Tensorflow 2 Object Detection: Train model

This notebook walks you through training a custom object detection model using the Tensorflow Object Detection API and Tensorflow 2.

The notebook is split into the following parts:

  • Install the Tensorflow Object Detection API
  • Prepare data for use with the OD API
  • Write custom training configuration
  • Train detector
  • Export model inference graph
  • Test trained model

Microcontroller Detection

Installation

Installing the Tensorflow Object Detection API became a lot easier with the relase of Tensorflow 2. The following few cells are all that is needed in order to install the OD API.

In [ ]:
!pip install tensorflow=="2.6.0"
In [ ]:
import os
import pathlib

# Clone the tensorflow models repository if it doesn't already exist
if "models" in pathlib.Path.cwd().parts:
  while "models" in pathlib.Path.cwd().parts:
    os.chdir('..')
elif not pathlib.Path('models').exists():
  !git clone --depth 1 https://github.com/tensorflow/models
Cloning into 'models'...
remote: Enumerating objects: 2797, done.
remote: Counting objects: 100% (2797/2797), done.
remote: Compressing objects: 100% (2439/2439), done.
remote: Total 2797 (delta 563), reused 1415 (delta 322), pack-reused 0
Receiving objects: 100% (2797/2797), 57.73 MiB | 17.76 MiB/s, done.
Resolving deltas: 100% (563/563), done.
In [ ]:
# Install the Object Detection API
%%bash
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .
Processing /content/models/research
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Building wheels for collected packages: object-detection, py-cpuinfo
  Building wheel for object-detection (setup.py): started
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  Stored in directory: /root/.cache/pip/wheels/f1/93/7b/127daf0c3a5a49feb2fecd468d508067c733fba5192f726ad1
Successfully built object-detection py-cpuinfo
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object_detection/protos/input_reader.proto: warning: Import object_detection/protos/image_resizer.proto but not used.
In [ ]:
#run model builder test
!python /content/models/research/object_detection/builders/model_builder_tf2_test.py
2020-07-18 15:53:31.348956: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Running tests under Python 3.6.9: /usr/bin/python3
[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model
2020-07-18 15:53:33.836001: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-07-18 15:53:33.887663: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:33.888362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
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2020-07-18 15:53:35.144603: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:35.145370: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:35.145966: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-07-18 15:53:35.146307: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F
2020-07-18 15:53:35.177665: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000179999 Hz
2020-07-18 15:53:35.177869: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x24caf40 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-18 15:53:35.177903: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-07-18 15:53:35.325361: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:35.326101: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x24cad80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-07-18 15:53:35.326130: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2020-07-18 15:53:35.327390: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:35.327991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-07-18 15:53:35.328035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 15:53:35.328081: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-07-18 15:53:35.328123: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-07-18 15:53:35.328145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-07-18 15:53:35.328206: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-07-18 15:53:35.328230: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-07-18 15:53:35.328251: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-07-18 15:53:35.328332: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:35.328929: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:35.329492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-07-18 15:53:35.333064: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 15:53:41.707389: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-18 15:53:41.707444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2020-07-18 15:53:41.707458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2020-07-18 15:53:41.771654: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:41.772409: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:53:41.772966: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-07-18 15:53:41.773014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
[       OK ] ModelBuilderTF2Test.test_create_center_net_model
[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model
[       OK ] ModelBuilderTF2Test.test_create_experimental_model
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature(True)
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature(True)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature(False)
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature(False)
[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner
[       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
[       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
[       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
[       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
[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul
[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config
[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config
I0718 15:53:50.383395 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b0
I0718 15:53:50.383588 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 64
I0718 15:53:50.383674 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 3
I0718 15:53:50.391403 139783477233536 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:53:50.430215 139783477233536 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:53:50.430354 139783477233536 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:53:50.546231 139783477233536 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:53:50.546369 139783477233536 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:53:50.855049 139783477233536 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:53:50.855231 139783477233536 efficientnet_model.py:146] round_filter input=40 output=40
I0718 15:53:51.163151 139783477233536 efficientnet_model.py:146] round_filter input=40 output=40
I0718 15:53:51.163348 139783477233536 efficientnet_model.py:146] round_filter input=80 output=80
I0718 15:53:51.631662 139783477233536 efficientnet_model.py:146] round_filter input=80 output=80
I0718 15:53:51.631822 139783477233536 efficientnet_model.py:146] round_filter input=112 output=112
I0718 15:53:52.096985 139783477233536 efficientnet_model.py:146] round_filter input=112 output=112
I0718 15:53:52.097150 139783477233536 efficientnet_model.py:146] round_filter input=192 output=192
I0718 15:53:52.894778 139783477233536 efficientnet_model.py:146] round_filter input=192 output=192
I0718 15:53:52.894949 139783477233536 efficientnet_model.py:146] round_filter input=320 output=320
I0718 15:53:53.037482 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=1280
I0718 15:53:53.098095 139783477233536 efficientnet_model.py:459] 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')
I0718 15:53:53.196350 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b1
I0718 15:53:53.196521 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 88
I0718 15:53:53.196609 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 4
I0718 15:53:53.203763 139783477233536 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:53:53.242450 139783477233536 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:53:53.242589 139783477233536 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:53:53.471552 139783477233536 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:53:53.471700 139783477233536 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:53:53.948541 139783477233536 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:53:53.948699 139783477233536 efficientnet_model.py:146] round_filter input=40 output=40
I0718 15:53:54.426619 139783477233536 efficientnet_model.py:146] round_filter input=40 output=40
I0718 15:53:54.426785 139783477233536 efficientnet_model.py:146] round_filter input=80 output=80
I0718 15:53:55.058634 139783477233536 efficientnet_model.py:146] round_filter input=80 output=80
I0718 15:53:55.058791 139783477233536 efficientnet_model.py:146] round_filter input=112 output=112
I0718 15:53:55.696157 139783477233536 efficientnet_model.py:146] round_filter input=112 output=112
I0718 15:53:55.696341 139783477233536 efficientnet_model.py:146] round_filter input=192 output=192
I0718 15:53:56.479348 139783477233536 efficientnet_model.py:146] round_filter input=192 output=192
I0718 15:53:56.479512 139783477233536 efficientnet_model.py:146] round_filter input=320 output=320
I0718 15:53:56.808774 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=1280
I0718 15:53:56.868782 139783477233536 efficientnet_model.py:459] 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')
I0718 15:53:57.169081 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b2
I0718 15:53:57.169301 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 112
I0718 15:53:57.169383 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 5
I0718 15:53:57.179291 139783477233536 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:53:57.222126 139783477233536 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:53:57.222292 139783477233536 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:53:57.450467 139783477233536 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:53:57.450621 139783477233536 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:53:57.921869 139783477233536 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:53:57.922047 139783477233536 efficientnet_model.py:146] round_filter input=40 output=48
I0718 15:53:58.394853 139783477233536 efficientnet_model.py:146] round_filter input=40 output=48
I0718 15:53:58.395023 139783477233536 efficientnet_model.py:146] round_filter input=80 output=88
I0718 15:53:59.036375 139783477233536 efficientnet_model.py:146] round_filter input=80 output=88
I0718 15:53:59.036572 139783477233536 efficientnet_model.py:146] round_filter input=112 output=120
I0718 15:53:59.687438 139783477233536 efficientnet_model.py:146] round_filter input=112 output=120
I0718 15:53:59.687608 139783477233536 efficientnet_model.py:146] round_filter input=192 output=208
I0718 15:54:00.491297 139783477233536 efficientnet_model.py:146] round_filter input=192 output=208
I0718 15:54:00.491468 139783477233536 efficientnet_model.py:146] round_filter input=320 output=352
I0718 15:54:00.807165 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=1408
I0718 15:54:00.866840 139783477233536 efficientnet_model.py:459] 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')
I0718 15:54:00.966921 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b3
I0718 15:54:00.967070 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 160
I0718 15:54:00.967152 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 6
I0718 15:54:00.973970 139783477233536 efficientnet_model.py:146] round_filter input=32 output=40
I0718 15:54:01.011606 139783477233536 efficientnet_model.py:146] round_filter input=32 output=40
I0718 15:54:01.011738 139783477233536 efficientnet_model.py:146] round_filter input=16 output=24
I0718 15:54:01.250095 139783477233536 efficientnet_model.py:146] round_filter input=16 output=24
I0718 15:54:01.250275 139783477233536 efficientnet_model.py:146] round_filter input=24 output=32
I0718 15:54:01.742437 139783477233536 efficientnet_model.py:146] round_filter input=24 output=32
I0718 15:54:01.742615 139783477233536 efficientnet_model.py:146] round_filter input=40 output=48
I0718 15:54:02.214576 139783477233536 efficientnet_model.py:146] round_filter input=40 output=48
I0718 15:54:02.214745 139783477233536 efficientnet_model.py:146] round_filter input=80 output=96
I0718 15:54:03.244881 139783477233536 efficientnet_model.py:146] round_filter input=80 output=96
I0718 15:54:03.245072 139783477233536 efficientnet_model.py:146] round_filter input=112 output=136
I0718 15:54:04.066666 139783477233536 efficientnet_model.py:146] round_filter input=112 output=136
I0718 15:54:04.066832 139783477233536 efficientnet_model.py:146] round_filter input=192 output=232
I0718 15:54:05.061048 139783477233536 efficientnet_model.py:146] round_filter input=192 output=232
I0718 15:54:05.061232 139783477233536 efficientnet_model.py:146] round_filter input=320 output=384
I0718 15:54:05.387217 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=1536
I0718 15:54:05.445710 139783477233536 efficientnet_model.py:459] 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')
I0718 15:54:05.552754 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b4
I0718 15:54:05.552904 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 224
I0718 15:54:05.552979 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 7
I0718 15:54:05.559571 139783477233536 efficientnet_model.py:146] round_filter input=32 output=48
I0718 15:54:05.599698 139783477233536 efficientnet_model.py:146] round_filter input=32 output=48
I0718 15:54:05.599832 139783477233536 efficientnet_model.py:146] round_filter input=16 output=24
I0718 15:54:05.849825 139783477233536 efficientnet_model.py:146] round_filter input=16 output=24
I0718 15:54:05.849990 139783477233536 efficientnet_model.py:146] round_filter input=24 output=32
I0718 15:54:06.503325 139783477233536 efficientnet_model.py:146] round_filter input=24 output=32
I0718 15:54:06.503524 139783477233536 efficientnet_model.py:146] round_filter input=40 output=56
I0718 15:54:07.158834 139783477233536 efficientnet_model.py:146] round_filter input=40 output=56
I0718 15:54:07.159014 139783477233536 efficientnet_model.py:146] round_filter input=80 output=112
I0718 15:54:08.160660 139783477233536 efficientnet_model.py:146] round_filter input=80 output=112
I0718 15:54:08.160824 139783477233536 efficientnet_model.py:146] round_filter input=112 output=160
I0718 15:54:09.162787 139783477233536 efficientnet_model.py:146] round_filter input=112 output=160
I0718 15:54:09.162958 139783477233536 efficientnet_model.py:146] round_filter input=192 output=272
I0718 15:54:10.759003 139783477233536 efficientnet_model.py:146] round_filter input=192 output=272
I0718 15:54:10.759187 139783477233536 efficientnet_model.py:146] round_filter input=320 output=448
I0718 15:54:11.075441 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=1792
I0718 15:54:11.135566 139783477233536 efficientnet_model.py:459] 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')
I0718 15:54:11.260094 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b5
I0718 15:54:11.260264 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 288
I0718 15:54:11.260339 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 7
I0718 15:54:11.267058 139783477233536 efficientnet_model.py:146] round_filter input=32 output=48
I0718 15:54:11.304764 139783477233536 efficientnet_model.py:146] round_filter input=32 output=48
I0718 15:54:11.304882 139783477233536 efficientnet_model.py:146] round_filter input=16 output=24
I0718 15:54:11.677724 139783477233536 efficientnet_model.py:146] round_filter input=16 output=24
I0718 15:54:11.677884 139783477233536 efficientnet_model.py:146] round_filter input=24 output=40
I0718 15:54:12.480824 139783477233536 efficientnet_model.py:146] round_filter input=24 output=40
I0718 15:54:12.480993 139783477233536 efficientnet_model.py:146] round_filter input=40 output=64
I0718 15:54:13.280297 139783477233536 efficientnet_model.py:146] round_filter input=40 output=64
I0718 15:54:13.280489 139783477233536 efficientnet_model.py:146] round_filter input=80 output=128
I0718 15:54:14.424644 139783477233536 efficientnet_model.py:146] round_filter input=80 output=128
I0718 15:54:14.424811 139783477233536 efficientnet_model.py:146] round_filter input=112 output=176
I0718 15:54:15.571858 139783477233536 efficientnet_model.py:146] round_filter input=112 output=176
I0718 15:54:15.572030 139783477233536 efficientnet_model.py:146] round_filter input=192 output=304
I0718 15:54:17.033928 139783477233536 efficientnet_model.py:146] round_filter input=192 output=304
I0718 15:54:17.034095 139783477233536 efficientnet_model.py:146] round_filter input=320 output=512
I0718 15:54:17.526939 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=2048
I0718 15:54:17.587463 139783477233536 efficientnet_model.py:459] 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')
I0718 15:54:18.087429 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b6
I0718 15:54:18.087618 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 384
I0718 15:54:18.087708 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 8
I0718 15:54:18.094159 139783477233536 efficientnet_model.py:146] round_filter input=32 output=56
I0718 15:54:18.135271 139783477233536 efficientnet_model.py:146] round_filter input=32 output=56
I0718 15:54:18.135396 139783477233536 efficientnet_model.py:146] round_filter input=16 output=32
I0718 15:54:18.514646 139783477233536 efficientnet_model.py:146] round_filter input=16 output=32
I0718 15:54:18.514820 139783477233536 efficientnet_model.py:146] round_filter input=24 output=40
I0718 15:54:19.495274 139783477233536 efficientnet_model.py:146] round_filter input=24 output=40
I0718 15:54:19.495432 139783477233536 efficientnet_model.py:146] round_filter input=40 output=72
I0718 15:54:20.461184 139783477233536 efficientnet_model.py:146] round_filter input=40 output=72
I0718 15:54:20.461385 139783477233536 efficientnet_model.py:146] round_filter input=80 output=144
I0718 15:54:21.781459 139783477233536 efficientnet_model.py:146] round_filter input=80 output=144
I0718 15:54:21.781636 139783477233536 efficientnet_model.py:146] round_filter input=112 output=200
I0718 15:54:23.097401 139783477233536 efficientnet_model.py:146] round_filter input=112 output=200
I0718 15:54:23.097569 139783477233536 efficientnet_model.py:146] round_filter input=192 output=344
I0718 15:54:24.937327 139783477233536 efficientnet_model.py:146] round_filter input=192 output=344
I0718 15:54:24.937556 139783477233536 efficientnet_model.py:146] round_filter input=320 output=576
I0718 15:54:25.435920 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=2304
I0718 15:54:25.495490 139783477233536 efficientnet_model.py:459] 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')
I0718 15:54:25.657754 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b7
I0718 15:54:25.657919 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 384
I0718 15:54:25.657990 139783477233536 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 8
I0718 15:54:25.664875 139783477233536 efficientnet_model.py:146] round_filter input=32 output=64
I0718 15:54:25.713185 139783477233536 efficientnet_model.py:146] round_filter input=32 output=64
I0718 15:54:25.713321 139783477233536 efficientnet_model.py:146] round_filter input=16 output=32
I0718 15:54:26.226270 139783477233536 efficientnet_model.py:146] round_filter input=16 output=32
I0718 15:54:26.226435 139783477233536 efficientnet_model.py:146] round_filter input=24 output=48
I0718 15:54:27.412612 139783477233536 efficientnet_model.py:146] round_filter input=24 output=48
I0718 15:54:27.412778 139783477233536 efficientnet_model.py:146] round_filter input=40 output=80
I0718 15:54:29.042933 139783477233536 efficientnet_model.py:146] round_filter input=40 output=80
I0718 15:54:29.043100 139783477233536 efficientnet_model.py:146] round_filter input=80 output=160
I0718 15:54:30.741483 139783477233536 efficientnet_model.py:146] round_filter input=80 output=160
I0718 15:54:30.741668 139783477233536 efficientnet_model.py:146] round_filter input=112 output=224
I0718 15:54:32.468313 139783477233536 efficientnet_model.py:146] round_filter input=112 output=224
I0718 15:54:32.468503 139783477233536 efficientnet_model.py:146] round_filter input=192 output=384
I0718 15:54:34.713875 139783477233536 efficientnet_model.py:146] round_filter input=192 output=384
I0718 15:54:34.714047 139783477233536 efficientnet_model.py:146] round_filter input=320 output=640
I0718 15:54:35.418475 139783477233536 efficientnet_model.py:146] round_filter input=1280 output=2560
I0718 15:54:35.479586 139783477233536 efficientnet_model.py:459] 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')
[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update
[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold
[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto
[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto
[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size
[ RUN      ] ModelBuilderTF2Test.test_session
[  SKIPPED ] ModelBuilderTF2Test.test_session
[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor
[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture
[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture
[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor
----------------------------------------------------------------------
Ran 20 tests in 62.089s

OK (skipped=1)

Prepare data

To train a robust model, you need a lot of pictures that vary greatly from each other. You can either take the pictures yourself or you can download them from the internet.

After collecting the images you need to label them. For this I recommend using LabelImg - an free, open source graphical image annotation tool.

LabelImg

After labeling the images, split the data into a training and testing part and convert the xml label files to csv using the xml_to_csv.py script.

I uploaded my Microcontroller Detection data-set on Kaggle. The below four cells are used to download and extract the data-set.

In [ ]:
# Install Kaggle API
!pip install -q kaggle
!pip install -q kaggle-cli
     |████████████████████████████████| 81kB 6.4MB/s 
     |████████████████████████████████| 5.3MB 13.9MB/s 
     |████████████████████████████████| 112kB 60.9MB/s 
     |████████████████████████████████| 51kB 7.7MB/s 
     |████████████████████████████████| 122kB 57.3MB/s 
  Building wheel for kaggle-cli (setup.py) ... done
  Building wheel for pyperclip (setup.py) ... done
In [ ]:
# only for google colab
import os
os.environ['KAGGLE_USERNAME'] = "<username>" 
os.environ['KAGGLE_KEY'] = "<key>"
In [ ]:
!kaggle datasets download -d tannergi/microcontroller-detection --unzip
Downloading microcontroller-detection.zip to /content
 60% 5.00M/8.34M [00:00<00:00, 20.5MB/s]
100% 8.34M/8.34M [00:00<00:00, 27.8MB/s]
In [ ]:
!mv "Microcontroller Detection" microcontroller-detection
In [ ]:
!wget https://raw.githubusercontent.com/TannerGilbert/Tensorflow-Object-Detection-API-Train-Model/master/generate_tfrecord.py
--2020-07-18 15:54:55--  https://raw.githubusercontent.com/TannerGilbert/Tensorflow-Object-Detection-API-Train-Model/master/generate_tfrecord.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3382 (3.3K) [text/plain]
Saving to: ‘generate_tfrecord.py’

generate_tfrecord.p 100%[===================>]   3.30K  --.-KB/s    in 0s      

2020-07-18 15:54:55 (53.3 MB/s) - ‘generate_tfrecord.py’ saved [3382/3382]

In [ ]:
!wget https://raw.githubusercontent.com/TannerGilbert/Tensorflow-Object-Detection-API-Train-Model/master/training/labelmap.pbtxt
--2020-07-18 15:54:58--  https://raw.githubusercontent.com/TannerGilbert/Tensorflow-Object-Detection-API-Train-Model/master/training/labelmap.pbtxt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 177 [text/plain]
Saving to: ‘labelmap.pbtxt’

labelmap.pbtxt      100%[===================>]     177  --.-KB/s    in 0s      

2020-07-18 15:54:58 (12.3 MB/s) - ‘labelmap.pbtxt’ saved [177/177]

In [ ]:
!python generate_tfrecord.py --csv_input=microcontroller-detection/train_labels.csv --image_dir=microcontroller-detection/train --output_path=train.record
!python generate_tfrecord.py --csv_input=microcontroller-detection/test_labels.csv --image_dir=microcontroller-detection/test --output_path=test.record
2020-07-18 15:55:01.284990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Successfully created the TFRecords: /content/train.record
2020-07-18 15:55:04.972641: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
Successfully created the TFRecords: /content/test.record
In [ ]:
train_record_path = '/content/train.record'
test_record_path = '/content/test.record'
labelmap_path = '/content/labelmap.pbtxt'

Configuring training

Now that the data is ready it's time to create a training configuration. The OD API supports lots of models, each with its own config file. In this notebook I'm making use of EfficientDet, but you can replace it with any model available in the Tensorflow 2 Detection Model Zoo.

In [ ]:
batch_size = 16
num_steps = 8000
num_eval_steps = 1000
In [ ]:
!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d0_coco17_tpu-32.tar.gz
!tar -xf efficientdet_d0_coco17_tpu-32.tar.gz
--2020-07-18 15:55:08--  http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d0_coco17_tpu-32.tar.gz
Resolving download.tensorflow.org (download.tensorflow.org)... 108.177.126.128, 2a00:1450:4013:c01::80
Connecting to download.tensorflow.org (download.tensorflow.org)|108.177.126.128|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 30736482 (29M) [application/x-tar]
Saving to: ‘efficientdet_d0_coco17_tpu-32.tar.gz’

efficientdet_d0_coc 100%[===================>]  29.31M  71.6MB/s    in 0.4s    

2020-07-18 15:55:09 (71.6 MB/s) - ‘efficientdet_d0_coco17_tpu-32.tar.gz’ saved [30736482/30736482]

In [ ]:
fine_tune_checkpoint = 'efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0'
In [ ]:
!wget https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/configs/tf2/ssd_efficientdet_d0_512x512_coco17_tpu-8.config

base_config_path = 'ssd_efficientdet_d0_512x512_coco17_tpu-8.config'
--2020-07-18 15:55:13--  https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/configs/tf2/ssd_efficientdet_d0_512x512_coco17_tpu-8.config
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4630 (4.5K) [text/plain]
Saving to: ‘ssd_efficientdet_d0_512x512_coco17_tpu-8.config’

ssd_efficientdet_d0 100%[===================>]   4.52K  --.-KB/s    in 0s      

2020-07-18 15:55:13 (74.9 MB/s) - ‘ssd_efficientdet_d0_512x512_coco17_tpu-8.config’ saved [4630/4630]

In [ ]:
# edit configuration file (from https://colab.research.google.com/drive/1sLqFKVV94wm-lglFq_0kGo2ciM0kecWD)

import re

with open(base_config_path) as f:
    config = f.read()

with open('model_config.config', 'w') as f:
  
  # Set labelmap path
  config = re.sub('label_map_path: ".*?"', 
             'label_map_path: "{}"'.format(labelmap_path), config)
  
  # Set fine_tune_checkpoint path
  config = re.sub('fine_tune_checkpoint: ".*?"',
                  'fine_tune_checkpoint: "{}"'.format(fine_tune_checkpoint), config)
  
  # Set train tf-record file path
  config = re.sub('(input_path: ".*?)(PATH_TO_BE_CONFIGURED/train)(.*?")', 
                  'input_path: "{}"'.format(train_record_path), config)
  
  # Set test tf-record file path
  config = re.sub('(input_path: ".*?)(PATH_TO_BE_CONFIGURED/val)(.*?")', 
                  'input_path: "{}"'.format(test_record_path), config)
  
  # Set number of classes.
  config = re.sub('num_classes: [0-9]+',
                  'num_classes: {}'.format(4), config)
  
  # Set batch size
  config = re.sub('batch_size: [0-9]+',
                  'batch_size: {}'.format(batch_size), config)
  
  # Set training steps
  config = re.sub('num_steps: [0-9]+',
                  'num_steps: {}'.format(num_steps), config)
  
  # Set fine-tune checkpoint type to detection
  config = re.sub('fine_tune_checkpoint_type: "classification"', 
             'fine_tune_checkpoint_type: "{}"'.format('detection'), config)
  
  f.write(config)
In [ ]:
%cat model_config.config
 # SSD with EfficientNet-b0 + BiFPN feature extractor,
# shared box predictor and focal loss (a.k.a EfficientDet-d0).
# See EfficientDet, Tan et al, https://arxiv.org/abs/1911.09070
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from an EfficientNet-b0 checkpoint.
#
# Train on TPU-8

model {
  ssd {
    inplace_batchnorm_update: true
    freeze_batchnorm: false
    num_classes: 4
    add_background_class: false
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    encode_background_as_zeros: true
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: [1.0, 2.0, 0.5]
        scales_per_octave: 3
      }
    }
    image_resizer {
      keep_aspect_ratio_resizer {
        min_dimension: 512
        max_dimension: 512
        pad_to_max_dimension: true
        }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        depth: 64
        class_prediction_bias_init: -4.6
        conv_hyperparams {
          force_use_bias: true
          activation: SWISH
          regularizer {
            l2_regularizer {
              weight: 0.00004
            }
          }
          initializer {
            random_normal_initializer {
              stddev: 0.01
              mean: 0.0
            }
          }
          batch_norm {
            scale: true
            decay: 0.99
            epsilon: 0.001
          }
        }
        num_layers_before_predictor: 3
        kernel_size: 3
        use_depthwise: true
      }
    }
    feature_extractor {
      type: 'ssd_efficientnet-b0_bifpn_keras'
      bifpn {
        min_level: 3
        max_level: 7
        num_iterations: 3
        num_filters: 64
      }
      conv_hyperparams {
        force_use_bias: true
        activation: SWISH
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.99,
          epsilon: 0.001,
        }
      }
    }
    loss {
      classification_loss {
        weighted_sigmoid_focal {
          alpha: 0.25
          gamma: 1.5
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    normalize_loc_loss_by_codesize: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.5
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  fine_tune_checkpoint: "efficientdet_d0_coco17_tpu-32/checkpoint/ckpt-0"
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint_type: "detection"
  batch_size: 16
  sync_replicas: true
  startup_delay_steps: 0
  replicas_to_aggregate: 8
  use_bfloat16: true
  num_steps: 8000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_scale_crop_and_pad_to_square {
      output_size: 512
      scale_min: 0.1
      scale_max: 2.0
    }
  }
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: 8e-2
          total_steps: 300000
          warmup_learning_rate: .001
          warmup_steps: 2500
        }
      }
      momentum_optimizer_value: 0.9
    }
    use_moving_average: false
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
}

train_input_reader: {
  label_map_path: "/content/labelmap.pbtxt"
  tf_record_input_reader {
    input_path: "/content/train.record"
  }
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
  batch_size: 16;
}

eval_input_reader: {
  label_map_path: "/content/labelmap.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path: "/content/test.record"
  }
}
In [ ]:
model_dir = 'training/'
pipeline_config_path = 'model_config.config'

Train detector

In [ ]:
!python /content/models/research/object_detection/model_main_tf2.py \
    --pipeline_config_path={pipeline_config_path} \
    --model_dir={model_dir} \
    --alsologtostderr \
    --num_train_steps={num_steps} \
    --sample_1_of_n_eval_examples=1 \
    --num_eval_steps={num_eval_steps}
2020-07-18 15:55:18.541351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 15:55:20.730572: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-07-18 15:55:20.744101: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.744746: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-07-18 15:55:20.744782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 15:55:20.746525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-07-18 15:55:20.754937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-07-18 15:55:20.755316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-07-18 15:55:20.757284: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-07-18 15:55:20.758312: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-07-18 15:55:20.762422: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-07-18 15:55:20.762544: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.763225: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.763788: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-07-18 15:55:20.764113: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F
2020-07-18 15:55:20.769083: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000179999 Hz
2020-07-18 15:55:20.769275: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x23bcd80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-18 15:55:20.769302: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-07-18 15:55:20.856829: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.857598: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x23bcbc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-07-18 15:55:20.857631: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2020-07-18 15:55:20.857811: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.858474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-07-18 15:55:20.858558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 15:55:20.858626: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-07-18 15:55:20.858659: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-07-18 15:55:20.858685: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-07-18 15:55:20.858708: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-07-18 15:55:20.858730: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-07-18 15:55:20.858753: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-07-18 15:55:20.858858: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.859494: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:20.860023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-07-18 15:55:20.860069: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 15:55:21.384629: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-18 15:55:21.384686: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2020-07-18 15:55:21.384701: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2020-07-18 15:55:21.384914: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:21.385568: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 15:55:21.386097: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-07-18 15:55:21.386147: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0718 15:55:21.388301 139647596537728 mirrored_strategy.py:500] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: 8000
I0718 15:55:21.393160 139647596537728 config_util.py:552] Maybe overwriting train_steps: 8000
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0718 15:55:21.393323 139647596537728 config_util.py:552] Maybe overwriting use_bfloat16: False
I0718 15:55:21.409111 139647596537728 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b0
I0718 15:55:21.409232 139647596537728 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 64
I0718 15:55:21.409301 139647596537728 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 3
I0718 15:55:21.419074 139647596537728 efficientnet_model.py:146] round_filter input=32 output=32
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.449922 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.452643 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.460427 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.463117 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.487277 139647596537728 efficientnet_model.py:146] round_filter input=32 output=32
I0718 15:55:21.487384 139647596537728 efficientnet_model.py:146] round_filter input=16 output=16
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.508268 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.510931 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.518724 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.521116 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.590935 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.593564 139647596537728 cross_device_ops.py:440] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
I0718 15:55:21.620893 139647596537728 efficientnet_model.py:146] round_filter input=16 output=16
I0718 15:55:21.620997 139647596537728 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:55:22.095125 139647596537728 efficientnet_model.py:146] round_filter input=24 output=24
I0718 15:55:22.095307 139647596537728 efficientnet_model.py:146] round_filter input=40 output=40
I0718 15:55:22.474871 139647596537728 efficientnet_model.py:146] round_filter input=40 output=40
I0718 15:55:22.475042 139647596537728 efficientnet_model.py:146] round_filter input=80 output=80
I0718 15:55:23.045157 139647596537728 efficientnet_model.py:146] round_filter input=80 output=80
I0718 15:55:23.045339 139647596537728 efficientnet_model.py:146] round_filter input=112 output=112
I0718 15:55:23.627793 139647596537728 efficientnet_model.py:146] round_filter input=112 output=112
I0718 15:55:23.628000 139647596537728 efficientnet_model.py:146] round_filter input=192 output=192
I0718 15:55:24.393873 139647596537728 efficientnet_model.py:146] round_filter input=192 output=192
I0718 15:55:24.394054 139647596537728 efficientnet_model.py:146] round_filter input=320 output=320
I0718 15:55:24.571159 139647596537728 efficientnet_model.py:146] round_filter input=1280 output=1280
I0718 15:55:24.642750 139647596537728 efficientnet_model.py:459] 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')
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0718 15:55:24.849716 139647596537728 dataset_builder.py:83] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
W0718 15:55:24.853544 139647596537728 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/object_detection/builders/dataset_builder.py:175: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
W0718 15:55:24.870084 139647596537728 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/object_detection/builders/dataset_builder.py:175: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.map()
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/object_detection/inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
W0718 15:55:35.259865 139647596537728 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/object_detection/inputs.py:79: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/object_detection/inputs.py:259: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0718 15:55:41.317755 139647596537728 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/object_detection/inputs.py:259: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
2020-07-18 15:56:19.728733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-07-18 15:56:21.173091: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._groundtruth_lists
W0718 15:56:29.761919 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._groundtruth_lists
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads
W0718 15:56:29.762498 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._sorted_head_names
W0718 15:56:29.762557 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._sorted_head_names
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers
W0718 15:56:29.762616 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.0
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.1
W0718 15:56:29.762973 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.2
W0718 15:56:29.763030 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.3
W0718 15:56:29.763087 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.3
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.4
W0718 15:56:29.763144 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings
W0718 15:56:29.763216 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background
W0718 15:56:29.763274 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower
W0718 15:56:29.763345 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower
W0718 15:56:29.763403 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0
W0718 15:56:29.763488 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers
W0718 15:56:29.763558 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4
W0718 15:56:29.763852 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0
W0718 15:56:29.763909 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1
W0718 15:56:29.763967 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2
W0718 15:56:29.764024 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3
W0718 15:56:29.764081 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4
W0718 15:56:29.764138 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0
W0718 15:56:29.764207 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1
W0718 15:56:29.764265 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2
W0718 15:56:29.764327 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0
W0718 15:56:29.764384 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1
W0718 15:56:29.764441 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2
W0718 15:56:29.764498 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.depthwise_kernel
W0718 15:56:29.764617 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.pointwise_kernel
W0718 15:56:29.764679 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.bias
W0718 15:56:29.764738 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0
W0718 15:56:29.764797 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1
W0718 15:56:29.764855 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.2
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4
W0718 15:56:29.764971 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.5
W0718 15:56:29.765028 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7
W0718 15:56:29.765086 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.8
W0718 15:56:29.765143 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1
W0718 15:56:29.765213 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.2
W0718 15:56:29.765273 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4
W0718 15:56:29.765335 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.5
W0718 15:56:29.765393 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7
W0718 15:56:29.765450 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.8
W0718 15:56:29.765506 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1
W0718 15:56:29.765563 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.2
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4
W0718 15:56:29.765678 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.5
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WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7
W0718 15:56:29.765793 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.8
W0718 15:56:29.765850 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1
W0718 15:56:29.765907 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.2
W0718 15:56:29.765964 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4
W0718 15:56:29.766020 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.5
W0718 15:56:29.766077 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7
W0718 15:56:29.810621 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.8
W0718 15:56:29.810745 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1
W0718 15:56:29.810834 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.2
W0718 15:56:29.810914 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4
W0718 15:56:29.810986 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.5
W0718 15:56:29.811067 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7
W0718 15:56:29.811146 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.8
W0718 15:56:29.811270 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1
W0718 15:56:29.811366 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.2
W0718 15:56:29.811450 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4
W0718 15:56:29.811532 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.5
W0718 15:56:29.811616 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7
W0718 15:56:29.811705 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.8
W0718 15:56:29.811781 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1
W0718 15:56:29.811860 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.2
W0718 15:56:29.811955 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4
W0718 15:56:29.812035 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.5
W0718 15:56:29.812120 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7
W0718 15:56:29.812219 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.8
W0718 15:56:29.812301 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1
W0718 15:56:29.812401 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.2
W0718 15:56:29.812483 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4
W0718 15:56:29.812563 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.5
W0718 15:56:29.812645 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7
W0718 15:56:29.812726 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.8
W0718 15:56:29.812806 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1
W0718 15:56:29.812887 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.2
W0718 15:56:29.812968 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4
W0718 15:56:29.813050 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.5
W0718 15:56:29.813129 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7
W0718 15:56:29.813226 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.8
W0718 15:56:29.813346 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1
W0718 15:56:29.813433 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.2
W0718 15:56:29.813519 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.2
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4
W0718 15:56:29.813604 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.5
W0718 15:56:29.813692 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.5
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7
W0718 15:56:29.813778 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.8
W0718 15:56:29.813867 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.8
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.depthwise_kernel
W0718 15:56:29.813955 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.pointwise_kernel
W0718 15:56:29.814041 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.bias
W0718 15:56:29.814128 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.0.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.depthwise_kernel
W0718 15:56:29.814244 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.pointwise_kernel
W0718 15:56:29.814334 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.bias
W0718 15:56:29.814417 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.1.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.depthwise_kernel
W0718 15:56:29.814499 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.pointwise_kernel
W0718 15:56:29.814582 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.bias
W0718 15:56:29.814663 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.BoxPredictionTower.2.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.depthwise_kernel
W0718 15:56:29.814744 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.pointwise_kernel
W0718 15:56:29.814825 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.bias
W0718 15:56:29.814907 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.0.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.depthwise_kernel
W0718 15:56:29.814988 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.pointwise_kernel
W0718 15:56:29.815070 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.bias
W0718 15:56:29.815150 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.1.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.depthwise_kernel
W0718 15:56:29.815248 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.pointwise_kernel
W0718 15:56:29.815335 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.bias
W0718 15:56:29.815419 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower.2.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.depthwise_kernel
W0718 15:56:29.815531 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.depthwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.pointwise_kernel
W0718 15:56:29.815632 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.pointwise_kernel
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias
W0718 15:56:29.815710 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.axis
W0718 15:56:29.815792 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma
W0718 15:56:29.815867 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta
W0718 15:56:29.815943 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_mean
W0718 15:56:29.816020 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_variance
W0718 15:56:29.816096 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.axis
W0718 15:56:29.816186 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma
W0718 15:56:29.816267 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.beta
W0718 15:56:29.816352 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_mean
W0718 15:56:29.816429 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_variance
W0718 15:56:29.816505 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.axis
W0718 15:56:29.816605 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma
W0718 15:56:29.816684 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta
W0718 15:56:29.816772 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean
W0718 15:56:29.816852 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance
W0718 15:56:29.816948 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis
W0718 15:56:29.817027 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma
W0718 15:56:29.817115 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta
W0718 15:56:29.817224 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean
W0718 15:56:29.817324 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance
W0718 15:56:29.817402 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis
W0718 15:56:29.817478 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma
W0718 15:56:29.817553 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta
W0718 15:56:29.817629 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean
W0718 15:56:29.817703 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance
W0718 15:56:29.817779 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis
W0718 15:56:29.817855 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma
W0718 15:56:29.817929 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta
W0718 15:56:29.818004 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean
W0718 15:56:29.818079 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance
W0718 15:56:29.818154 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis
W0718 15:56:29.818247 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma
W0718 15:56:29.818345 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta
W0718 15:56:29.818425 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean
W0718 15:56:29.818501 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance
W0718 15:56:29.818577 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis
W0718 15:56:29.818654 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma
W0718 15:56:29.818728 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta
W0718 15:56:29.818803 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean
W0718 15:56:29.818878 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance
W0718 15:56:29.818954 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis
W0718 15:56:29.819030 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma
W0718 15:56:29.819106 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta
W0718 15:56:29.819215 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean
W0718 15:56:29.819300 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance
W0718 15:56:29.819400 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis
W0718 15:56:29.819477 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma
W0718 15:56:29.819554 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta
W0718 15:56:29.819630 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean
W0718 15:56:29.819706 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance
W0718 15:56:29.819782 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis
W0718 15:56:29.819859 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma
W0718 15:56:29.819932 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta
W0718 15:56:29.820010 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean
W0718 15:56:29.820087 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance
W0718 15:56:29.820164 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis
W0718 15:56:29.820260 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma
W0718 15:56:29.820369 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta
W0718 15:56:29.820448 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean
W0718 15:56:29.820524 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance
W0718 15:56:29.820601 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis
W0718 15:56:29.820678 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma
W0718 15:56:29.820750 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta
W0718 15:56:29.820826 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean
W0718 15:56:29.820902 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance
W0718 15:56:29.820977 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis
W0718 15:56:29.821054 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma
W0718 15:56:29.821150 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta
W0718 15:56:29.821259 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean
W0718 15:56:29.821367 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance
W0718 15:56:29.821450 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis
W0718 15:56:29.821529 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma
W0718 15:56:29.821611 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta
W0718 15:56:29.821691 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean
W0718 15:56:29.821772 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance
W0718 15:56:29.821853 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.axis
W0718 15:56:29.821935 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.gamma
W0718 15:56:29.822015 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.beta
W0718 15:56:29.822103 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_mean
W0718 15:56:29.822192 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_variance
W0718 15:56:29.822274 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.axis
W0718 15:56:29.822359 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.gamma
W0718 15:56:29.822437 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.beta
W0718 15:56:29.822513 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_mean
W0718 15:56:29.822589 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_variance
W0718 15:56:29.822665 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.axis
W0718 15:56:29.822742 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.gamma
W0718 15:56:29.822818 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta
W0718 15:56:29.822895 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_mean
W0718 15:56:29.822970 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_variance
W0718 15:56:29.823045 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.axis
W0718 15:56:29.823122 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.gamma
W0718 15:56:29.823216 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.beta
W0718 15:56:29.823318 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_mean
W0718 15:56:29.823413 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_variance
W0718 15:56:29.823491 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.axis
W0718 15:56:29.823567 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.gamma
W0718 15:56:29.823643 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.beta
W0718 15:56:29.823720 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_mean
W0718 15:56:29.823795 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_variance
W0718 15:56:29.823871 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.axis
W0718 15:56:29.823946 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.gamma
W0718 15:56:29.824021 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta
W0718 15:56:29.824096 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_mean
W0718 15:56:29.824186 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_variance
W0718 15:56:29.824268 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.axis
W0718 15:56:29.824372 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.gamma
W0718 15:56:29.824465 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.beta
W0718 15:56:29.824541 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_mean
W0718 15:56:29.824617 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_variance
W0718 15:56:29.824712 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.axis
W0718 15:56:29.824794 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.gamma
W0718 15:56:29.824875 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.beta
W0718 15:56:29.824963 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_mean
W0718 15:56:29.825040 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_variance
W0718 15:56:29.825116 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.axis
W0718 15:56:29.825203 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma
W0718 15:56:29.825282 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.beta
W0718 15:56:29.825365 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_mean
W0718 15:56:29.825443 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_variance
W0718 15:56:29.825519 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.axis
W0718 15:56:29.825596 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.gamma
W0718 15:56:29.825672 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.beta
W0718 15:56:29.825749 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_mean
W0718 15:56:29.825831 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_variance
W0718 15:56:29.825907 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.axis
W0718 15:56:29.825983 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.gamma
W0718 15:56:29.826059 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta
W0718 15:56:29.826136 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_mean
W0718 15:56:29.826230 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_variance
W0718 15:56:29.826308 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.axis
W0718 15:56:29.826394 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.gamma
W0718 15:56:29.826470 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.beta
W0718 15:56:29.826546 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_mean
W0718 15:56:29.826622 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_variance
W0718 15:56:29.826699 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.axis
W0718 15:56:29.826775 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.gamma
W0718 15:56:29.826850 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.beta
W0718 15:56:29.826926 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_mean
W0718 15:56:29.827002 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_variance
W0718 15:56:29.827077 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.axis
W0718 15:56:29.827152 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma
W0718 15:56:29.827246 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.beta
W0718 15:56:29.827331 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_mean
W0718 15:56:29.827410 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_variance
W0718 15:56:29.827486 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.moving_variance
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.axis
W0718 15:56:29.827564 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.axis
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.gamma
W0718 15:56:29.827640 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.gamma
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.beta
W0718 15:56:29.827715 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.beta
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_mean
W0718 15:56:29.827791 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_mean
WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_variance
W0718 15:56:29.827867 139647596537728 util.py:144] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7.moving_variance
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
W0718 15:56:29.827985 139647596537728 util.py:152] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0718 15:56:29.836683 139647596537728 dataset_builder.py:83] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:Gradients do not exist for variables ['top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.
W0718 15:56:41.910216 139643864684288 optimizer_v2.py:1223] Gradients do not exist for variables ['top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.
WARNING:tensorflow:Gradients do not exist for variables ['top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.
W0718 15:56:55.353491 139643864684288 optimizer_v2.py:1223] Gradients do not exist for variables ['top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.
INFO:tensorflow:Step 100 per-step time 0.963s loss=1.618
I0718 15:58:53.005567 139647596537728 model_lib_v2.py:635] Step 100 per-step time 0.963s loss=1.618
INFO:tensorflow:Step 200 per-step time 0.973s loss=0.983
I0718 16:00:31.596000 139647596537728 model_lib_v2.py:635] Step 200 per-step time 0.973s loss=0.983
INFO:tensorflow:Step 300 per-step time 0.948s loss=0.584
I0718 16:02:10.137982 139647596537728 model_lib_v2.py:635] Step 300 per-step time 0.948s loss=0.584
INFO:tensorflow:Step 400 per-step time 1.031s loss=0.462
I0718 16:03:48.128544 139647596537728 model_lib_v2.py:635] Step 400 per-step time 1.031s loss=0.462
INFO:tensorflow:Step 500 per-step time 1.013s loss=0.371
I0718 16:05:27.004443 139647596537728 model_lib_v2.py:635] Step 500 per-step time 1.013s loss=0.371
INFO:tensorflow:Step 600 per-step time 1.018s loss=0.309
I0718 16:07:05.286844 139647596537728 model_lib_v2.py:635] Step 600 per-step time 1.018s loss=0.309
INFO:tensorflow:Step 700 per-step time 0.952s loss=0.286
I0718 16:08:43.153319 139647596537728 model_lib_v2.py:635] Step 700 per-step time 0.952s loss=0.286
INFO:tensorflow:Step 800 per-step time 0.952s loss=0.268
I0718 16:10:20.226912 139647596537728 model_lib_v2.py:635] Step 800 per-step time 0.952s loss=0.268
INFO:tensorflow:Step 900 per-step time 0.954s loss=0.301
I0718 16:11:58.051970 139647596537728 model_lib_v2.py:635] Step 900 per-step time 0.954s loss=0.301
INFO:tensorflow:Step 1000 per-step time 1.007s loss=0.272
I0718 16:13:35.937324 139647596537728 model_lib_v2.py:635] Step 1000 per-step time 1.007s loss=0.272
INFO:tensorflow:Step 1100 per-step time 0.956s loss=0.262
I0718 16:15:12.069109 139647596537728 model_lib_v2.py:635] Step 1100 per-step time 0.956s loss=0.262
INFO:tensorflow:Step 1200 per-step time 1.039s loss=0.250
I0718 16:16:49.407042 139647596537728 model_lib_v2.py:635] Step 1200 per-step time 1.039s loss=0.250
INFO:tensorflow:Step 1300 per-step time 0.976s loss=0.197
I0718 16:18:26.032222 139647596537728 model_lib_v2.py:635] Step 1300 per-step time 0.976s loss=0.197
INFO:tensorflow:Step 1400 per-step time 0.951s loss=0.237
I0718 16:20:03.382388 139647596537728 model_lib_v2.py:635] Step 1400 per-step time 0.951s loss=0.237
INFO:tensorflow:Step 1500 per-step time 0.977s loss=0.306
I0718 16:21:41.338461 139647596537728 model_lib_v2.py:635] Step 1500 per-step time 0.977s loss=0.306
INFO:tensorflow:Step 1600 per-step time 1.063s loss=0.237
I0718 16:23:20.234978 139647596537728 model_lib_v2.py:635] Step 1600 per-step time 1.063s loss=0.237
INFO:tensorflow:Step 1700 per-step time 0.974s loss=0.213
I0718 16:24:58.889896 139647596537728 model_lib_v2.py:635] Step 1700 per-step time 0.974s loss=0.213
INFO:tensorflow:Step 1800 per-step time 1.016s loss=0.300
I0718 16:26:36.388681 139647596537728 model_lib_v2.py:635] Step 1800 per-step time 1.016s loss=0.300
INFO:tensorflow:Step 1900 per-step time 0.954s loss=0.254
I0718 16:28:14.356321 139647596537728 model_lib_v2.py:635] Step 1900 per-step time 0.954s loss=0.254
INFO:tensorflow:Step 2000 per-step time 0.970s loss=0.242
I0718 16:29:51.866037 139647596537728 model_lib_v2.py:635] Step 2000 per-step time 0.970s loss=0.242
INFO:tensorflow:Step 2100 per-step time 1.017s loss=0.194
I0718 16:31:29.485513 139647596537728 model_lib_v2.py:635] Step 2100 per-step time 1.017s loss=0.194
INFO:tensorflow:Step 2200 per-step time 1.022s loss=0.186
I0718 16:33:07.731748 139647596537728 model_lib_v2.py:635] Step 2200 per-step time 1.022s loss=0.186
INFO:tensorflow:Step 2300 per-step time 1.008s loss=0.309
I0718 16:34:45.874040 139647596537728 model_lib_v2.py:635] Step 2300 per-step time 1.008s loss=0.309
INFO:tensorflow:Step 2400 per-step time 1.015s loss=0.148
I0718 16:36:22.904197 139647596537728 model_lib_v2.py:635] Step 2400 per-step time 1.015s loss=0.148
INFO:tensorflow:Step 2500 per-step time 0.927s loss=0.150
I0718 16:38:01.355399 139647596537728 model_lib_v2.py:635] Step 2500 per-step time 0.927s loss=0.150
INFO:tensorflow:Step 2600 per-step time 0.947s loss=0.297
I0718 16:39:40.349664 139647596537728 model_lib_v2.py:635] Step 2600 per-step time 0.947s loss=0.297
INFO:tensorflow:Step 2700 per-step time 0.967s loss=0.207
I0718 16:41:19.720731 139647596537728 model_lib_v2.py:635] Step 2700 per-step time 0.967s loss=0.207
INFO:tensorflow:Step 2800 per-step time 0.961s loss=0.190
I0718 16:42:57.518537 139647596537728 model_lib_v2.py:635] Step 2800 per-step time 0.961s loss=0.190
INFO:tensorflow:Step 2900 per-step time 1.003s loss=0.212
I0718 16:44:35.050038 139647596537728 model_lib_v2.py:635] Step 2900 per-step time 1.003s loss=0.212
INFO:tensorflow:Step 3000 per-step time 1.063s loss=0.248
I0718 16:46:12.523824 139647596537728 model_lib_v2.py:635] Step 3000 per-step time 1.063s loss=0.248
INFO:tensorflow:Step 3100 per-step time 0.973s loss=0.149
I0718 16:47:48.734216 139647596537728 model_lib_v2.py:635] Step 3100 per-step time 0.973s loss=0.149
INFO:tensorflow:Step 3200 per-step time 0.941s loss=0.140
I0718 16:49:26.268930 139647596537728 model_lib_v2.py:635] Step 3200 per-step time 0.941s loss=0.140
INFO:tensorflow:Step 3300 per-step time 1.040s loss=0.151
I0718 16:51:03.988835 139647596537728 model_lib_v2.py:635] Step 3300 per-step time 1.040s loss=0.151
INFO:tensorflow:Step 3400 per-step time 0.949s loss=0.128
I0718 16:52:41.263417 139647596537728 model_lib_v2.py:635] Step 3400 per-step time 0.949s loss=0.128
INFO:tensorflow:Step 3500 per-step time 1.038s loss=0.147
I0718 16:54:19.543487 139647596537728 model_lib_v2.py:635] Step 3500 per-step time 1.038s loss=0.147
INFO:tensorflow:Step 3600 per-step time 0.931s loss=0.118
I0718 16:55:57.125929 139647596537728 model_lib_v2.py:635] Step 3600 per-step time 0.931s loss=0.118
INFO:tensorflow:Step 3700 per-step time 0.946s loss=0.137
I0718 16:57:34.303002 139647596537728 model_lib_v2.py:635] Step 3700 per-step time 0.946s loss=0.137
INFO:tensorflow:Step 3800 per-step time 0.991s loss=0.131
I0718 16:59:12.041098 139647596537728 model_lib_v2.py:635] Step 3800 per-step time 0.991s loss=0.131
INFO:tensorflow:Step 3900 per-step time 0.990s loss=0.170
I0718 17:00:49.065743 139647596537728 model_lib_v2.py:635] Step 3900 per-step time 0.990s loss=0.170
INFO:tensorflow:Step 4000 per-step time 0.962s loss=0.139
I0718 17:02:26.259342 139647596537728 model_lib_v2.py:635] Step 4000 per-step time 0.962s loss=0.139
INFO:tensorflow:Step 4100 per-step time 0.952s loss=0.147
I0718 17:04:03.287443 139647596537728 model_lib_v2.py:635] Step 4100 per-step time 0.952s loss=0.147
INFO:tensorflow:Step 4200 per-step time 0.958s loss=0.192
I0718 17:05:40.161522 139647596537728 model_lib_v2.py:635] Step 4200 per-step time 0.958s loss=0.192
INFO:tensorflow:Step 4300 per-step time 1.007s loss=0.132
I0718 17:07:18.131650 139647596537728 model_lib_v2.py:635] Step 4300 per-step time 1.007s loss=0.132
INFO:tensorflow:Step 4400 per-step time 0.895s loss=0.155
I0718 17:08:55.491564 139647596537728 model_lib_v2.py:635] Step 4400 per-step time 0.895s loss=0.155
INFO:tensorflow:Step 4500 per-step time 0.936s loss=0.203
I0718 17:10:32.971879 139647596537728 model_lib_v2.py:635] Step 4500 per-step time 0.936s loss=0.203
INFO:tensorflow:Step 4600 per-step time 1.011s loss=0.169
I0718 17:12:09.851675 139647596537728 model_lib_v2.py:635] Step 4600 per-step time 1.011s loss=0.169
INFO:tensorflow:Step 4700 per-step time 0.990s loss=0.142
I0718 17:13:47.197745 139647596537728 model_lib_v2.py:635] Step 4700 per-step time 0.990s loss=0.142
INFO:tensorflow:Step 4800 per-step time 0.929s loss=0.124
I0718 17:15:23.954118 139647596537728 model_lib_v2.py:635] Step 4800 per-step time 0.929s loss=0.124
INFO:tensorflow:Step 4900 per-step time 0.946s loss=0.129
I0718 17:17:00.635682 139647596537728 model_lib_v2.py:635] Step 4900 per-step time 0.946s loss=0.129
INFO:tensorflow:Step 5000 per-step time 0.989s loss=0.110
I0718 17:18:37.576071 139647596537728 model_lib_v2.py:635] Step 5000 per-step time 0.989s loss=0.110
INFO:tensorflow:Step 5100 per-step time 0.916s loss=0.145
I0718 17:20:15.425503 139647596537728 model_lib_v2.py:635] Step 5100 per-step time 0.916s loss=0.145
INFO:tensorflow:Step 5200 per-step time 0.948s loss=0.165
I0718 17:21:52.617373 139647596537728 model_lib_v2.py:635] Step 5200 per-step time 0.948s loss=0.165
INFO:tensorflow:Step 5300 per-step time 0.958s loss=0.180
I0718 17:23:30.207345 139647596537728 model_lib_v2.py:635] Step 5300 per-step time 0.958s loss=0.180
INFO:tensorflow:Step 5400 per-step time 0.927s loss=0.158
I0718 17:25:07.380574 139647596537728 model_lib_v2.py:635] Step 5400 per-step time 0.927s loss=0.158
INFO:tensorflow:Step 5500 per-step time 0.983s loss=0.118
I0718 17:26:44.486347 139647596537728 model_lib_v2.py:635] Step 5500 per-step time 0.983s loss=0.118
INFO:tensorflow:Step 5600 per-step time 0.965s loss=0.112
I0718 17:28:22.722653 139647596537728 model_lib_v2.py:635] Step 5600 per-step time 0.965s loss=0.112
INFO:tensorflow:Step 5700 per-step time 0.966s loss=0.122
I0718 17:30:01.129547 139647596537728 model_lib_v2.py:635] Step 5700 per-step time 0.966s loss=0.122
INFO:tensorflow:Step 5800 per-step time 1.025s loss=0.120
I0718 17:31:41.212644 139647596537728 model_lib_v2.py:635] Step 5800 per-step time 1.025s loss=0.120
INFO:tensorflow:Step 5900 per-step time 0.954s loss=0.105
I0718 17:33:20.486520 139647596537728 model_lib_v2.py:635] Step 5900 per-step time 0.954s loss=0.105
INFO:tensorflow:Step 6000 per-step time 1.005s loss=0.122
I0718 17:35:00.397906 139647596537728 model_lib_v2.py:635] Step 6000 per-step time 1.005s loss=0.122
INFO:tensorflow:Step 6100 per-step time 0.985s loss=0.110
I0718 17:36:40.049550 139647596537728 model_lib_v2.py:635] Step 6100 per-step time 0.985s loss=0.110
INFO:tensorflow:Step 6200 per-step time 1.072s loss=0.108
I0718 17:38:19.901260 139647596537728 model_lib_v2.py:635] Step 6200 per-step time 1.072s loss=0.108
INFO:tensorflow:Step 6300 per-step time 0.970s loss=0.119
I0718 17:39:59.510702 139647596537728 model_lib_v2.py:635] Step 6300 per-step time 0.970s loss=0.119
INFO:tensorflow:Step 6400 per-step time 0.991s loss=0.101
I0718 17:41:39.184242 139647596537728 model_lib_v2.py:635] Step 6400 per-step time 0.991s loss=0.101
INFO:tensorflow:Step 6500 per-step time 1.013s loss=0.127
I0718 17:43:19.403757 139647596537728 model_lib_v2.py:635] Step 6500 per-step time 1.013s loss=0.127
INFO:tensorflow:Step 6600 per-step time 0.938s loss=0.106
I0718 17:44:59.473459 139647596537728 model_lib_v2.py:635] Step 6600 per-step time 0.938s loss=0.106
INFO:tensorflow:Step 6700 per-step time 1.028s loss=0.093
I0718 17:46:39.841076 139647596537728 model_lib_v2.py:635] Step 6700 per-step time 1.028s loss=0.093
INFO:tensorflow:Step 6800 per-step time 1.011s loss=0.117
I0718 17:48:20.955692 139647596537728 model_lib_v2.py:635] Step 6800 per-step time 1.011s loss=0.117
INFO:tensorflow:Step 6900 per-step time 0.978s loss=0.108
I0718 17:50:01.963084 139647596537728 model_lib_v2.py:635] Step 6900 per-step time 0.978s loss=0.108
INFO:tensorflow:Step 7000 per-step time 1.031s loss=0.093
I0718 17:51:42.939702 139647596537728 model_lib_v2.py:635] Step 7000 per-step time 1.031s loss=0.093
INFO:tensorflow:Step 7100 per-step time 1.013s loss=0.111
I0718 17:53:23.458900 139647596537728 model_lib_v2.py:635] Step 7100 per-step time 1.013s loss=0.111
INFO:tensorflow:Step 7200 per-step time 1.048s loss=0.120
I0718 17:55:04.379149 139647596537728 model_lib_v2.py:635] Step 7200 per-step time 1.048s loss=0.120
INFO:tensorflow:Step 7300 per-step time 1.031s loss=0.113
I0718 17:56:45.987596 139647596537728 model_lib_v2.py:635] Step 7300 per-step time 1.031s loss=0.113
INFO:tensorflow:Step 7400 per-step time 1.027s loss=0.105
I0718 17:58:27.480935 139647596537728 model_lib_v2.py:635] Step 7400 per-step time 1.027s loss=0.105
INFO:tensorflow:Step 7500 per-step time 1.141s loss=0.098
I0718 18:00:08.795779 139647596537728 model_lib_v2.py:635] Step 7500 per-step time 1.141s loss=0.098
INFO:tensorflow:Step 7600 per-step time 1.074s loss=0.116
I0718 18:01:50.352721 139647596537728 model_lib_v2.py:635] Step 7600 per-step time 1.074s loss=0.116
INFO:tensorflow:Step 7700 per-step time 1.087s loss=0.126
I0718 18:03:31.442188 139647596537728 model_lib_v2.py:635] Step 7700 per-step time 1.087s loss=0.126
INFO:tensorflow:Step 7800 per-step time 0.987s loss=0.116
I0718 18:05:12.665795 139647596537728 model_lib_v2.py:635] Step 7800 per-step time 0.987s loss=0.116
INFO:tensorflow:Step 7900 per-step time 1.020s loss=0.098
I0718 18:06:54.143270 139647596537728 model_lib_v2.py:635] Step 7900 per-step time 1.020s loss=0.098
INFO:tensorflow:Step 8000 per-step time 0.951s loss=0.104
I0718 18:08:34.783083 139647596537728 model_lib_v2.py:635] Step 8000 per-step time 0.951s loss=0.104
In [ ]:
%load_ext tensorboard
%tensorboard --logdir '/content/training/train'

Export model inference graph

In [ ]:
output_directory = 'inference_graph'

!python /content/models/research/object_detection/exporter_main_v2.py \
    --trained_checkpoint_dir {model_dir} \
    --output_directory {output_directory} \
    --pipeline_config_path {pipeline_config_path}
2020-07-18 18:08:42.450130: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
I0718 18:08:46.165607 140076592244608 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet EfficientNet backbone version: efficientnet-b0
I0718 18:08:46.165880 140076592244608 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num filters: 64
I0718 18:08:46.166004 140076592244608 ssd_efficientnet_bifpn_feature_extractor.py:147] EfficientDet BiFPN num iterations: 3
I0718 18:08:46.182794 140076592244608 efficientnet_model.py:146] round_filter input=32 output=32
2020-07-18 18:08:46.199456: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-07-18 18:08:46.233530: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.234444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-07-18 18:08:46.234508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 18:08:46.238096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-07-18 18:08:46.240872: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-07-18 18:08:46.241611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-07-18 18:08:46.244906: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-07-18 18:08:46.252590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-07-18 18:08:46.259536: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-07-18 18:08:46.259688: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.260626: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.261449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-07-18 18:08:46.261832: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F
2020-07-18 18:08:46.270587: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000179999 Hz
2020-07-18 18:08:46.270846: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1ef4d80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-07-18 18:08:46.270892: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-07-18 18:08:46.402611: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.403746: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1ef4bc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-07-18 18:08:46.403784: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2020-07-18 18:08:46.404057: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.404912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-07-18 18:08:46.404971: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 18:08:46.405042: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-07-18 18:08:46.405067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-07-18 18:08:46.405088: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-07-18 18:08:46.405113: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-07-18 18:08:46.405133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-07-18 18:08:46.405156: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-07-18 18:08:46.405264: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.406338: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:46.407156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-07-18 18:08:46.407264: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-07-18 18:08:47.115951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-07-18 18:08:47.116008: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 
2020-07-18 18:08:47.116021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N 
2020-07-18 18:08:47.116274: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:47.116933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-07-18 18:08:47.117559: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-07-18 18:08:47.117605: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
I0718 18:08:47.169928 140076592244608 efficientnet_model.py:146] round_filter input=32 output=32
I0718 18:08:47.170113 140076592244608 efficientnet_model.py:146] round_filter input=16 output=16
I0718 18:08:47.278512 140076592244608 efficientnet_model.py:146] round_filter input=16 output=16
I0718 18:08:47.278703 140076592244608 efficientnet_model.py:146] round_filter input=24 output=24
I0718 18:08:47.579567 140076592244608 efficientnet_model.py:146] round_filter input=24 output=24
I0718 18:08:47.579728 140076592244608 efficientnet_model.py:146] round_filter input=40 output=40
I0718 18:08:47.875831 140076592244608 efficientnet_model.py:146] round_filter input=40 output=40
I0718 18:08:47.876023 140076592244608 efficientnet_model.py:146] round_filter input=80 output=80
I0718 18:08:48.427931 140076592244608 efficientnet_model.py:146] round_filter input=80 output=80
I0718 18:08:48.428107 140076592244608 efficientnet_model.py:146] round_filter input=112 output=112
I0718 18:08:48.882806 140076592244608 efficientnet_model.py:146] round_filter input=112 output=112
I0718 18:08:48.882960 140076592244608 efficientnet_model.py:146] round_filter input=192 output=192
I0718 18:08:49.500107 140076592244608 efficientnet_model.py:146] round_filter input=192 output=192
I0718 18:08:49.500282 140076592244608 efficientnet_model.py:146] round_filter input=320 output=320
I0718 18:08:49.637418 140076592244608 efficientnet_model.py:146] round_filter input=1280 output=1280
I0718 18:08:49.707412 140076592244608 efficientnet_model.py:459] 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')
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f654d31ae80>, because it is not built.
W0718 18:09:16.331918 140076592244608 save_impl.py:76] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f654d31ae80>, because it is not built.
2020-07-18 18:09:43.326048: W tensorflow/python/util/util.cc:329] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:03.976670 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:03.977004 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:03.977253 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:03.977445 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:08.289783 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:08.290111 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:08.290342 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:08.290518 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:08.290705 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:08.290864 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:10.784348 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:10.784663 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:10.784868 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:10.785037 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:11.120451 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:11.120755 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:11.120958 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:11.121135 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:11.121331 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:11.121485 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
W0718 18:10:11.873308 140076592244608 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:19.641722 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348a58>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348ac8>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540348dd8>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:19.642035 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d198>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d208>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654034d518>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
I0718 18:10:19.642466 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f28>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f98>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f6540649f60>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], False), {}).
INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
I0718 18:10:19.642683 140076592244608 def_function.py:830] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b8d0>, TensorSpec(shape=(None, 64, 64, 40), dtype=tf.float32, name='feature_pyramid/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b860>, TensorSpec(shape=(None, 32, 32, 112), dtype=tf.float32, name='feature_pyramid/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f654095b588>, TensorSpec(shape=(None, 16, 16, 320), dtype=tf.float32, name='feature_pyramid/2/1'))], True), {}).
INFO:tensorflow:Assets written to: inference_graph/saved_model/assets
I0718 18:10:20.931694 140076592244608 builder_impl.py:775] Assets written to: inference_graph/saved_model/assets
INFO:tensorflow:Writing pipeline config file to inference_graph/pipeline.config
I0718 18:10:22.774637 140076592244608 config_util.py:254] Writing pipeline config file to inference_graph/pipeline.config
In [ ]:
from google.colab import files
files.download(f'/content/{output_directory}/saved_model/saved_model.pb') 

Test trained model on test images

based on Object Detection API Demo and Inference from saved model tf2 colab.

In [ ]:
import io
import os
import scipy.misc
import numpy as np
import six
import time
import glob
from IPython.display import display

from six import BytesIO

import matplotlib
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont

import tensorflow as tf
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

%matplotlib inline
In [ ]:
def load_image_into_numpy_array(path):
  """Load an image from file into a numpy array.

  Puts image into numpy array to feed into tensorflow graph.
  Note that by convention we put it into a numpy array with shape
  (height, width, channels), where channels=3 for RGB.

  Args:
    path: a file path (this can be local or on colossus)

  Returns:
    uint8 numpy array with shape (img_height, img_width, 3)
  """
  img_data = tf.io.gfile.GFile(path, 'rb').read()
  image = Image.open(BytesIO(img_data))
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)
In [ ]:
category_index = label_map_util.create_category_index_from_labelmap(labelmap_path, use_display_name=True)
In [ ]:
tf.keras.backend.clear_session()
model = tf.saved_model.load(f'/content/{output_directory}/saved_model')
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WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_91461) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_bifpn_layer_call_and_return_conditional_losses_70203) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference___call___22430) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
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WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:tensorflow:Importing a function (__inference_EfficientDet-D0_layer_call_and_return_conditional_losses_95375) with ops with custom gradients. Will likely fail if a gradient is requested.
In [ ]:
def run_inference_for_single_image(model, image):
  image = np.asarray(image)
  # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
  input_tensor = tf.convert_to_tensor(image)
  # The model expects a batch of images, so add an axis with `tf.newaxis`.
  input_tensor = input_tensor[tf.newaxis,...]

  # Run inference
  model_fn = model.signatures['serving_default']
  output_dict = model_fn(input_tensor)

  # All outputs are batches tensors.
  # Convert to numpy arrays, and take index [0] to remove the batch dimension.
  # We're only interested in the first num_detections.
  num_detections = int(output_dict.pop('num_detections'))
  output_dict = {key:value[0, :num_detections].numpy() 
                 for key,value in output_dict.items()}
  output_dict['num_detections'] = num_detections

  # detection_classes should be ints.
  output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
   
  # Handle models with masks:
  if 'detection_masks' in output_dict:
    # Reframe the the bbox mask to the image size.
    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
              output_dict['detection_masks'], output_dict['detection_boxes'],
               image.shape[0], image.shape[1])      
    detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
                                       tf.uint8)
    output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
    
  return output_dict
In [ ]:
for image_path in glob.glob('microcontroller-detection/test/*.jpg'):
  image_np = load_image_into_numpy_array(image_path)
  output_dict = run_inference_for_single_image(model, image_np)
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,
      output_dict['detection_boxes'],
      output_dict['detection_classes'],
      output_dict['detection_scores'],
      category_index,
      instance_masks=output_dict.get('detection_masks_reframed', None),
      use_normalized_coordinates=True,
      line_thickness=8)
  display(Image.fromarray(image_np))