From 588dfddbe3fe1c62d78932f5bd34512cd6fe451d Mon Sep 17 00:00:00 2001 From: Ashzad-ML Date: Wed, 10 Jul 2024 15:00:58 +0530 Subject: [PATCH] Updated the code for tensorflow==2.13.1 --- 2. Training and Detection.ipynb | 2863 ++++++++++--------------------- requirements.txt | 161 ++ 2 files changed, 1024 insertions(+), 2000 deletions(-) create mode 100644 requirements.txt diff --git a/2. Training and Detection.ipynb b/2. Training and Detection.ipynb index 7827d4dd7..807e549a3 100644 --- a/2. Training and Detection.ipynb +++ b/2. Training and Detection.ipynb @@ -110,20 +110,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "id": "K-Cmz2edpfDE", "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: wget in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (3.2)\n" - ] - } - ], + "outputs": [], "source": [ "if os.name=='nt':\n", " !pip install wget\n", @@ -132,11 +124,25 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "id": "iA1DIq5OpfDE" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'Tensorflow/models'...\n", + "remote: Enumerating objects: 97454, done.\u001b[K\n", + "remote: Counting objects: 100% (21/21), done.\u001b[K\n", + "remote: Compressing objects: 100% (14/14), done.\u001b[K\n", + "remote: Total 97454 (delta 8), reused 17 (delta 7), pack-reused 97433\u001b[K\n", + "Receiving objects: 100% (97454/97454), 613.50 MiB | 15.22 MiB/s, done.\n", + "Resolving deltas: 100% (70949/70949), done.\n" + ] + } + ], "source": [ "if not os.path.exists(os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection')):\n", " !git clone https://github.com/tensorflow/models {paths['APIMODEL_PATH']}" @@ -144,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": { "id": "rJjMHbnDs3Tv" }, @@ -153,1571 +159,346 @@ "name": "stdout", "output_type": "stream", "text": [ - "100% [..............................................] 1468733 / 1468733 1 file(s) moved.\n", - " 1 file(s) copied.\n", - "running build\n", - "running build_py\n", - "copying object_detection\\protos\\anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\argmax_matcher_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\bipartite_matcher_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\box_coder_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\box_predictor_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\calibration_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\center_net_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\eval_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\faster_rcnn_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\faster_rcnn_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\flexible_grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\fpn_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\graph_rewriter_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\grid_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\hyperparams_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\image_resizer_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\input_reader_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\keypoint_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\losses_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\matcher_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\mean_stddev_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\model_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\multiscale_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\optimizer_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\pipeline_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\post_processing_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\preprocessor_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\region_similarity_calculator_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\square_box_coder_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\ssd_anchor_generator_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\ssd_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\string_int_label_map_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\target_assigner_pb2.py -> build\\lib\\object_detection\\protos\n", - "copying object_detection\\protos\\train_pb2.py -> build\\lib\\object_detection\\protos\n", - "running egg_info\n", - "writing object_detection.egg-info\\PKG-INFO\n", - "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n", - "writing requirements to object_detection.egg-info\\requires.txt\n", - "writing top-level names to object_detection.egg-info\\top_level.txt\n", - "reading manifest file 'object_detection.egg-info\\SOURCES.txt'\n", - "writing manifest file 'object_detection.egg-info\\SOURCES.txt'\n", - "running install\n", - "running bdist_egg\n", - "running egg_info\n", - "writing object_detection.egg-info\\PKG-INFO\n", - "writing dependency_links to object_detection.egg-info\\dependency_links.txt\n", - "writing requirements to 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build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\oid_challenge_evaluation.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\oid_challenge_evaluation_utils.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\oid_challenge_evaluation_utils_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\oid_vrd_challenge_evaluation.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\oid_vrd_challenge_evaluation_utils.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\oid_vrd_challenge_evaluation_utils_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\tf_example_parser.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\tf_example_parser_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "copying build\\lib\\object_detection\\metrics\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\metrics\n", - "creating build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\bidirectional_feature_pyramid_generators.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\bidirectional_feature_pyramid_generators_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_hourglass_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_hourglass_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_mobilenet_v2_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_mobilenet_v2_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_mobilenet_v2_fpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_mobilenet_v2_fpn_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_resnet_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_resnet_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_resnet_v1_fpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\center_net_resnet_v1_fpn_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\embedded_ssd_mobilenet_v1_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\embedded_ssd_mobilenet_v1_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_inception_resnet_v2_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_inception_resnet_v2_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_inception_resnet_v2_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_inception_resnet_v2_keras_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_inception_v2_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_inception_v2_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_mobilenet_v1_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_mobilenet_v1_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_nas_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_nas_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_pnas_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_pnas_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_resnet_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_resnet_keras_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_resnet_v1_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_resnet_v1_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_resnet_v1_fpn_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\faster_rcnn_resnet_v1_fpn_keras_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\feature_map_generators.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\feature_map_generators_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "creating build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\convert_keras_models.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\hourglass_network.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\hourglass_network_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\inception_resnet_v2.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\inception_resnet_v2_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\mobilenet_v1.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\mobilenet_v1_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\mobilenet_v2.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\mobilenet_v2_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\model_utils.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\resnet_v1.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\resnet_v1_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\test_utils.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\keras_models\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\\keras_models\n", - "copying build\\lib\\object_detection\\models\\ssd_efficientnet_bifpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_efficientnet_bifpn_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_feature_extractor_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_inception_v2_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_inception_v2_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_inception_v3_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_inception_v3_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobiledet_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobiledet_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_edgetpu_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_edgetpu_feature_extractor_testbase.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_edgetpu_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_fpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_fpn_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_fpn_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_fpn_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_ppn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v1_ppn_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_fpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_fpn_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_fpn_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_fpn_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_mnasfpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v2_mnasfpn_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v3_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v3_feature_extractor_testbase.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_mobilenet_v3_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_pnasnet_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_pnasnet_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_fpn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_fpn_feature_extractor_testbase.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_fpn_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_fpn_feature_extractor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_fpn_keras_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_ppn_feature_extractor.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_ppn_feature_extractor_testbase.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\ssd_resnet_v1_ppn_feature_extractor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\models\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\models\n", - "copying build\\lib\\object_detection\\model_hparams.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_lib.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_lib_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_lib_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_lib_v2.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_main.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_main_tf2.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "copying build\\lib\\object_detection\\model_tpu_main.py -> build\\bdist.win-amd64\\egg\\object_detection\n", - "creating build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\convolutional_box_predictor.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\convolutional_box_predictor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\convolutional_keras_box_predictor.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\convolutional_keras_box_predictor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "creating build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\box_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\box_head_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\class_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\class_head_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keras_box_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keras_box_head_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keras_class_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keras_class_head_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keras_mask_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keras_mask_head_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keypoint_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\keypoint_head_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\mask_head.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\mask_head_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\heads\\__init__.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\\heads\n", - "copying build\\lib\\object_detection\\predictors\\mask_rcnn_box_predictor.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\mask_rcnn_box_predictor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\mask_rcnn_keras_box_predictor.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\mask_rcnn_keras_box_predictor_tf2_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\rfcn_box_predictor.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying build\\lib\\object_detection\\predictors\\rfcn_box_predictor_tf1_test.py -> build\\bdist.win-amd64\\egg\\object_detection\\predictors\n", - "copying 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https://files.pythonhosted.org/packages/56/3f/0a1b147f23830ca027f9e93cb4985957dbef690649f249063718c6e5c19b/matplotlib-3.4.1-cp37-cp37m-win_amd64.whl#sha256=c45e7bf89ea33a2adaef34774df4e692c7436a18a48bcb0e47a53e698a39fa39\n", - "Best match: matplotlib 3.4.1\n", - "Processing matplotlib-3.4.1-cp37-cp37m-win_amd64.whl\n", - "Installing matplotlib-3.4.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\matplotlib-3.4.1-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n", - "Adding matplotlib 3.4.1 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\matplotlib-3.4.1-py3.7-win-amd64.egg\n", - "Searching for pillow\n", - "Reading https://pypi.org/simple/pillow/\n", - "Downloading https://files.pythonhosted.org/packages/84/41/b233abcfb9bae12768bffae92e2c96d2d807cd92a3cef7d24987df0e3d64/Pillow-8.2.0-cp37-cp37m-win_amd64.whl#sha256=3b570f84a6161cf8865c4e08adf629441f56e32f180f7aa4ccbd2e0a5a02cba2\n", - "Best match: Pillow 8.2.0\n", - "Processing Pillow-8.2.0-cp37-cp37m-win_amd64.whl\n", - "Installing Pillow-8.2.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding Pillow 8.2.0 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pillow-8.2.0-py3.7-win-amd64.egg\n", - "Searching for apache-beam\n", - "Reading https://pypi.org/simple/apache-beam/\n", - "Downloading https://files.pythonhosted.org/packages/2d/c9/8a706032b946c5486482d928fe5eb274d39eddfc1ea41e684ce723b760ef/apache_beam-2.28.0-cp37-cp37m-win_amd64.whl#sha256=605c0329bc47c17275ed24cd13a0bfe6fe64fad81588551cd3d3507644c1b09d\n", - "Best match: apache-beam 2.28.0\n", - "Processing apache_beam-2.28.0-cp37-cp37m-win_amd64.whl\n", - "Installing apache_beam-2.28.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\apache_beam-2.28.0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n", - "Adding apache-beam 2.28.0 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\apache_beam-2.28.0-py3.7-win-amd64.egg\n", - "Searching for avro-python3\n", - "Reading https://pypi.org/simple/avro-python3/\n", - "Downloading https://files.pythonhosted.org/packages/cc/97/7a6970380ca8db9139a3cc0b0e3e0dd3e4bc584fb3644e1d06e71e1a55f0/avro-python3-1.10.2.tar.gz#sha256=3b63f24e6b04368c3e4a6f923f484be0230d821aad65ac36108edbff29e9aaab\n", - "Best match: avro-python3 1.10.2\n", - "Processing avro-python3-1.10.2.tar.gz\n", - "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-o0qh6aes\\avro-python3-1.10.2\\setup.cfg\n", - "Running avro-python3-1.10.2\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-o0qh6aes\\avro-python3-1.10.2\\egg-dist-tmp-u9g8xvl0\n", - "Copying avro_python3-1.10.2-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding avro-python3 1.10.2 to easy-install.pth file\n", - "Installing avro script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\avro_python3-1.10.2-py3.7.egg\n", - "Searching for tensorflow>=2.4.0\n", - "Reading https://pypi.org/simple/tensorflow/\n", - "Downloading https://files.pythonhosted.org/packages/ab/7a/eebd927c1f10f97a51d8276d933b0459bd0d705920c3095ec433bf529d33/tensorflow-2.5.0rc0-cp37-cp37m-win_amd64.whl#sha256=48738c7eb1262a13b50df1ff6860c9fa056ae942c7b29bb5c6cc78a025862840\n", - "Best match: tensorflow 2.5.0rc0\n", - "Processing tensorflow-2.5.0rc0-cp37-cp37m-win_amd64.whl\n", - "Installing tensorflow-2.5.0rc0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow-2.5.0rc0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n", - "Adding tensorflow 2.5.0rc0 to easy-install.pth file\n", - "Installing estimator_ckpt_converter-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing estimator_ckpt_converter.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing import_pb_to_tensorboard-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing import_pb_to_tensorboard.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing saved_model_cli-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing saved_model_cli.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tensorboard-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tensorboard.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tf_upgrade_v2-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tf_upgrade_v2.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tflite_convert-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tflite_convert.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing toco-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing toco.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing toco_from_protos-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing toco_from_protos.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow-2.5.0rc0-py3.7-win-amd64.egg\n", - "Searching for tensorflow-model-optimization>=0.4.1\n", - "Reading https://pypi.org/simple/tensorflow-model-optimization/\n", - "Downloading https://files.pythonhosted.org/packages/55/38/4fd48ea1bfcb0b6e36d949025200426fe9c3a8bfae029f0973d85518fa5a/tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl#sha256=06db9b86d871e1dcc40a90515cf0a0f3228ac71cac3f4c49b5c2aef96f45852b\n", - "Best match: tensorflow-model-optimization 0.5.0\n", - "Processing tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl\n", - "Installing tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_model_optimization-0.5.0-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding tensorflow-model-optimization 0.5.0 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_model_optimization-0.5.0-py3.7.egg\n", - "Searching for tensorflow-hub>=0.6.0\n", - "Reading https://pypi.org/simple/tensorflow-hub/\n", - "Downloading https://files.pythonhosted.org/packages/f4/1f/ca72fa5400954903aa6f3354142cd66374604384fb13429af9e41b12120d/tensorflow_hub-0.11.0-py2.py3-none-any.whl#sha256=19399a8abef10682b4f739a5aa78b43da3937df17f5d2afb0547945798787674\n", - "Best match: tensorflow-hub 0.11.0\n", - "Processing tensorflow_hub-0.11.0-py2.py3-none-any.whl\n", - "Installing tensorflow_hub-0.11.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_hub-0.11.0-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding tensorflow-hub 0.11.0 to easy-install.pth file\n", - "Installing make_image_classifier-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing make_image_classifier.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing make_nearest_neighbour_index-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing make_nearest_neighbour_index.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_hub-0.11.0-py3.7.egg\n", - "Searching for tensorflow-datasets\n", - "Reading https://pypi.org/simple/tensorflow-datasets/\n", - "Downloading https://files.pythonhosted.org/packages/d3/91/c3e36695ca04e6f3c2d920887d7dc36550f6bbb03d7d5fd03c2172b06d97/tensorflow_datasets-4.2.0-py3-none-any.whl#sha256=be2532e7de3c7aac3e95a094c3c95b9b1f1a7f2e98daeb16d212dda4382de264\n", - "Best match: tensorflow-datasets 4.2.0\n", - "Processing tensorflow_datasets-4.2.0-py3-none-any.whl\n", - "Installing tensorflow_datasets-4.2.0-py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_datasets-4.2.0-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding tensorflow-datasets 4.2.0 to easy-install.pth file\n", - "Installing tfds-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing tfds.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_datasets-4.2.0-py3.7.egg\n", - "Searching for tensorflow-addons\n", - "Reading https://pypi.org/simple/tensorflow-addons/\n", - "Downloading https://files.pythonhosted.org/packages/0f/87/56e435b4cd3c23e6aeaf438f9f517f08ac406ae7a0d6615e42295280eb7b/tensorflow_addons-0.12.1-cp37-cp37m-win_amd64.whl#sha256=259233e45494354b25c1e45581f0ff7a99e50a38256b353b25113f971c4d364b\n", - "Best match: tensorflow-addons 0.12.1\n", - "Processing tensorflow_addons-0.12.1-cp37-cp37m-win_amd64.whl\n", - "Installing tensorflow_addons-0.12.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_addons-0.12.1-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n", - "Adding tensorflow-addons 0.12.1 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow_addons-0.12.1-py3.7-win-amd64.egg\n", - "Searching for seqeval\n", - "Reading https://pypi.org/simple/seqeval/\n", - "Downloading https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f\n", - "Best match: seqeval 1.2.2\n", - "Processing seqeval-1.2.2.tar.gz\n", - "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-t9lsj_fq\\seqeval-1.2.2\\setup.cfg\n", - "Running seqeval-1.2.2\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-t9lsj_fq\\seqeval-1.2.2\\egg-dist-tmp-dopodyzy\n", - "Copying seqeval-1.2.2-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding seqeval 1.2.2 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\seqeval-1.2.2-py3.7.egg\n", - "Searching for sentencepiece\n", - "Reading https://pypi.org/simple/sentencepiece/\n", - "Downloading https://files.pythonhosted.org/packages/2b/6d/b34d43118887d514f19fea262c09ed1fc357f86a2c42d71fe46a84818e55/sentencepiece-0.1.95-cp37-cp37m-win_amd64.whl#sha256=c2add7d87c30898661de5b9e492bd99c5b184c731dec3c7dd3d2c956e4003446\n", - "Best match: sentencepiece 0.1.95\n", - "Processing sentencepiece-0.1.95-cp37-cp37m-win_amd64.whl\n", - "Installing sentencepiece-0.1.95-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding sentencepiece 0.1.95 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\sentencepiece-0.1.95-py3.7-win-amd64.egg\n", - "Searching for pyyaml>=5.1\n", - "Reading https://pypi.org/simple/pyyaml/\n", - "Downloading https://files.pythonhosted.org/packages/66/7a/9f3a49dea61e98ac8bd3b6b619da4fcb59053232a6e11e9e865dab7c16e0/PyYAML-5.4.1-cp37-cp37m-win_amd64.whl#sha256=08682f6b72c722394747bddaf0aa62277e02557c0fd1c42cb853016a38f8dedf\n", - "Best match: PyYAML 5.4.1\n", - "Processing PyYAML-5.4.1-cp37-cp37m-win_amd64.whl\n", - "Installing PyYAML-5.4.1-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding PyYAML 5.4.1 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyyaml-5.4.1-py3.7-win-amd64.egg\n", - "Searching for py-cpuinfo>=3.3.0\n", - "Reading https://pypi.org/simple/py-cpuinfo/\n", - "Downloading https://files.pythonhosted.org/packages/f6/f5/8e6e85ce2e9f6e05040cf0d4e26f43a4718bcc4bce988b433276d4b1a5c1/py-cpuinfo-7.0.0.tar.gz#sha256=9aa2e49675114959697d25cf57fec41c29b55887bff3bc4809b44ac6f5730097\n", - "Best match: py-cpuinfo 7.0.0\n", - "Processing py-cpuinfo-7.0.0.tar.gz\n", - "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-vtzj0arj\\py-cpuinfo-7.0.0\\setup.cfg\n", - "Running py-cpuinfo-7.0.0\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-vtzj0arj\\py-cpuinfo-7.0.0\\egg-dist-tmp-_xlgbztg\n", - "creating d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\py_cpuinfo-7.0.0-py3.7.egg\n", - "Extracting py_cpuinfo-7.0.0-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding py-cpuinfo 7.0.0 to easy-install.pth file\n", - "Installing cpuinfo-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing cpuinfo.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\py_cpuinfo-7.0.0-py3.7.egg\n", - "Searching for psutil>=5.4.3\n", - "Reading https://pypi.org/simple/psutil/\n", - "Downloading https://files.pythonhosted.org/packages/71/ce/35107e81e7eae55c847313f872d4258a71d2640fa04f57c5520fc81473ce/psutil-5.8.0-cp37-cp37m-win_amd64.whl#sha256=fcc01e900c1d7bee2a37e5d6e4f9194760a93597c97fee89c4ae51701de03563\n", - "Best match: psutil 5.8.0\n", - "Processing psutil-5.8.0-cp37-cp37m-win_amd64.whl\n", - "Installing psutil-5.8.0-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\psutil-5.8.0-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n", - "Adding psutil 5.8.0 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\psutil-5.8.0-py3.7-win-amd64.egg\n", - "Searching for opencv-python-headless\n", - "Reading https://pypi.org/simple/opencv-python-headless/\n", - "Downloading https://files.pythonhosted.org/packages/7f/4d/4a6353f32552be7f36c0e7179275718d30c3287c9c4ab4d1ba99e26a0948/opencv_python_headless-4.5.1.48-cp37-cp37m-win_amd64.whl#sha256=fe02a943b1a28b505e954fbce24e867119a3eb4351f93adad55c6cfe81a70484\n", - "Best match: opencv-python-headless 4.5.1.48\n", - "Processing opencv_python_headless-4.5.1.48-cp37-cp37m-win_amd64.whl\n", - "Installing opencv_python_headless-4.5.1.48-cp37-cp37m-win_amd64.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\opencv_python_headless-4.5.1.48-py3.7-win-amd64.egg\\EGG-INFO\\requires.txt\n", - "Adding opencv-python-headless 4.5.1.48 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\opencv_python_headless-4.5.1.48-py3.7-win-amd64.egg\n", - "Searching for oauth2client\n", - "Reading https://pypi.org/simple/oauth2client/\n", - "Downloading https://files.pythonhosted.org/packages/95/a9/4f25a14d23f0786b64875b91784607c2277eff25d48f915e39ff0cff505a/oauth2client-4.1.3-py2.py3-none-any.whl#sha256=b8a81cc5d60e2d364f0b1b98f958dbd472887acaf1a5b05e21c28c31a2d6d3ac\n", - "Best match: oauth2client 4.1.3\n", - "Processing oauth2client-4.1.3-py2.py3-none-any.whl\n", - "Installing oauth2client-4.1.3-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\oauth2client-4.1.3-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding oauth2client 4.1.3 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\oauth2client-4.1.3-py3.7.egg\n", - "Searching for kaggle>=1.3.9\n", - "Reading https://pypi.org/simple/kaggle/\n", - "Downloading https://files.pythonhosted.org/packages/3a/e7/3bac01547d2ed3d308ac92a0878fbdb0ed0f3d41fb1906c319ccbba1bfbc/kaggle-1.5.12.tar.gz#sha256=b4d87d107bff743aaa805c2b382c3661c4c175cdb159656d4972be2a9cef42cb\n", - "Best match: kaggle 1.5.12\n", - "Processing kaggle-1.5.12.tar.gz\n", - "Writing C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-0zog1127\\kaggle-1.5.12\\setup.cfg\n", - "Running kaggle-1.5.12\\setup.py -q bdist_egg --dist-dir C:\\Users\\User\\AppData\\Local\\Temp\\easy_install-0zog1127\\kaggle-1.5.12\\egg-dist-tmp-h924dpw_\n", - "Copying kaggle-1.5.12-py3.7.egg to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding kaggle 1.5.12 to easy-install.pth file\n", - "Installing kaggle-script.py script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "Installing kaggle.exe script to D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\kaggle-1.5.12-py3.7.egg\n", - "Searching for google-cloud-bigquery>=0.31.0\n", - "Reading https://pypi.org/simple/google-cloud-bigquery/\n", - "Downloading https://files.pythonhosted.org/packages/f7/c1/510fbde69f8ca9d0253326230fe1c36819f94099532312f1174e870f1112/google_cloud_bigquery-2.13.1-py2.py3-none-any.whl#sha256=e0cc41fd5d53d59abf6b87e6d7d9863746c2ee6f136def670938dfb4ee5a4ba3\n", - "Best match: google-cloud-bigquery 2.13.1\n", - "Processing google_cloud_bigquery-2.13.1-py2.py3-none-any.whl\n", - "Installing google_cloud_bigquery-2.13.1-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_cloud_bigquery-2.13.1-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding google-cloud-bigquery 2.13.1 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_cloud_bigquery-2.13.1-py3.7.egg\n", - "Searching for google-api-python-client>=1.6.7\n", - "Reading https://pypi.org/simple/google-api-python-client/\n", - "Downloading https://files.pythonhosted.org/packages/94/f8/49f913ebb8b8d142a55ae398192704297a4f37d7ec879eb3b6b38f6342fd/google_api_python_client-2.1.0-py2.py3-none-any.whl#sha256=921fe10cdff22d1f5b8af7473f9a298efb991fb6ea67dadd6c37fbecfb0575f4\n", - "Best match: google-api-python-client 2.1.0\n", - "Processing google_api_python_client-2.1.0-py2.py3-none-any.whl\n", - "Installing google_api_python_client-2.1.0-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_api_python_client-2.1.0-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding google-api-python-client 2.1.0 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\google_api_python_client-2.1.0-py3.7.egg\n", - "Searching for gin-config\n", - "Reading https://pypi.org/simple/gin-config/\n", - "Downloading 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pytz-2021.1-py2.py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "Adding pytz 2021.1 to easy-install.pth file\n", - "\n", - "Installed d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pytz-2021.1-py3.7.egg\n", - "Searching for pyparsing>=2.4.0\n", - "Reading https://pypi.org/simple/pyparsing/\n", - "Downloading https://files.pythonhosted.org/packages/ff/c5/14a5edf227544a6da3cb99a1481fc894db9a81306c9a18b736d3ac9fe8d5/pyparsing-3.0.0b2-py3-none-any.whl#sha256=f4896b4cc085a1f8f8ae53a1a90db5a86b3825ff73eb974dffee3d9e701007f4\n", - "Best match: pyparsing 3.0.0b2\n", - "Processing pyparsing-3.0.0b2-py3-none-any.whl\n", - "Installing pyparsing-3.0.0b2-py3-none-any.whl to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\n", - "writing requirements to d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\pyparsing-3.0.0b2-py3.7.egg\\EGG-INFO\\requires.txt\n", - "Adding pyparsing 3.0.0b2 to easy-install.pth file\n", - "\n", - "Installed 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google-api-python-client-2.137.0 google-auth-2.32.0 google-auth-httplib2-0.2.0 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 googleapis-common-protos-1.63.2 grpcio-1.64.1 h5py-3.11.0 hdfs-2.7.3 httplib2-0.22.0 idna-3.7 immutabledict-4.2.0 importlib-resources-6.4.0 joblib-1.4.2 js2py-0.74 jsonpickle-3.2.2 jsonschema-4.23.0 jsonschema-specifications-2023.12.1 kaggle-1.6.14 keras-2.13.1 kiwisolver-1.4.5 libclang-18.1.1 lvis-0.5.3 lxml-5.2.2 markdown-3.6 matplotlib-3.7.5 numpy-1.24.3 oauth2client-4.1.3 oauthlib-3.2.2 object-detection-0.1 objsize-0.7.0 opencv-python-4.10.0.84 opencv-python-headless-4.10.0.84 opt-einsum-3.3.0 orjson-3.10.6 pandas-2.0.3 pillow-10.4.0 pkgutil-resolve-name-1.3.10 portalocker-2.10.0 promise-2.3 proto-plus-1.24.0 protobuf-3.20.3 py-cpuinfo-9.0.0 pyarrow-16.1.0 pyarrow-hotfix-0.6 pyasn1-0.6.0 pyasn1-modules-0.4.0 pycocotools-2.0.7 pydot-1.4.2 pyjsparser-2.7.1 pymongo-4.8.0 pyparsing-2.4.7 python-slugify-8.0.4 pytz-2024.1 pyyaml-6.0.1 redis-5.0.7 referencing-0.35.1 regex-2024.5.15 requests-2.31.0 requests-oauthlib-2.0.0 rpds-py-0.19.0 rsa-4.9 sacrebleu-2.2.0 scikit-learn-1.3.2 scipy-1.10.1 sentencepiece-0.2.0 seqeval-1.2.2 tabulate-0.9.0 tensorboard-2.13.0 tensorboard-data-server-0.7.2 tensorflow-2.13.1 tensorflow-datasets-4.9.2 tensorflow-estimator-2.13.0 tensorflow-hub-0.16.1 tensorflow-io-gcs-filesystem-0.34.0 tensorflow-metadata-1.14.0 tensorflow-model-optimization-0.8.0 tensorflow-text-2.13.0 tensorflow_io-0.34.0 termcolor-2.4.0 text-unidecode-1.3 tf-keras-2.15.0 tf-models-official-2.13.2 tf-slim-1.1.0 threadpoolctl-3.5.0 toml-0.10.2 tqdm-4.66.4 typing-extensions-4.5.0 tzdata-2024.1 tzlocal-5.2 uritemplate-4.1.1 urllib3-2.2.2 webencodings-0.5.1 werkzeug-3.0.3 wheel-0.43.0 wrapt-1.16.0 zstandard-0.22.0\n", + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.1.2 is available.\n", + "You should consider upgrading via the '/home/dev/workspace/fixedtfod/tfod/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" ] } ], @@ -1739,285 +520,389 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2021-04-03 11:13:18.258898: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n", - "Running tests under Python 3.7.3: D:\\YouTube\\OD\\TFODCourse\\tfod\\Scripts\\python.exe\n", - "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model\n", - "2021-04-03 11:13:24.537172: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll\n", - "2021-04-03 11:13:24.563275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n", - "pciBusID: 0000:2b:00.0 name: GeForce RTX 2070 SUPER computeCapability: 7.5\n", - "coreClock: 1.785GHz coreCount: 40 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s\n", - "2021-04-03 11:13:24.563310: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll\n", - "2021-04-03 11:13:25.034685: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll\n", - "2021-04-03 11:13:25.034709: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll\n", - "2021-04-03 11:13:25.115325: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll\n", - "2021-04-03 11:13:25.149186: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll\n", - "2021-04-03 11:13:25.150141: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_11.dll'; dlerror: cusolver64_11.dll not found\n", - "2021-04-03 11:13:25.356866: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll\n", - "2021-04-03 11:13:25.879448: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll\n", - "2021-04-03 11:13:25.879478: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1766] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n", - "Skipping registering GPU devices...\n", - "2021-04-03 11:13:25.879799: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2021-04-03 11:13:25.880455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n", - "2021-04-03 11:13:25.880465: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] \n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 1.8s\n", - "I0403 11:13:26.103002 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 1.8s\n", - "[ OK ] ModelBuilderTF2Test.test_create_center_net_model\n", + "Requirement already satisfied: tensorflow==2.13.1 in ./tfod/lib/python3.8/site-packages (2.13.1)\n", + "Requirement already satisfied: astunparse>=1.6.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (1.6.3)\n", + "Requirement already satisfied: flatbuffers>=23.1.21 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (24.3.25)\n", + "Requirement already satisfied: typing-extensions<4.6.0,>=3.6.6 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (4.5.0)\n", + "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (0.4.0)\n", + "Requirement already satisfied: keras<2.14,>=2.13.1 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (2.13.1)\n", + "Requirement already satisfied: six>=1.12.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (1.16.0)\n", + "Requirement already satisfied: libclang>=13.0.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (18.1.1)\n", + "Requirement already satisfied: setuptools in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (56.0.0)\n", + "Requirement already satisfied: termcolor>=1.1.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (2.4.0)\n", + "Requirement already satisfied: packaging in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (24.1)\n", + "Requirement already satisfied: wrapt>=1.11.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (1.16.0)\n", + "Requirement already satisfied: grpcio<2.0,>=1.24.3 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (1.64.1)\n", + "Requirement already satisfied: tensorboard<2.14,>=2.13 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (2.13.0)\n", + "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (0.34.0)\n", + "Requirement already satisfied: numpy<=1.24.3,>=1.22 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (1.24.3)\n", + "Requirement already satisfied: tensorflow-estimator<2.14,>=2.13.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (2.13.0)\n", + "Requirement already satisfied: opt-einsum>=2.3.2 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (3.3.0)\n", + "Requirement already satisfied: h5py>=2.9.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (3.11.0)\n", + "Requirement already satisfied: absl-py>=1.0.0 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (1.4.0)\n", + "Requirement already satisfied: google-pasta>=0.1.1 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (0.2.0)\n", + "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in ./tfod/lib/python3.8/site-packages (from tensorflow==2.13.1) (3.20.3)\n", + "Requirement already satisfied: wheel<1.0,>=0.23.0 in ./tfod/lib/python3.8/site-packages (from astunparse>=1.6.0->tensorflow==2.13.1) (0.43.0)\n", + "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in ./tfod/lib/python3.8/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.1) (1.0.0)\n", + "Requirement already satisfied: requests<3,>=2.21.0 in ./tfod/lib/python3.8/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2.31.0)\n", + "Requirement already satisfied: werkzeug>=1.0.1 in ./tfod/lib/python3.8/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.0.3)\n", + "Requirement already satisfied: markdown>=2.6.8 in ./tfod/lib/python3.8/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.6)\n", + "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in ./tfod/lib/python3.8/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.1) (0.7.2)\n", + "Requirement already satisfied: google-auth<3,>=1.6.3 in ./tfod/lib/python3.8/site-packages (from tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2.32.0)\n", + "Requirement already satisfied: rsa<5,>=3.1.4 in ./tfod/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (4.9)\n", + "Requirement already satisfied: cachetools<6.0,>=2.0.0 in ./tfod/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (5.3.3)\n", + "Requirement already satisfied: pyasn1-modules>=0.2.1 in ./tfod/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (0.4.0)\n", + "Requirement already satisfied: requests-oauthlib>=0.7.0 in ./tfod/lib/python3.8/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2.0.0)\n", + "Requirement already satisfied: importlib-metadata>=4.4 in ./tfod/lib/python3.8/site-packages (from markdown>=2.6.8->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (8.0.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in ./tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2024.7.4)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in ./tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in ./tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.7)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in ./tfod/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2.2.2)\n", + "Requirement already satisfied: MarkupSafe>=2.1.1 in ./tfod/lib/python3.8/site-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2.1.5)\n", + "Requirement already satisfied: zipp>=0.5 in ./tfod/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.19.2)\n", + "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in ./tfod/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (0.6.0)\n", + "Requirement already satisfied: oauthlib>=3.0.0 in ./tfod/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.2.2)\n", + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.1.2 is available.\n", + "You should consider upgrading via the '/home/dev/workspace/fixedtfod/tfod/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install tensorflow==2.13.1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-07-10 14:54:23.019173: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2024-07-10 14:54:23.697801: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "2024-07-10 14:54:25.255964: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.276813: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.276909: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "Running tests under Python 3.8.16: /home/dev/workspace/fixedtfod/tfod/bin/python\n", + "[ RUN ] ModelBuilderTF2Test.test_create_center_net_deepmac\n", + "2024-07-10 14:54:25.285493: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.285596: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.285673: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.471020: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.471128: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.471162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1726] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n", + "2024-07-10 14:54:25.471241: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] could not open file to read NUMA node: /sys/bus/pci/devices/0000:0c:00.0/numa_node\n", + "Your kernel may have been built without NUMA support.\n", + "2024-07-10 14:54:25.471292: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21769 MB memory: -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:0c:00.0, compute capability: 8.6\n", + "WARNING:tensorflow:`tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removed in a future release. Please use `tf.keras.layers.BatchNormalization` with parameter `synchronized` set to True.\n", + "W0710 14:54:31.072097 139952443078464 batch_normalization.py:1531] `tf.keras.layers.experimental.SyncBatchNormalization` endpoint is deprecated and will be removed in a future release. Please use `tf.keras.layers.BatchNormalization` with parameter `synchronized` set to True.\n", + "/home/dev/workspace/fixedtfod/tfod/lib/python3.8/site-packages/object_detection/builders/model_builder.py:1112: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead\n", + " logging.warn(('Building experimental DeepMAC meta-arch.'\n", + "W0710 14:54:31.279329 139952443078464 model_builder.py:1112] Building experimental DeepMAC meta-arch. Some features may be omitted.\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 6.23s\n", + "I0710 14:54:31.511672 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_center_net_deepmac): 6.23s\n", + "[ OK ] ModelBuilderTF2Test.test_create_center_net_deepmac\n", + "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.58s\n", + "I0710 14:54:32.096110 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 0.58s\n", + "[ OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n", + "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.23s\n", + "I0710 14:54:32.331552 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.23s\n", + "[ OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n", "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.16s\n", - "I0403 11:13:26.261001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.16s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.22s\n", + "I0710 14:54:32.552535 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.22s\n", "[ OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n", + "[ RUN ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.48s\n", + "I0710 14:54:34.036960 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.48s\n", + "[ OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n", "[ RUN ] ModelBuilderTF2Test.test_create_experimental_model\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n", - "I0403 11:13:26.261001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n", + "I0710 14:54:34.041210 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_create_experimental_model\n", "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.01s\n", - "I0403 11:13:26.274001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.01s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s\n", + "I0710 14:54:34.061099 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.02s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n", "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n", - "I0403 11:13:26.283001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n", + "I0710 14:54:34.075255 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.01s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n", "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n", - "I0403 11:13:26.292001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.01s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n", + "I0710 14:54:34.091609 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n", "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.06s\n", - "I0403 11:13:26.349001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.06s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.2s\n", + "I0710 14:54:34.295457 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.2s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n", - "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.06s\n", - "I0403 11:13:26.406001 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.06s\n", + "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s\n", + "I0710 14:54:34.377296 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n", "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.06s\n", - "I0403 11:13:26.465714 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.06s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.09s\n", + "I0710 14:54:34.464040 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.09s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n", "[ RUN ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.06s\n", - "I0403 11:13:26.523714 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.06s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s\n", + "I0710 14:54:34.550852 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s\n", "[ OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n", "[ RUN ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.1s\n", - "I0403 11:13:26.621714 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.1s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.08s\n", + "I0710 14:54:34.632366 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.08s\n", "[ OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n", "[ RUN ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n", - "I0403 11:13:26.638714 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n", + "I0710 14:54:34.656425 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.02s\n", "[ OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n", "[ RUN ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n", - "I0403 11:13:26.744284 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n", - "I0403 11:13:26.744284 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n", - "I0403 11:13:26.744284 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n", - "I0403 11:13:26.745285 4864 efficientnet_model.py:147] round_filter input=32 output=32\n", - "I0403 11:13:26.755285 4864 efficientnet_model.py:147] round_filter input=32 output=32\n", - "I0403 11:13:26.755285 4864 efficientnet_model.py:147] round_filter input=16 output=16\n", - "I0403 11:13:26.786285 4864 efficientnet_model.py:147] round_filter input=16 output=16\n", - "I0403 11:13:26.786285 4864 efficientnet_model.py:147] round_filter input=24 output=24\n", - "I0403 11:13:26.864284 4864 efficientnet_model.py:147] round_filter input=24 output=24\n", - "I0403 11:13:26.864284 4864 efficientnet_model.py:147] round_filter input=40 output=40\n", - "I0403 11:13:26.944523 4864 efficientnet_model.py:147] round_filter input=40 output=40\n", - "I0403 11:13:26.944523 4864 efficientnet_model.py:147] round_filter input=80 output=80\n", - "I0403 11:13:27.066525 4864 efficientnet_model.py:147] round_filter input=80 output=80\n", - "I0403 11:13:27.066525 4864 efficientnet_model.py:147] round_filter input=112 output=112\n", - "I0403 11:13:27.189525 4864 efficientnet_model.py:147] round_filter input=112 output=112\n", - "I0403 11:13:27.189525 4864 efficientnet_model.py:147] round_filter input=192 output=192\n", - "I0403 11:13:27.365525 4864 efficientnet_model.py:147] round_filter input=192 output=192\n", - "I0403 11:13:27.366525 4864 efficientnet_model.py:147] round_filter input=320 output=320\n", - "I0403 11:13:27.411523 4864 efficientnet_model.py:147] round_filter input=1280 output=1280\n", - "I0403 11:13:27.435521 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:27.470155 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n", - "I0403 11:13:27.470155 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n", - "I0403 11:13:27.471155 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n", - "I0403 11:13:27.472155 4864 efficientnet_model.py:147] round_filter input=32 output=32\n", - "I0403 11:13:27.480155 4864 efficientnet_model.py:147] round_filter input=32 output=32\n", - "I0403 11:13:27.480155 4864 efficientnet_model.py:147] round_filter input=16 output=16\n", - "I0403 11:13:27.541155 4864 efficientnet_model.py:147] round_filter input=16 output=16\n", - "I0403 11:13:27.541155 4864 efficientnet_model.py:147] round_filter input=24 output=24\n", - "I0403 11:13:27.658154 4864 efficientnet_model.py:147] round_filter input=24 output=24\n", - "I0403 11:13:27.658154 4864 efficientnet_model.py:147] round_filter input=40 output=40\n", - "I0403 11:13:27.827155 4864 efficientnet_model.py:147] round_filter input=40 output=40\n", - "I0403 11:13:27.827155 4864 efficientnet_model.py:147] round_filter input=80 output=80\n", - "I0403 11:13:27.989430 4864 efficientnet_model.py:147] round_filter input=80 output=80\n", - "I0403 11:13:27.989430 4864 efficientnet_model.py:147] round_filter input=112 output=112\n", - "I0403 11:13:28.154430 4864 efficientnet_model.py:147] round_filter input=112 output=112\n", - "I0403 11:13:28.154430 4864 efficientnet_model.py:147] round_filter input=192 output=192\n", - "I0403 11:13:28.376430 4864 efficientnet_model.py:147] round_filter input=192 output=192\n", - "I0403 11:13:28.376430 4864 efficientnet_model.py:147] round_filter input=320 output=320\n", - "I0403 11:13:28.473672 4864 efficientnet_model.py:147] round_filter input=1280 output=1280\n", - "I0403 11:13:28.496715 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:28.537719 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n", - "I0403 11:13:28.537719 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n", - "I0403 11:13:28.537719 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n", - "I0403 11:13:28.538719 4864 efficientnet_model.py:147] round_filter input=32 output=32\n", - "I0403 11:13:28.546719 4864 efficientnet_model.py:147] round_filter input=32 output=32\n", - "I0403 11:13:28.546719 4864 efficientnet_model.py:147] round_filter input=16 output=16\n", - "I0403 11:13:28.609718 4864 efficientnet_model.py:147] round_filter input=16 output=16\n", - "I0403 11:13:28.609718 4864 efficientnet_model.py:147] round_filter input=24 output=24\n", - "I0403 11:13:28.732719 4864 efficientnet_model.py:147] round_filter input=24 output=24\n", - "I0403 11:13:28.732719 4864 efficientnet_model.py:147] round_filter input=40 output=48\n", - "I0403 11:13:28.861715 4864 efficientnet_model.py:147] round_filter input=40 output=48\n", - "I0403 11:13:28.861715 4864 efficientnet_model.py:147] round_filter input=80 output=88\n", - "I0403 11:13:29.032945 4864 efficientnet_model.py:147] round_filter input=80 output=88\n", - "I0403 11:13:29.032945 4864 efficientnet_model.py:147] round_filter input=112 output=120\n", - "I0403 11:13:29.207945 4864 efficientnet_model.py:147] round_filter input=112 output=120\n", - "I0403 11:13:29.208945 4864 efficientnet_model.py:147] round_filter input=192 output=208\n", - "I0403 11:13:29.516621 4864 efficientnet_model.py:147] round_filter input=192 output=208\n", - "I0403 11:13:29.516621 4864 efficientnet_model.py:147] round_filter input=320 output=352\n", - "I0403 11:13:29.615617 4864 efficientnet_model.py:147] round_filter input=1280 output=1408\n", - "I0403 11:13:29.639617 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:29.680617 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n", - "I0403 11:13:29.680617 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n", - "I0403 11:13:29.680617 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n", - "I0403 11:13:29.681617 4864 efficientnet_model.py:147] round_filter input=32 output=40\n", - "I0403 11:13:29.690620 4864 efficientnet_model.py:147] round_filter input=32 output=40\n", - "I0403 11:13:29.690620 4864 efficientnet_model.py:147] round_filter input=16 output=24\n", - "I0403 11:13:29.751616 4864 efficientnet_model.py:147] round_filter input=16 output=24\n", - "I0403 11:13:29.751616 4864 efficientnet_model.py:147] round_filter input=24 output=32\n", - "I0403 11:13:29.870619 4864 efficientnet_model.py:147] round_filter input=24 output=32\n", - "I0403 11:13:29.870619 4864 efficientnet_model.py:147] round_filter input=40 output=48\n", - "I0403 11:13:29.991739 4864 efficientnet_model.py:147] round_filter input=40 output=48\n", - "I0403 11:13:29.991739 4864 efficientnet_model.py:147] round_filter input=80 output=96\n", - "I0403 11:13:30.196747 4864 efficientnet_model.py:147] round_filter input=80 output=96\n", - "I0403 11:13:30.196747 4864 efficientnet_model.py:147] round_filter input=112 output=136\n", - "I0403 11:13:30.409746 4864 efficientnet_model.py:147] round_filter input=112 output=136\n", - "I0403 11:13:30.409746 4864 efficientnet_model.py:147] round_filter input=192 output=232\n", - "I0403 11:13:30.688942 4864 efficientnet_model.py:147] round_filter input=192 output=232\n", - "I0403 11:13:30.688942 4864 efficientnet_model.py:147] round_filter input=320 output=384\n", - "I0403 11:13:30.791941 4864 efficientnet_model.py:147] round_filter input=1280 output=1536\n", - "I0403 11:13:30.815942 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:30.861942 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n", - "I0403 11:13:30.861942 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n", - "I0403 11:13:30.861942 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n", - "I0403 11:13:30.862942 4864 efficientnet_model.py:147] round_filter input=32 output=48\n", - "I0403 11:13:30.870942 4864 efficientnet_model.py:147] round_filter input=32 output=48\n", - "I0403 11:13:30.870942 4864 efficientnet_model.py:147] round_filter input=16 output=24\n", - "I0403 11:13:30.934946 4864 efficientnet_model.py:147] round_filter input=16 output=24\n", - "I0403 11:13:30.934946 4864 efficientnet_model.py:147] round_filter input=24 output=32\n", - "I0403 11:13:31.095081 4864 efficientnet_model.py:147] round_filter input=24 output=32\n", - "I0403 11:13:31.095081 4864 efficientnet_model.py:147] round_filter input=40 output=56\n", - "I0403 11:13:31.255081 4864 efficientnet_model.py:147] round_filter input=40 output=56\n", - "I0403 11:13:31.255081 4864 efficientnet_model.py:147] round_filter input=80 output=112\n", - "I0403 11:13:31.605649 4864 efficientnet_model.py:147] round_filter input=80 output=112\n", - "I0403 11:13:31.605649 4864 efficientnet_model.py:147] round_filter input=112 output=160\n", - "I0403 11:13:31.866196 4864 efficientnet_model.py:147] round_filter input=112 output=160\n", - "I0403 11:13:31.866196 4864 efficientnet_model.py:147] round_filter input=192 output=272\n", - "I0403 11:13:32.256984 4864 efficientnet_model.py:147] round_filter input=192 output=272\n", - "I0403 11:13:32.256984 4864 efficientnet_model.py:147] round_filter input=320 output=448\n", - "I0403 11:13:32.364983 4864 efficientnet_model.py:147] round_filter input=1280 output=1792\n", - "I0403 11:13:32.390983 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:32.442983 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n", - "I0403 11:13:32.442983 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n", - "I0403 11:13:32.442983 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n", - "I0403 11:13:32.444984 4864 efficientnet_model.py:147] round_filter input=32 output=48\n", - "I0403 11:13:32.452983 4864 efficientnet_model.py:147] round_filter input=32 output=48\n", - "I0403 11:13:32.452983 4864 efficientnet_model.py:147] round_filter input=16 output=24\n", - "I0403 11:13:32.547165 4864 efficientnet_model.py:147] round_filter input=16 output=24\n", - "I0403 11:13:32.548164 4864 efficientnet_model.py:147] round_filter input=24 output=40\n", - "I0403 11:13:32.745163 4864 efficientnet_model.py:147] round_filter input=24 output=40\n", - "I0403 11:13:32.745163 4864 efficientnet_model.py:147] round_filter input=40 output=64\n", - "I0403 11:13:32.946163 4864 efficientnet_model.py:147] round_filter input=40 output=64\n", - "I0403 11:13:32.946163 4864 efficientnet_model.py:147] round_filter input=80 output=128\n", - "I0403 11:13:33.234887 4864 efficientnet_model.py:147] round_filter input=80 output=128\n", - "I0403 11:13:33.234887 4864 efficientnet_model.py:147] round_filter input=112 output=176\n", - "I0403 11:13:33.636457 4864 efficientnet_model.py:147] round_filter input=112 output=176\n", - "I0403 11:13:33.636457 4864 efficientnet_model.py:147] round_filter input=192 output=304\n", - "I0403 11:13:34.092802 4864 efficientnet_model.py:147] round_filter input=192 output=304\n", - "I0403 11:13:34.092802 4864 efficientnet_model.py:147] round_filter input=320 output=512\n", - "I0403 11:13:34.278800 4864 efficientnet_model.py:147] round_filter input=1280 output=2048\n", - "I0403 11:13:34.307799 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:34.367801 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n", - "I0403 11:13:34.367801 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n", - "I0403 11:13:34.367801 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n", - "I0403 11:13:34.368801 4864 efficientnet_model.py:147] round_filter input=32 output=56\n", - "I0403 11:13:34.377801 4864 efficientnet_model.py:147] round_filter input=32 output=56\n", - "I0403 11:13:34.377801 4864 efficientnet_model.py:147] round_filter input=16 output=32\n", - "I0403 11:13:34.472801 4864 efficientnet_model.py:147] round_filter input=16 output=32\n", - "I0403 11:13:34.472801 4864 efficientnet_model.py:147] round_filter input=24 output=40\n", - "I0403 11:13:34.711953 4864 efficientnet_model.py:147] round_filter input=24 output=40\n", - "I0403 11:13:34.711953 4864 efficientnet_model.py:147] round_filter input=40 output=72\n", - "I0403 11:13:34.956957 4864 efficientnet_model.py:147] round_filter input=40 output=72\n", - "I0403 11:13:34.956957 4864 efficientnet_model.py:147] round_filter input=80 output=144\n", - "I0403 11:13:35.292506 4864 efficientnet_model.py:147] round_filter input=80 output=144\n", - "I0403 11:13:35.292506 4864 efficientnet_model.py:147] round_filter input=112 output=200\n", - "I0403 11:13:35.651663 4864 efficientnet_model.py:147] round_filter input=112 output=200\n", - "I0403 11:13:35.652663 4864 efficientnet_model.py:147] round_filter input=192 output=344\n", - "I0403 11:13:36.332050 4864 efficientnet_model.py:147] round_filter input=192 output=344\n", - "I0403 11:13:36.332050 4864 efficientnet_model.py:147] round_filter input=320 output=576\n", - "I0403 11:13:36.524049 4864 efficientnet_model.py:147] round_filter input=1280 output=2304\n", - "I0403 11:13:36.554049 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "I0403 11:13:36.624295 4864 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n", - "I0403 11:13:36.624295 4864 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n", - "I0403 11:13:36.624295 4864 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n", - "I0403 11:13:36.625340 4864 efficientnet_model.py:147] round_filter input=32 output=64\n", - "I0403 11:13:36.634599 4864 efficientnet_model.py:147] round_filter input=32 output=64\n", - "I0403 11:13:36.634599 4864 efficientnet_model.py:147] round_filter input=16 output=32\n", - "I0403 11:13:36.760170 4864 efficientnet_model.py:147] round_filter input=16 output=32\n", - "I0403 11:13:36.760170 4864 efficientnet_model.py:147] round_filter input=24 output=48\n", - "I0403 11:13:37.042175 4864 efficientnet_model.py:147] round_filter input=24 output=48\n", - "I0403 11:13:37.042175 4864 efficientnet_model.py:147] round_filter input=40 output=80\n" + "I0710 14:54:34.815559 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b0\n", + "I0710 14:54:34.815698 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 64\n", + "I0710 14:54:34.815765 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 3\n", + "I0710 14:54:34.817827 139952443078464 efficientnet_model.py:143] round_filter input=32 output=32\n", + "I0710 14:54:34.843388 139952443078464 efficientnet_model.py:143] round_filter input=32 output=32\n", + "I0710 14:54:34.843654 139952443078464 efficientnet_model.py:143] round_filter input=16 output=16\n", + "I0710 14:54:34.920144 139952443078464 efficientnet_model.py:143] round_filter input=16 output=16\n", + "I0710 14:54:34.920305 139952443078464 efficientnet_model.py:143] round_filter input=24 output=24\n", + "I0710 14:54:35.099544 139952443078464 efficientnet_model.py:143] round_filter input=24 output=24\n", + "I0710 14:54:35.099686 139952443078464 efficientnet_model.py:143] round_filter input=40 output=40\n", + "I0710 14:54:35.262030 139952443078464 efficientnet_model.py:143] round_filter input=40 output=40\n", + "I0710 14:54:35.262162 139952443078464 efficientnet_model.py:143] round_filter input=80 output=80\n", + "I0710 14:54:35.472486 139952443078464 efficientnet_model.py:143] round_filter input=80 output=80\n", + "I0710 14:54:35.472631 139952443078464 efficientnet_model.py:143] round_filter input=112 output=112\n", + "I0710 14:54:35.732017 139952443078464 efficientnet_model.py:143] round_filter input=112 output=112\n", + "I0710 14:54:35.732163 139952443078464 efficientnet_model.py:143] round_filter input=192 output=192\n", + "I0710 14:54:36.008328 139952443078464 efficientnet_model.py:143] round_filter input=192 output=192\n", + "I0710 14:54:36.008464 139952443078464 efficientnet_model.py:143] round_filter input=320 output=320\n", + "I0710 14:54:36.078224 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=1280\n", + "I0710 14:54:36.112273 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "I0710 14:54:36.157663 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b1\n", + "I0710 14:54:36.157819 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 88\n", + "I0710 14:54:36.157894 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 4\n", + "I0710 14:54:36.159371 139952443078464 efficientnet_model.py:143] round_filter input=32 output=32\n", + "I0710 14:54:36.175934 139952443078464 efficientnet_model.py:143] round_filter input=32 output=32\n", + "I0710 14:54:36.176075 139952443078464 efficientnet_model.py:143] round_filter input=16 output=16\n", + "I0710 14:54:36.307551 139952443078464 efficientnet_model.py:143] round_filter input=16 output=16\n", + "I0710 14:54:36.307690 139952443078464 efficientnet_model.py:143] round_filter input=24 output=24\n", + "I0710 14:54:36.499695 139952443078464 efficientnet_model.py:143] round_filter input=24 output=24\n", + "I0710 14:54:36.499832 139952443078464 efficientnet_model.py:143] round_filter input=40 output=40\n", + "I0710 14:54:36.697473 139952443078464 efficientnet_model.py:143] round_filter input=40 output=40\n", + "I0710 14:54:36.697613 139952443078464 efficientnet_model.py:143] round_filter input=80 output=80\n", + "I0710 14:54:36.960014 139952443078464 efficientnet_model.py:143] round_filter input=80 output=80\n", + "I0710 14:54:36.960144 139952443078464 efficientnet_model.py:143] round_filter input=112 output=112\n", + "I0710 14:54:37.249803 139952443078464 efficientnet_model.py:143] round_filter input=112 output=112\n", + "I0710 14:54:37.249938 139952443078464 efficientnet_model.py:143] round_filter input=192 output=192\n", + "I0710 14:54:37.646457 139952443078464 efficientnet_model.py:143] round_filter input=192 output=192\n", + "I0710 14:54:37.646593 139952443078464 efficientnet_model.py:143] round_filter input=320 output=320\n", + "I0710 14:54:37.875990 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=1280\n", + "I0710 14:54:37.915863 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "I0403 11:13:37.327793 4864 efficientnet_model.py:147] round_filter input=40 output=80\n", - "I0403 11:13:37.327793 4864 efficientnet_model.py:147] round_filter input=80 output=160\n", - "I0403 11:13:37.769363 4864 efficientnet_model.py:147] round_filter input=80 output=160\n", - "I0403 11:13:37.769363 4864 efficientnet_model.py:147] round_filter input=112 output=224\n", - "I0403 11:13:38.237657 4864 efficientnet_model.py:147] round_filter input=112 output=224\n", - "I0403 11:13:38.237657 4864 efficientnet_model.py:147] round_filter input=192 output=384\n", - "I0403 11:13:39.077081 4864 efficientnet_model.py:147] round_filter input=192 output=384\n", - "I0403 11:13:39.077081 4864 efficientnet_model.py:147] round_filter input=320 output=640\n", - "I0403 11:13:39.359760 4864 efficientnet_model.py:147] round_filter input=1280 output=2560\n", - "I0403 11:13:39.393760 4864 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 12.84s\n", - "I0403 11:13:39.477760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 12.84s\n", + "I0710 14:54:37.966329 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b2\n", + "I0710 14:54:37.966464 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 112\n", + "I0710 14:54:37.966529 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 5\n", + "I0710 14:54:37.967953 139952443078464 efficientnet_model.py:143] round_filter input=32 output=32\n", + "I0710 14:54:37.981044 139952443078464 efficientnet_model.py:143] round_filter input=32 output=32\n", + "I0710 14:54:37.981180 139952443078464 efficientnet_model.py:143] round_filter input=16 output=16\n", + "I0710 14:54:38.095564 139952443078464 efficientnet_model.py:143] round_filter input=16 output=16\n", + "I0710 14:54:38.095707 139952443078464 efficientnet_model.py:143] round_filter input=24 output=24\n", + "I0710 14:54:38.430339 139952443078464 efficientnet_model.py:143] round_filter input=24 output=24\n", + "I0710 14:54:38.430475 139952443078464 efficientnet_model.py:143] round_filter input=40 output=48\n", + "I0710 14:54:38.669786 139952443078464 efficientnet_model.py:143] round_filter input=40 output=48\n", + "I0710 14:54:38.669939 139952443078464 efficientnet_model.py:143] round_filter input=80 output=88\n", + "I0710 14:54:38.936804 139952443078464 efficientnet_model.py:143] round_filter input=80 output=88\n", + "I0710 14:54:38.936939 139952443078464 efficientnet_model.py:143] round_filter input=112 output=120\n", + "I0710 14:54:39.241975 139952443078464 efficientnet_model.py:143] round_filter input=112 output=120\n", + "I0710 14:54:39.242114 139952443078464 efficientnet_model.py:143] round_filter input=192 output=208\n", + "I0710 14:54:39.603561 139952443078464 efficientnet_model.py:143] round_filter input=192 output=208\n", + "I0710 14:54:39.603697 139952443078464 efficientnet_model.py:143] round_filter input=320 output=352\n", + "I0710 14:54:39.745383 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=1408\n", + "I0710 14:54:39.776885 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "I0710 14:54:39.827839 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b3\n", + "I0710 14:54:39.828043 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 160\n", + "I0710 14:54:39.828121 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 6\n", + "I0710 14:54:39.829835 139952443078464 efficientnet_model.py:143] round_filter input=32 output=40\n", + "I0710 14:54:39.846067 139952443078464 efficientnet_model.py:143] round_filter input=32 output=40\n", + "I0710 14:54:39.846230 139952443078464 efficientnet_model.py:143] round_filter input=16 output=24\n", + "I0710 14:54:39.980979 139952443078464 efficientnet_model.py:143] round_filter input=16 output=24\n", + "I0710 14:54:39.981193 139952443078464 efficientnet_model.py:143] round_filter input=24 output=32\n", + "I0710 14:54:40.187443 139952443078464 efficientnet_model.py:143] round_filter input=24 output=32\n", + "I0710 14:54:40.187582 139952443078464 efficientnet_model.py:143] round_filter input=40 output=48\n", + "I0710 14:54:40.383304 139952443078464 efficientnet_model.py:143] round_filter input=40 output=48\n", + "I0710 14:54:40.383508 139952443078464 efficientnet_model.py:143] round_filter input=80 output=96\n", + "I0710 14:54:40.757490 139952443078464 efficientnet_model.py:143] round_filter input=80 output=96\n", + "I0710 14:54:40.757634 139952443078464 efficientnet_model.py:143] round_filter input=112 output=136\n", + "I0710 14:54:41.139020 139952443078464 efficientnet_model.py:143] round_filter input=112 output=136\n", + "I0710 14:54:41.139195 139952443078464 efficientnet_model.py:143] round_filter input=192 output=232\n", + "I0710 14:54:41.580224 139952443078464 efficientnet_model.py:143] round_filter input=192 output=232\n", + "I0710 14:54:41.580358 139952443078464 efficientnet_model.py:143] round_filter input=320 output=384\n", + "I0710 14:54:41.722403 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=1536\n", + "I0710 14:54:41.752321 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "I0710 14:54:41.807769 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b4\n", + "I0710 14:54:41.807913 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 224\n", + "I0710 14:54:41.807980 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 7\n", + "I0710 14:54:41.809493 139952443078464 efficientnet_model.py:143] round_filter input=32 output=48\n", + "I0710 14:54:41.826463 139952443078464 efficientnet_model.py:143] round_filter input=32 output=48\n", + "I0710 14:54:41.826611 139952443078464 efficientnet_model.py:143] round_filter input=16 output=24\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0710 14:54:41.941309 139952443078464 efficientnet_model.py:143] round_filter input=16 output=24\n", + "I0710 14:54:41.941447 139952443078464 efficientnet_model.py:143] round_filter input=24 output=32\n", + "I0710 14:54:42.240670 139952443078464 efficientnet_model.py:143] round_filter input=24 output=32\n", + "I0710 14:54:42.240821 139952443078464 efficientnet_model.py:143] round_filter input=40 output=56\n", + "I0710 14:54:42.553384 139952443078464 efficientnet_model.py:143] round_filter input=40 output=56\n", + "I0710 14:54:42.553577 139952443078464 efficientnet_model.py:143] round_filter input=80 output=112\n", + "I0710 14:54:42.945340 139952443078464 efficientnet_model.py:143] round_filter input=80 output=112\n", + "I0710 14:54:42.945491 139952443078464 efficientnet_model.py:143] round_filter input=112 output=160\n", + "I0710 14:54:43.462821 139952443078464 efficientnet_model.py:143] round_filter input=112 output=160\n", + "I0710 14:54:43.462957 139952443078464 efficientnet_model.py:143] round_filter input=192 output=272\n", + "I0710 14:54:44.345771 139952443078464 efficientnet_model.py:143] round_filter input=192 output=272\n", + "I0710 14:54:44.345919 139952443078464 efficientnet_model.py:143] round_filter input=320 output=448\n", + "I0710 14:54:44.491524 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=1792\n", + "I0710 14:54:44.519813 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "I0710 14:54:44.581676 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b5\n", + "I0710 14:54:44.581808 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 288\n", + "I0710 14:54:44.581870 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 7\n", + "I0710 14:54:44.583272 139952443078464 efficientnet_model.py:143] round_filter input=32 output=48\n", + "I0710 14:54:44.598665 139952443078464 efficientnet_model.py:143] round_filter input=32 output=48\n", + "I0710 14:54:44.598804 139952443078464 efficientnet_model.py:143] round_filter input=16 output=24\n", + "I0710 14:54:44.758918 139952443078464 efficientnet_model.py:143] round_filter input=16 output=24\n", + "I0710 14:54:44.759062 139952443078464 efficientnet_model.py:143] round_filter input=24 output=40\n", + "I0710 14:54:45.112659 139952443078464 efficientnet_model.py:143] round_filter input=24 output=40\n", + "I0710 14:54:45.112802 139952443078464 efficientnet_model.py:143] round_filter input=40 output=64\n", + "I0710 14:54:45.457576 139952443078464 efficientnet_model.py:143] round_filter input=40 output=64\n", + "I0710 14:54:45.457725 139952443078464 efficientnet_model.py:143] round_filter input=80 output=128\n", + "I0710 14:54:45.972878 139952443078464 efficientnet_model.py:143] round_filter input=80 output=128\n", + "I0710 14:54:45.973048 139952443078464 efficientnet_model.py:143] round_filter input=112 output=176\n", + "I0710 14:54:46.472792 139952443078464 efficientnet_model.py:143] round_filter input=112 output=176\n", + "I0710 14:54:46.472924 139952443078464 efficientnet_model.py:143] round_filter input=192 output=304\n", + "I0710 14:54:47.076003 139952443078464 efficientnet_model.py:143] round_filter input=192 output=304\n", + "I0710 14:54:47.076139 139952443078464 efficientnet_model.py:143] round_filter input=320 output=512\n", + "I0710 14:54:47.346386 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=2048\n", + "I0710 14:54:47.374636 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "I0710 14:54:47.444739 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b6\n", + "I0710 14:54:47.444874 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 384\n", + "I0710 14:54:47.444939 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 8\n", + "I0710 14:54:47.446310 139952443078464 efficientnet_model.py:143] round_filter input=32 output=56\n", + "I0710 14:54:47.461347 139952443078464 efficientnet_model.py:143] round_filter input=32 output=56\n", + "I0710 14:54:47.461477 139952443078464 efficientnet_model.py:143] round_filter input=16 output=32\n", + "I0710 14:54:47.623290 139952443078464 efficientnet_model.py:143] round_filter input=16 output=32\n", + "I0710 14:54:47.623423 139952443078464 efficientnet_model.py:143] round_filter input=24 output=40\n", + "I0710 14:54:48.001881 139952443078464 efficientnet_model.py:143] round_filter input=24 output=40\n", + "I0710 14:54:48.002019 139952443078464 efficientnet_model.py:143] round_filter input=40 output=72\n", + "I0710 14:54:48.396046 139952443078464 efficientnet_model.py:143] round_filter input=40 output=72\n", + "I0710 14:54:48.396190 139952443078464 efficientnet_model.py:143] round_filter input=80 output=144\n", + "I0710 14:54:48.909229 139952443078464 efficientnet_model.py:143] round_filter input=80 output=144\n", + "I0710 14:54:48.909363 139952443078464 efficientnet_model.py:143] round_filter input=112 output=200\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I0710 14:54:49.605669 139952443078464 efficientnet_model.py:143] round_filter input=112 output=200\n", + "I0710 14:54:49.605807 139952443078464 efficientnet_model.py:143] round_filter input=192 output=344\n", + "I0710 14:54:52.644131 139952443078464 efficientnet_model.py:143] round_filter input=192 output=344\n", + "I0710 14:54:52.644273 139952443078464 efficientnet_model.py:143] round_filter input=320 output=576\n", + "I0710 14:54:53.184411 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=2304\n", + "I0710 14:54:53.216851 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "I0710 14:54:53.294514 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:161] EfficientDet EfficientNet backbone version: efficientnet-b7\n", + "I0710 14:54:53.294652 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:163] EfficientDet BiFPN num filters: 384\n", + "I0710 14:54:53.294716 139952443078464 ssd_efficientnet_bifpn_feature_extractor.py:164] EfficientDet BiFPN num iterations: 8\n", + "I0710 14:54:53.296106 139952443078464 efficientnet_model.py:143] round_filter input=32 output=64\n", + "I0710 14:54:53.310557 139952443078464 efficientnet_model.py:143] round_filter input=32 output=64\n", + "I0710 14:54:53.310680 139952443078464 efficientnet_model.py:143] round_filter input=16 output=32\n", + "I0710 14:54:53.521605 139952443078464 efficientnet_model.py:143] round_filter input=16 output=32\n", + "I0710 14:54:53.521744 139952443078464 efficientnet_model.py:143] round_filter input=24 output=48\n", + "I0710 14:54:53.965472 139952443078464 efficientnet_model.py:143] round_filter input=24 output=48\n", + "I0710 14:54:53.965615 139952443078464 efficientnet_model.py:143] round_filter input=40 output=80\n", + "I0710 14:54:54.431227 139952443078464 efficientnet_model.py:143] round_filter input=40 output=80\n", + "I0710 14:54:54.431363 139952443078464 efficientnet_model.py:143] round_filter input=80 output=160\n", + "I0710 14:54:55.069571 139952443078464 efficientnet_model.py:143] round_filter input=80 output=160\n", + "I0710 14:54:55.069709 139952443078464 efficientnet_model.py:143] round_filter input=112 output=224\n", + "I0710 14:54:55.708192 139952443078464 efficientnet_model.py:143] round_filter input=112 output=224\n", + "I0710 14:54:55.708329 139952443078464 efficientnet_model.py:143] round_filter input=192 output=384\n", + "I0710 14:54:56.797708 139952443078464 efficientnet_model.py:143] round_filter input=192 output=384\n", + "I0710 14:54:56.797858 139952443078464 efficientnet_model.py:143] round_filter input=320 output=640\n", + "I0710 14:54:57.137669 139952443078464 efficientnet_model.py:143] round_filter input=1280 output=2560\n", + "I0710 14:54:57.227303 139952443078464 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 22.66s\n", + "I0710 14:54:57.321321 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 22.66s\n", "[ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n", "[ RUN ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n", - "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n", - "I0403 11:13:39.481760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n", + "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.01s\n", + "I0710 14:54:57.348123 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.01s\n", "[ OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n", "[ RUN ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n", - "I0403 11:13:39.482760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n", + "I0710 14:54:57.349507 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n", "[ RUN ] ModelBuilderTF2Test.test_invalid_model_config_proto\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n", - "I0403 11:13:39.482760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n", + "I0710 14:54:57.349872 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n", "[ RUN ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n", - "I0403 11:13:39.483760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n", + "I0710 14:54:57.350933 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n", "[ RUN ] ModelBuilderTF2Test.test_session\n", "[ SKIPPED ] ModelBuilderTF2Test.test_session\n", "[ RUN ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n", - "I0403 11:13:39.484760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n", + "I0710 14:54:57.351973 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n", "[ RUN ] ModelBuilderTF2Test.test_unknown_meta_architecture\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n", - "I0403 11:13:39.485760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n", + "I0710 14:54:57.352291 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n", "[ RUN ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n", "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n", - "I0403 11:13:39.485760 4864 test_util.py:2096] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n", + "I0710 14:54:57.353089 139952443078464 test_util.py:2462] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n", "[ OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n", "----------------------------------------------------------------------\n", - "Ran 21 tests in 15.178s\n", + "Ran 24 tests in 32.074s\n", "\n", "OK (skipped=1)\n" ] @@ -2029,109 +914,14 @@ "!python {VERIFICATION_SCRIPT}" ] }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: tensorflow in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages\\tensorflow-2.5.0rc0-py3.7-win-amd64.egg (2.5.0rc0)\n", - "Requirement already satisfied: absl-py~=0.10 in d:\\youtube\\od\\tfodcourse\\tfod\\lib\\site-packages (from tensorflow) (0.12.0)\n", - "Collecting astunparse~=1.6.3\n", - " Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n", - "Collecting flatbuffers~=1.12.0\n", - " Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB)\n", - "Collecting gast==0.4.0\n", - " Using cached gast-0.4.0-py3-none-any.whl (9.8 kB)\n", - "Collecting 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currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "apache-beam 2.28.0 requires crcmod<2.0,>=1.7, which is not installed.\n", - "apache-beam 2.28.0 requires dill<0.3.2,>=0.3.1.1, which is not installed.\n", - "apache-beam 2.28.0 requires fastavro<2,>=0.21.4, which is not installed.\n", - "apache-beam 2.28.0 requires future<1.0.0,>=0.18.2, which is not installed.\n", - "apache-beam 2.28.0 requires hdfs<3.0.0,>=2.1.0, which is not installed.\n", - "apache-beam 2.28.0 requires httplib2<0.18.0,>=0.8, which is not installed.\n", - "apache-beam 2.28.0 requires mock<3.0.0,>=1.0.1, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires attrs>=18.1.0, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires dill, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires future, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires importlib-resources, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires promise, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires tensorflow-metadata, which is not installed.\n", - "tensorflow-datasets 4.2.0 requires tqdm, which is not installed.\n", - "apache-beam 2.28.0 requires avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you have avro-python3 1.10.2 which is incompatible.\n" - ] - } - ], - "source": [ - "!pip install tensorflow --upgrade" - ] - }, { "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": true + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } }, "outputs": [ { @@ -2209,13 +999,14 @@ } ], "source": [ + "#no need to run this\n", "!pip uninstall protobuf matplotlib -y\n", "!pip install protobuf matplotlib==3.2" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -2224,7 +1015,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": { "scrolled": true }, @@ -2233,111 +1024,175 @@ "name": "stdout", "output_type": "stream", "text": [ - "Package Version Location\n", - "----------------------------- ------------------- --------------------------------------------------------\n", - "absl-py 0.12.0\n", - "apache-beam 2.28.0\n", + "Package Version\n", + "----------------------------- -----------\n", + "absl-py 1.4.0\n", + "apache-beam 2.57.0\n", + "array-record 0.4.0\n", + "asttokens 2.4.1\n", "astunparse 1.6.3\n", + "async-timeout 4.0.3\n", + "attrs 23.2.0\n", "avro-python3 1.10.2\n", "backcall 0.2.0\n", - "cached-property 1.5.2\n", - "cachetools 4.2.1\n", - "certifi 2020.12.5\n", - "chardet 4.0.0\n", - "colorama 0.4.4\n", - "contextlib2 0.6.0.post1\n", - "cycler 0.10.0\n", - "cython 3.0a6\n", - "dataclasses 0.8\n", - "decorator 5.0.3\n", - "flatbuffers 1.12\n", + "backports.zoneinfo 0.2.1\n", + "bleach 6.1.0\n", + "cachetools 5.3.3\n", + "certifi 2024.7.4\n", + "charset-normalizer 3.3.2\n", + "click 8.1.7\n", + "cloudpickle 2.2.1\n", + "colorama 0.4.6\n", + "comm 0.2.2\n", + "contextlib2 21.6.0\n", + "contourpy 1.1.1\n", + "crcmod 1.7\n", + "cycler 0.12.1\n", + "Cython 3.0.10\n", + "debugpy 1.8.2\n", + "decorator 5.1.1\n", + "dill 0.3.1.1\n", + "dm-tree 0.1.8\n", + "dnspython 2.6.1\n", + "docopt 0.6.2\n", + "etils 1.3.0\n", + "executing 2.0.1\n", + "fastavro 1.9.5\n", + "fasteners 0.19\n", + "flatbuffers 24.3.25\n", + "fonttools 4.53.1\n", "gast 0.4.0\n", - 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"Pygments 2.8.1\n", - "pymongo 3.11.3\n", - "pyparsing 3.0.0b2\n", - "PyQt5 5.15.4\n", - "PyQt5-Qt5 5.15.2\n", - "PyQt5-sip 12.8.1\n", - "python-dateutil 2.8.1\n", - "pytz 2021.1\n", - "pywin32 300\n", - "pyyaml 5.4.1\n", - "pyzmq 22.0.3\n", - "requests 2.25.1\n", - "requests-oauthlib 1.3.0\n", - "rsa 4.7.2\n", - "scipy 1.6.2\n", - "sentencepiece 0.1.95\n", + "Pygments 2.18.0\n", + "pyjsparser 2.7.1\n", + "pymongo 4.8.0\n", + "pyparsing 2.4.7\n", + "python-dateutil 2.9.0.post0\n", + "python-slugify 8.0.4\n", + "pytz 2024.1\n", + "PyYAML 6.0.1\n", + "pyzmq 26.0.3\n", + "redis 5.0.7\n", + "referencing 0.35.1\n", + "regex 2024.5.15\n", + "requests 2.31.0\n", + "requests-oauthlib 2.0.0\n", + "rpds-py 0.19.0\n", + "rsa 4.9\n", + "sacrebleu 2.2.0\n", + "scikit-learn 1.3.2\n", + "scipy 1.10.1\n", + "sentencepiece 0.2.0\n", "seqeval 1.2.2\n", - "setuptools 54.2.0\n", - "six 1.15.0\n", - "slim 0.1 d:\\youtube\\od\\tfodcourse\\tensorflow\\models\\research\\slim\n", - "tensorboard 2.4.1\n", - "tensorboard-plugin-wit 1.8.0\n", - "tensorflow 2.5.0rc0\n", - "tensorflow-addons 0.12.1\n", - "tensorflow-datasets 4.2.0\n", - "tensorflow-hub 0.11.0\n", - "tensorflow-model-optimization 0.5.0\n", - "termcolor 1.1.0\n", - "tf-estimator-nightly 2.5.0.dev2021032501\n", - "tf-models-official 2.4.0\n", + "setuptools 56.0.0\n", + "six 1.16.0\n", + "stack-data 0.6.3\n", + "tabulate 0.9.0\n", + "tensorboard 2.13.0\n", + "tensorboard-data-server 0.7.2\n", + "tensorflow 2.13.1\n", + "tensorflow-datasets 4.9.2\n", + "tensorflow-estimator 2.13.0\n", + "tensorflow-hub 0.16.1\n", + "tensorflow-io 0.34.0\n", + "tensorflow-io-gcs-filesystem 0.34.0\n", + "tensorflow-metadata 1.14.0\n", + "tensorflow-model-optimization 0.8.0\n", + "tensorflow-text 2.13.0\n", + "termcolor 2.4.0\n", + "text-unidecode 1.3\n", + "tf-keras 2.15.0\n", + "tf-models-official 2.13.2\n", "tf-slim 1.1.0\n", - "tornado 6.1\n", - "traitlets 5.0.5\n", - "typing-extensions 3.7.4.3\n", - "urllib3 1.26.4\n", - "wcwidth 0.2.5\n", - "Werkzeug 1.0.1\n", - "wget 3.2\n", - "wheel 0.36.2\n", - "wrapt 1.12.1\n", - "zipp 3.4.1\n" + "threadpoolctl 3.5.0\n", + "toml 0.10.2\n", + "tornado 6.4.1\n", + "tqdm 4.66.4\n", + "traitlets 5.14.3\n", + "typing_extensions 4.5.0\n", + "tzdata 2024.1\n", + "tzlocal 5.2\n", + "uritemplate 4.1.1\n", + "urllib3 2.2.2\n", + "wcwidth 0.2.13\n", + "webencodings 0.5.1\n", + "Werkzeug 3.0.3\n", + "wheel 0.43.0\n", + "wrapt 1.16.0\n", + "zipp 3.19.2\n", + "zstandard 0.22.0\n", + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 24.1.2 is available.\n", + "You should consider upgrading via the '/home/dev/workspace/fixedtfod/tfod/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0m" ] } ], @@ -2347,7 +1202,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -2360,24 +1215,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "100% [............................................] 20515344 / 20515344 1 file(s) moved.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/checkpoint\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/checkpoint/ckpt-0.index\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/pipeline.config\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/saved_model.pb\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n", - "x ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8/saved_model/variables/variables.index\n" + "--2024-07-10 14:55:07-- http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz\n", + "Resolving download.tensorflow.org (download.tensorflow.org)... 142.250.183.123, 142.250.192.123, 142.251.42.91, ...\n", + "Connecting to download.tensorflow.org (download.tensorflow.org)|142.250.183.123|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 20515344 (20M) [application/x-tar]\n", + "Saving to: ‘ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz’\n", + "\n", + "ssd_mobilenet_v2_fp 100%[===================>] 19.56M 6.73MB/s in 2.9s \n", + "\n", + "2024-07-10 14:55:11 (6.73 MB/s) - ‘ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz’ saved [20515344/20515344]\n", + "\n", + "mv: cannot move 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz' to 'Tensorflow/workspace/pre-trained-models': No such file or directory\n", + "/bin/bash: line 1: cd: Tensorflow/workspace/pre-trained-models: No such file or directory\n" ] } ], @@ -2403,11 +1253,24 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "id": "p1BVDWo7pfDC" }, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'Tensorflow/workspace/annotations/label_map.pbtxt'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m labels \u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThumbsUp\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;241m1\u001b[39m}, {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThumbsDown\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;241m2\u001b[39m}, {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThankYou\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;241m3\u001b[39m}, {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLiveLong\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;241m4\u001b[39m}]\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mLABELMAP\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m labels:\n\u001b[1;32m 5\u001b[0m f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mitem \u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/workspace/fixedtfod/tfod/lib/python3.8/site-packages/IPython/core/interactiveshell.py:284\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 279\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 281\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 282\u001b[0m )\n\u001b[0;32m--> 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Tensorflow/workspace/annotations/label_map.pbtxt'" + ] + } + ], "source": [ "labels = [{'name':'ThumbsUp', 'id':1}, {'name':'ThumbsDown', 'id':2}, {'name':'ThankYou', 'id':3}, {'name':'LiveLong', 'id':4}]\n", "\n", @@ -4177,9 +3040,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.16" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..8d28dba22 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,161 @@ +absl-py==1.4.0 +apache-beam==2.57.0 +array-record==0.4.0 +asttokens==2.4.1 +astunparse==1.6.3 +async-timeout==4.0.3 +attrs==23.2.0 +avro-python3==1.10.2 +backcall==0.2.0 +backports.zoneinfo==0.2.1 +bleach==6.1.0 +cachetools==5.3.3 +certifi==2024.7.4 +charset-normalizer==3.3.2 +click==8.1.7 +cloudpickle==2.2.1 +colorama==0.4.6 +comm==0.2.2 +contextlib2==21.6.0 +contourpy==1.1.1 +crcmod==1.7 +cycler==0.12.1 +Cython==3.0.10 +debugpy==1.8.2 +decorator==5.1.1 +dill==0.3.1.1 +dm-tree==0.1.8 +dnspython==2.6.1 +docopt==0.6.2 +etils==1.3.0 +executing==2.0.1 +fastavro==1.9.5 +fasteners==0.19 +flatbuffers==24.3.25 +fonttools==4.53.1 +gast==0.4.0 +gin-config==0.5.0 +google-api-core==2.19.1 +google-api-python-client==2.136.0 +google-auth==2.32.0 +google-auth-httplib2==0.2.0 +google-auth-oauthlib==1.0.0 +google-pasta==0.2.0 +googleapis-common-protos==1.63.2 +grpcio==1.64.1 +h5py==3.11.0 +hdfs==2.7.3 +httplib2==0.22.0 +idna==3.7 +immutabledict==4.2.0 +importlib_metadata==8.0.0 +importlib_resources==6.4.0 +ipykernel==6.29.5 +ipython==8.12.3 +jedi==0.19.1 +joblib==1.4.2 +Js2Py==0.74 +jsonpickle==3.2.2 +jsonschema==4.23.0 +jsonschema-specifications==2023.12.1 +jupyter_client==8.6.2 +jupyter_core==5.7.2 +kaggle==1.6.14 +keras==2.13.1 +kiwisolver==1.4.5 +libclang==18.1.1 +lvis==0.5.3 +lxml==5.2.2 +Markdown==3.6 +MarkupSafe==2.1.5 +matplotlib==3.7.5 +matplotlib-inline==0.1.7 +nest-asyncio==1.6.0 +numpy==1.24.3 +oauth2client==4.1.3 +oauthlib==3.2.2 +object-detection==0.1 +objsize==0.7.0 +opencv-python==4.10.0.84 +opencv-python-headless==4.10.0.84 +opt-einsum==3.3.0 +orjson==3.10.6 +packaging==24.1 +pandas==2.0.3 +parso==0.8.4 +pexpect==4.9.0 +pickleshare==0.7.5 +pillow==10.4.0 +pkgutil_resolve_name==1.3.10 +platformdirs==4.2.2 +portalocker==2.10.0 +promise==2.3 +prompt_toolkit==3.0.47 +proto-plus==1.24.0 +protobuf==3.20.3 +psutil==6.0.0 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py-cpuinfo==9.0.0 +pyarrow==16.1.0 +pyarrow-hotfix==0.6 +pyasn1==0.6.0 +pyasn1_modules==0.4.0 +pycocotools==2.0.7 +pydot==1.4.2 +Pygments==2.18.0 +pyjsparser==2.7.1 +pymongo==4.8.0 +pyparsing==2.4.7 +python-dateutil==2.9.0.post0 +python-slugify==8.0.4 +pytz==2024.1 +PyYAML==6.0.1 +pyzmq==26.0.3 +redis==5.0.7 +referencing==0.35.1 +regex==2024.5.15 +requests==2.31.0 +requests-oauthlib==2.0.0 +rpds-py==0.19.0 +rsa==4.9 +sacrebleu==2.2.0 +scikit-learn==1.3.2 +scipy==1.10.1 +sentencepiece==0.2.0 +seqeval==1.2.2 +six==1.16.0 +stack-data==0.6.3 +tabulate==0.9.0 +tensorboard==2.13.0 +tensorboard-data-server==0.7.2 +tensorflow==2.13.1 +tensorflow-datasets==4.9.2 +tensorflow-estimator==2.13.0 +tensorflow-hub==0.16.1 +tensorflow-io==0.34.0 +tensorflow-io-gcs-filesystem==0.34.0 +tensorflow-metadata==1.14.0 +tensorflow-model-optimization==0.8.0 +tensorflow-text==2.13.0 +termcolor==2.4.0 +text-unidecode==1.3 +tf-keras==2.15.0 +tf-models-official==2.13.2 +tf-slim==1.1.0 +threadpoolctl==3.5.0 +toml==0.10.2 +tornado==6.4.1 +tqdm==4.66.4 +traitlets==5.14.3 +typing_extensions==4.5.0 +tzdata==2024.1 +tzlocal==5.2 +uritemplate==4.1.1 +urllib3==2.2.2 +wcwidth==0.2.13 +webencodings==0.5.1 +Werkzeug==3.0.3 +wrapt==1.16.0 +zipp==3.19.2 +zstandard==0.22.0