'shape_like_input_data': {'shape': int64_array([2, 2, 2, 2])}, + 'slice_like': {'axes': int64_array([2, 3])} + }, + nodes_with_edges_only=True + ) + SliceLikeToStridedSlice().find_and_replace_pattern(graph) + ref_graph = build_graph( + nodes_attributes, + same_input_shapes_dims_edges, + nodes_with_edges_only=True + ) + + flag, resp = compare_graphs(graph, ref_graph, 'result') + self.assertTrue(flag, resp) + + def test_2(self): + graph = build_graph( + nodes_attributes, + edges, + update_attributes={ + 'input_data': {'shape': int64_array([1, 224, 224, 3])}, + 'shape_like_input_data': {'shape': int64_array([2, 2, 2, 2, 2])}, + 'slice_like': {'axes': int64_array([2, 3])} + }, + nodes_with_edges_only=True + ) + SliceLikeToStridedSlice().find_and_replace_pattern(graph) + ref_graph = build_graph( + nodes_attributes, + last_axis_index, + nodes_with_edges_only=True + ) + + flag, resp = compare_graphs(graph, ref_graph, 'result') + self.assertTrue(flag, resp) + + def test_3(self): + graph = build_graph( + nodes_attributes, + edges, + update_attributes={ + 'input_data': {'shape': int64_array([1, 224, 224, 3])}, + 'shape_like_input_data': {'shape': int64_array([2, 2, 2, 2, 2])}, + 'slice_like': {'axes': int64_array([1, 2])} + }, + nodes_with_edges_only=True + ) + SliceLikeToStridedSlice().find_and_replace_pattern(graph) + ref_graph = build_graph( + nodes_attributes, + input_part_shape_edges, + nodes_with_edges_only=True + ) + flag, resp = compare_graphs(graph, ref_graph, 'result') + self.assertTrue(flag, resp) diff --git a/model-optimizer/extensions/middle/UselessStridedSlice.py b/model-optimizer/extensions/middle/UselessStridedSlice.py deleted file mode 100644 index 55fcf8c0406..00000000000 --- a/model-optimizer/extensions/middle/UselessStridedSlice.py +++ /dev/null @@ -1,92 +0,0 @@ -""" - Copyright (C) 2018-2020 Intel Corporation - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -""" - -import logging as log - -import numpy as np - -from extensions.middle.ConvertGroupedStridedSlice import ConvertGroupedStridedSlice -from extensions.middle.SliceConverter import ConvertSlice -from mo.front.common.partial_infer.utils import int64_array -from mo.graph.graph import Graph -from mo.middle.passes.eliminate import remove_op_node_with_data_node -from mo.middle.replacement import MiddleReplacementPattern - - -class UselessStridedSliceEraser(MiddleReplacementPattern): - enabled = True - force_shape_inference = True - - def run_before(self): - return [ConvertGroupedStridedSlice] - - def run_after(self): - return [ConvertSlice] - - @staticmethod - def pattern(): - return dict( - nodes=[('strided_slice', dict(kind='op', op='StridedSlice'))], - edges=[] - ) - - @staticmethod - def replace_pattern(graph: Graph, match: dict): - node_ss = match['strided_slice'] - # slices = [elem for elem in node_ss.slices if elem is not None] - # node_ss.slices = np.array(slices) - - if node_ss.out_port(0).data.get_value() is not None: - # StridedSlices(SS) in shape-calculating sub-graphs that should not be deleted that easily - # Example: - # In RetinaNetFilteredDetectionsReplacement we have SS that slices first batch - # We delete such SS for batch 1, but it should be performed while reshaping the model - return - - output_data_node = node_ss.out_node(0) - input_data_node = node_ss.in_node(0) - - out_shape = output_data_node.shape - - if not np.all(node_ss.shrink_axis_mask == 0): - out_shape = list(out_shape) - for i in range(len(node_ss.shrink_axis_mask)): - if node_ss.shrink_axis_mask[i] == 1: - out_shape.insert(i, 1) - out_shape = int64_array(out_shape) - - if not np.all(node_ss.new_axis_mask == 0): - out_shape = list(out_shape) - for i in reversed(range(len(node_ss.new_axis_mask))): - if node_ss.new_axis_mask[i] == 1: - out_shape.pop(i) - out_shape = int64_array(out_shape) - - if np.array_equal(input_data_node.shape, out_shape) and \ - all(elem.step == 1 for elem in match['strided_slice'].slices): - if not np.all(node_ss.shrink_axis_mask == 0): - ConvertGroupedStridedSlice.add_squeeze_for_shrink(graph, node_ss) - if not np.all(node_ss.new_axis_mask == 0): - ConvertGroupedStridedSlice.add_unsqueeze_for_new(graph, node_ss) - - log.info("Useless StridedSlice op '{}' has been detected".format(match['strided_slice'].id)) - # remove inputs to Strided Slice so it has just one input with data so we can use 'remove_op_node' function - graph.remove_edge(match['strided_slice'].in_node(1).id, match['strided_slice'].id) - graph.remove_edge(match['strided_slice'].in_node(2).id, match['strided_slice'].id) - if len(match['strided_slice'].in_nodes()) > 3: - graph.remove_edge(match['strided_slice'].in_node(3).id, match['strided_slice'].id) - - remove_op_node_with_data_node(graph, match['strided_slice']) diff --git a/model-optimizer/extensions/middle/UselessStridedSlice_test.py b/model-optimizer/extensions/middle/UselessStridedSlice_test.py deleted file mode 100644 index 4b90ff98fbf..00000000000 --- a/model-optimizer/extensions/middle/UselessStridedSlice_test.py +++ /dev/null @@ -1,282 +0,0 @@ -""" - Copyright (C) 2018-2020 Intel Corporation - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -""" -import unittest - -from extensions.middle.UselessStridedSlice import UselessStridedSliceEraser -from mo.front.common.partial_infer.utils import int64_array -from mo.middle.passes.eliminate import shape_inference -from mo.utils.ir_engine.compare_graphs import compare_graphs -from mo.utils.unittest.graph import build_graph - -nodes_attributes = { - # input data - 'placeholder': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'}, - 'placeholder_data': {'value': None, 'shape': int64_array([4, 1, 6]), 'kind': 'data', 'data_type': None}, - # - 'strided_slice': {'type': 'StridedSlice', 'kind': 'op', 'op': 'StridedSlice', - 'shrink_axis_mask': int64_array([0, 0, 0]), 'new_axis_mask': int64_array([0, 0, 0]), - 'slices': [slice(0, 4, 1), slice(0, 1, 1), slice(0, 6, 1)]}, - 'strided_slice_data': {'value': None, 'shape': int64_array([4, 1, 6]), 'kind': 'data'}, - 'strided_slice_input_1_data': {'value': None, 'shape': int64_array([3]), 'kind': 'data'}, - 'strided_slice_input_2_data': {'value': None, 'shape': int64_array([3]), 'kind': 'data'}, - 'strided_slice_input_3_data': {'value': None, 'shape': int64_array([3]), 'kind': 'data'}, - # - 'strided_slice_2': {'type': 'StridedSlice', 'kind': 'op', 'op': 'StridedSlice', - 'shrink_axis_mask': int64_array([0, 0, 0]), 'new_axis_mask': int64_array([0, 0, 0]), - 'slices': [slice(0, 4, 1), slice(0, 1, 1), slice(0, 6, 1)]}, - 'strided_slice_2_data': {'value': None, 'shape': int64_array([4, 1, 6]), 'kind': 'data'}, - # Output operation - 'output_op': {'kind': 'op', 'op': 'Result'}, - # squeeze op - 'squeeze': {'type': 'Squeeze', 'kind': 'op', 'op': 'Squeeze'}, - 'squeeze_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': int64_array([1])}, - 'squeeze_const_data': {'kind': 'data'}, - # unsqueeze op - 'unsqueeze': {'type': None, 'kind': 'op', 'op': 'Unsqueeze'}, - 'unsqueeze_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': int64_array([1])}, - 'unsqueeze_const_data': {'kind': 'data'}, - 'unsqueeze_data': {'value': None, 'shape': int64_array([4, 6]), 'kind': 'data'}, -} - - -class UselessStridedSliceTests(unittest.TestCase): - def test_single_stride_slice_removal(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'output_op'), - ], - {}, - nodes_with_edges_only=True - ) - - UselessStridedSliceEraser().find_and_replace_pattern(graph) - shape_inference(graph) - - graph_ref = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'output_op'), - ], - {'placeholder_data': {'shape': int64_array([4, 1, 6])}}, - nodes_with_edges_only=True - ) - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) - - def test_single_stride_slice_with_shrink_removal(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'output_op'), - ], - {'strided_slice': {'shrink_axis_mask': int64_array([0, 1, 0])}, - 'strided_slice_data': {'shape': int64_array([4, 6])}}, - nodes_with_edges_only=True - ) - graph.graph['layout'] = 'NCHW' - - UselessStridedSliceEraser().find_and_replace_pattern(graph) - shape_inference(graph) - - graph_ref = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'squeeze'), - ('squeeze_const', 'squeeze_const_data'), - ('squeeze_const_data', 'squeeze'), - ('squeeze', 'strided_slice_data'), - ('strided_slice_data', 'output_op') - ], - {'placeholder_data': {'shape': int64_array([4, 1, 6])}, - 'strided_slice_data': {'shape': int64_array([4, 6])}}, - nodes_with_edges_only=True - ) - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) - - def test_single_stride_slice_with_new_removal(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'output_op'), - ], - {'strided_slice': {'new_axis_mask': int64_array([0, 1, 0, 0])}, - 'strided_slice_data': {'shape': int64_array([4, 1, 1, 6])}}, - nodes_with_edges_only=True - ) - graph.graph['layout'] = 'NCHW' - - UselessStridedSliceEraser().find_and_replace_pattern(graph) - shape_inference(graph) - - graph_ref = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'unsqueeze'), - ('unsqueeze_const', 'unsqueeze_const_data'), - ('unsqueeze_const_data', 'unsqueeze'), - ('unsqueeze', 'strided_slice_data'), - ('strided_slice_data', 'output_op') - ], - {'placeholder_data': {'shape': int64_array([4, 1, 6])}, - 'strided_slice_data': {'shape': int64_array([4, 1, 1, 6])}}, - nodes_with_edges_only=True - ) - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) - - def test_single_stride_slice_with_shrink_and_new_removal(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'output_op'), - ], - {'strided_slice': {'shrink_axis_mask': int64_array([0, 1, 0, 0]), - 'new_axis_mask': int64_array([0, 0, 1, 0])}, - 'strided_slice_data': {'shape': int64_array([4, 1, 6])}}, - nodes_with_edges_only=True - ) - graph.graph['layout'] = 'NCHW' - - UselessStridedSliceEraser().find_and_replace_pattern(graph) - shape_inference(graph) - - graph_ref = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'unsqueeze'), - ('unsqueeze_const', 'unsqueeze_const_data'), - ('unsqueeze_const_data', 'unsqueeze'), - ('unsqueeze', 'unsqueeze_data'), - ('unsqueeze_data', 'squeeze'), - ('squeeze_const', 'squeeze_const_data'), - ('squeeze_const_data', 'squeeze'), - ('squeeze', 'strided_slice_data'), - ('strided_slice_data', 'output_op') - ], - {'placeholder_data': {'shape': int64_array([4, 1, 6])}, - 'unsqueeze_data': {'shape': int64_array([4, 1, 1, 6])}, - 'strided_slice_data': {'shape': int64_array([4, 1, 6])}, - 'unsqueeze_const': {'value': int64_array([2])}, - }, - nodes_with_edges_only=True - ) - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) - - def test_single_stride_slice_with_new_and_shrink_removal(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'output_op'), - ], - {'strided_slice': {'shrink_axis_mask': int64_array([0, 0, 1, 0]), - 'new_axis_mask': int64_array([0, 1, 0, 0])}, - 'strided_slice_data': {'shape': int64_array([4, 1, 6])}}, - nodes_with_edges_only=True - ) - graph.graph['layout'] = 'NCHW' - - UselessStridedSliceEraser().find_and_replace_pattern(graph) - shape_inference(graph) - - graph_ref = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'unsqueeze'), - ('unsqueeze_const', 'unsqueeze_const_data'), - ('unsqueeze_const_data', 'unsqueeze'), - ('unsqueeze', 'unsqueeze_data'), - ('unsqueeze_data', 'squeeze'), - ('squeeze_const', 'squeeze_const_data'), - ('squeeze_const_data', 'squeeze'), - ('squeeze', 'strided_slice_data'), - ('strided_slice_data', 'output_op') - ], - {'unsqueeze_data': {'shape': int64_array([4, 1, 1, 6])}, - 'strided_slice_data': {'shape': int64_array([4, 1, 6])}, - 'squeeze_const': {'value': int64_array([2])}, - }, - nodes_with_edges_only=True - ) - - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) - - def test_consecutive_stride_slices_removal(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'strided_slice_2'), - ('strided_slice_input_1_data', 'strided_slice_2'), - ('strided_slice_input_2_data', 'strided_slice_2'), - ('strided_slice_input_3_data', 'strided_slice_2'), - ('strided_slice_2', 'strided_slice_2_data'), - ('strided_slice_2_data', 'output_op'), - ], - {}, - nodes_with_edges_only=True - ) - - UselessStridedSliceEraser().find_and_replace_pattern(graph) - shape_inference(graph) - - graph_ref = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'output_op'), - ], - {'placeholder_data': {'shape': int64_array([4, 1, 6])}} - ) - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) - - def test_negative(self): - graph = build_graph(nodes_attributes, - [('placeholder', 'placeholder_data'), - ('placeholder_data', 'strided_slice'), - ('strided_slice_input_1_data', 'strided_slice'), - ('strided_slice_input_2_data', 'strided_slice'), - ('strided_slice_input_3_data', 'strided_slice'), - ('strided_slice', 'strided_slice_data'), - ('strided_slice_data', 'output_op'), - ], - {'strided_slice_data': {'value': []}}, - nodes_with_edges_only=True - ) - graph_ref = graph.copy() - UselessStridedSliceEraser().find_and_replace_pattern(graph) - (flag, resp) = compare_graphs(graph, graph_ref, 'output_op', check_op_attrs=True) - self.assertTrue(flag, resp) diff --git a/model-optimizer/extensions/middle/ssd_anchors_to_const.py b/model-optimizer/extensions/middle/ssd_anchors_to_const.py index bf9b6575e8c..a6896ca93cf 100644 --- a/model-optimizer/extensions/middle/ssd_anchors_to_const.py +++ b/model-optimizer/extensions/middle/ssd_anchors_to_const.py @@ -17,6 +17,7 @@ import numpy as np from extensions.middle.AnchorToPriorBox import AnchorToPriorBoxes +from extensions.middle.SliceLikeToStridedSlice import SliceLikeToStridedSlice from mo.graph.graph import Graph from mo.middle.replacement import MiddleReplacementPattern from mo.ops.const import Const @@ -33,6 +34,9 @@ class SsdAnchorsMiddleReplacer(MiddleReplacementPattern): def run_after(self): return [AnchorToPriorBoxes] + def run_before(self): + return [SliceLikeToStridedSlice] + def pattern(self): return dict( diff --git a/model-optimizer/extensions/ops/BlockLSTM.py b/model-optimizer/extensions/ops/BlockLSTM.py index 4256ad55e90..de670dec48b 100644 --- a/model-optimizer/extensions/ops/BlockLSTM.py +++ b/model-optimizer/extensions/ops/BlockLSTM.py @@ -26,7 +26,7 @@ class BlockLSTM(Op): mandatory_props = { 'op': __class__.op, 'infer': __class__.infer, - 'type': __class__.op, + 'type': None, } super().__init__(graph, mandatory_props, attrs) diff --git a/model-optimizer/extensions/ops/Cast.py b/model-optimizer/extensions/ops/Cast.py index 572d2170e33..abc25065fd4 100644 --- a/model-optimizer/extensions/ops/Cast.py +++ b/model-optimizer/extensions/ops/Cast.py @@ -30,6 +30,7 @@ class Cast(Op): mandatory_props = { 'op': __class__.op, 'type': 'Convert', + 'version': 'opset1', 'infer': __class__.infer, 'type_infer': __class__.type_infer, 'dst_type': None, diff --git a/model-optimizer/extensions/ops/DetectionOutput.py b/model-optimizer/extensions/ops/DetectionOutput.py index 8b70f1ee2b9..9a7f52c7207 100644 --- a/model-optimizer/extensions/ops/DetectionOutput.py +++ b/model-optimizer/extensions/ops/DetectionOutput.py @@ -28,6 +28,7 @@ class DetectionOutput(Op): super().__init__(graph, { 'type': self.op, 'op': self.op, + 'version': 'opset1', 'in_ports_count': 3, 'out_ports_count': 1, 'infer': multi_box_detection_infer, diff --git a/model-optimizer/extensions/ops/Enter.py b/model-optimizer/extensions/ops/Enter.py index 167c6b21884..8c9330580ea 100644 --- a/model-optimizer/extensions/ops/Enter.py +++ b/model-optimizer/extensions/ops/Enter.py @@ -25,7 +25,7 @@ class Enter(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'in_ports_count': 1, 'infer': Enter.enter_infer, diff --git a/model-optimizer/extensions/ops/Exit.py b/model-optimizer/extensions/ops/Exit.py index cd2a48ffd20..ba64e892648 100644 --- a/model-optimizer/extensions/ops/Exit.py +++ b/model-optimizer/extensions/ops/Exit.py @@ -25,7 +25,7 @@ class Exit(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': Exit.exit_infer, 'in_ports_count': 1, diff --git a/model-optimizer/extensions/ops/GRUCell.py b/model-optimizer/extensions/ops/GRUCell.py index d05980aacd4..fbcb2fb1542 100644 --- a/model-optimizer/extensions/ops/GRUCell.py +++ b/model-optimizer/extensions/ops/GRUCell.py @@ -39,6 +39,7 @@ class GRUCell(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 4, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/GatherTree.py b/model-optimizer/extensions/ops/GatherTree.py index 004845eaa29..4dc40de7a01 100644 --- a/model-optimizer/extensions/ops/GatherTree.py +++ b/model-optimizer/extensions/ops/GatherTree.py @@ -25,6 +25,7 @@ class GatherTree(Op): mandatory_props = { 'op': __class__.op, 'type': __class__.op, + 'version': 'opset1', 'infer': __class__.infer, 'in_ports_count': 4, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/Log.py b/model-optimizer/extensions/ops/Log.py deleted file mode 100644 index a5e07e1610c..00000000000 --- a/model-optimizer/extensions/ops/Log.py +++ /dev/null @@ -1,43 +0,0 @@ -""" - Copyright (C) 2018-2020 Intel Corporation - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -""" -import numpy as np - -from mo.graph.graph import Node, Graph -from mo.ops.op import Op - - -class LogOp(Op): - op = 'Log' - - def __init__(self, graph: Graph, attrs: dict): - mandatory_props = { - 'type': __class__.op, - 'op': __class__.op, - 'infer': __class__.infer, - 'in_ports_count': 1, - 'out_ports_count': 1, - } - super().__init__(graph, mandatory_props, attrs) - - @staticmethod - def infer(node: Node): - assert len(node.in_nodes()) == 1 - assert len(node.out_nodes()) == 1 - input_node = node.in_node() - - node.out_node(0).shape = input_node.shape.copy() - if input_node.has_valid('value'): - node.out_node(0).value = np.array(np.log(input_node.value)) diff --git a/model-optimizer/extensions/ops/MatMul.py b/model-optimizer/extensions/ops/MatMul.py index cc2b3211250..d96e47c144f 100644 --- a/model-optimizer/extensions/ops/MatMul.py +++ b/model-optimizer/extensions/ops/MatMul.py @@ -69,6 +69,7 @@ class MatMul(Op): mandatory_props = { 'type': self.op, 'op': self.op, + 'version': 'opset1', 'transpose_a': False, 'transpose_b': False, 'infer': __class__.infer, diff --git a/model-optimizer/extensions/ops/NextIteration.py b/model-optimizer/extensions/ops/NextIteration.py index 80b76025876..35b28e3ad45 100644 --- a/model-optimizer/extensions/ops/NextIteration.py +++ b/model-optimizer/extensions/ops/NextIteration.py @@ -25,7 +25,7 @@ class NextIteration(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'in_ports_count': 1, 'infer': NextIteration.enter_infer, diff --git a/model-optimizer/extensions/ops/RNNCell.py b/model-optimizer/extensions/ops/RNNCell.py index db62e3de8af..cb0a94a88cb 100644 --- a/model-optimizer/extensions/ops/RNNCell.py +++ b/model-optimizer/extensions/ops/RNNCell.py @@ -39,6 +39,7 @@ class RNNCell(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 4, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/ReduceOps.py b/model-optimizer/extensions/ops/ReduceOps.py index 65d05fa79c4..7867acefd15 100644 --- a/model-optimizer/extensions/ops/ReduceOps.py +++ b/model-optimizer/extensions/ops/ReduceOps.py @@ -90,6 +90,7 @@ class ReduceOp(Op): super().__init__(graph, { 'op': self.op, 'type': self.op_type, + 'version': 'opset1', 'infer': reduce_infer, 'keep_dims': 0, 'in_ports_count': 2, diff --git a/model-optimizer/extensions/ops/TensorArray.py b/model-optimizer/extensions/ops/TensorArray.py index 9426142c0f2..fcb6e2975cf 100644 --- a/model-optimizer/extensions/ops/TensorArray.py +++ b/model-optimizer/extensions/ops/TensorArray.py @@ -25,7 +25,7 @@ class TensorArray(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': TensorArray.array_infer, } diff --git a/model-optimizer/extensions/ops/TensorArrayGather.py b/model-optimizer/extensions/ops/TensorArrayGather.py index eddc0a8228f..82a0362008a 100644 --- a/model-optimizer/extensions/ops/TensorArrayGather.py +++ b/model-optimizer/extensions/ops/TensorArrayGather.py @@ -26,7 +26,7 @@ class TensorArrayGather(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': TensorArrayGather.array_infer, } diff --git a/model-optimizer/extensions/ops/TensorArrayRead.py b/model-optimizer/extensions/ops/TensorArrayRead.py index d16b2f4dcaa..0fd4782f486 100644 --- a/model-optimizer/extensions/ops/TensorArrayRead.py +++ b/model-optimizer/extensions/ops/TensorArrayRead.py @@ -25,7 +25,7 @@ class TensorArrayReader(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': TensorArrayReader.array_infer, } diff --git a/model-optimizer/extensions/ops/TensorArrayScatter.py b/model-optimizer/extensions/ops/TensorArrayScatter.py index 176c34d3aa7..6575f2736d7 100644 --- a/model-optimizer/extensions/ops/TensorArrayScatter.py +++ b/model-optimizer/extensions/ops/TensorArrayScatter.py @@ -26,7 +26,7 @@ class TensorArrayScatter(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': TensorArrayScatter.array_infer, } diff --git a/model-optimizer/extensions/ops/TensorArraySize.py b/model-optimizer/extensions/ops/TensorArraySize.py index c4951d5dd02..114def202a4 100644 --- a/model-optimizer/extensions/ops/TensorArraySize.py +++ b/model-optimizer/extensions/ops/TensorArraySize.py @@ -25,7 +25,7 @@ class TensorArraySize(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': TensorArraySize.array_infer, } diff --git a/model-optimizer/extensions/ops/TensorArrayWrite.py b/model-optimizer/extensions/ops/TensorArrayWrite.py index fa4d50a91a2..6c8171cf154 100644 --- a/model-optimizer/extensions/ops/TensorArrayWrite.py +++ b/model-optimizer/extensions/ops/TensorArrayWrite.py @@ -26,7 +26,7 @@ class TensorArrayWriter(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': TensorArrayWriter.array_infer, } diff --git a/model-optimizer/extensions/ops/accum.py b/model-optimizer/extensions/ops/accum.py index 12a614dd521..d52833928cc 100644 --- a/model-optimizer/extensions/ops/accum.py +++ b/model-optimizer/extensions/ops/accum.py @@ -27,6 +27,7 @@ class AccumOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'extension', 'top_height': 0, 'top_width': 0, 'size_divisible_by': 0, diff --git a/model-optimizer/extensions/ops/activation_ops.py b/model-optimizer/extensions/ops/activation_ops.py index fb105eaeb92..cd4282bf4ae 100644 --- a/model-optimizer/extensions/ops/activation_ops.py +++ b/model-optimizer/extensions/ops/activation_ops.py @@ -34,6 +34,7 @@ class Activation(Op): 'type': self.op, 'op': self.op, 'operation': self.operation, + 'version': 'opset1', 'infer': self.infer, 'in_ports_count': 1, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/argmax.py b/model-optimizer/extensions/ops/argmax.py index 52c1a93a16d..366f130327c 100644 --- a/model-optimizer/extensions/ops/argmax.py +++ b/model-optimizer/extensions/ops/argmax.py @@ -31,6 +31,7 @@ class ArgMaxOp(Op): 'type': __class__.op, 'op': __class__.op, 'infer': ArgMaxOp.argmax_infer, + 'output_type': np.int64, 'in_ports_count': 2, 'out_ports_count': 1, } diff --git a/model-optimizer/extensions/ops/axpy.py b/model-optimizer/extensions/ops/axpy.py index ce3fff5235e..c2438765448 100644 --- a/model-optimizer/extensions/ops/axpy.py +++ b/model-optimizer/extensions/ops/axpy.py @@ -27,7 +27,7 @@ class AxpyOp(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': None }, attrs) diff --git a/model-optimizer/extensions/ops/bn.py b/model-optimizer/extensions/ops/bn.py index 94b5e2e9ad4..ae3edc02bc5 100644 --- a/model-optimizer/extensions/ops/bn.py +++ b/model-optimizer/extensions/ops/bn.py @@ -27,7 +27,7 @@ class BNOp(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'in_ports_count': 5, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/box_nms.py b/model-optimizer/extensions/ops/box_nms.py index 9f96affb158..affcccc8ba9 100644 --- a/model-optimizer/extensions/ops/box_nms.py +++ b/model-optimizer/extensions/ops/box_nms.py @@ -27,7 +27,7 @@ class BoxNms(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'coord_start': 2, 'force_suppress': False, diff --git a/model-optimizer/extensions/ops/bucketize.py b/model-optimizer/extensions/ops/bucketize.py index 9255a732dac..9e98a6268aa 100644 --- a/model-optimizer/extensions/ops/bucketize.py +++ b/model-optimizer/extensions/ops/bucketize.py @@ -28,6 +28,7 @@ class Bucketize(Op): 'kind': 'op', 'type': __class__.op, 'op': __class__.op, + 'version': 'extension', 'type_infer': self.type_infer, 'infer': self.infer, 'in_ports_count': 2, diff --git a/model-optimizer/extensions/ops/constant_fill.py b/model-optimizer/extensions/ops/constant_fill.py index 2e9714165b3..46cabbf914d 100644 --- a/model-optimizer/extensions/ops/constant_fill.py +++ b/model-optimizer/extensions/ops/constant_fill.py @@ -30,7 +30,7 @@ class ConstantFill(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'input_as_shape': 1, 'in_ports_count': 1, diff --git a/model-optimizer/extensions/ops/copyop.py b/model-optimizer/extensions/ops/copyop.py index 20d5d8a03d3..dbd0977a4d0 100644 --- a/model-optimizer/extensions/ops/copyop.py +++ b/model-optimizer/extensions/ops/copyop.py @@ -27,7 +27,7 @@ class CopyOp(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': None }, attrs) diff --git a/model-optimizer/extensions/ops/correlation.py b/model-optimizer/extensions/ops/correlation.py index c214ad2784b..b0396435f0f 100644 --- a/model-optimizer/extensions/ops/correlation.py +++ b/model-optimizer/extensions/ops/correlation.py @@ -31,6 +31,7 @@ class CorrelationOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'extension', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': CorrelationOp.corr_infer diff --git a/model-optimizer/extensions/ops/ctc_greedy_decoder.py b/model-optimizer/extensions/ops/ctc_greedy_decoder.py index 716f173adb9..9262eac5bfa 100644 --- a/model-optimizer/extensions/ops/ctc_greedy_decoder.py +++ b/model-optimizer/extensions/ops/ctc_greedy_decoder.py @@ -27,6 +27,7 @@ class CTCGreedyDecoderOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'in_ports_count': 2, 'out_ports_count': 1, 'infer': CTCGreedyDecoderOp.ctc_greedy_decoder_infer diff --git a/model-optimizer/extensions/ops/data_augmentation.py b/model-optimizer/extensions/ops/data_augmentation.py index 8676c1dad67..55d9074438e 100644 --- a/model-optimizer/extensions/ops/data_augmentation.py +++ b/model-optimizer/extensions/ops/data_augmentation.py @@ -29,6 +29,7 @@ class DataAugmentationOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'extension', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': DataAugmentationOp.data_augmentation_infer diff --git a/model-optimizer/extensions/ops/depth_to_space.py b/model-optimizer/extensions/ops/depth_to_space.py index a559f08de50..8b5fe68aee7 100644 --- a/model-optimizer/extensions/ops/depth_to_space.py +++ b/model-optimizer/extensions/ops/depth_to_space.py @@ -30,6 +30,7 @@ class DepthToSpaceOp(Op): mandatory_props = { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'mode': 'blocks_first', diff --git a/model-optimizer/extensions/ops/detectionoutput_onnx.py b/model-optimizer/extensions/ops/detectionoutput_onnx.py index ab89c12956b..0b41185a35c 100644 --- a/model-optimizer/extensions/ops/detectionoutput_onnx.py +++ b/model-optimizer/extensions/ops/detectionoutput_onnx.py @@ -28,6 +28,7 @@ class ExperimentalDetectronDetectionOutput(Op): mandatory_props = dict( type=__class__.op, op=__class__.op, + version='experimental', infer=__class__.infer, type_infer=self.type_infer, in_ports_count=4, diff --git a/model-optimizer/extensions/ops/elementwise.py b/model-optimizer/extensions/ops/elementwise.py index ab9915b4c7c..754287fb9a6 100644 --- a/model-optimizer/extensions/ops/elementwise.py +++ b/model-optimizer/extensions/ops/elementwise.py @@ -35,6 +35,7 @@ class Elementwise(Op): super().__init__(graph, { 'op': self.op, 'type': self.op_type, + 'version': 'opset1', 'infer': lambda node: eltwise_infer(node, self.operation), 'type_infer': self.type_infer, 'can_be_bias': True, @@ -202,6 +203,7 @@ class Round(Elementwise): enabled = False op = 'Round' op_type = None + version = 'extension' operation = staticmethod(lambda a: np.round(a)) diff --git a/model-optimizer/extensions/ops/exp.py b/model-optimizer/extensions/ops/exp.py deleted file mode 100644 index c04beb933f7..00000000000 --- a/model-optimizer/extensions/ops/exp.py +++ /dev/null @@ -1,44 +0,0 @@ -""" - Copyright (C) 2018-2020 Intel Corporation - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -""" - -import numpy as np - -from mo.graph.graph import Node, Graph -from mo.ops.op import Op - - -class ExpOp(Op): - op = 'Exp' - - def __init__(self, graph: Graph, attrs: dict): - mandatory_props = { - 'type': __class__.op, - 'op': __class__.op, - 'infer': __class__.infer, - 'in_ports_count': 1, - 'out_ports_count': 1, - } - super().__init__(graph, mandatory_props, attrs) - - @staticmethod - def infer(node: Node): - assert len(node.in_nodes()) == 1 - assert len(node.out_nodes()) == 1 - input_node = node.in_node() - assert input_node.has_valid('shape') - node.out_node().shape = input_node.shape.copy() - if input_node.has_valid('value'): - node.out_node().value = np.exp(input_node.value) diff --git a/model-optimizer/extensions/ops/exp_test.py b/model-optimizer/extensions/ops/exp_test.py deleted file mode 100644 index 30eee108cfd..00000000000 --- a/model-optimizer/extensions/ops/exp_test.py +++ /dev/null @@ -1,76 +0,0 @@ -""" - Copyright (C) 2018-2020 Intel Corporation - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. -""" - -import unittest - -import numpy as np - -from extensions.ops.exp import ExpOp -from mo.graph.graph import Node -from mo.utils.unittest.graph import build_graph - -nodes_attributes = {'node_1': {'op': 'Identity', 'kind': 'op'}, - 'exp': {'op': 'Exp', 'kind': 'op'}, - 'node_3': {'op': 'Identity', 'kind': 'op'}, - 'op_output': {'kind': 'op', 'op': 'Result'} - } - - -class TestExpOp(unittest.TestCase): - def test_shape_only(self): - graph = build_graph(nodes_attributes, - [('node_1', 'exp'), - ('exp', 'node_3'), - ('node_3', 'op_output') - ], - {'node_3': {'shape': None}, - 'node_1': {'shape': np.array([1, 3, 10, 20])}, - }) - - exp_node = Node(graph, 'exp') - ExpOp.infer(exp_node) - exp_shape = np.array([1, 3, 10, 20]) - res_shape = graph.node['node_3']['shape'] - for i in range(0, len(exp_shape)): - self.assertEqual(exp_shape[i], res_shape[i]) - - def test_shape_and_value(self): - graph = build_graph(nodes_attributes, - [('node_1', 'exp'), - ('exp', 'node_3'), - ('node_3', 'op_output') - ], - { - 'node_3': { - 'shape': None, - 'value': None, - }, - 'node_1': { - 'shape': np.array([2]), - 'value': np.array([0, 1], dtype=np.float32), - }, - }) - - exp_node = Node(graph, 'exp') - ExpOp.infer(exp_node) - exp_shape = np.array([2]) - exp_value = np.array([1, 2.7182818], dtype=np.float32) - res_shape = graph.node['node_3']['shape'] - res_value = graph.node['node_3']['value'] - for i in range(0, len(exp_shape)): - self.assertEqual(exp_shape[i], res_shape[i]) - for i in range(0, len(exp_value)): - self.assertAlmostEqual(exp_value[i], res_value[i], places=6) diff --git a/model-optimizer/extensions/ops/fakequantize.py b/model-optimizer/extensions/ops/fakequantize.py index 40569f43646..f9981b1b8dd 100644 --- a/model-optimizer/extensions/ops/fakequantize.py +++ b/model-optimizer/extensions/ops/fakequantize.py @@ -43,6 +43,7 @@ class FakeQuantize(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'levels': None, 'is_eltwise': True, # flag to switch between dumping FakeQuantize as statistics and keeping it as layer in IR diff --git a/model-optimizer/extensions/ops/gather.py b/model-optimizer/extensions/ops/gather.py index 1a25a69de27..6c444cb52c9 100644 --- a/model-optimizer/extensions/ops/gather.py +++ b/model-optimizer/extensions/ops/gather.py @@ -30,6 +30,7 @@ class Gather(Op): super().__init__(graph, { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'infer': self.infer, diff --git a/model-optimizer/extensions/ops/grn.py b/model-optimizer/extensions/ops/grn.py index 8f20683dd25..afcb881238f 100644 --- a/model-optimizer/extensions/ops/grn.py +++ b/model-optimizer/extensions/ops/grn.py @@ -26,6 +26,7 @@ class GRNOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': copy_shape_infer diff --git a/model-optimizer/extensions/ops/hard_sigmoid.py b/model-optimizer/extensions/ops/hard_sigmoid.py index f410932db15..52126dec3b5 100644 --- a/model-optimizer/extensions/ops/hard_sigmoid.py +++ b/model-optimizer/extensions/ops/hard_sigmoid.py @@ -28,6 +28,7 @@ class HardSigmoid(Op): super().__init__(graph, { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'infer': self.infer, 'in_ports_count': 3, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/interpolate.py b/model-optimizer/extensions/ops/interpolate.py index 27f465a9f77..0a0e9d27aa9 100644 --- a/model-optimizer/extensions/ops/interpolate.py +++ b/model-optimizer/extensions/ops/interpolate.py @@ -26,6 +26,7 @@ class Interpolate(Op): mandatory_props = { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'axes': None, 'mode': None, diff --git a/model-optimizer/extensions/ops/lstm_cell.py b/model-optimizer/extensions/ops/lstm_cell.py index aaae4c23797..218266d7ee9 100644 --- a/model-optimizer/extensions/ops/lstm_cell.py +++ b/model-optimizer/extensions/ops/lstm_cell.py @@ -42,6 +42,7 @@ class LSTMCell(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'infer': __class__.infer, 'in_ports_count': 5, 'out_ports_count': 2, diff --git a/model-optimizer/extensions/ops/mvn.py b/model-optimizer/extensions/ops/mvn.py index 80ae4ea05b6..79cc28687ce 100644 --- a/model-optimizer/extensions/ops/mvn.py +++ b/model-optimizer/extensions/ops/mvn.py @@ -29,6 +29,7 @@ class MVN(Op): 'kind': 'op', 'type': __class__.op, 'op': __class__.op, + 'version': 'opset2', 'eps': None, 'across_channels': 0, 'normalize_variance': 1, diff --git a/model-optimizer/extensions/ops/non_max_suppression.py b/model-optimizer/extensions/ops/non_max_suppression.py index 82b512dc4c3..66bca864660 100644 --- a/model-optimizer/extensions/ops/non_max_suppression.py +++ b/model-optimizer/extensions/ops/non_max_suppression.py @@ -19,7 +19,9 @@ import numpy as np from mo.front.common.partial_infer.utils import int64_array from mo.graph.graph import Node, Graph +from mo.middle.passes.convert_data_type import np_data_type_to_destination_type from mo.ops.op import Op +from mo.utils.error import Error class NonMaxSuppression(Op): @@ -29,7 +31,9 @@ class NonMaxSuppression(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset3', 'infer': __class__.infer, + 'output_type': np.int64, 'center_point_box': 0, 'box_encoding': 'corner', 'in_ports_count': 5, @@ -43,15 +47,16 @@ class NonMaxSuppression(Op): def supported_attrs(self): if self.ir_version < 10: - return [ - 'center_point_box', - ] + return ['center_point_box'] else: - return [ - 'sort_result_descending', - 'box_encoding' - ] - + version = self.get_opset() + if version == 'opset3': + return ['sort_result_descending', 'box_encoding', + ('output_type', lambda node: np_data_type_to_destination_type(node.output_type))] + elif version == 'opset1': + return ['sort_result_descending', 'box_encoding'] + else: + raise Error('Unsupported operation opset version "{}"'.format(version)) @staticmethod def infer(node: Node): @@ -76,5 +81,10 @@ class NonMaxSuppression(Op): @staticmethod def type_infer(node): - node.out_port(0).set_data_type(np.int64 if node.graph.graph['cmd_params'].generate_experimental_IR_V10 else - np.int32) + if not node.graph.graph['cmd_params'].generate_experimental_IR_V10: + node.out_port(0).set_data_type(np.int32) + else: + if node.get_opset() == 'opset3': + node.out_port(0).set_data_type(node.output_type) + else: + node.out_port(0).set_data_type(np.int64) diff --git a/model-optimizer/extensions/ops/non_max_suppression_test.py b/model-optimizer/extensions/ops/non_max_suppression_test.py new file mode 100644 index 00000000000..bf3c1ff3af5 --- /dev/null +++ b/model-optimizer/extensions/ops/non_max_suppression_test.py @@ -0,0 +1,90 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import unittest + +import numpy as np + +from extensions.ops.non_max_suppression import NonMaxSuppression +from mo.front.common.partial_infer.utils import int64_array +from mo.graph.graph import Node +from mo.utils.unittest.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, result, \ + connect, FakeAttr + + +class TestNonMaxSuppressionInfer(unittest.TestCase): + def setUp(self): + nodes = { + **regular_op_with_shaped_data('boxes', [10, 100, 4], {'type': 'Parameter'}), + **regular_op_with_shaped_data('scores', [10, 5, 100], {'type': 'Parameter'}), + **valued_const_with_data('max_output_per_class', int64_array(10)), + **regular_op_with_shaped_data('nms', None, {'op': 'NonMaxSuppression', 'type': 'NonMaxSuppression', + 'name': 'nms'}), + **result('output'), + } + + self.graph = build_graph(nodes, [ + *connect('boxes', '0:nms'), + *connect('scores', '1:nms'), + *connect('max_output_per_class', '2:nms'), + *connect('nms', 'output'), + ], nodes_with_edges_only=True) + + def test_nms_infer_v7(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=False, ir_version=7) + + nms_node = Node(self.graph, 'nms') + nms_node['version'] = 'opset1' + NonMaxSuppression.infer(nms_node) + NonMaxSuppression.type_infer(nms_node) + + self.assertTrue(np.array_equal(nms_node.out_port(0).data.get_shape(), [100, 3])) + self.assertTrue(nms_node.out_port(0).get_data_type() == np.int32) + + def test_nms_infer_v10_opset1(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=True, ir_version=10) + + nms_node = Node(self.graph, 'nms') + nms_node['version'] = 'opset1' + NonMaxSuppression.infer(nms_node) + NonMaxSuppression.type_infer(nms_node) + + self.assertTrue(np.array_equal(nms_node.out_port(0).data.get_shape(), [100, 3])) + self.assertTrue(nms_node.out_port(0).get_data_type() == np.int64) + + def test_nms_infer_v10_i64_opset3(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=True, ir_version=10) + + nms_node = Node(self.graph, 'nms') + nms_node['version'] = 'opset3' + nms_node['output_type'] = np.int64 + NonMaxSuppression.infer(nms_node) + NonMaxSuppression.type_infer(nms_node) + + self.assertTrue(np.array_equal(nms_node.out_port(0).data.get_shape(), [100, 3])) + self.assertTrue(nms_node.out_port(0).get_data_type() == np.int64) + + def test_nms_infer_v10_i32_opset3(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=True, ir_version=10) + + nms_node = Node(self.graph, 'nms') + nms_node['version'] = 'opset3' + nms_node['output_type'] = np.int32 + NonMaxSuppression.infer(nms_node) + NonMaxSuppression.type_infer(nms_node) + + self.assertTrue(np.array_equal(nms_node.out_port(0).data.get_shape(), [100, 3])) + self.assertTrue(nms_node.out_port(0).get_data_type() == np.int32) diff --git a/model-optimizer/extensions/ops/normalize.py b/model-optimizer/extensions/ops/normalize.py index 429a54cbf68..bbb47df0e6b 100644 --- a/model-optimizer/extensions/ops/normalize.py +++ b/model-optimizer/extensions/ops/normalize.py @@ -27,7 +27,6 @@ class NormalizeOp(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'kind': 'op', 'type': __class__.op, 'op': __class__.op, 'eps': None, diff --git a/model-optimizer/extensions/ops/normalize_l2.py b/model-optimizer/extensions/ops/normalize_l2.py index ffd9045fd04..a0d19f1b7a8 100644 --- a/model-optimizer/extensions/ops/normalize_l2.py +++ b/model-optimizer/extensions/ops/normalize_l2.py @@ -25,9 +25,9 @@ class NormalizeL2Op(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'kind': 'op', 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'eps': None, 'p': None, 'eps_mode': None, diff --git a/model-optimizer/extensions/ops/one_hot.py b/model-optimizer/extensions/ops/one_hot.py index fa635bb5d24..948390bf6a8 100644 --- a/model-optimizer/extensions/ops/one_hot.py +++ b/model-optimizer/extensions/ops/one_hot.py @@ -27,6 +27,7 @@ class OneHot(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'axis': -1, 'infer': __class__.infer, 'on_value': None, diff --git a/model-optimizer/extensions/ops/parameter.py b/model-optimizer/extensions/ops/parameter.py index dc5a4e0290b..a260b731402 100644 --- a/model-optimizer/extensions/ops/parameter.py +++ b/model-optimizer/extensions/ops/parameter.py @@ -28,6 +28,7 @@ class Parameter(Op): mandatory_props = { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'infer': self.infer, 'is_input': True, diff --git a/model-optimizer/extensions/ops/power_file.py b/model-optimizer/extensions/ops/power_file.py index 5a8e46b377c..32b20120504 100644 --- a/model-optimizer/extensions/ops/power_file.py +++ b/model-optimizer/extensions/ops/power_file.py @@ -27,6 +27,7 @@ class PowerFileOp(Op): super().__init__(graph, { 'type': __class__.op, 'op': __class__.op, + 'version': 'extension', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': copy_shape_infer diff --git a/model-optimizer/extensions/ops/prediction_heatmap.py b/model-optimizer/extensions/ops/prediction_heatmap.py index 80b8968388e..94eabb42d1e 100644 --- a/model-optimizer/extensions/ops/prediction_heatmap.py +++ b/model-optimizer/extensions/ops/prediction_heatmap.py @@ -27,6 +27,7 @@ class PredictionHeatmapOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'extension', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': PredictionHeatmapOp.infer diff --git a/model-optimizer/extensions/ops/prelu.py b/model-optimizer/extensions/ops/prelu.py index 4ebe5998ab9..716af9a86da 100644 --- a/model-optimizer/extensions/ops/prelu.py +++ b/model-optimizer/extensions/ops/prelu.py @@ -30,6 +30,7 @@ class PreluOp(Op): super().__init__(graph, { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'infer': self.infer, diff --git a/model-optimizer/extensions/ops/priorbox.py b/model-optimizer/extensions/ops/priorbox.py index e925f58f50a..e21d0f66435 100644 --- a/model-optimizer/extensions/ops/priorbox.py +++ b/model-optimizer/extensions/ops/priorbox.py @@ -29,6 +29,7 @@ class PriorBoxOp(Op): mandatory_props = { 'type': self.op, 'op': self.op, + 'version': 'opset1', 'flip': 1, 'max_size': np.array([]), 'min_size': np.array([]), diff --git a/model-optimizer/extensions/ops/priorbox_clustered.py b/model-optimizer/extensions/ops/priorbox_clustered.py index db7e91434f0..3f9847eb5b0 100644 --- a/model-optimizer/extensions/ops/priorbox_clustered.py +++ b/model-optimizer/extensions/ops/priorbox_clustered.py @@ -29,6 +29,7 @@ class PriorBoxClusteredOp(Op): mandatory_props = { 'type': self.op, 'op': self.op, + 'version': 'opset1', 'in_ports_count': 2, 'out_ports_count': 1, 'infer': self.priorbox_clustered_infer, diff --git a/model-optimizer/extensions/ops/priorgridgenerator_onnx.py b/model-optimizer/extensions/ops/priorgridgenerator_onnx.py index 676556ee894..176a1a3466f 100644 --- a/model-optimizer/extensions/ops/priorgridgenerator_onnx.py +++ b/model-optimizer/extensions/ops/priorgridgenerator_onnx.py @@ -26,6 +26,7 @@ class ExperimentalDetectronPriorGridGenerator(Op): mandatory_props = dict( type=__class__.op, op=__class__.op, + version='experimental', infer=__class__.infer, ) super().__init__(graph, mandatory_props, attrs) diff --git a/model-optimizer/extensions/ops/proposal.py b/model-optimizer/extensions/ops/proposal.py index ba92ac1d438..afd25a78021 100644 --- a/model-optimizer/extensions/ops/proposal.py +++ b/model-optimizer/extensions/ops/proposal.py @@ -27,6 +27,7 @@ class ProposalOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'post_nms_topn': 300, # default in caffe-shared 'infer': ProposalOp.proposal_infer, 'in_ports_count': 3, diff --git a/model-optimizer/extensions/ops/proposal_onnx.py b/model-optimizer/extensions/ops/proposal_onnx.py index 637c85718c1..3265d218782 100644 --- a/model-optimizer/extensions/ops/proposal_onnx.py +++ b/model-optimizer/extensions/ops/proposal_onnx.py @@ -26,6 +26,7 @@ class ExperimentalDetectronGenerateProposalsSingleImage(Op): mandatory_props = dict( type=__class__.op, op=__class__.op, + version='experimental', infer=__class__.infer ) diff --git a/model-optimizer/extensions/ops/psroipooling.py b/model-optimizer/extensions/ops/psroipooling.py index f2ab82db338..a04152af9c3 100644 --- a/model-optimizer/extensions/ops/psroipooling.py +++ b/model-optimizer/extensions/ops/psroipooling.py @@ -27,6 +27,7 @@ class PSROIPoolingOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset2', 'mode': 'average', 'in_ports_count': 2, 'out_ports_count': 1, @@ -83,6 +84,7 @@ class DeformablePSROIPoolingOp(PSROIPoolingOp): updated_attrs = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'mode': 'bilinear_deformable', 'in_ports_count': 3, 'trans_std': 0, diff --git a/model-optimizer/extensions/ops/range.py b/model-optimizer/extensions/ops/range.py index c3cc7880749..4bb80c092ea 100644 --- a/model-optimizer/extensions/ops/range.py +++ b/model-optimizer/extensions/ops/range.py @@ -29,6 +29,7 @@ class Range(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'in_ports_count': 3, 'out_ports_count': 1, 'infer': __class__.infer, diff --git a/model-optimizer/extensions/ops/rank.py b/model-optimizer/extensions/ops/rank.py index 867c2c0ad00..c295758601b 100644 --- a/model-optimizer/extensions/ops/rank.py +++ b/model-optimizer/extensions/ops/rank.py @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ +import numpy as np from mo.graph.graph import Graph from mo.ops.op import Op @@ -23,9 +24,12 @@ class Rank(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'op': __class__.op, 'type': None, + 'op': self.op, + + 'output_type': np.int64, 'infer': None, + 'in_ports_count': 1, 'out_ports_count': 1, } diff --git a/model-optimizer/extensions/ops/regionyolo.py b/model-optimizer/extensions/ops/regionyolo.py index 98c0827634d..bab2835d7e3 100644 --- a/model-optimizer/extensions/ops/regionyolo.py +++ b/model-optimizer/extensions/ops/regionyolo.py @@ -30,6 +30,7 @@ class RegionYoloOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': RegionYoloOp.regionyolo_infer diff --git a/model-optimizer/extensions/ops/reorgyolo.py b/model-optimizer/extensions/ops/reorgyolo.py index 58f26350345..366554508c4 100644 --- a/model-optimizer/extensions/ops/reorgyolo.py +++ b/model-optimizer/extensions/ops/reorgyolo.py @@ -27,6 +27,7 @@ class ReorgYoloOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset2', 'infer': ReorgYoloOp.reorgyolo_infer } super().__init__(graph, mandatory_props, attrs) diff --git a/model-optimizer/extensions/ops/reverse_sequence.py b/model-optimizer/extensions/ops/reverse_sequence.py index 1c8310ed85a..f87ab3cb559 100644 --- a/model-optimizer/extensions/ops/reverse_sequence.py +++ b/model-optimizer/extensions/ops/reverse_sequence.py @@ -24,6 +24,7 @@ class ReverseSequence(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { 'type': __class__.op, + 'version': 'opset1', 'seq_axis': None, 'batch_axis': 0, 'op': __class__.op, diff --git a/model-optimizer/extensions/ops/roifeatureextractor_onnx.py b/model-optimizer/extensions/ops/roifeatureextractor_onnx.py index 255fff062b6..87d6a06d7df 100644 --- a/model-optimizer/extensions/ops/roifeatureextractor_onnx.py +++ b/model-optimizer/extensions/ops/roifeatureextractor_onnx.py @@ -26,6 +26,7 @@ class ExperimentalDetectronROIFeatureExtractor(Op): mandatory_props = dict( type=__class__.op, op=__class__.op, + version='experimental', infer=__class__.infer, in_ports_count=5, out_ports_count=2, diff --git a/model-optimizer/extensions/ops/select.py b/model-optimizer/extensions/ops/select.py index 4d64b39f7af..8e7f4dcd3c4 100644 --- a/model-optimizer/extensions/ops/select.py +++ b/model-optimizer/extensions/ops/select.py @@ -28,6 +28,7 @@ class Select(Op): mandatory_props = { 'op': __class__.op, 'type': __class__.op, + 'version': 'opset1', 'in_ports_count': 3, 'out_ports_count': 1, 'infer': __class__.infer, diff --git a/model-optimizer/extensions/ops/simplernms.py b/model-optimizer/extensions/ops/simplernms.py index af7490a93b0..4400ce5e07c 100644 --- a/model-optimizer/extensions/ops/simplernms.py +++ b/model-optimizer/extensions/ops/simplernms.py @@ -30,6 +30,7 @@ class SimplerNMSOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'in_ports_count': 3, 'out_ports_count': 1, 'infer': SimplerNMSOp.simplernms_infer diff --git a/model-optimizer/extensions/ops/size.py b/model-optimizer/extensions/ops/size.py index cb853af0a66..282af22d5df 100644 --- a/model-optimizer/extensions/ops/size.py +++ b/model-optimizer/extensions/ops/size.py @@ -14,25 +14,41 @@ limitations under the License. """ -import networkx as nx import numpy as np -from mo.graph.graph import Node +from mo.graph.graph import Node, Graph from mo.ops.op import Op class Size(Op): op = 'Size' - def __init__(self, graph: nx.MultiDiGraph, attrs: dict): + def __init__(self, graph: Graph, attrs: dict): + assert 'output_type' in attrs, 'Size has mandatory `output_type` attribute' + mandatory_props = { - 'op': __class__.op, - 'infer': __class__.infer, + 'type': None, + 'op': self.op, + + 'output_type': np.int64, + 'infer': self.infer, } super().__init__(graph, mandatory_props, attrs) @staticmethod def infer(node: Node): - size = np.prod(node.in_node().shape) - value = np.array(size, dtype=np.int) - node.out_node().shape = np.array(value.shape, dtype=np.int64) + name = node.soft_get('name', node.id) + connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()] + assert len(connected_in_ports) == 1, \ + 'Size operation should have exact one input node, but it has {}'.format(len(connected_in_ports)) + + input_shape = node.in_port(0).data.get_shape() + assert input_shape is not None, \ + 'Input shape is undefined for Size node `{}`'.format(node.soft_get('name', node.id)) + + assert node.has_valid('output_type'), \ + '`output_type` attribute is not set for Size node `{}`'.format(name) + assert node.output_type in [np.int64, np.int32], \ + 'Size `output_type` attribute must be int32 or int64, `{}` found'.format(np.dtype(node.output_type).name) + + node.out_port(0).data.set_value(np.array(np.prod(input_shape), dtype=node.output_type)) diff --git a/model-optimizer/extensions/ops/slice_like.py b/model-optimizer/extensions/ops/slice_like.py new file mode 100644 index 00000000000..ed02881c828 --- /dev/null +++ b/model-optimizer/extensions/ops/slice_like.py @@ -0,0 +1,54 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" +import numpy as np + +from mo.front.caffe.extractors.utils import get_canonical_axis_index +from mo.front.common.partial_infer.utils import int64_array +from mo.graph.graph import Graph +from mo.ops.op import Op + + +class SliceLike(Op): + op = 'slice_like' + enabled = True + + def __init__(self, graph: Graph, attrs: dict): + assert 'axes' in attrs, 'Please set mandatory `axes` attribute for `slice_like` operation' + super().__init__(graph, { + 'type': None, + 'op': self.op, + 'in_ports_count': 2, + 'out_ports_count': 1, + 'infer': self.infer, + }, attrs) + + @staticmethod + def infer(node): + input_shape = node.in_port(0).data.get_shape() + shape_like = node.in_port(1).data.get_shape() + + new_shape = np.copy(input_shape) + if node.axes is not None: + node.axes = sorted([get_canonical_axis_index(input_shape, i) for i in node.axes]) + for i in node.axes: + new_shape[i] = shape_like[i] + else: + assert input_shape.size == shape_like.size,\ + 'Input shape ranks are inconsistent: {} and {}'.format(input_shape.size, shape_like.size) + node.axes = int64_array(range(shape_like.size)) + new_shape = np.copy(shape_like) + + node.out_port(0).data.set_shape(new_shape) diff --git a/model-optimizer/extensions/ops/slice_like_test.py b/model-optimizer/extensions/ops/slice_like_test.py new file mode 100644 index 00000000000..dc1abd88187 --- /dev/null +++ b/model-optimizer/extensions/ops/slice_like_test.py @@ -0,0 +1,71 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" +import unittest + +import numpy as np + +from extensions.ops.slice_like import SliceLike +from mo.front.common.partial_infer.utils import int64_array +from mo.graph.graph import Node +from mo.utils.unittest.graph import build_graph + +nodes_attributes = { + 'input_data': {'kind': 'data', 'shape': int64_array([3, 4]), 'value': None}, + 'shape_like_data': {'kind': 'data', 'shape': int64_array([2, 3]), 'value': None}, + 'slice_like': {'kind': 'op', 'op': 'slice_data'}, + 'out_data': {'kind': 'data', 'shape': None, 'value': None} +} + +edges = [ + ('input_data', 'slice_like', {'in': 0}), + ('shape_like_data', 'slice_like', {'in': 1}), + ('slice_like', 'out_data') +] + + +class SliceLikeTest(unittest.TestCase): + + def test_1(self): + graph = build_graph(nodes_attributes, edges, {'slice_like': {'axes': None}}) + slice_like = Node(graph, 'slice_like') + SliceLike.infer(slice_like) + ref_shape = int64_array([2, 3]) + res_shape = graph.node['out_data']['shape'] + self.assertTrue(np.array_equal(res_shape, ref_shape)) + + def test_2(self): + graph = build_graph(nodes_attributes, edges, {'slice_like': {'axes': (0, 1)}}) + slice_like = Node(graph, 'slice_like') + SliceLike.infer(slice_like) + ref_shape = int64_array([2, 3]) + res_shape = graph.node['out_data']['shape'] + self.assertTrue(np.array_equal(res_shape, ref_shape)) + + def test_3(self): + graph = build_graph(nodes_attributes, edges, {'slice_like': {'axes': (0,)}}) + slice_like = Node(graph, 'slice_like') + SliceLike.infer(slice_like) + ref_shape = int64_array([2, 4]) + res_shape = graph.node['out_data']['shape'] + self.assertTrue(np.array_equal(res_shape, ref_shape)) + + def test_4(self): + graph = build_graph(nodes_attributes, edges, {'slice_like': {'axes': (-1,)}}) + slice_like = Node(graph, 'slice_like') + SliceLike.infer(slice_like) + ref_shape = int64_array([3, 3]) + res_shape = graph.node['out_data']['shape'] + self.assertTrue(np.array_equal(res_shape, ref_shape)) diff --git a/model-optimizer/extensions/ops/space_to_depth.py b/model-optimizer/extensions/ops/space_to_depth.py index f91d365292e..4f0a7244771 100644 --- a/model-optimizer/extensions/ops/space_to_depth.py +++ b/model-optimizer/extensions/ops/space_to_depth.py @@ -30,6 +30,7 @@ class SpaceToDepth(Op): mandatory_props = { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'mode': 'blocks_first', diff --git a/model-optimizer/extensions/ops/sparse_fill_empty_rows.py b/model-optimizer/extensions/ops/sparse_fill_empty_rows.py index 4f76e5e5dfd..012fa40bb59 100644 --- a/model-optimizer/extensions/ops/sparse_fill_empty_rows.py +++ b/model-optimizer/extensions/ops/sparse_fill_empty_rows.py @@ -43,6 +43,7 @@ class SparseFillEmptyRows(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 4, 'out_ports_count': 3 diff --git a/model-optimizer/extensions/ops/sparse_reshape.py b/model-optimizer/extensions/ops/sparse_reshape.py index 7866863288a..2a2c75ce6c2 100644 --- a/model-optimizer/extensions/ops/sparse_reshape.py +++ b/model-optimizer/extensions/ops/sparse_reshape.py @@ -28,8 +28,7 @@ class SparseReshape(Op): def __init__(self, graph: Graph, attrs: dict): mandatory_props = { - 'kind': 'op', - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'infer': self.infer, 'in_ports_count': 3, @@ -44,7 +43,6 @@ class SparseReshape(Op): def infer(node: Node): input_indices_shape = node.in_port(0).data.get_shape() input_shape_value = node.in_port(1).data.get_value() - input_shape_shape = node.in_port(1).data.get_shape() new_shape_value = node.in_port(2).data.get_value() new_shape_shape = node.in_port(2).data.get_shape() @@ -62,4 +60,4 @@ class SparseReshape(Op): output_indices_shape = np.concatenate((input_indices_shape[0:1], new_shape_shape)) node.out_port(0).data.set_shape(output_indices_shape) - #TODO: implement for constant input indices value + # TODO: implement for constant input indices value diff --git a/model-optimizer/extensions/ops/sparse_segment_mean.py b/model-optimizer/extensions/ops/sparse_segment_mean.py index 8eecc7febab..80e2c8d969c 100644 --- a/model-optimizer/extensions/ops/sparse_segment_mean.py +++ b/model-optimizer/extensions/ops/sparse_segment_mean.py @@ -40,6 +40,7 @@ class SparseSegmentMean(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 3, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/sparse_segment_sqrtn.py b/model-optimizer/extensions/ops/sparse_segment_sqrtn.py index 84473c93456..f2b83619f30 100644 --- a/model-optimizer/extensions/ops/sparse_segment_sqrtn.py +++ b/model-optimizer/extensions/ops/sparse_segment_sqrtn.py @@ -40,6 +40,7 @@ class SparseSegmentSqrtN(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 3, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/sparse_segment_sum.py b/model-optimizer/extensions/ops/sparse_segment_sum.py index 47989ab9023..18db477248d 100644 --- a/model-optimizer/extensions/ops/sparse_segment_sum.py +++ b/model-optimizer/extensions/ops/sparse_segment_sum.py @@ -40,6 +40,7 @@ class SparseSegmentSum(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 3, 'out_ports_count': 1, diff --git a/model-optimizer/extensions/ops/sparse_to_dense.py b/model-optimizer/extensions/ops/sparse_to_dense.py index bfb3f360469..50867cc3f7b 100644 --- a/model-optimizer/extensions/ops/sparse_to_dense.py +++ b/model-optimizer/extensions/ops/sparse_to_dense.py @@ -39,9 +39,9 @@ class SparseToDense(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'kind': 'op', 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'type_infer': self.type_infer, 'infer': self.infer, 'in_ports_count': 4, diff --git a/model-optimizer/extensions/ops/sparse_weighted_sum.py b/model-optimizer/extensions/ops/sparse_weighted_sum.py index ad7b275dc2d..ff76b211f1c 100644 --- a/model-optimizer/extensions/ops/sparse_weighted_sum.py +++ b/model-optimizer/extensions/ops/sparse_weighted_sum.py @@ -25,15 +25,14 @@ class ExperimentalSparseWeightedSum(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'kind': 'op', 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'reduce_op': None, 'type_infer': self.type_infer, 'infer': self.infer, 'in_ports_count': 6, 'out_ports_count': 1, - 'version': 'experimental', }, attrs) def supported_attrs(self): diff --git a/model-optimizer/extensions/ops/spatial_transformer.py b/model-optimizer/extensions/ops/spatial_transformer.py index 49888ef4848..62f3ffeb280 100644 --- a/model-optimizer/extensions/ops/spatial_transformer.py +++ b/model-optimizer/extensions/ops/spatial_transformer.py @@ -27,6 +27,7 @@ class SpatialTransformOp(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'in_ports_count': 1, 'out_ports_count': 1, 'infer': SpatialTransformOp.sp_infer diff --git a/model-optimizer/extensions/ops/split.py b/model-optimizer/extensions/ops/split.py index 23935ed0420..5cefb616115 100644 --- a/model-optimizer/extensions/ops/split.py +++ b/model-optimizer/extensions/ops/split.py @@ -164,6 +164,7 @@ class AttributedVariadicSplit(VariadicSplitBase): super().__init__(graph, { 'op': self.op, 'type': 'VariadicSplit', + 'version': 'opset1', 'infer': self.infer, @@ -236,6 +237,7 @@ class Split(SplitBase): super().__init__(graph, { 'op': self.op, 'type': self.op, + 'version': 'opset1', 'infer': self.infer, @@ -261,6 +263,7 @@ class AttributedSplit(SplitBase): super().__init__(graph, { 'op': self.op, 'type': 'Split', + 'version': 'opset1', 'axis': 1, diff --git a/model-optimizer/extensions/ops/stop_gradient.py b/model-optimizer/extensions/ops/stop_gradient.py index 45df1af3eba..51378870135 100644 --- a/model-optimizer/extensions/ops/stop_gradient.py +++ b/model-optimizer/extensions/ops/stop_gradient.py @@ -25,7 +25,7 @@ class StopGradientOp(Op): def __init__(self, graph: Graph, attrs: dict): super().__init__(graph, { - 'type': __class__.op, + 'type': None, 'op': __class__.op, 'identity': True, 'in_ports_count': 1, diff --git a/model-optimizer/extensions/ops/tensor_iterator.py b/model-optimizer/extensions/ops/tensor_iterator.py index 49fbd6a4972..5d43ffb25a9 100644 --- a/model-optimizer/extensions/ops/tensor_iterator.py +++ b/model-optimizer/extensions/ops/tensor_iterator.py @@ -34,6 +34,7 @@ class TensorIterator(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'input_port_map': [], # a list of dicts with such attrs as external_port_id, etc. 'output_port_map': [], # a list of dicts with such attrs as external_port_id, etc. 'back_edges': [], # a list of dicts with such attrs as from_layer, from_port, etc. diff --git a/model-optimizer/extensions/ops/topk.py b/model-optimizer/extensions/ops/topk.py index cde7c67d6da..dd39d2b6b73 100644 --- a/model-optimizer/extensions/ops/topk.py +++ b/model-optimizer/extensions/ops/topk.py @@ -17,6 +17,7 @@ import numpy as np from mo.graph.graph import Graph +from mo.middle.passes.convert_data_type import np_data_type_to_destination_type from mo.ops.op import Op, PermuteAttrs from mo.utils.error import Error @@ -29,8 +30,11 @@ class TopK(Op): super().__init__(graph, { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset3', 'infer': self.infer, 'type_infer': self.type_infer, + + 'index_element_type': np.int32, 'axis': None, 'mode': 'max', 'sort': 'none', @@ -41,7 +45,14 @@ class TopK(Op): }, attrs) def backend_attrs(self): - return ['axis', 'mode', 'sort'] + version = self.get_opset() + if version == 'opset3': + return ['axis', 'mode', 'sort', + ('index_element_type', lambda node: np_data_type_to_destination_type(node.index_element_type))] + elif version == 'opset1': + return ['axis', 'mode', 'sort'] + else: + raise Error('Unknown opset version "{}"'.format(version)) @staticmethod def infer(node): @@ -75,4 +86,7 @@ class TopK(Op): @staticmethod def type_infer(node): node.out_port(0).set_data_type(node.in_port(0).get_data_type()) - node.out_port(1).set_data_type(np.int32) + if node.get_opset() == 'opset3': + node.out_port(1).set_data_type(node.index_element_type) + else: + node.out_port(1).set_data_type(np.int32) diff --git a/model-optimizer/extensions/ops/topk_test.py b/model-optimizer/extensions/ops/topk_test.py new file mode 100644 index 00000000000..f06962268e5 --- /dev/null +++ b/model-optimizer/extensions/ops/topk_test.py @@ -0,0 +1,101 @@ +""" + Copyright (C) 2018-2020 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import unittest + +import numpy as np + +from extensions.ops.topk import TopK +from mo.front.common.partial_infer.utils import int64_array +from mo.graph.graph import Node +from mo.utils.unittest.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, result, \ + connect, FakeAttr + + +class TestTopKInfer(unittest.TestCase): + def setUp(self): + nodes = { + **regular_op_with_shaped_data('data', [20, 100, 4], {'type': 'Parameter', 'value': None, + '_out_port_data_type': {0: np.float32}}), + **valued_const_with_data('k', int64_array(10)), + **regular_op_with_shaped_data('topk', None, {'op': 'TopK', 'type': 'TopK', 'name': 'topk', 'axis': 1}), + 'topk_d2': {'kind': 'data', 'shape': None, 'value': None}, + **result('output_1'), + **result('output_2'), + } + + self.graph = build_graph(nodes, [ + *connect('data', '0:topk'), + *connect('k', '1:topk'), + ('topk', 'topk_d', {'out': 0}), + ('topk', 'topk_d2', {'out': 1}), + ('topk_d', 'output_1'), + ('topk_d2', 'output_2'), + ], nodes_with_edges_only=True) + + def test_topk_infer_v7(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=False, ir_version=7) + + topk_node = Node(self.graph, 'topk') + topk_node['version'] = 'opset1' + TopK.infer(topk_node) + TopK.type_infer(topk_node) + + self.assertTrue(np.array_equal(topk_node.out_port(0).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(np.array_equal(topk_node.out_port(1).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(topk_node.out_port(0).get_data_type() == np.float32) + self.assertTrue(topk_node.out_port(1).get_data_type() == np.int32) + + def test_topk_infer_v10_opset1(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=True, ir_version=10) + + topk_node = Node(self.graph, 'topk') + topk_node['version'] = 'opset1' + TopK.infer(topk_node) + TopK.type_infer(topk_node) + + self.assertTrue(np.array_equal(topk_node.out_port(0).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(np.array_equal(topk_node.out_port(1).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(topk_node.out_port(0).get_data_type() == np.float32) + self.assertTrue(topk_node.out_port(1).get_data_type() == np.int32) + + def test_topk_infer_v10_i64_opset3(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=True, ir_version=10) + + topk_node = Node(self.graph, 'topk') + topk_node['version'] = 'opset3' + topk_node['index_element_type'] = np.int64 + TopK.infer(topk_node) + TopK.type_infer(topk_node) + + self.assertTrue(np.array_equal(topk_node.out_port(0).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(np.array_equal(topk_node.out_port(1).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(topk_node.out_port(0).get_data_type() == np.float32) + self.assertTrue(topk_node.out_port(1).get_data_type() == np.int64) + + def test_topk_infer_v10_i32_opset3(self): + self.graph.graph['cmd_params'] = FakeAttr(generate_experimental_IR_V10=True, ir_version=10) + + topk_node = Node(self.graph, 'topk') + topk_node['version'] = 'opset3' + topk_node['index_element_type'] = np.int32 + TopK.infer(topk_node) + TopK.type_infer(topk_node) + + self.assertTrue(np.array_equal(topk_node.out_port(0).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(np.array_equal(topk_node.out_port(1).data.get_shape(), int64_array([20, 10, 4]))) + self.assertTrue(topk_node.out_port(0).get_data_type() == np.float32) + self.assertTrue(topk_node.out_port(1).get_data_type() == np.int32) diff --git a/model-optimizer/extensions/ops/topkrois_onnx.py b/model-optimizer/extensions/ops/topkrois_onnx.py index 43b3ed70fa3..126585efb94 100644 --- a/model-optimizer/extensions/ops/topkrois_onnx.py +++ b/model-optimizer/extensions/ops/topkrois_onnx.py @@ -26,6 +26,7 @@ class ExperimentalDetectronTopKROIs(Op): mandatory_props = dict( type=__class__.op, op=__class__.op, + version='experimental', infer=__class__.infer ) super().__init__(graph, mandatory_props, attrs) diff --git a/model-optimizer/extensions/ops/transpose.py b/model-optimizer/extensions/ops/transpose.py index 63807c5125d..930a57b219f 100644 --- a/model-optimizer/extensions/ops/transpose.py +++ b/model-optimizer/extensions/ops/transpose.py @@ -29,6 +29,7 @@ class Transpose(Op): super().__init__(graph, { 'type': __class__.op, 'op': __class__.op, + 'version': 'opset1', 'infer': self.infer, 'force_precision_in_ports': {1: 'int64'}, 'in_ports_count': 2, diff --git a/model-optimizer/extensions/ops/unique.py b/model-optimizer/extensions/ops/unique.py index 38916f1c439..35b367ffef4 100644 --- a/model-optimizer/extensions/ops/unique.py +++ b/model-optimizer/extensions/ops/unique.py @@ -47,6 +47,7 @@ class Unique(Op): mandatory_props = { 'type': __class__.op, 'op': __class__.op, + 'version': 'experimental', 'infer': __class__.infer, 'in_ports_count': 1, 'out_ports_count': 3 diff --git a/model-optimizer/install_prerequisites/protobuf-3.6.1-py3.5-win-amd64.egg b/model-optimizer/install_prerequisites/protobuf-3.6.1-py3.5-win-amd64.egg new file mode 100644 index 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