diff --git a/.github/workflows/linux.yml b/.github/workflows/linux.yml index 900d29a34d7fd7..7cbd748b3d9a10 100644 --- a/.github/workflows/linux.yml +++ b/.github/workflows/linux.yml @@ -1427,7 +1427,7 @@ jobs: export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/performance_tests/ -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-tf_hub_performance.html --self-contained-html -v env: - TYPE: ${{ github.event_name == 'schedule' && 'nightly' || 'precommit'}} + TYPE: ${{ github.event_name == 'schedule' && 'nightly' || 'nightly'}} TEST_DEVICE: CPU - name: Upload Test Results diff --git a/tests/model_hub_tests/models_hub_common/constants.py b/tests/model_hub_tests/models_hub_common/constants.py index 8830543ed8bec2..e659537b248322 100644 --- a/tests/model_hub_tests/models_hub_common/constants.py +++ b/tests/model_hub_tests/models_hub_common/constants.py @@ -10,11 +10,13 @@ loading and heating and includes measurement only one of 2 models - got through convert and read_model. Both "converted" and "read_model" modes will be 2 * runtime_measure_duration ''' -runtime_measure_duration = os.environ.get('RUNTIME_MEASURE_DURATION', '60') +precommit_runtime_measure_duration = os.environ.get('PRECOMMIT_RUNTIME_MEASURE_DURATION', '60') +nightly_runtime_measure_duration = os.environ.get('NIGHTLY_RUNTIME_MEASURE_DURATION', '15') ''' @brief Time in seconds of heating before measurement ''' -runtime_heat_duration = os.environ.get('RUNTIME_HEAT_DURATION', '5') +precommit_runtime_heat_duration = os.environ.get('PRECOMMIT_RUNTIME_HEAT_DURATION', '5') +nigtly_runtime_heat_duration = os.environ.get('NIGHTLY_RUNTIME_HEAT_DURATION', '5') tf_hub_cache_dir = os.environ.get('TFHUB_CACHE_DIR', diff --git a/tests/model_hub_tests/models_hub_common/test_performance_model.py b/tests/model_hub_tests/models_hub_common/test_performance_model.py index 90704aa0d6939f..b778cb8968ac28 100644 --- a/tests/model_hub_tests/models_hub_common/test_performance_model.py +++ b/tests/model_hub_tests/models_hub_common/test_performance_model.py @@ -120,14 +120,12 @@ def get_read_model(self, model_path: str): core = ov.Core() return core.read_model(model=model_path) - def heat_hardware(self, ov_model, inputs) -> None: - _, heat_n_repeats, _ = utils.measure(utils.nano_secs(const.runtime_heat_duration), ov_model, (inputs,)) + def heat_hardware(self, ov_model, inputs, conf) -> None: + _, heat_n_repeats, _ = utils.measure(conf.runtime_heat_duration, ov_model, (inputs,)) print('heat done in {} repeats'.format(heat_n_repeats)) - def measure_inference(self, ov_model, inputs) -> ModelResults: - time_slices, infer_n_repeats, real_runtime = utils.measure(utils.nano_secs(const.runtime_measure_duration), - ov_model, - (inputs,)) + def measure_inference(self, ov_model, inputs, conf) -> ModelResults: + time_slices, infer_n_repeats, real_runtime = utils.measure(conf.runtime_measure_duration, ov_model, (inputs,)) print('measurement done in {} repeats'.format(infer_n_repeats)) infer_throughput = float(infer_n_repeats * (10 ** 9)) / real_runtime infer_mean_time_ns = np.mean(time_slices) @@ -141,15 +139,15 @@ def measure_inference(self, ov_model, inputs) -> ModelResults: results.infer_variance = infer_variance return results - def infer_model(self, ov_model, inputs) -> ModelResults: - self.heat_hardware(ov_model, inputs) - return self.measure_inference(ov_model, inputs) + def infer_model(self, ov_model, inputs, conf) -> ModelResults: + self.heat_hardware(ov_model, inputs, conf) + return self.measure_inference(ov_model, inputs, conf) def compile_model(self, model, ie_device): core = ov.Core() return core.compile_model(model, ie_device) - def __run(self, model_name, model_link, ie_device): + def __run(self, model_name, model_link, ie_device, conf): results = Results() results.status = None try: @@ -168,11 +166,11 @@ def __run(self, model_name, model_link, ie_device): results.status = Status.INFER_CONVERTED_MODEL results.converted_model_results = utils.call_with_timer('Infer converted model', self.infer_model, - (converted_model, inputs)) + (converted_model, inputs, conf)) results.status = Status.INFER_READ_MODEL results.read_model_results = utils.call_with_timer('Infer read model', self.infer_model, - (read_model, inputs)) + (read_model, inputs, conf)) infer_time_ratio = (results.converted_model_results.infer_mean_time / results.read_model_results.infer_mean_time) @@ -196,10 +194,10 @@ def __run(self, model_name, model_link, ie_device): ex_type=ex_type.__name__, ex_value=ex_value) return results - def run(self, model_name, model_link, ie_device): + def run(self, model_name, model_link, ie_device, conf): self.result = Results() t0 = time.time() - self.result = multiprocessing_run(self.__run, [model_name, model_link, ie_device], model_name, + self.result = multiprocessing_run(self.__run, [model_name, model_link, ie_device, conf], model_name, self.infer_timeout) t1 = time.time() utils.print_stat('test run time {} secs', (t1 - t0)) diff --git a/tests/model_hub_tests/models_hub_common/utils.py b/tests/model_hub_tests/models_hub_common/utils.py index a45d9aa77d1784..48ee41753c7974 100644 --- a/tests/model_hub_tests/models_hub_common/utils.py +++ b/tests/model_hub_tests/models_hub_common/utils.py @@ -32,6 +32,28 @@ def get_models_list(file_name: str): return models +def get_skipped_model_links(filename: str): + links = set() + if not os.path.exists(filename): + return links + with open(filename) as f: + for model_info in f: + model_info = model_info.strip() + model_name, model_link = model_info.split(',') + links.add(model_link) + return links + + +def get_models_list_not_skipped(model_list_file: str, skip_list_file: str): + skipped_links = get_skipped_model_links(skip_list_file) + not_skipped_models = [] + for model_name, model_link, mark, reason in get_models_list(model_list_file): + if model_link in skipped_links: + continue + not_skipped_models.append((model_name, model_link, mark, reason)) + return not_skipped_models + + def compare_two_tensors(ov_res, fw_res, eps): is_ok = True if not np.allclose(ov_res, fw_res, atol=eps, rtol=eps, equal_nan=True): diff --git a/tests/model_hub_tests/performance_tests/nightly_models b/tests/model_hub_tests/performance_tests/nightly_models new file mode 100644 index 00000000000000..841d0f8047083f --- /dev/null +++ b/tests/model_hub_tests/performance_tests/nightly_models @@ -0,0 +1,689 @@ +universal-sentence-encoder,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/universal-sentence-encoder/versions/2 +imagenet/mobilenet_v1_100_224/classification,https://www.kaggle.com/models/google/mobilenet-v1/frameworks/tensorFlow2/variations/100-224-classification/versions/2 +imagenet/mobilenet_v2_100_224/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/100-224-classification/versions/2 +universal-sentence-encoder-multilingual,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/multilingual/versions/2 +universal-sentence-encoder-large,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/large/versions/2 +imagenet/mobilenet_v2_075_224/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/075-224-classification/versions/2 +movenet/singlepose/lightning,https://www.kaggle.com/models/google/movenet/frameworks/tensorFlow2/variations/singlepose-lightning/versions/4 +imagenet/mobilenet_v1_025_224/classification,https://www.kaggle.com/models/google/mobilenet-v1/frameworks/tensorFlow2/variations/025-224-classification/versions/2 +bert_en_uncased_preprocess,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/en-uncased-preprocess/versions/3 +tf2-preview/mobilenet_v2/feature_vector,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/tf2-preview-feature-vector/versions/4 +magenta/arbitrary-image-stylization-v1-256,https://www.kaggle.com/models/google/arbitrary-image-stylization-v1/frameworks/tensorFlow1/variations/256/versions/2 +nnlm-en-dim128,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/en-dim128/versions/1 +bert_en_uncased_L-12_H-768_A-12,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/en-uncased-l-12-h-768-a-12/versions/4 +bert_uncased_L-12_H-768_A-12,https://www.kaggle.com/models/google/bert/frameworks/tensorFlow1/variations/uncased-l-12-h-768-a-12/versions/1 +elmo,https://www.kaggle.com/models/google/elmo/frameworks/tensorFlow1/variations/elmo/versions/3 +universal-sentence-encoder-multilingual-large,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/multilingual-large/versions/2 +small_bert/bert_en_uncased_L-4_H-256_A-4,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/bert-en-uncased-l-4-h-256-a-4/versions/2 +imagenet/resnet_v2_50/feature_vector,https://www.kaggle.com/models/google/resnet-v2/frameworks/tensorFlow2/variations/50-feature-vector/versions/2 +spice,https://www.kaggle.com/models/google/spice/frameworks/tensorFlow1/variations/spice/versions/2 +movenet/singlepose/thunder,https://www.kaggle.com/models/google/movenet/frameworks/tensorFlow2/variations/singlepose-thunder/versions/4 +yamnet,https://www.kaggle.com/models/google/yamnet/frameworks/tensorFlow2/variations/yamnet/versions/1 +tf2-preview/nnlm-en-dim128,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/tf2-preview-en-dim128/versions/1 +universal-sentence-encoder-lite,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow1/variations/lite/versions/2 +tf2-preview/mobilenet_v2/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/tf2-preview-classification/versions/4 +imagenet/inception_v3/feature_vector,https://www.kaggle.com/models/google/inception-v3/frameworks/tensorFlow2/variations/feature-vector/versions/2 +imagenet/mobilenet_v2_140_224/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/140-224-classification/versions/2 +efficientnet/lite0/feature-vector,https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tensorFlow1/variations/lite0-feature-vector/versions/2 +i3d-kinetics-400,https://www.kaggle.com/models/deepmind/i3d-kinetics/frameworks/tensorFlow1/variations/400/versions/1 +imagenet/mobilenet_v2_035_224/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/035-224-classification/versions/2 +tf2-preview/gnews-swivel-20dim,https://www.kaggle.com/models/google/gnews-swivel/frameworks/tensorFlow2/variations/tf2-preview-20dim/versions/1 +faster_rcnn/openimages_v4/inception_resnet_v2,https://www.kaggle.com/models/google/faster-rcnn-inception-resnet-v2/frameworks/tensorFlow1/variations/faster-rcnn-openimages-v4-inception-resnet-v2/versions/1 +imagenet/mobilenet_v2_140_224/feature_vector,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/140-224-feature-vector/versions/2 +imagenet/mobilenet_v2_100_224/feature_vector,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/100-224-feature-vector/versions/2 +bert_en_cased_L-12_H-768_A-12,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/en-cased-l-12-h-768-a-12/versions/4 +universal-sentence-encoder-cmlm/en-base,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/cmlm-en-base/versions/1 +openimages_v4/ssd/mobilenet_v2,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow1/variations/openimages-v4-ssd-mobilenet-v2/versions/1 +efficientnet/lite0/classification,https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tensorFlow1/variations/lite0-classification/versions/2 +universal-sentence-encoder-xling-many,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow1/variations/xling-many/versions/1 +imagenet/mobilenet_v3_large_100_224/classification,https://www.kaggle.com/models/google/mobilenet-v3/frameworks/tensorFlow2/variations/large-100-224-classification/versions/1 +nnlm-en-dim128-with-normalization,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/en-dim128-with-normalization/versions/1 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+vggish,https://www.kaggle.com/models/google/vggish/frameworks/tensorFlow2/variations/vggish/versions/1 +bert_multi_cased_L-12_H-768_A-12,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/multi-cased-l-12-h-768-a-12/versions/4 +imagenet/mobilenet_v2_130_224/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/130-224-classification/versions/2 +tf2-preview/inception_v3/feature_vector,https://www.kaggle.com/models/google/inception-v3/frameworks/tensorFlow2/variations/tf2-preview-feature-vector/versions/4 +mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT,https://www.kaggle.com/models/google/mobilebert/frameworks/tensorFlow1/variations/uncased-l-24-h-128-b-512-a-4-f-4-opt/versions/1 +bert_en_uncased_L-24_H-1024_A-16,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/en-uncased-l-24-h-1024-a-16/versions/4 +imagenet/mobilenet_v3_small_100_224/feature_vector,https://www.kaggle.com/models/google/mobilenet-v3/frameworks/tensorFlow2/variations/small-100-224-feature-vector/versions/1 +efficientdet/lite0/detection,https://www.kaggle.com/models/tensorflow/efficientdet/frameworks/tensorFlow2/variations/lite0-detection/versions/1 +small_bert/bert_en_uncased_L-2_H-128_A-2,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/bert-en-uncased-l-2-h-128-a-2/versions/2 +albert_base,https://www.kaggle.com/models/google/albert/frameworks/tensorFlow1/variations/base/versions/3 +nnlm-ja-dim128,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/ja-dim128/versions/1 +universal-sentence-encoder-multilingual-qa,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/multilingual-qa/versions/2 +nnlm-ja-dim128-with-normalization,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/ja-dim128-with-normalization/versions/1 +LaBSE,https://www.kaggle.com/models/google/labse/frameworks/tensorFlow2/variations/labse/versions/2 +nnlm-en-dim50-with-normalization,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/en-dim50-with-normalization/versions/1 +resnet_50/feature_vector,https://www.kaggle.com/models/tensorflow/resnet-50/frameworks/tensorFlow2/variations/feature-vector/versions/1 +universal-sentence-encoder-qa,https://www.kaggle.com/models/google/universal-sentence-encoder/frameworks/tensorFlow2/variations/qa/versions/2 +biggan-deep-256,https://www.kaggle.com/models/deepmind/biggan/frameworks/tensorFlow1/variations/deep-256/versions/1 +efficientdet/lite2/detection,https://www.kaggle.com/models/tensorflow/efficientdet/frameworks/tensorFlow2/variations/lite2-detection/versions/1 +imagenet/mobilenet_v2_050_224/classification,https://www.kaggle.com/models/google/mobilenet-v2/frameworks/tensorFlow2/variations/050-224-classification/versions/2 +mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT/squadv1,https://www.kaggle.com/models/google/mobilebert/frameworks/tensorFlow1/variations/uncased-l-24-h-128-b-512-a-4-f-4-opt-squadv1/versions/1 +delf,https://www.kaggle.com/models/google/delf/frameworks/tensorFlow1/variations/delf/versions/1 +ssd_mobilenet_v2,https://www.kaggle.com/models/tensorflow/ssd-mobilenet-v2/frameworks/tensorFlow2/variations/ssd-mobilenet-v2/versions/1 +imagenet/mobilenet_v3_large_075_224/feature_vector,https://www.kaggle.com/models/google/mobilenet-v3/frameworks/tensorFlow2/variations/large-075-224-feature-vector/versions/1 +centernet/hourglass_512x512_kpts,https://www.kaggle.com/models/tensorflow/centernet-hourglass/frameworks/tensorFlow2/variations/512x512-kpts/versions/1 +bert_cased_L-12_H-768_A-12,https://www.kaggle.com/models/google/bert/frameworks/tensorFlow1/variations/cased-l-12-h-768-a-12/versions/1 +biggan-512,https://www.kaggle.com/models/deepmind/biggan/frameworks/tensorFlow1/variations/512/versions/2 +tf2-preview/gnews-swivel-20dim-with-oov,https://www.kaggle.com/models/google/gnews-swivel/frameworks/tensorFlow2/variations/tf2-preview-20dim-with-oov/versions/1 +tf2-preview/nnlm-es-dim50-with-normalization,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/tf2-preview-es-dim50-with-normalization/versions/1 +efficientnet/b4/feature-vector,https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tensorFlow2/variations/b4-feature-vector/versions/1 +enformer,https://www.kaggle.com/models/deepmind/enformer/frameworks/tensorFlow2/variations/enformer/versions/1 +efficientnet/b0/feature-vector,https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tensorFlow2/variations/b0-feature-vector/versions/1 +efficientnet/b0/feature-vector,https://www.kaggle.com/models/google/efficientnet/frameworks/tensorFlow1/variations/b0-feature-vector/versions/1 +aiy/vision/classifier/food_V1,https://www.kaggle.com/models/google/aiy/frameworks/tensorFlow1/variations/vision-classifier-food-v1/versions/1 +albert_en_base,https://www.kaggle.com/models/tensorflow/albert/frameworks/tensorFlow2/variations/en-base/versions/3 +imagenet/inception_v3/classification,https://www.kaggle.com/models/google/inception-v3/frameworks/tensorFlow2/variations/classification/versions/2 +nonsemantic-speech-benchmark/trill-distilled,https://www.kaggle.com/models/google/nonsemantic-speech-benchmark/frameworks/tensorFlow2/variations/trill-distilled/versions/3 +efficientnet/lite4/feature-vector,https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tensorFlow1/variations/lite4-feature-vector/versions/2 +resnet_50/classification,https://www.kaggle.com/models/tensorflow/resnet-50/frameworks/tensorFlow2/variations/classification/versions/1 +imagenet/efficientnet_v2_imagenet21k_ft1k_m/feature_vector,https://www.kaggle.com/models/google/efficientnet-v2/frameworks/tensorFlow2/variations/imagenet21k-ft1k-m-feature-vector/versions/2 +imagenet/inception_v1/classification,https://www.kaggle.com/models/google/inception-v1/frameworks/tensorFlow2/variations/classification/versions/2 +bit/m-r50x1,https://www.kaggle.com/models/google/bit/frameworks/tensorFlow2/variations/m-r50x1/versions/1 +progan-128,https://www.kaggle.com/models/google/progan-128/frameworks/tensorFlow1/variations/progan-128/versions/1 +biggan-256,https://www.kaggle.com/models/deepmind/biggan/frameworks/tensorFlow1/variations/256/versions/2 +nnlm-de-dim50-with-normalization,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/de-dim50-with-normalization/versions/1 +imagenet/efficientnet_v2_imagenet21k_b0/feature_vector,https://www.kaggle.com/models/google/efficientnet-v2/frameworks/tensorFlow2/variations/imagenet21k-b0-feature-vector/versions/1 +bert_multi_cased_preprocess,https://www.kaggle.com/models/tensorflow/bert/frameworks/tensorFlow2/variations/multi-cased-preprocess/versions/3 +imagenet/efficientnet_v2_imagenet21k_xl/feature_vector,https://www.kaggle.com/models/google/efficientnet-v2/frameworks/tensorFlow2/variations/imagenet21k-xl-feature-vector/versions/1 +efficientnet/lite2/feature-vector,https://www.kaggle.com/models/tensorflow/efficientnet/frameworks/tensorFlow1/variations/lite2-feature-vector/versions/2 +tfgan/eval/inception,https://www.kaggle.com/models/tensorflow/inception/frameworks/tensorFlow1/variations/tfgan-eval-inception/versions/1 +efficientdet/lite2/feature-vector,https://www.kaggle.com/models/tensorflow/efficientdet/frameworks/tensorFlow2/variations/lite2-feature-vector/versions/1 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+compare_gan/model_13_cifar10_resnet_cifar,https://www.kaggle.com/models/google/compare-gan/frameworks/tensorFlow1/variations/model-13-cifar10-resnet-cifar/versions/1 +vit_b16_classification,https://www.kaggle.com/models/spsayakpaul/vision-transformer/frameworks/tensorFlow2/variations/vit-b16-classification/versions/1 +compare_gan/model_11_cifar10_resnet_cifar,https://www.kaggle.com/models/google/compare-gan/frameworks/tensorFlow1/variations/model-11-cifar10-resnet-cifar/versions/1 +wiki40b-lm-fi,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/fi/versions/1 +vit_r26_s32_medaug_fe,https://www.kaggle.com/models/spsayakpaul/vision-transformer/frameworks/tensorFlow2/variations/vit-r26-s32-medaug-fe/versions/1 +image_augmentation/flipx_crop_rotate_color,https://www.kaggle.com/models/google/image-augmentation/frameworks/tensorFlow1/variations/flipx-crop-rotate-color/versions/1 +circularnet_3,https://www.kaggle.com/models/google/circularnet/frameworks/tensorFlow2/variations/3/versions/1 +random-nnlm-en-dim50,https://www.kaggle.com/models/google/random-nnlm-en/frameworks/tensorFlow1/variations/dim50/versions/1 +mixer_b16_i21k_classification,https://www.kaggle.com/models/spsayakpaul/mlp-mixer/frameworks/tensorFlow2/variations/mixer-b16-i21k-classification/versions/1 +mixer_b16_sam_classification,https://www.kaggle.com/models/spsayakpaul/mlp-mixer/frameworks/tensorFlow2/variations/mixer-b16-sam-classification/versions/1 +wav2vec2-robust,https://www.kaggle.com/models/kaggle/wav2vec2/frameworks/tensorFlow2/variations/robust/versions/1 +wiki40b-lm-lv,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/lv/versions/1 +sentence-t5/st5-11b,https://www.kaggle.com/models/google/sentence-t5/frameworks/tensorFlow2/variations/st5-11b/versions/1 +gtr/gtr-xxl,https://www.kaggle.com/models/google/gtr/frameworks/tensorFlow2/variations/gtr-xxl/versions/1 +mmt/data_mscoco,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/data-mscoco/versions/1 +vit_r26_s32_lightaug_classification,https://www.kaggle.com/models/spsayakpaul/vision-transformer/frameworks/tensorFlow2/variations/vit-r26-s32-lightaug-classification/versions/1 +compare_gan/s3gan_10_256x256,https://www.kaggle.com/models/google/compare-gan/frameworks/tensorFlow1/variations/s3gan-10-256x256/versions/1 +mixer_b16_i1k_classification,https://www.kaggle.com/models/spsayakpaul/mlp-mixer/frameworks/tensorFlow2/variations/mixer-b16-i1k-classification/versions/1 +HRNet/msegpcontext-hrnetv2-w48,https://www.kaggle.com/models/google/hrnet/frameworks/tensorFlow2/variations/hrnet-msegpcontext-hrnetv2-w48/versions/1 +mmt/baseline-ft_baseline-no-bert-transfer,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/baseline-ft-baseline-no-bert-transfer/versions/1 +wiki40b-lm-el,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/el/versions/1 +wiki40b-lm-ro,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/ro/versions/1 +wiki40b-lm-ms,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/ms/versions/1 +wiki40b-lm-th,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/th/versions/1 +mmt/data_sbu,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/data-sbu/versions/1 +mmt/data_combined-instance,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/data-combined-instance/versions/1 +vila/image,https://www.kaggle.com/models/google/vila/frameworks/tensorFlow2/variations/image/versions/1 +mmt/architecture_vilbert-4block,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/architecture-vilbert-4block/versions/1 +mmt/architecture-ft_language-q-12,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/architecture-ft-language-q-12/versions/1 +mmt/architecture_single-modality,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/architecture-single-modality/versions/1 +compare_gan/model_4_lsun_bedroom_resnet19,https://www.kaggle.com/models/google/compare-gan/frameworks/tensorFlow1/variations/model-4-lsun-bedroom-resnet19/versions/1 +movinet/a3/stream/kinetics-600/classification,https://www.kaggle.com/models/google/movinet/frameworks/tensorFlow2/variations/a3-stream-kinetics-600-classification/versions/2 +mmt/architecture_vilbert-2block,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/architecture-vilbert-2block/versions/1 +jigsaw,https://www.kaggle.com/models/vtab/jigsaw/frameworks/tensorFlow1/variations/jigsaw/versions/1 +tf2-preview/nnlm-zh-dim50-with-normalization,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/tf2-preview-zh-dim50-with-normalization/versions/1 +wiki40b-lm-de,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/de/versions/1 +mixer_b32_sam_fe,https://www.kaggle.com/models/spsayakpaul/mlp-mixer/frameworks/tensorFlow2/variations/mixer-b32-sam-fe/versions/1 +mmt/data-ft_sbu,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/data-ft-sbu/versions/1 +mmt/data_cc-with-bert,https://www.kaggle.com/models/deepmind/mmt/frameworks/tensorFlow1/variations/data-cc-with-bert/versions/1 +tf2-preview/nnlm-id-dim50,https://www.kaggle.com/models/google/nnlm/frameworks/tensorFlow2/variations/tf2-preview-id-dim50/versions/1 +vgg19-block2-conv2-unpooling-decoder,https://www.kaggle.com/models/emilutz/vgg19-unpooling-encoder-decoder/frameworks/tensorFlow2/variations/vgg19-block2-conv2-unpooling-decoder/versions/1 +Wiki-words-500-with-normalization,https://www.kaggle.com/models/google/wiki-words/frameworks/tensorFlow2/variations/500-with-normalization/versions/1 +wiki40b-lm-zh-tw,https://www.kaggle.com/models/google/wiki40b-lm/frameworks/tensorFlow1/variations/zh-tw/versions/1 +centernet/hourglass_512x512,https://www.kaggle.com/models/tensorflow/centernet-hourglass/frameworks/tensorFlow2/variations/512x512/versions/1 +centernet/hourglass_1024x1024,https://www.kaggle.com/models/tensorflow/centernet-hourglass/frameworks/tensorFlow2/variations/1024x1024/versions/1 +centernet/hourglass_512x512_kpts,https://www.kaggle.com/models/tensorflow/centernet-hourglass/frameworks/tensorFlow2/variations/512x512-kpts/versions/1 +bit/s-r152x4,https://www.kaggle.com/models/google/bit/frameworks/tensorFlow2/variations/s-r152x4/versions/1 +small_bert/bert_uncased_L-8_H-128_A-2,https://www.kaggle.com/models/google/bert/frameworks/tensorFlow1/variations/bert-uncased-l-8-h-128-a-2/versions/2 +centernet/resnet50v2_512x512_kpts,https://www.kaggle.com/models/tensorflow/centernet-resnet/frameworks/tensorFlow2/variations/50v2-512x512-kpts/versions/1 +centernet/resnet50v1_fpn_512x512,https://www.kaggle.com/models/tensorflow/centernet-resnet/frameworks/tensorFlow2/variations/50v1-fpn-512x512/versions/1 +centernet/resnet50v1_fpn_512x512_kpts,https://www.kaggle.com/models/tensorflow/centernet-resnet/frameworks/tensorFlow2/variations/50v1-fpn-512x512-kpts/versions/1 diff --git a/tests/model_hub_tests/performance_tests/test_tf_hub_performance_model.py b/tests/model_hub_tests/performance_tests/test_tf_hub_performance_model.py index 08b4c785a2019b..b4be1e3016dc10 100644 --- a/tests/model_hub_tests/performance_tests/test_tf_hub_performance_model.py +++ b/tests/model_hub_tests/performance_tests/test_tf_hub_performance_model.py @@ -5,21 +5,39 @@ import gc import os import shutil +from collections import namedtuple +from enum import Enum import pytest import tensorflow_hub as hub -from models_hub_common.constants import no_clean_cache_dir -from models_hub_common.constants import tf_hub_cache_dir from models_hub_common.test_performance_model import TestModelPerformance -from models_hub_common.utils import get_models_list -from models_hub_common.utils import cleanup_dir +import models_hub_common.utils as utils +import models_hub_common.constants as const + + +Conf = namedtuple("Conf", "runtime_measure_duration runtime_heat_duration") + + +class TestType(Enum): + PRECOMMIT = 0 + NIGHTLY = 1 + + +def get_tests_conf(test_type: TestType) -> Conf: + options = {TestType.NIGHTLY: + Conf(utils.nano_secs(const.nightly_runtime_measure_duration), + utils.nano_secs(const.nigtly_runtime_heat_duration)), + TestType.PRECOMMIT: + Conf(utils.nano_secs(const.precommit_runtime_measure_duration), + utils.nano_secs(const.precommit_runtime_heat_duration))} + return options[test_type] def clean_cache(): - if not os.path.exists(tf_hub_cache_dir): + if not os.path.exists(const.tf_hub_cache_dir): return - for file_name in os.listdir(tf_hub_cache_dir): - file_path = os.path.join(tf_hub_cache_dir, file_name) + for file_name in os.listdir(const.tf_hub_cache_dir): + file_path = os.path.join(const.tf_hub_cache_dir, file_name) try: if os.path.isfile(file_path) or os.path.islink(file_path): os.unlink(file_path) @@ -35,16 +53,28 @@ def load_model(self, model_name, model_link): return hub.resolve(model_link) def teardown_method(self): - if not no_clean_cache_dir: - cleanup_dir(tf_hub_cache_dir) + if not const.no_clean_cache_dir: + utils.cleanup_dir(const.tf_hub_cache_dir) # deallocate memory after each test case gc.collect() @pytest.mark.parametrize("model_name,model_link,mark,reason", - get_models_list(os.path.join(os.path.dirname(__file__), "precommit_models"))) + utils.get_models_list(os.path.join(os.path.dirname(__file__), "precommit_models"))) @pytest.mark.precommit def test_convert_model_precommit(self, model_name, model_link, mark, reason, ie_device): assert mark is None or mark == 'skip', "Incorrect test case: {}, {}".format(model_name, model_link) if mark == 'skip': pytest.skip(reason) - self.run(model_name, model_link, ie_device) + self.run(model_name, model_link, ie_device, get_tests_conf(TestType.PRECOMMIT)) + + @pytest.mark.parametrize("model_name,model_link,mark,reason", + utils.get_models_list_not_skipped(os.path.join(os.path.dirname(__file__), + "nightly_models"), + os.path.join(os.path.dirname(__file__), + "nightly_models.skip"))) + @pytest.mark.nightly + def test_convert_model_all_models(self, model_name, model_link, mark, reason, ie_device): + assert mark is None or mark == 'skip', "Incorrect test case: {}, {}".format(model_name, model_link) + if mark == 'skip': + pytest.skip(reason) + self.run(model_name, model_link, ie_device, get_tests_conf(TestType.NIGHTLY))