6号笔记本 tensorflow cpu object detection api

6号笔记本环境配置

done
#
# To activate this environment, use
#
#     $ conda activate wind_202103
#
# To deactivate an active environment, use
#
#     $ conda deactivate


(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>

 

测试

(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>python object_detection/builders/model_builder_tf2_test.py
2021-04-12 14:15:52.486730: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
Running tests under Python 3.7.10: E:\Anaconda3\install\envs\wind_202103\python.exe
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model
2021-04-12 14:15:55.906964: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-12 14:15:55.914149: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-04-12 14:15:58.094186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3080 Laptop GPU computeCapability: 8.6
coreClock: 1.245GHz coreCount: 48 deviceMemorySize: 16.00GiB deviceMemoryBandwidth: 357.69GiB/s
2021-04-12 14:15:58.094455: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-04-12 14:15:58.144854: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-04-12 14:15:58.144929: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-04-12 14:15:58.170496: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-04-12 14:15:58.178093: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-04-12 14:15:58.182160: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2021-04-12 14:15:58.199838: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-04-12 14:15:58.203486: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-04-12 14:15:58.203589: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] 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.
Skipping registering GPU devices...
2021-04-12 14:15:58.204149: 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:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-12 14:15:58.204888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-12 14:15:58.207014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]
2021-04-12 14:15:58.211032: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 2.91s
I0412 14:15:58.526351 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 2.91s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.25s
I0412 14:15:58.776292 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.25s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints
[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s
I0412 14:15:58.776292 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s
[       OK ] ModelBuilderTF2Test.test_create_experimental_model
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s
I0412 14:15:58.807536 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s
I0412 14:15:58.823157 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s
I0412 14:15:58.838778 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s
I0412 14:15:58.932541 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s
I0412 14:15:59.027438 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s
I0412 14:15:59.138765 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s
I0412 14:15:59.232494 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s
I0412 14:15:59.341844 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s
I0412 14:15:59.374269 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s
[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config
I0412 14:15:59.589379 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0
I0412 14:15:59.589379 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64
I0412 14:15:59.589379 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3
I0412 14:15:59.589379 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:15:59.605001 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:15:59.605001 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:15:59.636244 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:15:59.636244 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:15:59.746711 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:15:59.746711 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:15:59.847079 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:15:59.847079 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:00.003327 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:00.003327 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:00.160682 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:00.160682 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:00.420666 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:00.420666 11224 efficientnet_model.py:147] round_filter input=320 output=320
I0412 14:16:00.484324 11224 efficientnet_model.py:147] round_filter input=1280 output=1280
I0412 14:16:00.515606 11224 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')
I0412 14:16:00.572247 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1
I0412 14:16:00.572247 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88
I0412 14:16:00.572247 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4
I0412 14:16:00.572247 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:00.587869 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:00.587869 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:00.665976 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:00.665976 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:00.822189 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:00.822189 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:16:00.981099 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:16:00.981099 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:01.184208 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:01.184208 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:01.391219 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:01.391219 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:01.672546 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:01.672546 11224 efficientnet_model.py:147] round_filter input=320 output=320
I0412 14:16:01.801679 11224 efficientnet_model.py:147] round_filter input=1280 output=1280
I0412 14:16:01.832922 11224 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')
I0412 14:16:01.879786 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2
I0412 14:16:01.879786 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112
I0412 14:16:01.879786 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5
I0412 14:16:01.895407 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:01.895407 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:01.911029 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:01.989136 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:01.989136 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:02.130882 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:02.130882 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:02.293226 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:02.293226 11224 efficientnet_model.py:147] round_filter input=80 output=88
I0412 14:16:02.497462 11224 efficientnet_model.py:147] round_filter input=80 output=88
I0412 14:16:02.497462 11224 efficientnet_model.py:147] round_filter input=112 output=120
I0412 14:16:02.780225 11224 efficientnet_model.py:147] round_filter input=112 output=120
I0412 14:16:02.780225 11224 efficientnet_model.py:147] round_filter input=192 output=208
I0412 14:16:03.053839 11224 efficientnet_model.py:147] round_filter input=192 output=208
I0412 14:16:03.053839 11224 efficientnet_model.py:147] round_filter input=320 output=352
I0412 14:16:03.178811 11224 efficientnet_model.py:147] round_filter input=1280 output=1408
I0412 14:16:03.210053 11224 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')
I0412 14:16:03.272538 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3
I0412 14:16:03.272538 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160
I0412 14:16:03.272538 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6
I0412 14:16:03.288159 11224 efficientnet_model.py:147] round_filter input=32 output=40
I0412 14:16:03.288159 11224 efficientnet_model.py:147] round_filter input=32 output=40
I0412 14:16:03.288159 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:03.367403 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:03.367403 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:03.525760 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:03.525760 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:03.681978 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:03.681978 11224 efficientnet_model.py:147] round_filter input=80 output=96
I0412 14:16:03.936995 11224 efficientnet_model.py:147] round_filter input=80 output=96
I0412 14:16:03.936995 11224 efficientnet_model.py:147] round_filter input=112 output=136
I0412 14:16:04.211657 11224 efficientnet_model.py:147] round_filter input=112 output=136
I0412 14:16:04.211657 11224 efficientnet_model.py:147] round_filter input=192 output=232
I0412 14:16:04.560706 11224 efficientnet_model.py:147] round_filter input=192 output=232
I0412 14:16:04.560706 11224 efficientnet_model.py:147] round_filter input=320 output=384
I0412 14:16:04.701331 11224 efficientnet_model.py:147] round_filter input=1280 output=1536
I0412 14:16:04.732574 11224 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')
I0412 14:16:04.795061 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4
I0412 14:16:04.795061 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224
I0412 14:16:04.795061 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7
I0412 14:16:04.795061 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:04.810693 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:04.810693 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:04.889998 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:04.889998 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:05.079239 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:05.079239 11224 efficientnet_model.py:147] round_filter input=40 output=56
I0412 14:16:05.410891 11224 efficientnet_model.py:147] round_filter input=40 output=56
I0412 14:16:05.410891 11224 efficientnet_model.py:147] round_filter input=80 output=112
I0412 14:16:05.725448 11224 efficientnet_model.py:147] round_filter input=80 output=112
I0412 14:16:05.725448 11224 efficientnet_model.py:147] round_filter input=112 output=160
I0412 14:16:06.054715 11224 efficientnet_model.py:147] round_filter input=112 output=160
I0412 14:16:06.054715 11224 efficientnet_model.py:147] round_filter input=192 output=272
I0412 14:16:06.576864 11224 efficientnet_model.py:147] round_filter input=192 output=272
I0412 14:16:06.576864 11224 efficientnet_model.py:147] round_filter input=320 output=448
I0412 14:16:06.717458 11224 efficientnet_model.py:147] round_filter input=1280 output=1792
I0412 14:16:06.749806 11224 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')
I0412 14:16:06.824114 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5
I0412 14:16:06.824114 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288
I0412 14:16:06.824114 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7
I0412 14:16:06.824114 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:06.839785 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:06.839785 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:06.964734 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:06.964734 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:07.215926 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:07.215926 11224 efficientnet_model.py:147] round_filter input=40 output=64
I0412 14:16:07.462391 11224 efficientnet_model.py:147] round_filter input=40 output=64
I0412 14:16:07.462391 11224 efficientnet_model.py:147] round_filter input=80 output=128
I0412 14:16:07.847848 11224 efficientnet_model.py:147] round_filter input=80 output=128
I0412 14:16:07.847848 11224 efficientnet_model.py:147] round_filter input=112 output=176
I0412 14:16:08.227632 11224 efficientnet_model.py:147] round_filter input=112 output=176
I0412 14:16:08.227632 11224 efficientnet_model.py:147] round_filter input=192 output=304
I0412 14:16:08.935734 11224 efficientnet_model.py:147] round_filter input=192 output=304
I0412 14:16:08.935734 11224 efficientnet_model.py:147] round_filter input=320 output=512
I0412 14:16:09.201296 11224 efficientnet_model.py:147] round_filter input=1280 output=2048
I0412 14:16:09.248161 11224 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')
I0412 14:16:09.327469 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6
I0412 14:16:09.327469 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384
I0412 14:16:09.327469 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8
I0412 14:16:09.330456 11224 efficientnet_model.py:147] round_filter input=32 output=56
I0412 14:16:09.330456 11224 efficientnet_model.py:147] round_filter input=32 output=56
I0412 14:16:09.330456 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:09.455431 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:09.455431 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:09.769093 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:09.769093 11224 efficientnet_model.py:147] round_filter input=40 output=72
I0412 14:16:10.072026 11224 efficientnet_model.py:147] round_filter input=40 output=72
I0412 14:16:10.072026 11224 efficientnet_model.py:147] round_filter input=80 output=144
I0412 14:16:10.504786 11224 efficientnet_model.py:147] round_filter input=80 output=144
I0412 14:16:10.504786 11224 efficientnet_model.py:147] round_filter input=112 output=200
I0412 14:16:10.973426 11224 efficientnet_model.py:147] round_filter input=112 output=200
I0412 14:16:10.973426 11224 efficientnet_model.py:147] round_filter input=192 output=344
I0412 14:16:11.731940 11224 efficientnet_model.py:147] round_filter input=192 output=344
I0412 14:16:11.731940 11224 efficientnet_model.py:147] round_filter input=320 output=576
I0412 14:16:12.084060 11224 efficientnet_model.py:147] round_filter input=1280 output=2304
I0412 14:16:12.130926 11224 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')
I0412 14:16:12.209056 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7
I0412 14:16:12.209056 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384
I0412 14:16:12.224833 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8
I0412 14:16:12.224833 11224 efficientnet_model.py:147] round_filter input=32 output=64
I0412 14:16:12.224833 11224 efficientnet_model.py:147] round_filter input=32 output=64
I0412 14:16:12.224833 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:12.388885 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:12.388885 11224 efficientnet_model.py:147] round_filter input=24 output=48
I0412 14:16:12.748528 11224 efficientnet_model.py:147] round_filter input=24 output=48
I0412 14:16:12.748528 11224 efficientnet_model.py:147] round_filter input=40 output=80
I0412 14:16:13.119887 11224 efficientnet_model.py:147] round_filter input=40 output=80
I0412 14:16:13.119887 11224 efficientnet_model.py:147] round_filter input=80 output=160
I0412 14:16:13.672224 11224 efficientnet_model.py:147] round_filter input=80 output=160
I0412 14:16:13.672224 11224 efficientnet_model.py:147] round_filter input=112 output=224
I0412 14:16:14.254858 11224 efficientnet_model.py:147] round_filter input=112 output=224
I0412 14:16:14.254858 11224 efficientnet_model.py:147] round_filter input=192 output=384
I0412 14:16:15.250613 11224 efficientnet_model.py:147] round_filter input=192 output=384
I0412 14:16:15.250613 11224 efficientnet_model.py:147] round_filter input=320 output=640
I0412 14:16:15.611495 11224 efficientnet_model.py:147] round_filter input=1280 output=2560
I0412 14:16:15.658359 11224 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')
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 16.39s
I0412 14:16:15.767739 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 16.39s
[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
I0412 14:16:15.767739 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
I0412 14:16:15.783332 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
I0412 14:16:15.792893 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
I0412 14:16:15.796538 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
I0412 14:16:15.801620 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
I0412 14:16:15.803169 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
I0412 14:16:15.805706 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 21 tests in 20.176s

OK (skipped=1)

(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>

 

 

 

 

 

安装 tensorflow-cpu

(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>
(base) F:\>conda activate wind_202103

(wind_202103) F:\>
(wind_202103) F:\>
(wind_202103) F:\>
(wind_202103) F:\>
(wind_202103) F:\>pip install tensorflow-cpu==2.2.2
Collecting tensorflow-cpu==2.2.2
  Downloading tensorflow_cpu-2.2.2-cp37-cp37m-win_amd64.whl (189.3 MB)
     |████████████████████████████████| 189.3 MB 1.1 MB/s
Collecting termcolor>=1.1.0
  Using cached termcolor-1.1.0-py3-none-any.whl
Collecting gast==0.3.3
  Using cached gast-0.3.3-py2.py3-none-any.whl (9.7 kB)
Collecting h5py<2.11.0,>=2.10.0
  Using cached h5py-2.10.0-cp37-cp37m-win_amd64.whl (2.5 MB)
Collecting opt-einsum>=2.3.2
  Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Collecting absl-py>=0.7.0
  Using cached absl_py-0.12.0-py3-none-any.whl (129 kB)
Collecting tensorflow-estimator<2.3.0,>=2.2.0
  Downloading tensorflow_estimator-2.2.0-py2.py3-none-any.whl (454 kB)
     |████████████████████████████████| 454 kB 1.3 MB/s
Requirement already satisfied: wheel>=0.26 in e:\anaconda3\install\envs\wind_202103\lib\site-packages (from tensorflow-cpu==2.2.2) (0.36.2)
Collecting protobuf>=3.8.0
  Downloading protobuf-3.15.8-cp37-cp37m-win_amd64.whl (904 kB)
     |████████████████████████████████| 904 kB 1.3 MB/s
Collecting six>=1.12.0
  Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting google-pasta>=0.1.8
  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
Collecting numpy<1.19.0,>=1.16.0
  Downloading numpy-1.18.5-cp37-cp37m-win_amd64.whl (12.7 MB)
     |████████████████████████████████| 12.7 MB 1.3 MB/s
Collecting wrapt>=1.11.1
  Using cached wrapt-1.12.1-cp37-cp37m-win_amd64.whl
Collecting astunparse==1.6.3
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(wind_202103) F:\>
(wind_202103) F:\>
(wind_202103) F:\>
(wind_202103) F:\>
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(wind_202103) F:\>

 

安装 object detection api

(wind_202103) F:\>cd F:\TensorflowProject\models-master\research

(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>protoc object_detection/protos/*.proto --python_out=.

(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>python -m pip install --use-feature=2020-resolver .
WARNING: --use-feature=2020-resolver no longer has any effect, since it is now the default dependency resolver in pip. This will become an error in pip 21.0.
Processing f:\tensorflowproject\models-master\research
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Building wheels for collected packages: object-detection, avro-python3, crcmod, dill, future, docopt, kaggle, py-cpuinfo, python-slugify, seqeval, promise
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Successfully built object-detection avro-python3 crcmod dill future docopt kaggle py-cpuinfo python-slugify seqeval promise
Installing collected packages: pyparsing, pycparser, pytz, packaging, numpy, googleapis-common-protos, cffi, threadpoolctl, text-unidecode, scipy, python-dateutil, pillow, kiwisolver, joblib, httplib2, grpcio, google-crc32c, google-api-core, cycler, uritemplate, typeguard, tqdm, tensorflow-metadata, tensorflow-estimator, tensorboard, scikit-learn, python-slugify, proto-plus, promise, pbr, matplotlib, importlib-resources, google-resumable-media, google-cloud-core, google-auth-httplib2, future, flatbuffers, docopt, dm-tree, dill, Cython, attrs, tf-slim, tensorflow-model-optimization, tensorflow-hub, tensorflow-datasets, tensorflow-addons, tensorflow, seqeval, sentencepiece, pyyaml, pymongo, pydot, pycocotools, pyarrow, py-cpuinfo, psutil, pandas, opencv-python-headless, opencv-python, oauth2client, mock, kaggle, hdfs, google-cloud-bigquery, google-api-python-client, gin-config, fastavro, dataclasses, crcmod, avro-python3, tf-models-official, lxml, lvis, contextlib2, apache-beam, object-detection
  Attempting uninstall: numpy
    Found existing installation: numpy 1.18.5
    Uninstalling numpy-1.18.5:
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  Attempting uninstall: grpcio
    Found existing installation: grpcio 1.37.0
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  Attempting uninstall: tensorflow-estimator
    Found existing installation: tensorflow-estimator 2.2.0
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  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.2.2
    Uninstalling tensorboard-2.2.2:
      Successfully uninstalled tensorboard-2.2.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-cpu 2.2.2 requires numpy<1.19.0,>=1.16.0, but you have numpy 1.19.5 which is incompatible.
tensorflow-cpu 2.2.2 requires tensorboard<2.3.0,>=2.2.0, but you have tensorboard 2.4.1 which is incompatible.
tensorflow-cpu 2.2.2 requires tensorflow-estimator<2.3.0,>=2.2.0, but you have tensorflow-estimator 2.4.0 which is incompatible.
Successfully installed Cython-0.29.22 apache-beam-2.28.0 attrs-20.3.0 avro-python3-1.9.2.1 cffi-1.14.5 contextlib2-0.6.0.post1 crcmod-1.7 cycler-0.10.0 dataclasses-0.6 dill-0.3.1.1 dm-tree-0.1.5 docopt-0.6.2 fastavro-1.3.5 flatbuffers-1.12 future-0.18.2 gin-config-0.4.0 google-api-core-1.26.3 google-api-python-client-2.1.0 google-auth-httplib2-0.1.0 google-cloud-bigquery-2.13.1 google-cloud-core-1.6.0 google-crc32c-1.1.2 google-resumable-media-1.2.0 googleapis-common-protos-1.53.0 grpcio-1.32.0 hdfs-2.6.0 httplib2-0.17.4 importlib-resources-5.1.2 joblib-1.0.1 kaggle-1.5.12 kiwisolver-1.3.1 lvis-0.5.3 lxml-4.6.3 matplotlib-3.4.1 mock-2.0.0 numpy-1.19.5 oauth2client-4.1.3 object-detection-0.1 opencv-python-4.5.1.48 opencv-python-headless-4.5.1.48 packaging-20.9 pandas-1.2.3 pbr-5.5.1 pillow-8.2.0 promise-2.3 proto-plus-1.18.1 psutil-5.8.0 py-cpuinfo-7.0.0 pyarrow-2.0.0 pycocotools-2.0.2 pycparser-2.20 pydot-1.4.2 pymongo-3.11.3 pyparsing-2.4.7 python-dateutil-2.8.1 python-slugify-4.0.1 pytz-2021.1 pyyaml-5.4.1 scikit-learn-0.24.1 scipy-1.6.2 sentencepiece-0.1.95 seqeval-1.2.2 tensorboard-2.4.1 tensorflow-2.4.1 tensorflow-addons-0.12.1 tensorflow-datasets-4.2.0 tensorflow-estimator-2.4.0 tensorflow-hub-0.11.0 tensorflow-metadata-0.29.0 tensorflow-model-optimization-0.5.0 text-unidecode-1.3 tf-models-official-2.4.0 tf-slim-1.1.0 threadpoolctl-2.1.0 tqdm-4.60.0 typeguard-2.12.0 uritemplate-3.0.1

(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>

 

 

 测试

(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>
(wind_202103) F:\TensorflowProject\models-master\research>python object_detection/builders/model_builder_tf2_test.py
2021-04-12 14:15:52.486730: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
Running tests under Python 3.7.10: E:\Anaconda3\install\envs\wind_202103\python.exe
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model
2021-04-12 14:15:55.906964: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-12 14:15:55.914149: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-04-12 14:15:58.094186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce RTX 3080 Laptop GPU computeCapability: 8.6
coreClock: 1.245GHz coreCount: 48 deviceMemorySize: 16.00GiB deviceMemoryBandwidth: 357.69GiB/s
2021-04-12 14:15:58.094455: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-04-12 14:15:58.144854: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-04-12 14:15:58.144929: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-04-12 14:15:58.170496: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-04-12 14:15:58.178093: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-04-12 14:15:58.182160: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2021-04-12 14:15:58.199838: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-04-12 14:15:58.203486: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-04-12 14:15:58.203589: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] 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.
Skipping registering GPU devices...
2021-04-12 14:15:58.204149: 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:  AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-12 14:15:58.204888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-12 14:15:58.207014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]
2021-04-12 14:15:58.211032: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 2.91s
I0412 14:15:58.526351 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 2.91s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.25s
I0412 14:15:58.776292 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.25s
[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints
[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s
I0412 14:15:58.776292 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s
[       OK ] ModelBuilderTF2Test.test_create_experimental_model
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s
I0412 14:15:58.807536 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s
I0412 14:15:58.823157 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s
I0412 14:15:58.838778 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s
I0412 14:15:58.932541 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s
I0412 14:15:59.027438 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.08s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s
I0412 14:15:59.138765 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.1s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s
I0412 14:15:59.232494 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s
I0412 14:15:59.341844 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.09s
[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s
I0412 14:15:59.374269 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.03s
[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config
I0412 14:15:59.589379 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0
I0412 14:15:59.589379 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64
I0412 14:15:59.589379 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3
I0412 14:15:59.589379 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:15:59.605001 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:15:59.605001 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:15:59.636244 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:15:59.636244 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:15:59.746711 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:15:59.746711 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:15:59.847079 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:15:59.847079 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:00.003327 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:00.003327 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:00.160682 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:00.160682 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:00.420666 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:00.420666 11224 efficientnet_model.py:147] round_filter input=320 output=320
I0412 14:16:00.484324 11224 efficientnet_model.py:147] round_filter input=1280 output=1280
I0412 14:16:00.515606 11224 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')
I0412 14:16:00.572247 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1
I0412 14:16:00.572247 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88
I0412 14:16:00.572247 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4
I0412 14:16:00.572247 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:00.587869 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:00.587869 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:00.665976 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:00.665976 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:00.822189 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:00.822189 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:16:00.981099 11224 efficientnet_model.py:147] round_filter input=40 output=40
I0412 14:16:00.981099 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:01.184208 11224 efficientnet_model.py:147] round_filter input=80 output=80
I0412 14:16:01.184208 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:01.391219 11224 efficientnet_model.py:147] round_filter input=112 output=112
I0412 14:16:01.391219 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:01.672546 11224 efficientnet_model.py:147] round_filter input=192 output=192
I0412 14:16:01.672546 11224 efficientnet_model.py:147] round_filter input=320 output=320
I0412 14:16:01.801679 11224 efficientnet_model.py:147] round_filter input=1280 output=1280
I0412 14:16:01.832922 11224 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')
I0412 14:16:01.879786 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2
I0412 14:16:01.879786 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112
I0412 14:16:01.879786 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5
I0412 14:16:01.895407 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:01.895407 11224 efficientnet_model.py:147] round_filter input=32 output=32
I0412 14:16:01.911029 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:01.989136 11224 efficientnet_model.py:147] round_filter input=16 output=16
I0412 14:16:01.989136 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:02.130882 11224 efficientnet_model.py:147] round_filter input=24 output=24
I0412 14:16:02.130882 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:02.293226 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:02.293226 11224 efficientnet_model.py:147] round_filter input=80 output=88
I0412 14:16:02.497462 11224 efficientnet_model.py:147] round_filter input=80 output=88
I0412 14:16:02.497462 11224 efficientnet_model.py:147] round_filter input=112 output=120
I0412 14:16:02.780225 11224 efficientnet_model.py:147] round_filter input=112 output=120
I0412 14:16:02.780225 11224 efficientnet_model.py:147] round_filter input=192 output=208
I0412 14:16:03.053839 11224 efficientnet_model.py:147] round_filter input=192 output=208
I0412 14:16:03.053839 11224 efficientnet_model.py:147] round_filter input=320 output=352
I0412 14:16:03.178811 11224 efficientnet_model.py:147] round_filter input=1280 output=1408
I0412 14:16:03.210053 11224 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')
I0412 14:16:03.272538 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3
I0412 14:16:03.272538 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160
I0412 14:16:03.272538 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6
I0412 14:16:03.288159 11224 efficientnet_model.py:147] round_filter input=32 output=40
I0412 14:16:03.288159 11224 efficientnet_model.py:147] round_filter input=32 output=40
I0412 14:16:03.288159 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:03.367403 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:03.367403 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:03.525760 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:03.525760 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:03.681978 11224 efficientnet_model.py:147] round_filter input=40 output=48
I0412 14:16:03.681978 11224 efficientnet_model.py:147] round_filter input=80 output=96
I0412 14:16:03.936995 11224 efficientnet_model.py:147] round_filter input=80 output=96
I0412 14:16:03.936995 11224 efficientnet_model.py:147] round_filter input=112 output=136
I0412 14:16:04.211657 11224 efficientnet_model.py:147] round_filter input=112 output=136
I0412 14:16:04.211657 11224 efficientnet_model.py:147] round_filter input=192 output=232
I0412 14:16:04.560706 11224 efficientnet_model.py:147] round_filter input=192 output=232
I0412 14:16:04.560706 11224 efficientnet_model.py:147] round_filter input=320 output=384
I0412 14:16:04.701331 11224 efficientnet_model.py:147] round_filter input=1280 output=1536
I0412 14:16:04.732574 11224 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')
I0412 14:16:04.795061 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4
I0412 14:16:04.795061 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224
I0412 14:16:04.795061 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7
I0412 14:16:04.795061 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:04.810693 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:04.810693 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:04.889998 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:04.889998 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:05.079239 11224 efficientnet_model.py:147] round_filter input=24 output=32
I0412 14:16:05.079239 11224 efficientnet_model.py:147] round_filter input=40 output=56
I0412 14:16:05.410891 11224 efficientnet_model.py:147] round_filter input=40 output=56
I0412 14:16:05.410891 11224 efficientnet_model.py:147] round_filter input=80 output=112
I0412 14:16:05.725448 11224 efficientnet_model.py:147] round_filter input=80 output=112
I0412 14:16:05.725448 11224 efficientnet_model.py:147] round_filter input=112 output=160
I0412 14:16:06.054715 11224 efficientnet_model.py:147] round_filter input=112 output=160
I0412 14:16:06.054715 11224 efficientnet_model.py:147] round_filter input=192 output=272
I0412 14:16:06.576864 11224 efficientnet_model.py:147] round_filter input=192 output=272
I0412 14:16:06.576864 11224 efficientnet_model.py:147] round_filter input=320 output=448
I0412 14:16:06.717458 11224 efficientnet_model.py:147] round_filter input=1280 output=1792
I0412 14:16:06.749806 11224 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')
I0412 14:16:06.824114 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5
I0412 14:16:06.824114 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288
I0412 14:16:06.824114 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7
I0412 14:16:06.824114 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:06.839785 11224 efficientnet_model.py:147] round_filter input=32 output=48
I0412 14:16:06.839785 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:06.964734 11224 efficientnet_model.py:147] round_filter input=16 output=24
I0412 14:16:06.964734 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:07.215926 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:07.215926 11224 efficientnet_model.py:147] round_filter input=40 output=64
I0412 14:16:07.462391 11224 efficientnet_model.py:147] round_filter input=40 output=64
I0412 14:16:07.462391 11224 efficientnet_model.py:147] round_filter input=80 output=128
I0412 14:16:07.847848 11224 efficientnet_model.py:147] round_filter input=80 output=128
I0412 14:16:07.847848 11224 efficientnet_model.py:147] round_filter input=112 output=176
I0412 14:16:08.227632 11224 efficientnet_model.py:147] round_filter input=112 output=176
I0412 14:16:08.227632 11224 efficientnet_model.py:147] round_filter input=192 output=304
I0412 14:16:08.935734 11224 efficientnet_model.py:147] round_filter input=192 output=304
I0412 14:16:08.935734 11224 efficientnet_model.py:147] round_filter input=320 output=512
I0412 14:16:09.201296 11224 efficientnet_model.py:147] round_filter input=1280 output=2048
I0412 14:16:09.248161 11224 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')
I0412 14:16:09.327469 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6
I0412 14:16:09.327469 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384
I0412 14:16:09.327469 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8
I0412 14:16:09.330456 11224 efficientnet_model.py:147] round_filter input=32 output=56
I0412 14:16:09.330456 11224 efficientnet_model.py:147] round_filter input=32 output=56
I0412 14:16:09.330456 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:09.455431 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:09.455431 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:09.769093 11224 efficientnet_model.py:147] round_filter input=24 output=40
I0412 14:16:09.769093 11224 efficientnet_model.py:147] round_filter input=40 output=72
I0412 14:16:10.072026 11224 efficientnet_model.py:147] round_filter input=40 output=72
I0412 14:16:10.072026 11224 efficientnet_model.py:147] round_filter input=80 output=144
I0412 14:16:10.504786 11224 efficientnet_model.py:147] round_filter input=80 output=144
I0412 14:16:10.504786 11224 efficientnet_model.py:147] round_filter input=112 output=200
I0412 14:16:10.973426 11224 efficientnet_model.py:147] round_filter input=112 output=200
I0412 14:16:10.973426 11224 efficientnet_model.py:147] round_filter input=192 output=344
I0412 14:16:11.731940 11224 efficientnet_model.py:147] round_filter input=192 output=344
I0412 14:16:11.731940 11224 efficientnet_model.py:147] round_filter input=320 output=576
I0412 14:16:12.084060 11224 efficientnet_model.py:147] round_filter input=1280 output=2304
I0412 14:16:12.130926 11224 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')
I0412 14:16:12.209056 11224 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7
I0412 14:16:12.209056 11224 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384
I0412 14:16:12.224833 11224 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8
I0412 14:16:12.224833 11224 efficientnet_model.py:147] round_filter input=32 output=64
I0412 14:16:12.224833 11224 efficientnet_model.py:147] round_filter input=32 output=64
I0412 14:16:12.224833 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:12.388885 11224 efficientnet_model.py:147] round_filter input=16 output=32
I0412 14:16:12.388885 11224 efficientnet_model.py:147] round_filter input=24 output=48
I0412 14:16:12.748528 11224 efficientnet_model.py:147] round_filter input=24 output=48
I0412 14:16:12.748528 11224 efficientnet_model.py:147] round_filter input=40 output=80
I0412 14:16:13.119887 11224 efficientnet_model.py:147] round_filter input=40 output=80
I0412 14:16:13.119887 11224 efficientnet_model.py:147] round_filter input=80 output=160
I0412 14:16:13.672224 11224 efficientnet_model.py:147] round_filter input=80 output=160
I0412 14:16:13.672224 11224 efficientnet_model.py:147] round_filter input=112 output=224
I0412 14:16:14.254858 11224 efficientnet_model.py:147] round_filter input=112 output=224
I0412 14:16:14.254858 11224 efficientnet_model.py:147] round_filter input=192 output=384
I0412 14:16:15.250613 11224 efficientnet_model.py:147] round_filter input=192 output=384
I0412 14:16:15.250613 11224 efficientnet_model.py:147] round_filter input=320 output=640
I0412 14:16:15.611495 11224 efficientnet_model.py:147] round_filter input=1280 output=2560
I0412 14:16:15.658359 11224 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')
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 16.39s
I0412 14:16:15.767739 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 16.39s
[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
I0412 14:16:15.767739 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
I0412 14:16:15.783332 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
I0412 14:16:15.792893 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
I0412 14:16:15.796538 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s
[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
I0412 14:16:15.801620 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
I0412 14:16:15.803169 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
I0412 14:16:15.805706 11224 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 21 tests in 20.176s

OK (skipped=1)

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