From 24f8c4105e9bbc6740bdf9a757cbc409d7601465 Mon Sep 17 00:00:00 2001 From: Stefania Hergane Date: Wed, 9 Aug 2023 23:20:07 +0300 Subject: [PATCH] [EISW-89824] [master] Rename VPUX to NPU (#19004) * Change `VPUX`/`VPU` occurrences to `NPU` * Switch `HARDWARE_AWARE_IGNORED_PATTERNS` VPU to NPU * Rename `MYRIAD plugin` * Rename vpu_patterns to npu_patterns in tools/pot * Rename vpu.json to npu.json in tools/pot * Rename restrict_for_vpu to restrict_for_npu in tools/pot * Change keembayOptimalBatchNum to npuOptimalBatchNum --------- Co-authored-by: Dan --- docs/OV_Runtime_UG/automatic_batching.md | 2 +- .../opset_report_omz_static.html | 846 +++++++++--------- .../nncf/ptq/basic_quantization_flow.md | 2 +- samples/cpp/benchmark_app/utils.cpp | 2 +- samples/cpp/speech_sample/README.md | 6 +- samples/cpp/speech_sample/speech_sample.hpp | 4 +- samples/python/speech_sample/README.md | 4 +- samples/python/speech_sample/arg_parser.py | 2 +- .../include/ie/vpu/myriad_config.hpp | 2 +- src/plugins/auto/src/auto_compiled_model.cpp | 2 +- src/plugins/auto/src/auto_schedule.cpp | 8 +- src/plugins/auto/src/plugin_config.cpp | 2 +- .../tests/unit/compile_model_metric_test.cpp | 4 +- .../auto/tests/unit/get_device_list.cpp | 18 +- .../tests/unit/select_device_failed_test.cpp | 10 +- .../include/api_conformance_helpers.hpp | 2 +- .../common_test_utils/CMakeLists.txt | 2 +- .../common_test_utils/common_utils.hpp | 2 +- .../include/common_test_utils/data_utils.hpp | 2 +- .../include/common_test_utils/file_utils.hpp | 2 +- .../include/common_test_utils/test_common.hpp | 2 +- .../common_test_utils/test_constants.hpp | 2 +- .../common_test_utils/src/test_constants.cpp | 2 +- .../functional_test_utils/crash_handler.hpp | 2 +- .../.automation/auto_plugin_test_config.yml | 192 ++-- .../.automation/desktop_test_config.yml | 128 +-- .../.automation/desktop_test_config_cache.yml | 128 +-- tests/time_tests/test_runner/test_timetest.py | 2 +- thirdparty/dependencies.cmake | 4 +- .../tools/benchmark/utils/constants.py | 4 +- tools/pot/README_dev.md | 2 +- tools/pot/docs/BestPractices.md | 4 +- tools/pot/docs/DefaultQuantizationUsage.md | 2 +- .../pot/algorithms/quantization/README.md | 4 +- .../quantization/accuracy_aware/algorithm.py | 2 +- .../accuracy_aware_common/mixed_precision.py | 4 +- .../fake_quantize_configuration.py | 2 +- .../pot/algorithms/quantization/utils.py | 2 +- .../pot/openvino/tools/pot/configs/config.py | 2 +- .../{vpu_patterns.py => npu_patterns.py} | 2 +- tools/pot/openvino/tools/pot/graph/utils.py | 4 +- tools/pot/tests/test_target_device.py | 2 +- 42 files changed, 711 insertions(+), 711 deletions(-) rename tools/pot/openvino/tools/pot/graph/{vpu_patterns.py => npu_patterns.py} (92%) diff --git a/docs/OV_Runtime_UG/automatic_batching.md b/docs/OV_Runtime_UG/automatic_batching.md index 5b558502234..7fc4732a8e8 100644 --- a/docs/OV_Runtime_UG/automatic_batching.md +++ b/docs/OV_Runtime_UG/automatic_batching.md @@ -11,7 +11,7 @@ The Automatic Batching Execution mode (or Auto-batching for short) performs automatic batching on-the-fly to improve device utilization by grouping inference requests together, without programming effort from the user. With Automatic Batching, gathering the input and scattering the output from the individual inference requests required for the batch happen transparently, without affecting the application code. -Auto Batching can be used :ref:`directly as a virtual device ` or as an :ref:`option for inference on CPU/GPU/VPU ` (by means of configuration/hint). These 2 ways are provided for the user to enable the BATCH devices **explicitly** or **implicitly**, with the underlying logic remaining the same. An example of the difference is that the CPU device doesn’t support implicitly to enable BATCH device, commands such as ``./benchmark_app -m -d CPU -hint tput`` will not apply BATCH device **implicitly**, but ``./benchmark_app -m -d "BATCH:CPU(16)`` can **explicitly** load BATCH device. +Auto Batching can be used :ref:`directly as a virtual device ` or as an :ref:`option for inference on CPU/GPU/NPU ` (by means of configuration/hint). These 2 ways are provided for the user to enable the BATCH devices **explicitly** or **implicitly**, with the underlying logic remaining the same. An example of the difference is that the CPU device doesn’t support implicitly to enable BATCH device, commands such as ``./benchmark_app -m -d CPU -hint tput`` will not apply BATCH device **implicitly**, but ``./benchmark_app -m -d "BATCH:CPU(16)`` can **explicitly** load BATCH device. Auto-batching primarily targets the existing code written for inferencing many requests, each instance with the batch size 1. To get corresponding performance improvements, the application **must be running multiple inference requests simultaneously**. Auto-batching can also be used via a particular *virtual* device. diff --git a/docs/_static/download/operation_conformance_table_files/opset_report_omz_static.html b/docs/_static/download/operation_conformance_table_files/opset_report_omz_static.html index dea9f8ca231..4f95c22d048 100644 --- a/docs/_static/download/operation_conformance_table_files/opset_report_omz_static.html +++ b/docs/_static/download/operation_conformance_table_files/opset_report_omz_static.html @@ -105,7 +105,7 @@ GPU NVIDIA TEMPLATE - VPUX + NPU @@ -119,7 +119,7 @@ 95 95 95 - 93 + 93 @@ -131,7 +131,7 @@ 76.84 % 26.32 % 97.89 % - 11.83 % + 11.83 % @@ -143,7 +143,7 @@ 94.92 % 55.09 % 100.0 % - 21.1 % + 21.1 % @@ -155,7 +155,7 @@ 98.25 % 88.98 % 99.96 % - 7.69 % + 7.69 % @@ -304,12 +304,12 @@ - ---
@@ -430,12 +430,12 @@ - ---
@@ -556,12 +556,12 @@ - ---
@@ -682,12 +682,12 @@ - ---
@@ -808,12 +808,12 @@ - ---
@@ -969,12 +969,12 @@ - 0.0 %
@@ -1100,12 +1100,12 @@ - ---
@@ -1226,12 +1226,12 @@ - ---
@@ -1352,12 +1352,12 @@ - ---
@@ -1478,12 +1478,12 @@ - ---
@@ -1604,12 +1604,12 @@ - ---
@@ -1730,12 +1730,12 @@ - ---
@@ -1891,12 +1891,12 @@ - 0.0 %
@@ -2022,12 +2022,12 @@ - ---
@@ -2183,12 +2183,12 @@ - 0.0 %
@@ -2349,12 +2349,12 @@ - 0.0 %
@@ -2480,12 +2480,12 @@ - ---
@@ -2606,12 +2606,12 @@ - ---
@@ -2767,12 +2767,12 @@ - 0.0 %
@@ -2898,12 +2898,12 @@ - ---
@@ -3024,12 +3024,12 @@ - ---
@@ -3185,12 +3185,12 @@ - ---
@@ -3311,12 +3311,12 @@ - ---
@@ -3472,12 +3472,12 @@ - 0.0 %
@@ -3638,12 +3638,12 @@ - 0.0 %
@@ -3804,12 +3804,12 @@ - 0.0 %
@@ -3935,12 +3935,12 @@ - ---
@@ -4096,12 +4096,12 @@ - 0.0 %
@@ -4227,12 +4227,12 @@ - ---
@@ -4388,12 +4388,12 @@ - 0.0 %
@@ -4554,12 +4554,12 @@ - 0.0 %
@@ -4720,12 +4720,12 @@ - 0.0 %
@@ -4851,12 +4851,12 @@ - ---
@@ -5012,12 +5012,12 @@ - 0.0 %
@@ -5143,12 +5143,12 @@ - ---
@@ -5269,12 +5269,12 @@ - ---
@@ -5395,12 +5395,12 @@ - ---
@@ -5521,12 +5521,12 @@ - ---
@@ -5647,12 +5647,12 @@ - ---
@@ -5773,12 +5773,12 @@ - ---
@@ -5934,12 +5934,12 @@ - 0.0 %
@@ -6100,12 +6100,12 @@ - 0.0 %
@@ -6266,12 +6266,12 @@ - 75.0 %
@@ -6432,12 +6432,12 @@ - 0.0 %
@@ -6563,12 +6563,12 @@ - ---
@@ -6689,12 +6689,12 @@ - ---
@@ -6815,12 +6815,12 @@ - ---
@@ -6976,12 +6976,12 @@ - 0.0 %
@@ -7142,12 +7142,12 @@ - 0.0 %
@@ -7308,12 +7308,12 @@ - 0.0 %
@@ -7439,12 +7439,12 @@ - ---
@@ -7565,12 +7565,12 @@ - ---
@@ -7691,12 +7691,12 @@ - ---
@@ -7817,12 +7817,12 @@ - ---
@@ -7943,12 +7943,12 @@ - ---
@@ -8069,12 +8069,12 @@ - ---
@@ -8097,7 +8097,7 @@ N/A N/A N/A - N/A + N/A @@ -8244,12 +8244,12 @@ - 0.0 %
@@ -8410,12 +8410,12 @@ - 0.0 %
@@ -8576,12 +8576,12 @@ - 0.0 %
@@ -8707,12 +8707,12 @@ - ---
@@ -8833,12 +8833,12 @@ - ---
@@ -8994,12 +8994,12 @@ - 60.0 %
@@ -9160,12 +9160,12 @@ - 0.0 %
@@ -9291,12 +9291,12 @@ - ---
@@ -9452,12 +9452,12 @@ - 22.73 %
@@ -9583,12 +9583,12 @@ - ---
@@ -9709,12 +9709,12 @@ - ---
@@ -9870,12 +9870,12 @@ - 100.0 %
@@ -10001,12 +10001,12 @@ - ---
@@ -10127,12 +10127,12 @@ - ---
@@ -10288,12 +10288,12 @@ - 100.0 %
@@ -10419,12 +10419,12 @@ - ---
@@ -10580,12 +10580,12 @@ - 0.0 %
@@ -10711,12 +10711,12 @@ - ---
@@ -10837,12 +10837,12 @@ - ---
@@ -10998,12 +10998,12 @@ - 0.0 %
@@ -11164,12 +11164,12 @@ - 0.0 %
@@ -11295,7 +11295,7 @@ - N/A + N/A @@ -11442,12 +11442,12 @@ - 66.67 %
@@ -11608,12 +11608,12 @@ - 100.0 %
@@ -11774,12 +11774,12 @@ - 0.0 %
@@ -11905,12 +11905,12 @@ - ---
@@ -12031,12 +12031,12 @@ - ---
@@ -12192,12 +12192,12 @@ - 0.0 %
@@ -12323,12 +12323,12 @@ - ---
@@ -12484,12 +12484,12 @@ - 100.0 %
@@ -12615,12 +12615,12 @@ - ---
@@ -12741,12 +12741,12 @@ - ---
@@ -12902,12 +12902,12 @@ - 55.56 %
@@ -13033,12 +13033,12 @@ - ---
@@ -13159,12 +13159,12 @@ - ---
@@ -13285,12 +13285,12 @@ - ---
@@ -13446,12 +13446,12 @@ - 0.0 %
@@ -13577,12 +13577,12 @@ - ---
@@ -13703,12 +13703,12 @@ - ---
@@ -13829,12 +13829,12 @@ - ---
@@ -13990,12 +13990,12 @@ - 0.0 %
@@ -14121,12 +14121,12 @@ - ---
@@ -14247,12 +14247,12 @@ - ---
@@ -14408,12 +14408,12 @@ - 0.0 %
@@ -14574,12 +14574,12 @@ - 100.0 %
@@ -14705,12 +14705,12 @@ - ---
@@ -14866,12 +14866,12 @@ - 0.0 %
@@ -14997,12 +14997,12 @@ - ---
@@ -15123,12 +15123,12 @@ - ---
@@ -15249,12 +15249,12 @@ - ---
@@ -15375,12 +15375,12 @@ - ---
@@ -15536,12 +15536,12 @@ - 50.0 %
@@ -15702,12 +15702,12 @@ - 0.0 %
@@ -15833,12 +15833,12 @@ - ---
@@ -15959,12 +15959,12 @@ - ---
@@ -16120,12 +16120,12 @@ - 0.0 %
@@ -16286,12 +16286,12 @@ - 0.0 %
@@ -16452,12 +16452,12 @@ - 0.0 %
@@ -16618,12 +16618,12 @@ - 100.0 %
@@ -16749,12 +16749,12 @@ - ---
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@@ -17001,12 +17001,12 @@ - ---
@@ -17162,12 +17162,12 @@ - 0.0 %
@@ -17293,12 +17293,12 @@ - ---
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@@ -17711,12 +17711,12 @@ - ---
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@@ -17963,12 +17963,12 @@ - ---
@@ -18089,12 +18089,12 @@ - ---
@@ -18215,12 +18215,12 @@ - ---
@@ -18341,12 +18341,12 @@ - ---
@@ -18502,12 +18502,12 @@ - 0.0 %
@@ -18668,12 +18668,12 @@ - ---
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@@ -18955,12 +18955,12 @@ - 0.0 %
@@ -19086,12 +19086,12 @@ - ---
@@ -19247,12 +19247,12 @@ - 0.0 %
@@ -19378,7 +19378,7 @@ - N/A + N/A @@ -19490,12 +19490,12 @@ - ---
@@ -19651,12 +19651,12 @@ - 0.0 %
@@ -19782,12 +19782,12 @@ - ---
@@ -19908,12 +19908,12 @@ - ---
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@@ -24380,12 +24380,12 @@ - 0.0 %
@@ -24511,7 +24511,7 @@ - N/A + N/A @@ -24658,12 +24658,12 @@ - 0.0 %
@@ -24824,12 +24824,12 @@ - 0.0 %
@@ -24990,12 +24990,12 @@ - 0.0 %
@@ -25156,12 +25156,12 @@ - 0.0 %
@@ -25287,12 +25287,12 @@ - ---
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@@ -26696,12 +26696,12 @@ - 100.0 %
@@ -26827,12 +26827,12 @@ - ---
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@@ -28608,12 +28608,12 @@ - 100.0 %
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@@ -29192,12 +29192,12 @@ - 50.0 %
@@ -29323,12 +29323,12 @@ - ---
@@ -29449,12 +29449,12 @@ - ---
@@ -29610,12 +29610,12 @@ - 25.0 %
@@ -29776,12 +29776,12 @@ - 71.43 %
@@ -29907,12 +29907,12 @@ - ---
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@@ -30234,12 +30234,12 @@ - 90.0 %
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diff --git a/docs/optimization_guide/nncf/ptq/basic_quantization_flow.md b/docs/optimization_guide/nncf/ptq/basic_quantization_flow.md index 5fc93dcc344..2b31176f1ef 100644 --- a/docs/optimization_guide/nncf/ptq/basic_quantization_flow.md +++ b/docs/optimization_guide/nncf/ptq/basic_quantization_flow.md @@ -191,7 +191,7 @@ Tune quantization parameters regex = '.*layer_.*' nncf.quantize(model, dataset, ignored_scope=nncf.IgnoredScope(patterns=regex)) -* ``target_device`` - defines the target device, the specificity of which will be taken into account during optimization. The following values are supported: ``ANY`` (default), ``CPU``, ``CPU_SPR``, ``GPU``, and ``VPU``. +* ``target_device`` - defines the target device, the specificity of which will be taken into account during optimization. The following values are supported: ``ANY`` (default), ``CPU``, ``CPU_SPR``, ``GPU``, and ``NPU``. .. code-block:: sh diff --git a/samples/cpp/benchmark_app/utils.cpp b/samples/cpp/benchmark_app/utils.cpp index 10cc2c3163c..af069169354 100644 --- a/samples/cpp/benchmark_app/utils.cpp +++ b/samples/cpp/benchmark_app/utils.cpp @@ -61,7 +61,7 @@ size_t InputInfo::depth() const { uint32_t device_default_device_duration_in_seconds(const std::string& device) { static const std::map deviceDefaultDurationInSeconds{{"CPU", 60}, {"GPU", 60}, - {"VPU", 60}, + {"NPU", 60}, {"UNKNOWN", 120}}; uint32_t duration = 0; for (const auto& deviceDurationInSeconds : deviceDefaultDurationInSeconds) { diff --git a/samples/cpp/speech_sample/README.md b/samples/cpp/speech_sample/README.md index 2771f141923..188a5b43cbc 100644 --- a/samples/cpp/speech_sample/README.md +++ b/samples/cpp/speech_sample/README.md @@ -96,7 +96,7 @@ Several execution modes are supported via the ``-d`` flag: - ``CPU`` - All calculations are performed on CPU device using CPU Plugin. - ``GPU`` - All calculations are performed on GPU device using GPU Plugin. -- ``VPU`` - All calculations are performed on VPU device using VPU Plugin. +- ``NPU`` - All calculations are performed on NPU device using NPU Plugin. - ``GNA_AUTO`` - GNA hardware is used if available and the driver is installed. Otherwise, the GNA device is emulated in fast-but-not-bit-exact mode. - ``GNA_HW`` - GNA hardware is used if available and the driver is installed. Otherwise, an error will occur. - ``GNA_SW`` - Deprecated. The GNA device is emulated in fast-but-not-bit-exact mode. @@ -144,7 +144,7 @@ Usage message: -i "" Required. Path(s) to input file(s). Usage for a single file/layer: or . Example of usage for several files/layers: :=,:=. -m "" Required. Path to an .xml file with a trained model (required if -rg is missing). -o "" Optional. Output file name(s) to save scores (inference results). Example of usage for a single file/layer: or . Example of usage for several files/layers: :=,:=. - -d "" Optional. Specify a target device to infer on. CPU, GPU, VPU, GNA_AUTO, GNA_HW, GNA_HW_WITH_SW_FBACK, GNA_SW_FP32, GNA_SW_EXACT and HETERO with combination of GNA as the primary device and CPU as a secondary (e.g. HETERO:GNA,CPU) are supported. The sample will look for a suitable plugin for device specified. + -d "" Optional. Specify a target device to infer on. CPU, GPU, NPU, GNA_AUTO, GNA_HW, GNA_HW_WITH_SW_FBACK, GNA_SW_FP32, GNA_SW_EXACT and HETERO with combination of GNA as the primary device and CPU as a secondary (e.g. HETERO:GNA,CPU) are supported. The sample will look for a suitable plugin for device specified. -pc Optional. Enables per-layer performance report. -q "" Optional. Input quantization mode for GNA: static (default) or user defined (use with -sf). -qb "" Optional. Weight resolution in bits for GNA quantization: 8 or 16 (default) @@ -162,7 +162,7 @@ Usage message: -compile_target "" Optional. Specify GNA compile target generation. May be one of GNA_TARGET_2_0, GNA_TARGET_3_0. By default, generation corresponds to the GNA HW available in the system or the latest fully supported generation by the software. See the GNA Plugin's GNA_COMPILE_TARGET config option description. -memory_reuse_off Optional. Disables memory optimizations for compiled model. - Available target devices: CPU GNA GPU VPU + Available target devices: CPU GNA GPU NPU .. _model-preparation-speech: diff --git a/samples/cpp/speech_sample/speech_sample.hpp b/samples/cpp/speech_sample/speech_sample.hpp index 9ba602a55e3..0bc3a24c950 100644 --- a/samples/cpp/speech_sample/speech_sample.hpp +++ b/samples/cpp/speech_sample/speech_sample.hpp @@ -24,7 +24,7 @@ static const char model_message[] = "Required. Path to an .xml file with a train /// @brief message for assigning calculation to device static const char target_device_message[] = - "Optional. Specify a target device to infer on. CPU, GPU, VPU, GNA_AUTO, GNA_HW, " + "Optional. Specify a target device to infer on. CPU, GPU, NPU, GNA_AUTO, GNA_HW, " "GNA_HW_WITH_SW_FBACK, GNA_SW_FP32, " "GNA_SW_EXACT and HETERO with combination of GNA as the primary device and CPU" " as a secondary (e.g. HETERO:GNA,CPU) are supported. " @@ -274,7 +274,7 @@ bool parse_and_check_command_line(int argc, char* argv[]) { "HETERO:GNA_HW,CPU", "HETERO:GNA_SW_EXACT,CPU", "HETERO:GNA_SW_FP32,CPU", - "VPU"}; + "NPU"}; if (std::find(supportedDevices.begin(), supportedDevices.end(), FLAGS_d) == supportedDevices.end()) { throw std::logic_error("Specified device is not supported."); diff --git a/samples/python/speech_sample/README.md b/samples/python/speech_sample/README.md index ba765f1e127..a4120b10ebf 100644 --- a/samples/python/speech_sample/README.md +++ b/samples/python/speech_sample/README.md @@ -95,7 +95,7 @@ Several execution modes are supported via the ``-d`` flag: - ``CPU`` - All calculations are performed on CPU device using CPU Plugin. - ``GPU`` - All calculations are performed on GPU device using GPU Plugin. -- ``VPU`` - All calculations are performed on VPU device using VPU Plugin. +- ``NPU`` - All calculations are performed on NPU device using NPU Plugin. - ``GNA_AUTO`` - GNA hardware is used if available and the driver is installed. Otherwise, the GNA device is emulated in fast-but-not-bit-exact mode. - ``GNA_HW`` - GNA hardware is used if available and the driver is installed. Otherwise, an error will occur. - ``GNA_SW`` - Deprecated. The GNA device is emulated in fast-but-not-bit-exact mode. @@ -155,7 +155,7 @@ Usage message: Usage for a single file/layer: or . Example of usage for several files/layers: :=,:=. -d DEVICE, --device DEVICE - Optional. Specify a target device to infer on. CPU, GPU, VPU, GNA_AUTO, GNA_HW, GNA_SW_FP32, + Optional. Specify a target device to infer on. CPU, GPU, NPU, GNA_AUTO, GNA_HW, GNA_SW_FP32, GNA_SW_EXACT and HETERO with combination of GNA as the primary device and CPU as a secondary (e.g. HETERO:GNA,CPU) are supported. The sample will look for a suitable plugin for device specified. Default value is CPU. diff --git a/samples/python/speech_sample/arg_parser.py b/samples/python/speech_sample/arg_parser.py index d6e8c41d834..c498eb286df 100644 --- a/samples/python/speech_sample/arg_parser.py +++ b/samples/python/speech_sample/arg_parser.py @@ -32,7 +32,7 @@ def build_arg_parser() -> argparse.ArgumentParser: 'Example of usage for several files/layers: :=,:=.') args.add_argument('-d', '--device', default='CPU', type=str, help='Optional. Specify a target device to infer on. ' - 'CPU, GPU, VPU, GNA_AUTO, GNA_HW, GNA_SW_FP32, GNA_SW_EXACT and HETERO with combination of GNA' + 'CPU, GPU, NPU, GNA_AUTO, GNA_HW, GNA_SW_FP32, GNA_SW_EXACT and HETERO with combination of GNA' ' as the primary device and CPU as a secondary (e.g. HETERO:GNA,CPU) are supported. ' 'The sample will look for a suitable plugin for device specified. Default value is CPU.') args.add_argument('-bs', '--batch_size', type=int, choices=range(1, 9), metavar='[1-8]', diff --git a/src/inference/include/ie/vpu/myriad_config.hpp b/src/inference/include/ie/vpu/myriad_config.hpp index 7fc7d774941..52d490b87d1 100644 --- a/src/inference/include/ie/vpu/myriad_config.hpp +++ b/src/inference/include/ie/vpu/myriad_config.hpp @@ -60,7 +60,7 @@ INFERENCE_ENGINE_1_0_DEPRECATED DECLARE_VPU_CONFIG(MYRIAD_PCIE); INFERENCE_ENGINE_1_0_DEPRECATED DECLARE_VPU_CONFIG(MYRIAD_USB); /** - * @brief Optimize vpu plugin execution to maximize throughput. + * @brief Optimize MYRIAD plugin execution to maximize throughput. * This option should be used with integer value which is the requested number of streams. * The only possible values are: * 1 diff --git a/src/plugins/auto/src/auto_compiled_model.cpp b/src/plugins/auto/src/auto_compiled_model.cpp index 3bfdd51628d..a827072bf32 100644 --- a/src/plugins/auto/src/auto_compiled_model.cpp +++ b/src/plugins/auto/src/auto_compiled_model.cpp @@ -201,7 +201,7 @@ ov::Any AutoCompiledModel::get_property(const std::string& name) const { LOG_WARNING_TAG("deduce optimal infer requset num for auto-batch failed :%s", iie.what()); } real = (std::max)(requests, optimal_batch_size); - } else if (device_info.device_name.find("VPU") != std::string::npos) { + } else if (device_info.device_name.find("NPU") != std::string::npos) { real = 8u; } else { real = upper_bound_streams_num ? 2 * upper_bound_streams_num : default_num_for_tput; diff --git a/src/plugins/auto/src/auto_schedule.cpp b/src/plugins/auto/src/auto_schedule.cpp index 4efcd6e0253..36e9c9f5462 100644 --- a/src/plugins/auto/src/auto_schedule.cpp +++ b/src/plugins/auto/src/auto_schedule.cpp @@ -301,10 +301,10 @@ void AutoSchedule::try_to_compile_model(AutoCompileContext& context, const std:: } // need to recompile model, unregister it's priority // there maybe potential issue. - // for example they are dGPU, VPU, iGPU, customer want to compile model with - // configure 0 dGPU, 1 VPU, if dGPU compile failed, - // the result will be not sure, maybe two models are compiled into VPU, - // maybe 0 is compiled to VPU, 1 is compiled to iGPU + // for example they are dGPU, NPU, iGPU, customer want to compile model with + // configure 0 dGPU, 1 NPU, if dGPU compile failed, + // the result will be not sure, maybe two models are compiled into NPU, + // maybe 0 is compiled to NPU, 1 is compiled to iGPU m_plugin->unregister_priority(m_context->m_model_priority, context.m_device_info.unique_name); // remove the current device from device_list auto erase_device = deviceChecker().check_and_return_if_device_in_list(device, device_list, true); diff --git a/src/plugins/auto/src/plugin_config.cpp b/src/plugins/auto/src/plugin_config.cpp index 4fcace66dd3..0f49680856d 100644 --- a/src/plugins/auto/src/plugin_config.cpp +++ b/src/plugins/auto/src/plugin_config.cpp @@ -8,7 +8,7 @@ namespace auto_plugin { // AUTO will enable the blocklist if // 1.No device priority passed to AUTO/MULTI.(eg. core.compile_model(model, "AUTO", configs);) // 2.No valid device parsed out from device priority (eg. core.compile_model(model, "AUTO:-CPU,-GPU", configs);). -const std::set PluginConfig::device_block_list = {"VPU", "GNA", "notIntelGPU"}; +const std::set PluginConfig::device_block_list = {"NPU", "GNA", "notIntelGPU"}; PluginConfig::PluginConfig() { set_default(); diff --git a/src/plugins/auto/tests/unit/compile_model_metric_test.cpp b/src/plugins/auto/tests/unit/compile_model_metric_test.cpp index 67fac96aabe..05161963605 100644 --- a/src/plugins/auto/tests/unit/compile_model_metric_test.cpp +++ b/src/plugins/auto/tests/unit/compile_model_metric_test.cpp @@ -192,13 +192,13 @@ TEST_P(ExecNetworkget_propertyOptimalNumInferReq, OPTIMAL_NUMBER_OF_INFER_REQUES metaDevices.push_back({actualDeviceName, metaConfig, actualCustomerNum, ""}); // enable autoBatch unsigned int gpuOptimalBatchNum = 8; - unsigned int keembayOptimalBatchNum = 1; + unsigned int npuOptimalBatchNum = 1; ov::hint::PerformanceMode mode = ov::hint::PerformanceMode::THROUGHPUT; std::tuple rangeOfStreams = std::make_tuple(1, 3); ON_CALL(*core, get_property(StrEq(ov::test::utils::DEVICE_GPU), StrEq(ov::optimal_batch_size.name()), _)) .WillByDefault(RETURN_MOCK_VALUE(gpuOptimalBatchNum)); ON_CALL(*core, get_property(StrEq(ov::test::utils::DEVICE_KEEMBAY), StrEq(ov::optimal_batch_size.name()), _)) - .WillByDefault(RETURN_MOCK_VALUE(keembayOptimalBatchNum)); + .WillByDefault(RETURN_MOCK_VALUE(npuOptimalBatchNum)); ON_CALL(*core, get_property(_, StrEq(ov::range_for_streams.name()), _)) .WillByDefault(RETURN_MOCK_VALUE(rangeOfStreams)); ON_CALL(*core, get_property(_, StrEq(ov::hint::performance_mode.name()), _)) diff --git a/src/plugins/auto/tests/unit/get_device_list.cpp b/src/plugins/auto/tests/unit/get_device_list.cpp index 50837ae17f1..5fc8d4eedb4 100644 --- a/src/plugins/auto/tests/unit/get_device_list.cpp +++ b/src/plugins/auto/tests/unit/get_device_list.cpp @@ -7,8 +7,8 @@ using Config = std::map; using namespace ov::mock_auto_plugin; -const std::vector availableDevs = {"CPU", "GPU", "VPU"}; -const std::vector availableDevsWithId = {"CPU", "GPU.0", "GPU.1", "VPU"}; +const std::vector availableDevs = {"CPU", "GPU", "NPU"}; +const std::vector availableDevsWithId = {"CPU", "GPU.0", "GPU.1", "NPU"}; using Params = std::tuple; using ConfigParams = std::tuple< std::vector, // Available devices retrieved from Core @@ -96,8 +96,8 @@ const std::vector testConfigsWithId = {Params{" ", " "}, Params{"CPU,,GPU", "CPU,GPU.0,GPU.1"}, Params{"CPU, ,GPU", "CPU, ,GPU.0,GPU.1"}, Params{"CPU,GPU,GPU.1", "CPU,GPU.0,GPU.1"}, - Params{"CPU,GPU,VPU,INVALID_DEVICE", "CPU,GPU.0,GPU.1,VPU,INVALID_DEVICE"}, - Params{"VPU,GPU,CPU,-GPU.0", "VPU,GPU.1,CPU"}, + Params{"CPU,GPU,NPU,INVALID_DEVICE", "CPU,GPU.0,GPU.1,NPU,INVALID_DEVICE"}, + Params{"NPU,GPU,CPU,-GPU.0", "NPU,GPU.1,CPU"}, Params{"-GPU.0,GPU,CPU", "GPU.1,CPU"}, Params{"-GPU.0,GPU", "GPU.1"}, Params{"-GPU,GPU.0", "GPU.0"}, @@ -131,13 +131,13 @@ const std::vector testConfigs = {Params{" ", " "}, Params{"CPU,GPU,GPU.0", "CPU,GPU"}, Params{"CPU,GPU,GPU.1", "CPU,GPU,GPU.1"}, Params{"CPU,GPU.1,GPU", "CPU,GPU.1,GPU"}, - Params{"CPU,VPU", "CPU,VPU"}, - Params{"CPU,-VPU", "CPU"}, + Params{"CPU,NPU", "CPU,NPU"}, + Params{"CPU,-NPU", "CPU"}, Params{"INVALID_DEVICE", "INVALID_DEVICE"}, Params{"CPU,-INVALID_DEVICE", "CPU"}, Params{"CPU,INVALID_DEVICE", "CPU,INVALID_DEVICE"}, Params{"-CPU,INVALID_DEVICE", "INVALID_DEVICE"}, - Params{"CPU,GPU,VPU", "CPU,GPU,VPU"}}; + Params{"CPU,GPU,NPU", "CPU,GPU,NPU"}}; const std::vector testConfigsWithIdNotInteldGPU = {Params{" ", " "}, Params{"", "CPU,GPU.0"}, @@ -147,8 +147,8 @@ const std::vector testConfigsWithIdNotInteldGPU = {Params{" ", " "}, Params{"CPU,,GPU", "CPU,GPU.0,GPU.1"}, Params{"CPU, ,GPU", "CPU, ,GPU.0,GPU.1"}, Params{"CPU,GPU,GPU.1", "CPU,GPU.0,GPU.1"}, - Params{"CPU,GPU,VPU,INVALID_DEVICE", "CPU,GPU.0,GPU.1,VPU,INVALID_DEVICE"}, - Params{"VPU,GPU,CPU,-GPU.0", "VPU,GPU.1,CPU"}, + Params{"CPU,GPU,NPU,INVALID_DEVICE", "CPU,GPU.0,GPU.1,NPU,INVALID_DEVICE"}, + Params{"NPU,GPU,CPU,-GPU.0", "NPU,GPU.1,CPU"}, Params{"-GPU.0,GPU,CPU", "GPU.1,CPU"}, Params{"-GPU.0,GPU", "GPU.1"}, Params{"-GPU,GPU.0", "GPU.0"}, diff --git a/src/plugins/auto/tests/unit/select_device_failed_test.cpp b/src/plugins/auto/tests/unit/select_device_failed_test.cpp index 5559621260d..92afffef4b6 100644 --- a/src/plugins/auto/tests/unit/select_device_failed_test.cpp +++ b/src/plugins/auto/tests/unit/select_device_failed_test.cpp @@ -127,7 +127,7 @@ TEST_P(AutoLoadFailedTest, LoadCNNetWork) { metaDevices.push_back(std::move(devInfo)); // set the return value of SelectDevice - // for example if there are three device, if will return GPU on the first call, and then MYRIAD + // for example if there are three device, if will return GPU on the first call, and then NPU // at last CPU ON_CALL(*plugin, select_device(Property(&std::vector::size, Eq(selDevsSize)), _, _)) .WillByDefault(Return(metaDevices[deviceConfigs.size() - selDevsSize])); @@ -181,12 +181,12 @@ TEST_P(AutoLoadFailedTest, LoadCNNetWork) { // { true, false, GENERAL, 3 device, 2, 3, 2} // // there are three devices for loading -// CPU load for accelerator success, but GPU will load faild and then select MYRIAD and load again +// CPU load for accelerator success, but GPU will load faild and then select NPU and load again // LoadExeNetworkImpl will not throw exception and can continue to run, -// it will select twice, first select GPU, second select MYRIAD -// it will load network three times(CPU, GPU, MYRIAD) +// it will select twice, first select GPU, second select NPU +// it will load network three times(CPU, GPU, NPU) // the inference request num is loadSuccessCount * optimalNum, in this test case optimalNum is 2 -// so inference request num is 4 (CPU 2, MYRIAD 2) +// so inference request num is 4 (CPU 2, NPU 2) // const std::vector testConfigs = { ConfigParams{true, diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp index bcf27f17fed..bdde3438192 100644 --- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp +++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp @@ -17,7 +17,7 @@ inline const std::string get_plugin_lib_name_by_device(const std::string& device { "HETERO", "openvino_hetero_plugin" }, { "BATCH", "openvino_auto_batch_plugin" }, { "MULTI", "openvino_auto_plugin" }, - { "VPU", "openvino_intel_vpu_plugin" }, + { "NPU", "openvino_intel_npu_plugin" }, { "CPU", "openvino_intel_cpu_plugin" }, { "GNA", "openvino_intel_gna_plugin" }, { "GPU", "openvino_intel_gpu_plugin" }, diff --git a/src/tests/test_utils/common_test_utils/CMakeLists.txt b/src/tests/test_utils/common_test_utils/CMakeLists.txt index 5fd7af8ce5d..a03a69b6120 100644 --- a/src/tests/test_utils/common_test_utils/CMakeLists.txt +++ b/src/tests/test_utils/common_test_utils/CMakeLists.txt @@ -85,7 +85,7 @@ function(add_common_utils ADD_TARGET_NAME) endfunction() -# Keep old name so that library can be used from VPU repo +# Keep old name so that library can be used from NPU repo set(TARGET_NAME commonTestUtils) set(NEW_TARGET_NAME common_test_utils) diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp index 57f5852e887..f639a1b2192 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/common_utils.hpp @@ -196,7 +196,7 @@ size_t getVmRSSInKB(); } // namespace test } // namespace ov -// openvino_contrib and vpu repo use CommonTestUtils:: +// openvino_contrib and NPU repo use CommonTestUtils:: // so we need to add these names to CommonTestUtils namespace namespace CommonTestUtils { using ov::test::utils::vec2str; diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp index e9c554f02fa..7141cd85790 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/data_utils.hpp @@ -483,7 +483,7 @@ OPENVINO_SUPPRESS_DEPRECATED_END } // namespace ov -// openvino_contrib and vpu repo use CommonTestUtils:: +// openvino_contrib and NPU repo use CommonTestUtils:: // so we need to add these names to CommonTestUtils namespace namespace CommonTestUtils { using ov::test::utils::ie_abs; diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/file_utils.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/file_utils.hpp index 28ba2d44a35..cb97b06a530 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/file_utils.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/file_utils.hpp @@ -365,7 +365,7 @@ private: } // namespace ov -// vpu repo uses CommonTestUtils:: +// NPU repo uses CommonTestUtils:: // so we need to add these names to CommonTestUtils namespace namespace CommonTestUtils { using ov::test::utils::fileExists; diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_common.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_common.hpp index f832f32dbbb..1480e6bbd5d 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_common.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_common.hpp @@ -18,7 +18,7 @@ class TestsCommon; // Temporal WA for applications.ai.vpu-accelerators.vpux-plugin repo // It requires to have TestCommon name in CommonTestUtils namespace -// Need to change namespace in VPU repo and then remove this WA +// Need to change namespace in NPU repo and then remove this WA namespace CommonTestUtils { using ov::test::TestsCommon; } // namespace CommonTestUtils diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp index 4e8c11e3ad3..3b1b8327c74 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_constants.hpp @@ -50,7 +50,7 @@ const unsigned int maxFileNameLength = 140; } // namespace test } // namespace ov -// openvino_contrib and vpu repo use CommonTestUtils:: +// openvino_contrib and NPU repo use CommonTestUtils:: // so we need to add these names to CommonTestUtils namespace namespace CommonTestUtils { using ov::test::utils::DEVICE_AUTO; diff --git a/src/tests/test_utils/common_test_utils/src/test_constants.cpp b/src/tests/test_utils/common_test_utils/src/test_constants.cpp index c9338438075..7018d0283f1 100644 --- a/src/tests/test_utils/common_test_utils/src/test_constants.cpp +++ b/src/tests/test_utils/common_test_utils/src/test_constants.cpp @@ -12,8 +12,8 @@ const char *DEVICE_AUTO = "AUTO"; const char *DEVICE_CPU = "CPU"; const char *DEVICE_GNA = "GNA"; const char *DEVICE_GPU = "GPU"; +const char *DEVICE_KEEMBAY = "NPU"; const char *DEVICE_BATCH = "BATCH"; -const char *DEVICE_KEEMBAY = "VPU"; const char *DEVICE_MULTI = "MULTI"; const char *DEVICE_TEMPLATE = "TEMPLATE"; const char *DEVICE_HETERO = "HETERO"; diff --git a/src/tests/test_utils/functional_test_utils/include/functional_test_utils/crash_handler.hpp b/src/tests/test_utils/functional_test_utils/include/functional_test_utils/crash_handler.hpp index b59d4b8646f..f8cc7858452 100644 --- a/src/tests/test_utils/functional_test_utils/include/functional_test_utils/crash_handler.hpp +++ b/src/tests/test_utils/functional_test_utils/include/functional_test_utils/crash_handler.hpp @@ -36,7 +36,7 @@ public: } // namespace test } // namespace ov -// openvino_contrib and vpu repo use CommonTestUtils:: +// openvino_contrib and NPU repo use CommonTestUtils:: // so we need to add these names to CommonTestUtils namespace namespace CommonTestUtils { using ov::test::utils::env; diff --git a/tests/time_tests/.automation/auto_plugin_test_config.yml b/tests/time_tests/.automation/auto_plugin_test_config.yml index 27cb4f05c74..0a884da2381 100644 --- a/tests/time_tests/.automation/auto_plugin_test_config.yml +++ b/tests/time_tests/.automation/auto_plugin_test_config.yml @@ -1,392 +1,392 @@ - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: 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${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: AUTO:CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: AUTO:GPU,CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf diff --git a/tests/time_tests/.automation/desktop_test_config.yml b/tests/time_tests/.automation/desktop_test_config.yml index 30149dd929d..a21ebe0d201 100644 --- a/tests/time_tests/.automation/desktop_test_config.yml +++ b/tests/time_tests/.automation/desktop_test_config.yml @@ -1,448 +1,448 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml + path: ${NPU_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml name: googlenet-v1 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml + path: ${NPU_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml name: googlenet-v1 precision: FP16 framework: tf - device: name: CPU 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yolo-v2-tiny-ava-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml name: yolo-v2-tiny-ava-0001 precision: FP16-INT8 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml name: squeezenet1.1 precision: FP16 framework: caffe - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml name: squeezenet1.1 precision: FP16 framework: caffe - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml name: squeezenet1.1 precision: FP16-INT8 framework: caffe - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml name: squeezenet1.1 precision: FP16-INT8 framework: caffe - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16-INT8 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf \ No newline at end of file diff --git a/tests/time_tests/.automation/desktop_test_config_cache.yml b/tests/time_tests/.automation/desktop_test_config_cache.yml index 94a2c8bedc9..a24be941289 100644 --- a/tests/time_tests/.automation/desktop_test_config_cache.yml +++ b/tests/time_tests/.automation/desktop_test_config_cache.yml @@ -1,7 +1,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx @@ -9,7 +9,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16 framework: onnx @@ -17,7 +17,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx @@ -25,7 +25,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml + path: ${NPU_MODELS_PKG}/resnet-50-pytorch/onnx/FP16-INT8/resnet-50-pytorch.xml name: resnet-50-pytorch precision: FP16-INT8 framework: onnx @@ -33,7 +33,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe @@ -41,7 +41,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16/mobilenet-v2.xml name: mobilenet-v2 precision: FP16 framework: caffe @@ -49,7 +49,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe @@ -57,7 +57,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml + path: ${NPU_MODELS_PKG}/mobilenet-v2/caffe/FP16-INT8/mobilenet-v2.xml name: mobilenet-v2 precision: FP16-INT8 framework: caffe @@ -65,7 +65,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf @@ -73,7 +73,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16 framework: tf @@ -81,7 +81,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16-INT8 framework: tf @@ -89,7 +89,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/faster-rcnn-resnet101-coco-sparse-60-0001/tf/FP16-INT8/faster-rcnn-resnet101-coco-sparse-60-0001.xml name: faster-rcnn-resnet101-coco-sparse-60-0001 precision: FP16-INT8 framework: tf @@ -97,7 +97,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml + path: ${NPU_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml name: googlenet-v1 precision: FP16 framework: tf @@ -105,7 +105,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml + path: ${NPU_MODELS_PKG}/googlenet-v1/tf/FP16/googlenet-v1.xml name: googlenet-v1 precision: FP16 framework: tf @@ -113,7 +113,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml + path: ${NPU_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml name: googlenet-v1 precision: FP16-INT8 framework: tf @@ -121,7 +121,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml + path: ${NPU_MODELS_PKG}/googlenet-v1/tf/FP16-INT8/googlenet-v1.xml name: googlenet-v1 precision: FP16-INT8 framework: tf @@ -129,7 +129,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml + path: ${NPU_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml name: googlenet-v3 precision: FP16 framework: tf @@ -137,7 +137,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml + path: ${NPU_MODELS_PKG}/googlenet-v3/tf/FP16/googlenet-v3.xml name: googlenet-v3 precision: FP16 framework: tf @@ -145,7 +145,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml + path: ${NPU_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml name: googlenet-v3 precision: FP16-INT8 framework: tf @@ -153,7 +153,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml + path: ${NPU_MODELS_PKG}/googlenet-v3/tf/FP16-INT8/googlenet-v3.xml name: googlenet-v3 precision: FP16-INT8 framework: tf @@ -161,7 +161,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml + path: ${NPU_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml name: ssd512 precision: FP16 framework: caffe @@ -169,7 +169,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml + path: ${NPU_MODELS_PKG}/ssd512/caffe/FP16/ssd512.xml name: ssd512 precision: FP16 framework: caffe @@ -177,7 +177,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml + path: ${NPU_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml name: ssd512 precision: FP16-INT8 framework: caffe @@ -185,7 +185,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml + path: ${NPU_MODELS_PKG}/ssd512/caffe/FP16-INT8/ssd512.xml name: ssd512 precision: FP16-INT8 framework: caffe @@ -193,7 +193,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml name: yolo-v2-ava-0001 precision: FP16 framework: tf @@ -201,7 +201,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16/yolo-v2-ava-0001.xml name: yolo-v2-ava-0001 precision: FP16 framework: tf @@ -209,7 +209,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml name: yolo-v2-ava-0001 precision: FP16-INT8 framework: tf @@ -217,7 +217,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-0001/tf/FP16-INT8/yolo-v2-ava-0001.xml name: yolo-v2-ava-0001 precision: FP16-INT8 framework: tf @@ -225,7 +225,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml name: yolo-v2-ava-sparse-35-0001 precision: FP16 framework: tf @@ -233,7 +233,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16/yolo-v2-ava-sparse-35-0001.xml name: yolo-v2-ava-sparse-35-0001 precision: FP16 framework: tf @@ -241,7 +241,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml name: yolo-v2-ava-sparse-35-0001 precision: FP16-INT8 framework: tf @@ -249,7 +249,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-35-0001/tf/FP16-INT8/yolo-v2-ava-sparse-35-0001.xml name: yolo-v2-ava-sparse-35-0001 precision: FP16-INT8 framework: tf @@ -257,7 +257,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml name: yolo-v2-ava-sparse-70-0001 precision: FP16 framework: tf @@ -265,7 +265,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16/yolo-v2-ava-sparse-70-0001.xml name: yolo-v2-ava-sparse-70-0001 precision: FP16 framework: tf @@ -273,7 +273,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml name: yolo-v2-ava-sparse-70-0001 precision: FP16-INT8 framework: tf @@ -281,7 +281,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-ava-sparse-70-0001/tf/FP16-INT8/yolo-v2-ava-sparse-70-0001.xml name: yolo-v2-ava-sparse-70-0001 precision: FP16-INT8 framework: tf @@ -289,7 +289,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml name: yolo-v2-tiny-ava-0001 precision: FP16 framework: tf @@ -297,7 +297,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16/yolo-v2-tiny-ava-0001.xml name: yolo-v2-tiny-ava-0001 precision: FP16 framework: tf @@ -305,7 +305,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml name: yolo-v2-tiny-ava-0001 precision: FP16-INT8 framework: tf @@ -313,7 +313,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-0001/tf/FP16-INT8/yolo-v2-tiny-ava-0001.xml name: yolo-v2-tiny-ava-0001 precision: FP16-INT8 framework: tf @@ -321,7 +321,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16 framework: tf @@ -329,7 +329,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16 framework: tf @@ -337,7 +337,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16-INT8 framework: tf @@ -345,7 +345,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-30-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-30-0001.xml name: yolo-v2-tiny-ava-sparse-30-0001 precision: FP16-INT8 framework: tf @@ -353,7 +353,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16 framework: tf @@ -361,7 +361,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16 framework: tf @@ -369,7 +369,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16-INT8 framework: tf @@ -377,7 +377,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/yolo-v2-tiny-ava-sparse-60-0001/tf/FP16-INT8/yolo-v2-tiny-ava-sparse-60-0001.xml name: yolo-v2-tiny-ava-sparse-60-0001 precision: FP16-INT8 framework: tf @@ -385,7 +385,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml name: squeezenet1.1 precision: FP16 framework: caffe @@ -393,7 +393,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16/squeezenet1.1.xml name: squeezenet1.1 precision: FP16 framework: caffe @@ -401,7 +401,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml name: squeezenet1.1 precision: FP16-INT8 framework: caffe @@ -409,7 +409,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml + path: ${NPU_MODELS_PKG}/squeezenet1.1/caffe/FP16-INT8/squeezenet1.1.xml name: squeezenet1.1 precision: FP16-INT8 framework: caffe @@ -417,7 +417,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16 framework: tf @@ -425,7 +425,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16 framework: tf @@ -433,7 +433,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16-INT8 framework: tf @@ -441,7 +441,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-0001/tf/FP16-INT8/icnet-camvid-ava-0001.xml name: icnet-camvid-ava-0001 precision: FP16-INT8 framework: tf @@ -449,7 +449,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf @@ -457,7 +457,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16 framework: tf @@ -465,7 +465,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf @@ -473,7 +473,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-30-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-30-0001.xml name: icnet-camvid-ava-sparse-30-0001 precision: FP16-INT8 framework: tf @@ -481,7 +481,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf @@ -489,7 +489,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16 framework: tf @@ -497,7 +497,7 @@ - device: name: CPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf @@ -505,7 +505,7 @@ - device: name: GPU model: - path: ${VPUX_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml + path: ${NPU_MODELS_PKG}/icnet-camvid-ava-sparse-60-0001/tf/FP16-INT8/icnet-camvid-ava-sparse-60-0001.xml name: icnet-camvid-ava-sparse-60-0001 precision: FP16-INT8 framework: tf diff --git a/tests/time_tests/test_runner/test_timetest.py b/tests/time_tests/test_runner/test_timetest.py index a090161394f..f7d7600b281 100644 --- a/tests/time_tests/test_runner/test_timetest.py +++ b/tests/time_tests/test_runner/test_timetest.py @@ -43,7 +43,7 @@ def test_timetest(instance, executable, niter, cl_cache_dir, model_cache, model_ :param niter: number of times to run executable :param cl_cache_dir: directory to store OpenCL cache :param cpu_cache: flag to enable model CPU cache - :param vpu_compiler: flag to change VPU compiler type + :param npu_compiler: flag to change NPU compiler type :param perf_hint: performance hint (optimize device for latency or throughput settings) :param model_cache_dir: directory to store IE model cache :param test_info: custom `test_info` field of built-in `request` pytest fixture diff --git a/thirdparty/dependencies.cmake b/thirdparty/dependencies.cmake index 3b9d9dc5b0f..47b23885e95 100644 --- a/thirdparty/dependencies.cmake +++ b/thirdparty/dependencies.cmake @@ -572,14 +572,14 @@ endif() # if(ENABLE_SAMPLES) - # Note: VPU requires 3.9.0 version, because it contains 'nlohmann::ordered_json' + # Note: NPU requires 3.9.0 version, because it contains 'nlohmann::ordered_json' find_package(nlohmann_json 3.9.0 QUIET) if(nlohmann_json_FOUND) # conan and vcpkg create imported target nlohmann_json::nlohmann_json else() add_subdirectory(thirdparty/json EXCLUDE_FROM_ALL) - # this is required only because of VPU plugin reused this + # this is required only because of NPU plugin reused this openvino_developer_export_targets(COMPONENT openvino_common TARGETS nlohmann_json) # for nlohmann library versions older than v3.0.0 diff --git a/tools/benchmark_tool/openvino/tools/benchmark/utils/constants.py b/tools/benchmark_tool/openvino/tools/benchmark/utils/constants.py index d9e3ca2ae71..1fb45e1f4a2 100644 --- a/tools/benchmark_tool/openvino/tools/benchmark/utils/constants.py +++ b/tools/benchmark_tool/openvino/tools/benchmark/utils/constants.py @@ -1,7 +1,7 @@ # Copyright (C) 2018-2023 Intel Corporation # SPDX-License-Identifier: Apache-2.0 -VPU_DEVICE_NAME = 'VPU' +NPU_DEVICE_NAME = 'NPU' CPU_DEVICE_NAME = 'CPU' GPU_DEVICE_NAME = 'GPU' HETERO_DEVICE_NAME = 'HETERO' @@ -22,7 +22,7 @@ BINARY_EXTENSIONS = ['.bin'] DEVICE_DURATION_IN_SECS = { CPU_DEVICE_NAME: 60, GPU_DEVICE_NAME: 60, - VPU_DEVICE_NAME: 60, + NPU_DEVICE_NAME: 60, GNA_DEVICE_NAME: 60, UNKNOWN_DEVICE_TYPE: 120 } diff --git a/tools/pot/README_dev.md b/tools/pot/README_dev.md index 785e409edd4..d524ec5bb59 100644 --- a/tools/pot/README_dev.md +++ b/tools/pot/README_dev.md @@ -13,7 +13,7 @@ Post-Training Optimization Tool includes standalone command-line tool and Python * Per-channel quantization for Convolutional and Fully-Connected layers. * Multiple domains: Computer Vision, Recommendation Systems. * Ability to implement custom calibration pipeline via supported [API](openvino/tools/pot/api/README.md). -* Compression for different HW targets such as CPU, GPU, VPU. +* Compression for different HW targets such as CPU, GPU, NPU. * Post-training sparsity. ## Usage diff --git a/tools/pot/docs/BestPractices.md b/tools/pot/docs/BestPractices.md index ab15576a06d..bee4d8fbcad 100644 --- a/tools/pot/docs/BestPractices.md +++ b/tools/pot/docs/BestPractices.md @@ -14,7 +14,7 @@ the fastest and easiest way to get a quantized model. It requires only some unan .. note:: - POT uses inference on the CPU during model optimization. It means that ability to infer the original floating-point model is essential for model optimization. In case of the 8-bit quantization, it is recommended to run POT on the same CPU architecture when optimizing for CPU or VNNI-based CPU when quantizing for a non-CPU device, such as GPU, VPU, or GNA. It should help to avoid the impact of the :doc:`saturation issue ` that occurs on AVX and SSE-based CPU devices. + POT uses inference on the CPU during model optimization. It means that ability to infer the original floating-point model is essential for model optimization. In case of the 8-bit quantization, it is recommended to run POT on the same CPU architecture when optimizing for CPU or VNNI-based CPU when quantizing for a non-CPU device, such as GPU, NPU, or GNA. It should help to avoid the impact of the :doc:`saturation issue ` that occurs on AVX and SSE-based CPU devices. Improving accuracy after the Default Quantization @@ -32,7 +32,7 @@ Parameters of the Default Quantization algorithm with basic settings are present # the quantization scheme. For the CPU: # performance - symmetric quantization of weights and activations. # mixed - symmetric weights and asymmetric activations. - # accuracy - the same as "mixed" for CPU, GPU, and GNA devices; asymmetric weights and activations for VPU device. + # accuracy - the same as "mixed" for CPU, GPU, and GNA devices; asymmetric weights and activations for NPU device. "stat_subset_size": 300 # Size of the subset to calculate activations statistics that can be used # for quantization parameters calculation. } diff --git a/tools/pot/docs/DefaultQuantizationUsage.md b/tools/pot/docs/DefaultQuantizationUsage.md index 674d08ba815..ec2edbacca6 100644 --- a/tools/pot/docs/DefaultQuantizationUsage.md +++ b/tools/pot/docs/DefaultQuantizationUsage.md @@ -90,7 +90,7 @@ Default Quantization algorithm has mandatory and optional parameters which are d * ``"target_device"`` - the following options are available: - * ``"ANY"`` (or ``"CPU"``) - default option to quantize models for CPU, GPU, or VPU + * ``"ANY"`` (or ``"CPU"``) - default option to quantize models for CPU, GPU, or NPU * ``"CPU_SPR"`` - to quantize models for CPU SPR (4th Generation Intel® Xeon® Scalable processor family) * ``"GNA"``, ``"GNA3"``, ``"GNA3.5"`` - to quantize models for GNA devices respectively. diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/README.md b/tools/pot/openvino/tools/pot/algorithms/quantization/README.md index 0444889bf44..3b8b9f352e5 100644 --- a/tools/pot/openvino/tools/pot/algorithms/quantization/README.md +++ b/tools/pot/openvino/tools/pot/algorithms/quantization/README.md @@ -2,9 +2,9 @@ ## Introduction -The primary optimization feature of the Post-training Optimization Tool (POT) is the uniform integer quantization which allows substantially increasing inference performance and reducing the model size. Different HW platforms can support different integer precisions and POT is designed to support all of them, for example, 8-bit for CPU, GPU, VPU, 16-bit for GNA. Moreover, POT makes the specification of HW settings transparent for the user by introducing a concept of the `target_device` parameter. +The primary optimization feature of the Post-training Optimization Tool (POT) is the uniform integer quantization which allows substantially increasing inference performance and reducing the model size. Different HW platforms can support different integer precisions and POT is designed to support all of them, for example, 8-bit for CPU, GPU, NPU, 16-bit for GNA. Moreover, POT makes the specification of HW settings transparent for the user by introducing a concept of the `target_device` parameter. -> **NOTE**: There is a special `target_device: "ANY"` which leads to portable quantized models compatible with CPU, GPU, and VPU devices. GNA-quantized models are compatible only with CPU. +> **NOTE**: There is a special `target_device: "ANY"` which leads to portable quantized models compatible with CPU, GPU, and NPU devices. GNA-quantized models are compatible only with CPU. During the quantization process, the POT tool runs inference of the optimizing model to estimate quantization parameters for input activations of the quantizable operation. It means that a calibration dataset is required to perform quantization. This dataset may have or not have annotation depending on the quantization algorithm that is used. diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware/algorithm.py index d08c970f1af..b1c29d88872 100644 --- a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware/algorithm.py +++ b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware/algorithm.py @@ -17,7 +17,7 @@ class AccuracyAwareQuantization(Algorithm): algos_by_devices = { 'ANY': 'AccuracyAwareCommon', 'CPU': 'AccuracyAwareCommon', - 'VPU': 'AccuracyAwareCommon', + 'NPU': 'AccuracyAwareCommon', 'GPU': 'AccuracyAwareCommon', 'GNA': 'AccuracyAwareGNA', 'GNA3': 'AccuracyAwareGNA', diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py index 70011eb9ccc..c99e9733fae 100644 --- a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py +++ b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py @@ -31,11 +31,11 @@ class INT4MixedQuantization(AccuracyAwareCommon): self.original_quantization_config.weights.bits = 8 self.original_quantization_config.activations.bits = 8 self._config.convert_to_mixed_preset = False - self._restrict_for_vpu = True + self._restrict_for_npu = True self._engine.calculate_metrics = True def _can_set_fq_to_low_bitwidth(self, node): - if self._restrict_for_vpu: + if self._restrict_for_npu: return (nu.get_node_output(node, 0)[0].type == 'Convolution') and \ ('group' not in nu.get_node_output(node, 0)[0]) return nu.get_node_output(node, 0)[0].type in OPERATIONS_WITH_WEIGHTS diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py index 4642201b558..35733a07b84 100644 --- a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py +++ b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py @@ -528,7 +528,7 @@ def _fake_quantize_to_types(model, hardware_config): def change_configurations_by_model_type(model, config, fq_configuration, hardware_config): - if config['model_type'] == 'transformer' and config['target_device'] in ['ANY', 'CPU', 'CPU_SPR', 'GPU', 'VPU']: + if config['model_type'] == 'transformer' and config['target_device'] in ['ANY', 'CPU', 'CPU_SPR', 'GPU', 'NPU']: change_configurations_by_model_type_transformer(model, fq_configuration, hardware_config) diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/utils.py b/tools/pot/openvino/tools/pot/algorithms/quantization/utils.py index 9ad333a72bf..be69ad8bc63 100644 --- a/tools/pot/openvino/tools/pot/algorithms/quantization/utils.py +++ b/tools/pot/openvino/tools/pot/algorithms/quantization/utils.py @@ -21,7 +21,7 @@ __HARDWARE_CONFIGS_MAP = {'ANY': 'cpu.json', 'GNA3': 'gna3.json', 'GNA3.5': 'gna3.json', 'GPU': 'gpu.json', # Same as cpu.json but without LSTM/GRUSequence quantization - 'VPU': 'vpu.json', + 'NPU': 'npu.json', 'CPU_SPR': 'cpu.json'} diff --git a/tools/pot/openvino/tools/pot/configs/config.py b/tools/pot/openvino/tools/pot/configs/config.py index 9c55cbd8e49..0b5b41adc30 100644 --- a/tools/pot/openvino/tools/pot/configs/config.py +++ b/tools/pot/openvino/tools/pot/configs/config.py @@ -347,7 +347,7 @@ class Config(Dict): aliases = {'symmetric': 'performance', 'asymmetric': 'accuracy'} preset = aliases.get(preset, preset) presets_aliases_by_device = { - 'VPU': {'accuracy': 'accuracy'}, + 'NPU': {'accuracy': 'accuracy'}, 'GNA': {'accuracy': 'accuracy', 'mixed': 'accuracy'}, 'GNA3': {'accuracy': 'accuracy', 'mixed': 'accuracy'}, 'GNA3.5': {'accuracy': 'accuracy', 'mixed': 'accuracy'}, diff --git a/tools/pot/openvino/tools/pot/graph/vpu_patterns.py b/tools/pot/openvino/tools/pot/graph/npu_patterns.py similarity index 92% rename from tools/pot/openvino/tools/pot/graph/vpu_patterns.py rename to tools/pot/openvino/tools/pot/graph/npu_patterns.py index 5da9861a34e..44a7012f192 100644 --- a/tools/pot/openvino/tools/pot/graph/vpu_patterns.py +++ b/tools/pot/openvino/tools/pot/graph/npu_patterns.py @@ -4,7 +4,7 @@ from openvino.tools.pot.graph.pattern_utils import get_clamp_mult_const_pattern, \ get_softmax_reshape_transpose_gather_matmul_pattern -def get_vpu_ignored_patterns(): +def get_npu_ignored_patterns(): return { 'blocks': [get_softmax_reshape_transpose_gather_matmul_pattern()], 'activations': [get_clamp_mult_const_pattern()], diff --git a/tools/pot/openvino/tools/pot/graph/utils.py b/tools/pot/openvino/tools/pot/graph/utils.py index 30844760d58..1358c74ae9f 100644 --- a/tools/pot/openvino/tools/pot/graph/utils.py +++ b/tools/pot/openvino/tools/pot/graph/utils.py @@ -10,7 +10,7 @@ import numpy as np from openvino.tools.pot.version import get_version from .cpu_patterns import get_cpu_ignored_patterns, get_cpu_spr_ignored_patterns from .gpu_patterns import get_gpu_ignored_patterns -from .vpu_patterns import get_vpu_ignored_patterns +from .npu_patterns import get_npu_ignored_patterns from .gna_patterns import get_gna_ignored_patterns, get_gna3_ignored_patterns from .special_operations import QUANTIZE_AGNOSTIC_OPERATIONS from .node_utils import get_all_node_outputs, get_input_shape @@ -19,7 +19,7 @@ HARDWARE_AWARE_IGNORED_PATTERNS = { 'ANY': get_cpu_ignored_patterns(), 'CPU': get_cpu_ignored_patterns(), 'GPU': get_gpu_ignored_patterns(), - 'VPU': get_vpu_ignored_patterns(), + 'NPU': get_npu_ignored_patterns(), 'GNA': get_gna_ignored_patterns(), 'GNA3': get_gna3_ignored_patterns(), 'GNA3.5': get_gna3_ignored_patterns(), diff --git a/tools/pot/tests/test_target_device.py b/tools/pot/tests/test_target_device.py index d42d294c8ae..2e6a8e429b1 100644 --- a/tools/pot/tests/test_target_device.py +++ b/tools/pot/tests/test_target_device.py @@ -7,7 +7,7 @@ from .utils.path import TOOL_CONFIG_PATH DEVICE = [ 'CPU', 'GPU', - 'VPU' + 'NPU' ]