diff --git a/.gitattributes b/.gitattributes index 781d37e0b6f..a28a8f83509 100644 --- a/.gitattributes +++ b/.gitattributes @@ -67,9 +67,11 @@ #POT attributes tools/pot/tests/data/test_cases_refs/* filter=lfs diff=lfs merge=lfs -text +tools/pot/tests/data/models/*/* filter=lfs diff=lfs merge=lfs -text +tools/pot/tests/data/reference_models/* filter=lfs diff=lfs merge=lfs -text +tools/pot/tests/data/video/* filter=lfs diff=lfs merge=lfs -text +tools/pot/tests/data/reference_fake_quantize_conf/* filter=lfs diff=lfs merge=lfs -text /tools/pot/tests/** -pot_package -/configs/accuracy_checker/** -pot_package -/configs/quantization/** -pot_package /tools/pot/tools/auxilary/** -pot_package /tools/pot/tools/run_series_experiments.py -pot_package /tools/pot/.pylintrc -pot_package diff --git a/tools/pot/openvino/tools/pot/benchmark/benchmark.py b/tools/pot/openvino/tools/pot/benchmark/benchmark.py index db0aa4646b4..07a6f3ef859 100644 --- a/tools/pot/openvino/tools/pot/benchmark/benchmark.py +++ b/tools/pot/openvino/tools/pot/benchmark/benchmark.py @@ -95,7 +95,7 @@ def benchmark_embedded_python_api(path_to_model_file): for key, value in input_info.items(): m = [] dt = np_d_type[value.precision] - for x in value.shape: + for x in value.input_data.shape: m.append(x) m[0] = m[0] * batch_size input_data[key] = np.empty(tuple(m), dtype=dt) @@ -122,7 +122,7 @@ def benchmark_embedded_python_api(path_to_model_file): infer_requests = exe_network.requests batch_size = ie_network.batch_size request_queue = InferRequestsQueue(infer_requests) - requests_input_data = get_dummy_inputs(batch_size, ie_network.inputs, infer_requests) + requests_input_data = get_dummy_inputs(batch_size, ie_network.input_info, infer_requests) infer_request = request_queue.get_idle_request() # For warming up diff --git a/tools/pot/tests/data/frame_extractor/0.png b/tools/pot/tests/data/frame_extractor/0.png new file mode 100644 index 00000000000..5562a70f5c7 --- /dev/null +++ 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--git a/tools/pot/tests/test_graph.py b/tools/pot/tests/test_graph.py index b7d82af9ed3..febe190f2c7 100755 --- a/tools/pot/tests/test_graph.py +++ b/tools/pot/tests/test_graph.py @@ -18,11 +18,11 @@ CPU_CONFIG_PATH = HARDWARE_CONFIG_PATH / 'cpu.json' GNA_CONFIG_PATH = HARDWARE_CONFIG_PATH / 'gna.json' TEST_MODELS = [ - # ('mobilenet-v2-pytorch', 'pytorch'), - # ('resnet-50-pytorch', 'pytorch'), - # ('googlenet-v3', 'tf'), - # ('ssd_mobilenet_v2_coco', 'tf'), - # ('densenet-121', 'caffe'), + ('mobilenetv2_example', 'pytorch'), + ('resnet_example', 'pytorch'), + ('googlenet_example', 'pytorch'), + ('mobilenetv2_ssd_example', 'pytorch'), + ('densenet121_example', 'pytorch'), ('multiple_out_ports_net', 'tf'), # multiple output ports in node case check, # ('rm_nnet4a', 'kaldi') ] @@ -53,16 +53,16 @@ def test_build_quantization_graph(tmp_path, models, model_name, model_framework) MODELS_FOR_TESTING_IGNORED_PARAMS = [ - # ('mobilenet-v2-pytorch', 'pytorch'), - # ('resnet-50-pytorch', 'pytorch'), - # ('googlenet-v3', 'tf'), + ('mobilenetv2_example', 'pytorch'), + ('resnet_example', 'pytorch'), + ('googlenet_example', 'pytorch'), # ('mtcnn', 'caffe') ] @pytest.mark.parametrize( - 'model_name, model_framework', MODELS_FOR_TESTING_IGNORED_PARAMS[1:], - ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_FOR_TESTING_IGNORED_PARAMS[1:]]) + 'model_name, model_framework', MODELS_FOR_TESTING_IGNORED_PARAMS, + ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_FOR_TESTING_IGNORED_PARAMS]) def test_build_quantization_graph_with_ignored_params( tmp_path, models, model_name, model_framework): if model_name in CASCADE_MAP: @@ -88,10 +88,10 @@ def test_build_quantization_graph_with_ignored_params( ] } - if model_name == 'resnet-50-pytorch': - ignored_params['scope'] = ['Conv_5/WithoutBiases', 'Conv_18/WithoutBiases'] - elif model_name == 'googlenet-v3': - node_name = 'InceptionV3/InceptionV3/Mixed_6c/Branch_2/Conv2d_0a_1x1/convolution' + if model_name == 'resnet_example': + ignored_params['scope'] = ['Conv_11/WithoutBiases', 'Conv_29/WithoutBiases'] + elif model_name == 'googlenet_example': + node_name = 'Conv_10/WithoutBiases' ignored_params['scope'] = [node_name] elif model_name == 'mtcnn': ignored_params = { @@ -117,8 +117,8 @@ def test_build_quantization_graph_with_ignored_params( @pytest.mark.parametrize( - 'model_name, model_framework', MODELS_FOR_TESTING_IGNORED_PARAMS[:], - ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_FOR_TESTING_IGNORED_PARAMS[:]]) + 'model_name, model_framework', MODELS_FOR_TESTING_IGNORED_PARAMS, + ids=['{}_{}'.format(m[0], m[1]) for m in MODELS_FOR_TESTING_IGNORED_PARAMS]) def test_build_quantization_graph_with_ignored_agnostic_params( tmp_path, models, model_name, model_framework): if model_name in CASCADE_MAP: @@ -155,22 +155,19 @@ def test_build_quantization_graph_with_ignored_agnostic_params( TEST_MODELS_REMOVAL = [ - # ('mobilenet-ssd', 'caffe', ['conv1/WithoutBiases', - # 'conv16_2_mbox_conf/WithoutBiases', - # 'conv15_1/WithoutBiases']), - # ('ssd_resnet50_512', 'mxnet', ['stage1_unit1_sc', - # 'stage3_unit1_sc', - # 'multi_feat_2_conv_3x3_relu_cls_pred_conv/WithoutBiases']), - # ('squeezenet1.1', 'pytorch', ['fire4/expand3x3/WithoutBiases', - # 'fire8/expand3x3/WithoutBiases']), - # ('mobilenet-v2-pytorch', 'pytorch', ['Conv_18/WithoutBiases', - # 'Conv_66/WithoutBiases', - # 'Conv_162/WithoutBiases']), - # ('googlenet-v3', 'tf', ['InceptionV3/InceptionV3/Conv2d_3b_1x1/convolution', - # 'InceptionV3/InceptionV3/Mixed_6a/Branch_1/Conv2d_0a_1x1/convolution', - # 'InceptionV3/InceptionV3/Mixed_6d/Branch_0/Conv2d_0a_1x1/convolution', - # 'InceptionV3/InceptionV3/Mixed_7c/Branch_2/Conv2d_0a_1x1/convolution', - # 'InceptionV3/InceptionV3/Mixed_6a/Branch_0/Conv2d_1a_1x1/convolution']), + ('mobilenetv2_ssd_example', 'caffe', ['Conv_8/WithoutBiases', + 'Conv_172/WithoutBiases', + 'Conv_129/WithoutBiases']), + ('squeezenet1_1_example', 'pytorch', ['Conv_14/WithoutBiases', + 'Conv_51/WithoutBiases']), + ('mobilenet_example', 'pytorch', ['Conv_10/WithoutBiases', + 'Conv_35/WithoutBiases', + 'Conv_73/WithoutBiases']), + ('googlenet_example', 'tf', ['Conv_3/WithoutBiases', + 'Conv_57/WithoutBiases', + 'Conv_65/WithoutBiases', + 'Conv_104/WithoutBiases', + 'Conv_39/WithoutBiases']), ('multiple_out_ports_net', 'tf', ['add_indices']) ] @@ -205,8 +202,8 @@ def test_cutting_fq_layers(_params, tmp_path, models): TEST_MODELS_WITH_PATTERNS = [ - # ('efficientnet-b0', 'tf'), - # ('se-resnet-50', 'caffe'), + ('efficientnet_b0_example', 'pytorch'), + ('mobilenet_v3_small_example', 'pytorch'), # ('image-retrieval-0001', 'dldt'), ('scaleshift_fuse', 'dldt'), ('scaleshift_no_fuse_1', 'dldt'), diff --git a/tools/pot/tests/test_image_loading.py b/tools/pot/tests/test_image_loading.py index 2644a4aaf7d..0836e3025ff 100644 --- a/tools/pot/tests/test_image_loading.py +++ b/tools/pot/tests/test_image_loading.py @@ -16,7 +16,6 @@ TEST_MODELS = [('mobilenet-v2-pytorch', 'pytorch')] def test_image_loading(): - pytest.skip() test_dir = Path(__file__).parent image_files = collect_img_files(str(test_dir / 'data/image_loading/image_files.txt')) @@ -29,13 +28,12 @@ def test_image_loading(): 'model_name, model_framework', TEST_MODELS, ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MODELS]) def test_check_image(tmp_path, models, model_name, model_framework): - pytest.skip() test_dir = Path(__file__).parent - path_imagenet = os.path.join(test_dir, "data/imagenet") + path_image_data = os.path.join(test_dir, "data/image_data") engine_config = Dict({"device": "CPU", "type": "simplified", - "data_source": path_imagenet}) + "data_source": path_image_data}) model = models.get(model_name, model_framework, tmp_path) model = load_model(model.model_params) @@ -43,8 +41,6 @@ def test_check_image(tmp_path, models, model_name, model_framework): num_images_from_data_loader = len(list(data_loader)) - # subtraction 1 is needed to exclude the text file - files = os.listdir(path_imagenet) - num_images_in_dir = len(files) - 1 + num_images_in_dir = len(os.listdir(path_image_data)) assert num_images_from_data_loader == num_images_in_dir diff --git a/tools/pot/tests/test_nightly.py b/tools/pot/tests/test_nightly.py index 5dedb3acea5..089ea1a7c12 100644 --- a/tools/pot/tests/test_nightly.py +++ b/tools/pot/tests/test_nightly.py @@ -35,12 +35,12 @@ TEST_MODELS_DEFAULT = [ ('googlenet-v3-pytorch', 'pytorch', 'FP32', { 'performance': {'accuracy@top1': 0.77562, 'accuracy@top5': 0.9363}, 'mixed': {'accuracy@top1': 0.77562, 'accuracy@top5': 0.9363}}), - # ('densenet-121', 'caffe', 'FP32', { - # 'performance': {'accuracy@top1': 0.73908, 'accuracy@top5': 0.91728}, - # 'mixed': {'accuracy@top1': 0.7389, 'accuracy@top5': 0.91714}}), - #('mobilenet-ssd', 'caffe', 'FP32', { - # 'performance': {'map': 0.71978}, - # 'mixed': {'map': 0.71931}}), + ('densenet-121', 'caffe', 'FP32', { + 'performance': {'accuracy@top1': 0.73908, 'accuracy@top5': 0.91728}, + 'mixed': {'accuracy@top1': 0.7389, 'accuracy@top5': 0.91714}}), + # ('mobilenet-ssd', 'caffe', 'FP32', { + # 'performance': {'map': 0.71978}, + # 'mixed': {'map': 0.71931}}), ('octave-resnet-26-0.25', 'mxnet', 'FP32', { 'performance': {'accuracy@top1': 0.7581, 'accuracy@top5': 0.9256}, 'mixed': {'accuracy@top1': 0.759, 'accuracy@top5': 0.92466}}), @@ -52,7 +52,7 @@ TEST_MODELS_DEFAULT = [ TEST_MODELS_ACC_AWARE = [ ('efficientnet-b0-pytorch', 'pytorch', 'CPU', {'performance': {'accuracy@top1': 0.7663, 'accuracy@top5': 0.9294}}), - #('mobilenet-ssd', 'caffe', 'CPU', {'performance': {'map': 0.7222}}), + # ('mobilenet-ssd', 'caffe', 'CPU', {'performance': {'map': 0.7222}}), ('ssd512', 'caffe', 'CPU', {'performance': {'map': 0.7917}}), ('mobilenet-v1-0.25-128', 'tf', 'GNA', {'performance': {'accuracy@top1': 0.4133, 'accuracy@top5': 0.6626}}) ] diff --git a/tools/pot/tests/test_ranger.py b/tools/pot/tests/test_ranger.py index 851bedca912..42178ab2cf1 100644 --- a/tools/pot/tests/test_ranger.py +++ b/tools/pot/tests/test_ranger.py @@ -1,17 +1,21 @@ # Copyright (C) 2020-2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 +import os +from pathlib import Path + import pytest from addict import Dict - +from openvino.tools.pot.data_loaders.creator import create_data_loader from openvino.tools.pot.engines.creator import create_engine from openvino.tools.pot.graph import load_model from openvino.tools.pot.pipeline.initializer import create_pipeline + from .utils.check_graph import check_model -from .utils.config import get_engine_config, merge_configs +from .utils.config import merge_configs TEST_MODELS = [ - # ('resnet-50-pytorch', 'pytorch') + ('resnet_example', 'pytorch') ] @@ -29,18 +33,23 @@ def test_ranger_graph(_params, tmp_path, models): 'name': 'Ranger', 'params': { 'target_device': 'ANY', - 'stat_subset_size': 100 + 'stat_subset_size': 1 } }] }) model = models.get(model_name, model_framework, tmp_path) - engine_config = get_engine_config(model_name) + test_dir = Path(__file__).parent + path_image_data = os.path.join(test_dir, 'data/image_data') + engine_config = Dict({'device': 'CPU', + 'type': 'simplified', + 'data_source': path_image_data}) config = merge_configs(model.model_params, engine_config, algorithm_config) model = load_model(config.model) - engine = create_engine(config.engine, data_loader=None, metric=None) + data_loader = create_data_loader(engine_config, model) + engine = create_engine(config.engine, data_loader=data_loader, metric=None) pipeline = create_pipeline(config.compression.algorithms, engine) optimized_model = pipeline.run(model) diff --git a/tools/pot/tests/test_sanity.py b/tools/pot/tests/test_sanity.py index 8c9f193d6a9..b89e03bcdc1 100644 --- a/tools/pot/tests/test_sanity.py +++ b/tools/pot/tests/test_sanity.py @@ -219,7 +219,6 @@ def test_simplified_mode(tmp_path, models): def test_frame_extractor_tool(): - pytest.skip() # hack due to strange python imports (same as in sample test) pot_dir = Path(__file__).parent.parent sys.path.append(str(pot_dir / 'tools/frame_extractor')) @@ -227,7 +226,7 @@ def test_frame_extractor_tool(): from tools.frame_extractor.extractor import extract_frames_and_make_dataset test_dir = Path(__file__).parent - test_video_path = test_dir / 'data/video/ImageNet_2012_f3_r5.avi' + test_video_path = test_dir / 'data/video/video_example.avi' output_dir = test_dir / 'data/frame_extractor' dataset_size, frame_step = 3, 1 @@ -252,10 +251,9 @@ TEST_MULTIPLE_OUT_PORTS = [('multiple_out_ports_net', 'tf')] 'model_name, model_framework', TEST_MULTIPLE_OUT_PORTS, ids=['{}_{}'.format(m[0], m[1]) for m in TEST_MULTIPLE_OUT_PORTS]) def test_multiport_outputs_model(tmp_path, models, model_name, model_framework): - pytest.skip() test_dir = Path(__file__).parent # one image as dataset - data_source = (test_dir / 'data/imagenet/ILSVRC2012_val_00000219.*').as_posix() + data_source = (test_dir / 'data/image_data/').as_posix() engine_config = Dict({'type': 'simplified', 'data_source': data_source, 'device': 'CPU'}) diff --git a/tools/pot/tests/test_scales.py b/tools/pot/tests/test_scales.py index 2d123212da7..d33f3b09f3c 100644 --- a/tools/pot/tests/test_scales.py +++ b/tools/pot/tests/test_scales.py @@ -104,7 +104,7 @@ CONFIGURATIONS = [('performance', 8, ids=['symmetric_{}_bits_{}_clipping_value_{}'.format(m[0], m[1], m[3]) for m in CONFIGURATIONS] ) def test_activation_scales(tmp_path, models, preset, bits, stats_path, clipping_value): - pytest.skip() + def normalize(_list): norm_coef = 0 for fq_name in _list: @@ -147,7 +147,7 @@ def test_activation_scales(tmp_path, models, preset, bits, stats_path, clipping_ def test_weights_scales(tmp_path, models): path_to_weights = os.path.join(os.path.dirname(os.path.realpath(__file__)), './data/reference_scale/mobilenet-v2-pytorch_weights.json') - pytest.skip() + model = models.get('mobilenet-v2-pytorch', 'pytorch', tmp_path) ref_weights = get_ref_stats(path_to_weights) weights = get_fq_nodes_stats_algo(model, False, 8, True) @@ -203,7 +203,6 @@ REFERENCES_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), def test_fake_quantize_configurations(tmp_path, models, model_name, model_framework, algo_mode): test_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), './data/reference_scale/test_data') - pytest.skip() config = _get_pytorch_accuracy_checker_config(test_dir) \ if model_framework == 'pytorch' else _get_tf_accuracy_checker_config(test_dir) diff --git a/tools/pot/tests/test_statistic_builder.py b/tools/pot/tests/test_statistic_builder.py index 9bd53076e9d..80f7437ca12 100644 --- a/tools/pot/tests/test_statistic_builder.py +++ b/tools/pot/tests/test_statistic_builder.py @@ -10,33 +10,34 @@ from openvino.tools.pot.graph import load_model from openvino.tools.pot.data_loaders.creator import create_data_loader from openvino.tools.pot.engines.creator import create_engine from openvino.tools.pot.statistics.collector import StatisticsCollector -# from openvino.tools.pot.algorithms.quantization.minmax.algorithm import MinMaxQuantization -# from openvino.tools.pot.algorithms.quantization.fast_bias_correction.algorithm import FastBiasCorrection -# from openvino.tools.pot.algorithms.quantization.channel_alignment.algorithm import ActivationChannelAlignment +from openvino.tools.pot.algorithms.quantization.minmax.algorithm import MinMaxQuantization +from openvino.tools.pot.algorithms.quantization.fast_bias_correction.algorithm import FastBiasCorrection +from openvino.tools.pot.algorithms.quantization.bias_correction.algorithm import BiasCorrection +from openvino.tools.pot.algorithms.quantization.channel_alignment.algorithm import ActivationChannelAlignment from openvino.tools.pot.statistics.utils import merge_stats_by_algo_names from openvino.tools.pot.statistics.statistic_graph_builder import StatisticGraphBuilder from tests.utils.config import PATHS2DATASETS_CONFIG from tests.utils.check_graph import check_model TEST_MODELS = [ - # ('resnet-50-pytorch', 'pytorch', 'symmetric', True, MinMaxQuantization, 'performance', 'perchannel', 0, - # 'max', 'min'), - # ('resnet-50-pytorch', 'pytorch', 'symmetric', True, MinMaxQuantization, 'performance', 'perchannel', 0, - # 'abs_max', 'min'), - # ('resnet-50-pytorch', 'pytorch', 'symmetric', True, MinMaxQuantization, 'mixed', 'pertensor', 0, - # 'abs_max', 'max'), - # ('resnet-50-pytorch', 'pytorch', 'symmetric', True, MinMaxQuantization, 'mixed', 'pertensor', 71, - # 'min', 'quantile'), - # ('resnet-50-pytorch', 'pytorch', 'symmetric', True, MinMaxQuantization, 'performance', 'perchannel', 71, - # 'quantile', 'abs_quantile'), - # ('mobilenet-v2-pytorch', 'pytorch', 'symmetric', True, ActivationChannelAlignment, 'mixed', - # 'perchannel', 2, None, None), - # ('squeezenet1.1', 'caffe', 'symmetric', True, FastBiasCorrection, 'mixed', 'perchannel', 0, - # None, None), - # ('mobilenet-ssd', 'caffe', 'symmetric', True, FastBiasCorrection, 'mixed', 'perchannel', 0, - # None, None), - # ('se-resnet-50', 'caffe', 'symmetric', True, BiasCorrection, 'mixed', 'perchannel', 2, - # None, None) + ('resnet_example', 'pytorch', 'symmetric', True, MinMaxQuantization, 'performance', 'perchannel', 0, + 'max', 'min'), + ('resnet_example', 'pytorch', 'symmetric', True, MinMaxQuantization, 'performance', 'perchannel', 0, + 'abs_max', 'min'), + ('resnet_example', 'pytorch', 'symmetric', True, MinMaxQuantization, 'mixed', 'pertensor', 0, + 'abs_max', 'max'), + ('resnet_example', 'pytorch', 'symmetric', True, MinMaxQuantization, 'mixed', 'pertensor', 23, + 'min', 'quantile'), + ('resnet_example', 'pytorch', 'symmetric', True, MinMaxQuantization, 'performance', 'perchannel', 23, + 'quantile', 'abs_quantile'), + ('mobilenetv2_example', 'pytorch', 'symmetric', True, ActivationChannelAlignment, 'mixed', + 'perchannel', 1, None, None), + ('squeezenet1_1_example', 'pytorch', 'symmetric', True, FastBiasCorrection, 'mixed', 'perchannel', 0, + None, None), + ('mobilenetv2_ssd_example', 'pytorch', 'symmetric', True, FastBiasCorrection, 'mixed', 'perchannel', 0, + None, None), + ('mobilenet_v3_small_example', 'pytorch', 'symmetric', True, BiasCorrection, 'mixed', 'perchannel', 1, + None, None) ]