logo_detection_colab.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"logo_detection.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMeVF7N7KtEyvzqWki6HQLu"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"F4Djw6Kin8c-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617955489170,"user_tz":-180,"elapsed":89776,"user":{"displayName":"Κοχλιαρίδης Βασίλης","photoUrl":"","userId":"17222540378759389139"}},"outputId":"8b1158c2-8f26-4f76-fd79-0140bd518ed0"},"source":["# Installation of object detection API.\n","# Following Tutorial: https://colab.research.google.com/github/Tony607/object_detection_demo/blob/master/tensorflow_object_detection_training_colab.ipynb#scrollTo=ecpHEnka8Kix\n","%cd /content\n","!apt-get update\n","!git clone --quiet https://github.com/tensorflow/models.git\n","!apt-get install -qq protobuf-compiler python-pil python-lxml python-tk\n","!pip install -q Cython contextlib2 pillow lxml matplotlib\n","!pip install -q pycocotools\n","\n","%cd /content/models/research\n","\n","!protoc object_detection/protos/*.proto --python_out=.\n","!cp object_detection/packages/tf2/setup.py .\n","!python -m pip install ."],"execution_count":1,"outputs":[{"output_type":"stream","text":["/content\n","Get:1 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Get:2 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Hit:3 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:4 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Hit:5 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:6 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n","Get:7 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease [15.9 kB]\n","Get:8 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Hit:9 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Ign:10 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Ign:11 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Hit:12 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  Release\n","Hit:13 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Get:14 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [1,750 kB]\n","Get:15 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [895 kB]\n","Get:16 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [2,476 kB]\n","Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,173 kB]\n","Get:18 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic/main amd64 Packages [39.5 kB]\n","Fetched 7,621 kB in 3s (2,995 kB/s)\n","Reading package lists... 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(0.3.3)\n","Requirement already satisfied: tensorboard~=2.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (2.4.1)\n","Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (1.1.2)\n","Requirement already satisfied: h5py~=2.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (2.10.0)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.6.7->tf-models-official->object-detection==0.1) (4.2.1)\n","Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official->object-detection==0.1) (1.53.0)\n","Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.6.7->tf-models-official->object-detection==0.1) (20.9)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval->tf-models-official->object-detection==0.1) (1.0.1)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle>=1.3.9->tf-models-official->object-detection==0.1) (1.3)\n","Requirement already satisfied: zipp>=0.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-resources; python_version < \"3.9\"->tensorflow-datasets->tf-models-official->object-detection==0.1) (3.4.1)\n","Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (1.8.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (1.0.1)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (0.4.3)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (3.3.4)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (1.3.0)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (3.8.1)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (3.1.0)\n","Building wheels for collected packages: object-detection, avro-python3, dill, future, py-cpuinfo, seqeval\n","  Building wheel for object-detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for object-detection: filename=object_detection-0.1-cp37-none-any.whl size=1643278 sha256=aa78cba82f2f26d3f8bd26151bdd7f8bd59f5af3206a6629a15d3d83b45df574\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-kvzfb3he/wheels/94/49/4b/39b051683087a22ef7e80ec52152a27249d1a644ccf4e442ea\n","  Building wheel for avro-python3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for avro-python3: filename=avro_python3-1.10.2-cp37-none-any.whl size=44011 sha256=da3360bbfb15d48e58ad23f6debcbd243e6b9daeb1c832558976f9b0e3ea4b13\n","  Stored in directory: /root/.cache/pip/wheels/ee/ee/18/c466221ca6900e3efce2f4ea9c329288808679aecdcb2838d3\n","  Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for dill: filename=dill-0.3.1.1-cp37-none-any.whl size=78532 sha256=58107bed75cf6313703cea54f7be2c784110a2dc075df38c50705d3963abd50c\n","  Stored in directory: /root/.cache/pip/wheels/59/b1/91/f02e76c732915c4015ab4010f3015469866c1eb9b14058d8e7\n","  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for future: filename=future-0.18.2-cp37-none-any.whl size=491058 sha256=d8176a00da3cc32291c82b08fbf183e7e3a4f36f210298cd0db36cfe5afb251f\n","  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n","  Building wheel for py-cpuinfo (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for py-cpuinfo: filename=py_cpuinfo-7.0.0-cp37-none-any.whl size=20070 sha256=852e073bca111289c9b973a3806119147463bceb3de72dcdd76e275e78a9187d\n","  Stored in directory: /root/.cache/pip/wheels/f1/93/7b/127daf0c3a5a49feb2fecd468d508067c733fba5192f726ad1\n","  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for seqeval: filename=seqeval-1.2.2-cp37-none-any.whl size=16172 sha256=1277face7866c62ced7ff842e9a6ea157991c6e52e4b0775c33d3d8cb9e509d0\n","  Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f\n","Successfully built object-detection avro-python3 dill future py-cpuinfo seqeval\n","\u001b[31mERROR: multiprocess 0.70.11.1 has requirement dill>=0.3.3, but you'll have dill 0.3.1.1 which is incompatible.\u001b[0m\n","\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n","\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n","\u001b[31mERROR: apache-beam 2.28.0 has requirement avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you'll have avro-python3 1.10.2 which is incompatible.\u001b[0m\n","Installing collected packages: avro-python3, requests, hdfs, pbr, mock, dill, future, fastavro, pyarrow, apache-beam, tf-slim, lvis, opencv-python-headless, py-cpuinfo, seqeval, dataclasses, pyyaml, tensorflow-addons, tensorflow-model-optimization, sentencepiece, tf-models-official, object-detection\n","  Found existing installation: requests 2.23.0\n","    Uninstalling requests-2.23.0:\n","      Successfully uninstalled requests-2.23.0\n","  Found existing installation: dill 0.3.3\n","    Uninstalling dill-0.3.3:\n","      Successfully uninstalled dill-0.3.3\n","  Found existing installation: future 0.16.0\n","    Uninstalling future-0.16.0:\n","      Successfully uninstalled future-0.16.0\n","  Found existing installation: pyarrow 3.0.0\n","    Uninstalling pyarrow-3.0.0:\n","      Successfully uninstalled pyarrow-3.0.0\n","  Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed apache-beam-2.28.0 avro-python3-1.10.2 dataclasses-0.6 dill-0.3.1.1 fastavro-1.3.5 future-0.18.2 hdfs-2.6.0 lvis-0.5.3 mock-2.0.0 object-detection-0.1 opencv-python-headless-4.5.1.48 pbr-5.5.1 py-cpuinfo-7.0.0 pyarrow-2.0.0 pyyaml-5.4.1 requests-2.25.1 sentencepiece-0.1.95 seqeval-1.2.2 tensorflow-addons-0.12.1 tensorflow-model-optimization-0.5.0 tf-models-official-2.4.0 tf-slim-1.1.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"idJ2At34pYkM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617955537033,"user_tz":-180,"elapsed":40964,"user":{"displayName":"Κοχλιαρίδης Βασίλης","photoUrl":"","userId":"17222540378759389139"}},"outputId":"34653dd6-e718-4475-edaa-d1f476035e88"},"source":["# Testing the installation of Object Detection API.\n","# Output should be: Ran 21 tests, OK: Skipped=1.\n","import os\n","%cd /content/models/research\n","!python object_detection/builders/model_builder_tf2_test.py"],"execution_count":2,"outputs":[{"output_type":"stream","text":["/content/models/research\n","2021-04-09 08:04:57.567915: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","Running tests under Python 3.7.10: /usr/bin/python3\n","[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model\n","2021-04-09 08:05:00.951036: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-04-09 08:05:00.952246: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n","2021-04-09 08:05:01.015258: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:01.016031: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7\n","coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s\n","2021-04-09 08:05:01.016078: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:05:01.151171: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-04-09 08:05:01.151295: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-04-09 08:05:01.298879: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-04-09 08:05:01.330108: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-04-09 08:05:01.588174: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-04-09 08:05:01.636300: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-04-09 08:05:01.640646: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-04-09 08:05:01.640824: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:01.641690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:01.645816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-04-09 08:05:01.646410: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-04-09 08:05:01.646606: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:01.647348: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7\n","coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s\n","2021-04-09 08:05:01.647386: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:05:01.647458: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-04-09 08:05:01.647506: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-04-09 08:05:01.647553: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-04-09 08:05:01.647594: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-04-09 08:05:01.647634: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-04-09 08:05:01.647703: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-04-09 08:05:01.647762: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-04-09 08:05:01.647886: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:01.648693: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:01.649374: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-04-09 08:05:01.652593: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:05:05.947106: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-04-09 08:05:05.947175: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-04-09 08:05:05.947198: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-04-09 08:05:05.954497: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:05.955334: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:05.956150: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:05:05.956872: 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.\n","2021-04-09 08:05:05.956942: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10637 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 5.95s\n","I0409 08:05:06.615354 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model): 5.95s\n","[       OK ] ModelBuilderTF2Test.test_create_center_net_model\n","[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.41s\n","I0409 08:05:07.022677 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.41s\n","[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n","[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n","I0409 08:05:07.023529 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_create_experimental_model\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n","I0409 08:05:07.055417 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n","I0409 08:05:07.079393 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n","I0409 08:05:07.103634 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.02s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.18s\n","I0409 08:05:07.284934 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.18s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.16s\n","I0409 08:05:07.449416 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.16s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.17s\n","I0409 08:05:07.617845 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.17s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n","[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.18s\n","I0409 08:05:07.798827 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.18s\n","[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n","[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.17s\n","I0409 08:05:07.972729 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.17s\n","[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n","[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.05s\n","I0409 08:05:08.025883 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.05s\n","[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n","[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n","I0409 08:05:08.387215 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n","I0409 08:05:08.387429 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n","I0409 08:05:08.387535 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n","I0409 08:05:08.393871 140318678677376 efficientnet_model.py:147] round_filter input=32 output=32\n","I0409 08:05:08.443757 140318678677376 efficientnet_model.py:147] round_filter input=32 output=32\n","I0409 08:05:08.443930 140318678677376 efficientnet_model.py:147] round_filter input=16 output=16\n","I0409 08:05:08.529484 140318678677376 efficientnet_model.py:147] round_filter input=16 output=16\n","I0409 08:05:08.529693 140318678677376 efficientnet_model.py:147] round_filter input=24 output=24\n","I0409 08:05:08.723135 140318678677376 efficientnet_model.py:147] round_filter input=24 output=24\n","I0409 08:05:08.723359 140318678677376 efficientnet_model.py:147] round_filter input=40 output=40\n","I0409 08:05:08.914937 140318678677376 efficientnet_model.py:147] round_filter input=40 output=40\n","I0409 08:05:08.915148 140318678677376 efficientnet_model.py:147] round_filter input=80 output=80\n","I0409 08:05:09.207387 140318678677376 efficientnet_model.py:147] round_filter input=80 output=80\n","I0409 08:05:09.207626 140318678677376 efficientnet_model.py:147] round_filter input=112 output=112\n","I0409 08:05:09.506832 140318678677376 efficientnet_model.py:147] round_filter input=112 output=112\n","I0409 08:05:09.507121 140318678677376 efficientnet_model.py:147] round_filter input=192 output=192\n","I0409 08:05:09.993500 140318678677376 efficientnet_model.py:147] round_filter input=192 output=192\n","I0409 08:05:09.993731 140318678677376 efficientnet_model.py:147] round_filter input=320 output=320\n","I0409 08:05:10.083311 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=1280\n","I0409 08:05:10.127873 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:10.209890 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n","I0409 08:05:10.210074 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n","I0409 08:05:10.210197 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n","I0409 08:05:10.215226 140318678677376 efficientnet_model.py:147] round_filter input=32 output=32\n","I0409 08:05:10.238002 140318678677376 efficientnet_model.py:147] round_filter input=32 output=32\n","I0409 08:05:10.238174 140318678677376 efficientnet_model.py:147] round_filter input=16 output=16\n","I0409 08:05:10.393918 140318678677376 efficientnet_model.py:147] round_filter input=16 output=16\n","I0409 08:05:10.394099 140318678677376 efficientnet_model.py:147] round_filter input=24 output=24\n","I0409 08:05:10.674160 140318678677376 efficientnet_model.py:147] round_filter input=24 output=24\n","I0409 08:05:10.674381 140318678677376 efficientnet_model.py:147] round_filter input=40 output=40\n","I0409 08:05:10.954687 140318678677376 efficientnet_model.py:147] round_filter input=40 output=40\n","I0409 08:05:10.954908 140318678677376 efficientnet_model.py:147] round_filter input=80 output=80\n","I0409 08:05:11.340251 140318678677376 efficientnet_model.py:147] round_filter input=80 output=80\n","I0409 08:05:11.340467 140318678677376 efficientnet_model.py:147] round_filter input=112 output=112\n","I0409 08:05:11.708379 140318678677376 efficientnet_model.py:147] round_filter input=112 output=112\n","I0409 08:05:11.708688 140318678677376 efficientnet_model.py:147] round_filter input=192 output=192\n","I0409 08:05:12.164279 140318678677376 efficientnet_model.py:147] round_filter input=192 output=192\n","I0409 08:05:12.164492 140318678677376 efficientnet_model.py:147] round_filter input=320 output=320\n","I0409 08:05:12.356348 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=1280\n","I0409 08:05:12.391298 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:12.492178 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n","I0409 08:05:12.492404 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n","I0409 08:05:12.492565 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n","I0409 08:05:12.497820 140318678677376 efficientnet_model.py:147] round_filter input=32 output=32\n","I0409 08:05:12.516844 140318678677376 efficientnet_model.py:147] round_filter input=32 output=32\n","I0409 08:05:12.517060 140318678677376 efficientnet_model.py:147] round_filter input=16 output=16\n","I0409 08:05:12.664510 140318678677376 efficientnet_model.py:147] round_filter input=16 output=16\n","I0409 08:05:12.664728 140318678677376 efficientnet_model.py:147] round_filter input=24 output=24\n","I0409 08:05:12.952893 140318678677376 efficientnet_model.py:147] round_filter input=24 output=24\n","I0409 08:05:12.953103 140318678677376 efficientnet_model.py:147] round_filter input=40 output=48\n","I0409 08:05:13.235241 140318678677376 efficientnet_model.py:147] round_filter input=40 output=48\n","I0409 08:05:13.235476 140318678677376 efficientnet_model.py:147] round_filter input=80 output=88\n","I0409 08:05:13.767797 140318678677376 efficientnet_model.py:147] round_filter input=80 output=88\n","I0409 08:05:13.768036 140318678677376 efficientnet_model.py:147] round_filter input=112 output=120\n","I0409 08:05:14.164850 140318678677376 efficientnet_model.py:147] round_filter input=112 output=120\n","I0409 08:05:14.165067 140318678677376 efficientnet_model.py:147] round_filter input=192 output=208\n","I0409 08:05:14.651765 140318678677376 efficientnet_model.py:147] round_filter input=192 output=208\n","I0409 08:05:14.651997 140318678677376 efficientnet_model.py:147] round_filter input=320 output=352\n","I0409 08:05:14.843280 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=1408\n","I0409 08:05:14.884400 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:14.977562 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n","I0409 08:05:14.977742 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n","I0409 08:05:14.977848 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n","I0409 08:05:14.982881 140318678677376 efficientnet_model.py:147] round_filter input=32 output=40\n","I0409 08:05:15.002264 140318678677376 efficientnet_model.py:147] round_filter input=32 output=40\n","I0409 08:05:15.002404 140318678677376 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:05:15.151209 140318678677376 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:05:15.151429 140318678677376 efficientnet_model.py:147] round_filter input=24 output=32\n","I0409 08:05:15.443192 140318678677376 efficientnet_model.py:147] round_filter input=24 output=32\n","I0409 08:05:15.443400 140318678677376 efficientnet_model.py:147] round_filter input=40 output=48\n","I0409 08:05:15.734965 140318678677376 efficientnet_model.py:147] round_filter input=40 output=48\n","I0409 08:05:15.735183 140318678677376 efficientnet_model.py:147] round_filter input=80 output=96\n","I0409 08:05:16.215502 140318678677376 efficientnet_model.py:147] round_filter input=80 output=96\n","I0409 08:05:16.215760 140318678677376 efficientnet_model.py:147] round_filter input=112 output=136\n","I0409 08:05:16.711775 140318678677376 efficientnet_model.py:147] round_filter input=112 output=136\n","I0409 08:05:16.711976 140318678677376 efficientnet_model.py:147] round_filter input=192 output=232\n","I0409 08:05:17.300191 140318678677376 efficientnet_model.py:147] round_filter input=192 output=232\n","I0409 08:05:17.300471 140318678677376 efficientnet_model.py:147] round_filter input=320 output=384\n","I0409 08:05:17.490301 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=1536\n","I0409 08:05:17.523353 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:17.632429 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n","I0409 08:05:17.632624 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n","I0409 08:05:17.632732 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n","I0409 08:05:17.637553 140318678677376 efficientnet_model.py:147] round_filter input=32 output=48\n","I0409 08:05:17.658103 140318678677376 efficientnet_model.py:147] round_filter input=32 output=48\n","I0409 08:05:17.658239 140318678677376 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:05:17.803352 140318678677376 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:05:17.803552 140318678677376 efficientnet_model.py:147] round_filter input=24 output=32\n","I0409 08:05:18.171132 140318678677376 efficientnet_model.py:147] round_filter input=24 output=32\n","I0409 08:05:18.171338 140318678677376 efficientnet_model.py:147] round_filter input=40 output=56\n","I0409 08:05:18.792259 140318678677376 efficientnet_model.py:147] round_filter input=40 output=56\n","I0409 08:05:18.792474 140318678677376 efficientnet_model.py:147] round_filter input=80 output=112\n","I0409 08:05:19.371341 140318678677376 efficientnet_model.py:147] round_filter input=80 output=112\n","I0409 08:05:19.371597 140318678677376 efficientnet_model.py:147] round_filter input=112 output=160\n","I0409 08:05:19.949872 140318678677376 efficientnet_model.py:147] round_filter input=112 output=160\n","I0409 08:05:19.950096 140318678677376 efficientnet_model.py:147] round_filter input=192 output=272\n","I0409 08:05:20.714380 140318678677376 efficientnet_model.py:147] round_filter input=192 output=272\n","I0409 08:05:20.714709 140318678677376 efficientnet_model.py:147] round_filter input=320 output=448\n","I0409 08:05:20.897584 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=1792\n","I0409 08:05:20.931081 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:21.046076 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n","I0409 08:05:21.046320 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n","I0409 08:05:21.046441 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n","I0409 08:05:21.051365 140318678677376 efficientnet_model.py:147] round_filter input=32 output=48\n","I0409 08:05:21.071342 140318678677376 efficientnet_model.py:147] round_filter input=32 output=48\n","I0409 08:05:21.071496 140318678677376 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:05:21.321293 140318678677376 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:05:21.321516 140318678677376 efficientnet_model.py:147] round_filter input=24 output=40\n","I0409 08:05:21.799882 140318678677376 efficientnet_model.py:147] round_filter input=24 output=40\n","I0409 08:05:21.800100 140318678677376 efficientnet_model.py:147] round_filter input=40 output=64\n","I0409 08:05:22.306603 140318678677376 efficientnet_model.py:147] round_filter input=40 output=64\n","I0409 08:05:22.306859 140318678677376 efficientnet_model.py:147] round_filter input=80 output=128\n","I0409 08:05:22.984377 140318678677376 efficientnet_model.py:147] round_filter input=80 output=128\n","I0409 08:05:22.984611 140318678677376 efficientnet_model.py:147] round_filter input=112 output=176\n","I0409 08:05:23.848069 140318678677376 efficientnet_model.py:147] round_filter input=112 output=176\n","I0409 08:05:23.848270 140318678677376 efficientnet_model.py:147] round_filter input=192 output=304\n","I0409 08:05:24.725726 140318678677376 efficientnet_model.py:147] round_filter input=192 output=304\n","I0409 08:05:24.725956 140318678677376 efficientnet_model.py:147] round_filter input=320 output=512\n","I0409 08:05:25.012452 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=2048\n","I0409 08:05:25.051733 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:25.175664 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n","I0409 08:05:25.175878 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n","I0409 08:05:25.175987 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n","I0409 08:05:25.180776 140318678677376 efficientnet_model.py:147] round_filter input=32 output=56\n","I0409 08:05:25.199652 140318678677376 efficientnet_model.py:147] round_filter input=32 output=56\n","I0409 08:05:25.199813 140318678677376 efficientnet_model.py:147] round_filter input=16 output=32\n","I0409 08:05:25.434848 140318678677376 efficientnet_model.py:147] round_filter input=16 output=32\n","I0409 08:05:25.435059 140318678677376 efficientnet_model.py:147] round_filter input=24 output=40\n","I0409 08:05:26.008742 140318678677376 efficientnet_model.py:147] round_filter input=24 output=40\n","I0409 08:05:26.008941 140318678677376 efficientnet_model.py:147] round_filter input=40 output=72\n","I0409 08:05:26.581878 140318678677376 efficientnet_model.py:147] round_filter input=40 output=72\n","I0409 08:05:26.582073 140318678677376 efficientnet_model.py:147] round_filter input=80 output=144\n","I0409 08:05:27.347131 140318678677376 efficientnet_model.py:147] round_filter input=80 output=144\n","I0409 08:05:27.347350 140318678677376 efficientnet_model.py:147] round_filter input=112 output=200\n","I0409 08:05:28.115521 140318678677376 efficientnet_model.py:147] round_filter input=112 output=200\n","I0409 08:05:28.115744 140318678677376 efficientnet_model.py:147] round_filter input=192 output=344\n","I0409 08:05:29.380634 140318678677376 efficientnet_model.py:147] round_filter input=192 output=344\n","I0409 08:05:29.380853 140318678677376 efficientnet_model.py:147] round_filter input=320 output=576\n","I0409 08:05:29.672312 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=2304\n","I0409 08:05:29.705785 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","I0409 08:05:29.852132 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n","I0409 08:05:29.852339 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n","I0409 08:05:29.852479 140318678677376 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n","I0409 08:05:29.857555 140318678677376 efficientnet_model.py:147] round_filter input=32 output=64\n","I0409 08:05:29.877781 140318678677376 efficientnet_model.py:147] round_filter input=32 output=64\n","I0409 08:05:29.877944 140318678677376 efficientnet_model.py:147] round_filter input=16 output=32\n","I0409 08:05:30.186811 140318678677376 efficientnet_model.py:147] round_filter input=16 output=32\n","I0409 08:05:30.187041 140318678677376 efficientnet_model.py:147] round_filter input=24 output=48\n","I0409 08:05:30.858807 140318678677376 efficientnet_model.py:147] round_filter input=24 output=48\n","I0409 08:05:30.859018 140318678677376 efficientnet_model.py:147] round_filter input=40 output=80\n","I0409 08:05:31.532013 140318678677376 efficientnet_model.py:147] round_filter input=40 output=80\n","I0409 08:05:31.532222 140318678677376 efficientnet_model.py:147] round_filter input=80 output=160\n","I0409 08:05:32.464462 140318678677376 efficientnet_model.py:147] round_filter input=80 output=160\n","I0409 08:05:32.464677 140318678677376 efficientnet_model.py:147] round_filter input=112 output=224\n","I0409 08:05:33.405546 140318678677376 efficientnet_model.py:147] round_filter input=112 output=224\n","I0409 08:05:33.405777 140318678677376 efficientnet_model.py:147] round_filter input=192 output=384\n","I0409 08:05:34.624375 140318678677376 efficientnet_model.py:147] round_filter input=192 output=384\n","I0409 08:05:34.624680 140318678677376 efficientnet_model.py:147] round_filter input=320 output=640\n","I0409 08:05:35.018883 140318678677376 efficientnet_model.py:147] round_filter input=1280 output=2560\n","I0409 08:05:35.053523 140318678677376 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 27.45s\n","I0409 08:05:35.478669 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 27.45s\n","[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n","[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n","I0409 08:05:35.486216 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n","[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n","I0409 08:05:35.488306 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n","[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n","I0409 08:05:35.488943 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n","[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n","I0409 08:05:35.491102 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n","[ RUN      ] ModelBuilderTF2Test.test_session\n","[  SKIPPED ] ModelBuilderTF2Test.test_session\n","[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n","I0409 08:05:35.492977 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n","[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n","I0409 08:05:35.493609 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n","[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n","INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n","I0409 08:05:35.494848 140318678677376 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n","[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n","----------------------------------------------------------------------\n","Ran 21 tests in 34.828s\n","\n","OK (skipped=1)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_C_LK2LNtLvY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617955601487,"user_tz":-180,"elapsed":25256,"user":{"displayName":"Κοχλιαρίδης Βασίλης","photoUrl":"","userId":"17222540378759389139"}},"outputId":"276a5498-d18f-4dbe-cdc5-9aa4a4608ae9"},"source":["# Creating a workspace directory.\n","# The directory will be linked with my google drive account.\n","# Annotations & Labelmap & Record, Model, Config files are already generated locally.\n","from google.colab import drive\n","\n","%cd object_detection/\n","%mkdir -p workspace/gdrive\n","\n","drive.mount('workspace/gdrive')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["/content/models/research/object_detection\n","Mounted at workspace/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"D_3YlGygyQmb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1617955611360,"user_tz":-180,"elapsed":1432,"user":{"displayName":"Κοχλιαρίδης Βασίλης","photoUrl":"","userId":"17222540378759389139"}},"outputId":"ba25b8d0-17cd-4a33-8cad-7d8f4aff95ed"},"source":["# Testing drive mounting: I will try to read the labelmap file, which I uploaded on my google drive account.\n","# Output should be the items of the labelmap.\n","labelmap_path = 'workspace/gdrive/MyDrive/logo-detection/Training/labelmap.pbtxt'\n","label_map = open(labelmap_path, 'r', encoding='utf-8')\n","lines = label_map.readlines()\n","label_map.close()\n","\n","lines"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['item {\\n',\n"," '\\tname: \"nike\"\\n',\n"," '\\tid: 1\\n',\n"," '}\\n',\n"," '\\n',\n"," 'item {\\n',\n"," '\\tname: \"jordans\"\\n',\n"," '\\tid: 2\\n',\n"," '}\\n',\n"," '\\n',\n"," 'item {\\n',\n"," '\\tname: \"puma\"\\n',\n"," '\\tid: 3\\n',\n"," '}\\n',\n"," '\\n',\n"," 'item {\\n',\n"," '\\tname: \"quicksilver\"\\n',\n"," '\\tid: 4\\n',\n"," '}\\n',\n"," '\\n',\n"," 'item {\\n',\n"," '\\tname: \"adi\"\\n',\n"," '\\tid: 5\\n',\n"," '}\\n',\n"," '\\n',\n"," 'item {\\n',\n"," '\\tname: \"kappa\"\\n',\n"," '\\tid: 6\\n',\n"," '}']"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"gc6bAnVs30Du","colab":{"base_uri":"https://localhost:8080/"},"outputId":"78275565-e8fb-4f58-c503-95b8e76c45e5"},"source":["# Training the model.\n","%cd /content/models/research/object_detection/\n","\n","\n","# Setting paths to Model directory & Pipeline config file:\n","os.environ['MODEL_DIR']='/content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/trained_model/'\n","os.environ['PIPELINE_CONFIG_PATH']='/content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/ssd_efficientdet_d4_colab.config'\n","os.environ['NUM_TRAIN_STEPS']='70000'\n","\n","\n","\n","# Validating environment variables.\n","!echo MODEL_DIR = ${MODEL_DIR}\n","!echo PIPELINE_CONFIG_PATH = ${PIPELINE_CONFIG_PATH}\n","\n","!python model_main_tf2.py --model_dir=${MODEL_DIR} --pipeline_config_path=${PIPELINE_CONFIG_PATH} --num_train_steps=${NUM_TRAIN_STEPS} --alsologtostderr"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/models/research/object_detection\n","MODEL_DIR = /content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/trained_model/\n","PIPELINE_CONFIG_PATH = /content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/ssd_efficientdet_d4_colab.config\n","2021-04-09 08:12:59.468747: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:13:02.382062: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-04-09 08:13:02.383007: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n","2021-04-09 08:13:02.406583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.407410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7\n","coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s\n","2021-04-09 08:13:02.407470: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:13:02.410541: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-04-09 08:13:02.410651: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-04-09 08:13:02.412582: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-04-09 08:13:02.412993: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-04-09 08:13:02.415209: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-04-09 08:13:02.416306: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-04-09 08:13:02.416649: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-04-09 08:13:02.416801: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.417665: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.418434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-04-09 08:13:02.419005: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n","2021-04-09 08:13:02.419149: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.419892: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n","pciBusID: 0000:00:04.0 name: Tesla K80 computeCapability: 3.7\n","coreClock: 0.8235GHz coreCount: 13 deviceMemorySize: 11.17GiB deviceMemoryBandwidth: 223.96GiB/s\n","2021-04-09 08:13:02.419932: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:13:02.419978: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-04-09 08:13:02.420021: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-04-09 08:13:02.420069: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n","2021-04-09 08:13:02.420115: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n","2021-04-09 08:13:02.420158: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n","2021-04-09 08:13:02.420201: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n","2021-04-09 08:13:02.420244: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","2021-04-09 08:13:02.420343: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.421114: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.421807: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n","2021-04-09 08:13:02.421868: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n","2021-04-09 08:13:02.867749: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n","2021-04-09 08:13:02.867814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n","2021-04-09 08:13:02.867836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n","2021-04-09 08:13:02.868240: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.869121: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.869933: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n","2021-04-09 08:13:02.870973: 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.\n","2021-04-09 08:13:02.871038: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10637 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n","INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n","I0409 08:13:02.872978 139792265414528 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n","INFO:tensorflow:Maybe overwriting train_steps: 70000\n","I0409 08:13:03.191008 139792265414528 config_util.py:552] Maybe overwriting train_steps: 70000\n","INFO:tensorflow:Maybe overwriting use_bfloat16: False\n","I0409 08:13:03.191310 139792265414528 config_util.py:552] Maybe overwriting use_bfloat16: False\n","I0409 08:13:03.205990 139792265414528 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n","I0409 08:13:03.206126 139792265414528 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n","I0409 08:13:03.206230 139792265414528 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n","I0409 08:13:03.214488 139792265414528 efficientnet_model.py:147] round_filter input=32 output=48\n","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',).\n","I0409 08:13:03.241718 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.243134 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.245377 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.246402 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.254676 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.258604 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","I0409 08:13:03.264914 139792265414528 efficientnet_model.py:147] round_filter input=32 output=48\n","I0409 08:13:03.265069 139792265414528 efficientnet_model.py:147] round_filter input=16 output=24\n","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',).\n","I0409 08:13:03.281233 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.282260 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.283977 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","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',).\n","I0409 08:13:03.286049 139792265414528 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n","I0409 08:13:03.589543 139792265414528 efficientnet_model.py:147] round_filter input=16 output=24\n","I0409 08:13:03.589728 139792265414528 efficientnet_model.py:147] round_filter input=24 output=32\n","I0409 08:13:04.176548 139792265414528 efficientnet_model.py:147] round_filter input=24 output=32\n","I0409 08:13:04.176792 139792265414528 efficientnet_model.py:147] round_filter input=40 output=56\n","I0409 08:13:04.799925 139792265414528 efficientnet_model.py:147] round_filter input=40 output=56\n","I0409 08:13:04.800128 139792265414528 efficientnet_model.py:147] round_filter input=80 output=112\n","I0409 08:13:05.706312 139792265414528 efficientnet_model.py:147] round_filter input=80 output=112\n","I0409 08:13:05.706516 139792265414528 efficientnet_model.py:147] round_filter input=112 output=160\n","I0409 08:13:06.599414 139792265414528 efficientnet_model.py:147] round_filter input=112 output=160\n","I0409 08:13:06.599682 139792265414528 efficientnet_model.py:147] round_filter input=192 output=272\n","I0409 08:13:07.752263 139792265414528 efficientnet_model.py:147] round_filter input=192 output=272\n","I0409 08:13:07.752455 139792265414528 efficientnet_model.py:147] round_filter input=320 output=448\n","I0409 08:13:08.043429 139792265414528 efficientnet_model.py:147] round_filter input=1280 output=1792\n","I0409 08:13:08.096472 139792265414528 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:546: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","rename to distribute_datasets_from_function\n","W0409 08:13:08.180535 139792265414528 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:546: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","rename to distribute_datasets_from_function\n","INFO:tensorflow:Reading unweighted datasets: ['/content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/Training/train.tfrecord']\n","I0409 08:13:08.190813 139792265414528 dataset_builder.py:163] Reading unweighted datasets: ['/content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/Training/train.tfrecord']\n","INFO:tensorflow:Reading record datasets for input file: ['/content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/Training/train.tfrecord']\n","I0409 08:13:08.191246 139792265414528 dataset_builder.py:80] Reading record datasets for input file: ['/content/models/research/object_detection/workspace/gdrive/MyDrive/logo-detection/Training/train.tfrecord']\n","INFO:tensorflow:Number of filenames to read: 1\n","I0409 08:13:08.191382 139792265414528 dataset_builder.py:81] Number of filenames to read: 1\n","WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n","W0409 08:13:08.191528 139792265414528 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n","W0409 08:13:08.193162 139792265414528 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.data.Dataset.map()\n","W0409 08:13:08.212391 139792265414528 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.data.Dataset.map()\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n","W0409 08:13:16.290331 139792265414528 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n","W0409 08:13:19.994853 139792265414528 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:282: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.cast` instead.\n","W0409 08:13:23.437183 139792265414528 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:282: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.cast` instead.\n","2021-04-09 08:13:27.114612: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n","2021-04-09 08:13:27.123264: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2299995000 Hz\n","/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n","  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n","2021-04-09 08:14:05.779692: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n","2021-04-09 08:14:07.754781: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n","2021-04-09 08:14:07.792274: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer\n","W0409 08:14:53.741720 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).step\n","W0409 08:14:53.742078 139792265414528 util.py:161] Unresolved object in checkpoint: (root).step\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter\n","W0409 08:14:53.742215 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer.iter\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1\n","W0409 08:14:53.742346 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer.beta_1\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2\n","W0409 08:14:53.742488 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer.beta_2\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay\n","W0409 08:14:53.742622 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer.decay\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-0.kernel\n","W0409 08:14:53.743040 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-0.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-1.gamma\n","W0409 08:14:53.743177 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-1.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-1.beta\n","W0409 08:14:53.743294 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-2.depthwise_kernel\n","W0409 08:14:53.743428 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-2.depthwise_kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.gamma\n","W0409 08:14:53.743560 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.beta\n","W0409 08:14:53.743687 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-4.kernel\n","W0409 08:14:53.743821 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-4.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-4.bias\n","W0409 08:14:53.743954 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-4.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-5.kernel\n","W0409 08:14:53.744107 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-5.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-5.bias\n","W0409 08:14:53.744259 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-5.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-6.kernel\n","W0409 08:14:53.744470 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-6.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-10.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-10.bias\n","W0409 08:14:53.745517 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-10.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-11.kernel\n","W0409 08:14:53.745642 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-11.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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(root).model._feature_extractor._efficientnet.layer_with_weights-13.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-13.beta\n","W0409 08:14:53.746124 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-13.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-14.kernel\n","W0409 08:14:53.746253 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-14.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-15.gamma\n","W0409 08:14:53.746383 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-15.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-15.beta\n","W0409 08:14:53.746540 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-15.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-16.depthwise_kernel\n","W0409 08:14:53.746678 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-16.depthwise_kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-19.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-20.kernel\n","W0409 08:14:53.747676 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-20.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-21.gamma\n","W0409 08:14:53.747799 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-21.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-25.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-25.beta\n","W0409 08:14:53.839212 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-25.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-26.kernel\n","W0409 08:14:53.839431 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-26.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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(root).model._feature_extractor._efficientnet.layer_with_weights-27.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-28.kernel\n","W0409 08:14:53.840142 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-28.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-29.gamma\n","W0409 08:14:53.840310 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-29.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._efficientnet.layer_with_weights-29.beta\n","W0409 08:14:53.840526 139792265414528 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state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.beta\n","W0409 08:14:54.396806 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.gamma\n","W0409 08:14:54.396990 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta\n","W0409 08:14:54.397151 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.gamma\n","W0409 08:14:54.397310 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.beta\n","W0409 08:14:54.397488 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for 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Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta\n","W0409 08:14:54.398665 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.gamma\n","W0409 08:14:54.398828 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for 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Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma\n","W0409 08:14:54.399877 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.beta\n","W0409 08:14:54.400027 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for 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Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta\n","W0409 08:14:54.401041 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.gamma\n","W0409 08:14:54.401192 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for 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Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma\n","W0409 08:14:54.402210 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.beta\n","W0409 08:14:54.402358 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for 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139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.0.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.kernel\n","W0409 08:14:54.452871 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.bias\n","W0409 08:14:54.453044 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.4.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.kernel\n","W0409 08:14:54.454271 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.bias\n","W0409 08:14:54.454474 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'm' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'm' for 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util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._efficientnet.layer_with_weights-2.depthwise_kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.gamma\n","W0409 08:14:54.457266 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.beta\n","W0409 08:14:54.457436 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._efficientnet.layer_with_weights-3.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for 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state 'v' for (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.pointwise_kernel\n","W0409 08:14:54.636472 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.pointwise_kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias\n","W0409 08:14:54.636631 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma\n","W0409 08:14:54.636801 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta\n","W0409 08:14:54.636950 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma\n","W0409 08:14:54.637126 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: 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Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.7.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.gamma\n","W0409 08:14:54.644306 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.10.beta\n","W0409 08:14:54.644486 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta\n","W0409 08:14:54.645467 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.7.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1.10.gamma\n","W0409 08:14:54.645626 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma\n","W0409 08:14:54.646625 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7.beta\n","W0409 08:14:54.646792 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta\n","W0409 08:14:54.647884 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7.gamma\n","W0409 08:14:54.648033 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma\n","W0409 08:14:54.649050 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4.beta\n","W0409 08:14:54.649249 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_post_combine_block.2.1.depthwise_kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_post_combine_block.2.1.pointwise_kernel\n","W0409 08:14:54.650376 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_post_combine_block.2.1.pointwise_kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_post_combine_block.2.1.bias\n","W0409 08:14:54.650549 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_post_combine_block.2.1.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for 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139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.0.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.kernel\n","W0409 08:14:54.698586 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.bias\n","W0409 08:14:54.698739 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.0.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.1.gamma\n","W0409 08:14:54.698887 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.1.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.1.beta\n","W0409 08:14:54.699040 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.3.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.4.0.1.0.kernel\n","W0409 08:14:54.699207 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.4.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.kernel\n","W0409 08:14:54.699850 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.bias\n","W0409 08:14:54.699993 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.0.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.1.gamma\n","W0409 08:14:54.700154 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.1.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.1.beta\n","W0409 08:14:54.700317 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.5.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.6.0.1.0.kernel\n","W0409 08:14:54.700496 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for 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139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.6.0.1.1.beta\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.0.kernel\n","W0409 08:14:54.701150 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.0.kernel\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.0.bias\n","W0409 08:14:54.701297 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.0.bias\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.1.gamma\n","W0409 08:14:54.701462 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.1.gamma\n","WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.1.beta\n","W0409 08:14:54.701625 139792265414528 util.py:161] Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model._feature_extractor._bifpn_stage.node_input_blocks.7.0.1.1.beta\n","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.\n","W0409 08:14:54.701776 139792265414528 util.py:169] 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.\n","WARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.\n","W0409 08:15:21.706777 139791513843456 utils.py:83] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:605: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use fn_output_signature instead\n","W0409 08:17:01.673454 139791513843456 deprecation.py:537] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:605: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use fn_output_signature instead\n","WARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.\n","W0409 08:17:20.650003 139791513843456 utils.py:83] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss.\n","2021-04-09 08:17:43.824330: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.52GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:43.852573: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.52GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.101933: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.60GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.117006: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.60GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.118761: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.60GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.935781: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.63GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.946022: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.62GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.953332: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.62GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:44.963243: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.61GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","2021-04-09 08:17:45.033887: W tensorflow/core/common_runtime/bfc_allocator.cc:248] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.\n","INFO:tensorflow:Step 16100 per-step time 2.232s loss=0.260\n","I0409 08:21:27.829232 139792265414528 model_lib_v2.py:682] Step 16100 per-step time 2.232s loss=0.260\n","INFO:tensorflow:Step 16200 per-step time 2.258s loss=0.200\n","I0409 08:25:11.079030 139792265414528 model_lib_v2.py:682] Step 16200 per-step time 2.258s loss=0.200\n","INFO:tensorflow:Step 16300 per-step time 2.125s loss=0.328\n","I0409 08:28:53.937831 139792265414528 model_lib_v2.py:682] Step 16300 per-step time 2.125s loss=0.328\n","INFO:tensorflow:Step 16400 per-step time 2.285s loss=0.153\n","I0409 08:32:36.511484 139792265414528 model_lib_v2.py:682] Step 16400 per-step time 2.285s loss=0.153\n","INFO:tensorflow:Step 16500 per-step time 2.262s loss=0.313\n","I0409 08:36:17.159912 139792265414528 model_lib_v2.py:682] Step 16500 per-step time 2.262s loss=0.313\n","INFO:tensorflow:Step 16600 per-step time 2.254s loss=0.667\n","I0409 08:39:59.336880 139792265414528 model_lib_v2.py:682] Step 16600 per-step time 2.254s loss=0.667\n","INFO:tensorflow:Step 16700 per-step time 2.204s loss=0.227\n","I0409 08:43:42.410728 139792265414528 model_lib_v2.py:682] Step 16700 per-step time 2.204s loss=0.227\n","INFO:tensorflow:Step 16800 per-step time 2.207s loss=0.209\n","I0409 08:47:26.259538 139792265414528 model_lib_v2.py:682] Step 16800 per-step time 2.207s loss=0.209\n","INFO:tensorflow:Step 16900 per-step time 2.187s loss=0.266\n","I0409 08:51:09.274861 139792265414528 model_lib_v2.py:682] Step 16900 per-step time 2.187s loss=0.266\n","INFO:tensorflow:Step 17000 per-step time 2.185s loss=0.142\n","I0409 08:54:52.583147 139792265414528 model_lib_v2.py:682] Step 17000 per-step time 2.185s loss=0.142\n","INFO:tensorflow:Step 17100 per-step time 2.244s loss=0.193\n","I0409 08:58:41.292306 139792265414528 model_lib_v2.py:682] Step 17100 per-step time 2.244s loss=0.193\n","INFO:tensorflow:Step 17200 per-step time 2.195s loss=0.223\n","I0409 09:02:24.545194 139792265414528 model_lib_v2.py:682] Step 17200 per-step time 2.195s loss=0.223\n","INFO:tensorflow:Step 17300 per-step time 2.382s loss=0.147\n","I0409 09:06:10.034780 139792265414528 model_lib_v2.py:682] Step 17300 per-step time 2.382s loss=0.147\n","INFO:tensorflow:Step 17400 per-step time 2.261s loss=0.179\n","I0409 09:09:55.823886 139792265414528 model_lib_v2.py:682] Step 17400 per-step time 2.261s loss=0.179\n","INFO:tensorflow:Step 17500 per-step time 2.268s loss=0.161\n","I0409 09:13:41.821529 139792265414528 model_lib_v2.py:682] Step 17500 per-step time 2.268s loss=0.161\n","INFO:tensorflow:Step 17600 per-step time 2.255s loss=0.253\n","I0409 09:17:28.797991 139792265414528 model_lib_v2.py:682] Step 17600 per-step time 2.255s loss=0.253\n","INFO:tensorflow:Step 17700 per-step time 2.371s loss=0.226\n","I0409 09:21:16.048060 139792265414528 model_lib_v2.py:682] Step 17700 per-step time 2.371s loss=0.226\n","INFO:tensorflow:Step 17800 per-step time 2.308s loss=0.275\n","I0409 09:25:03.276792 139792265414528 model_lib_v2.py:682] Step 17800 per-step time 2.308s loss=0.275\n","INFO:tensorflow:Step 17900 per-step time 2.233s loss=0.308\n","I0409 09:28:49.776143 139792265414528 model_lib_v2.py:682] Step 17900 per-step time 2.233s loss=0.308\n","INFO:tensorflow:Step 18000 per-step time 2.309s loss=0.253\n","I0409 09:32:36.650786 139792265414528 model_lib_v2.py:682] Step 18000 per-step time 2.309s loss=0.253\n","INFO:tensorflow:Step 18100 per-step time 2.235s loss=0.220\n","I0409 09:36:26.291856 139792265414528 model_lib_v2.py:682] Step 18100 per-step time 2.235s loss=0.220\n","INFO:tensorflow:Step 18200 per-step time 2.343s loss=0.184\n","I0409 09:40:12.573049 139792265414528 model_lib_v2.py:682] Step 18200 per-step time 2.343s loss=0.184\n","INFO:tensorflow:Step 18300 per-step time 2.328s loss=0.188\n","I0409 09:43:57.500305 139792265414528 model_lib_v2.py:682] Step 18300 per-step time 2.328s loss=0.188\n","INFO:tensorflow:Step 18400 per-step time 2.320s loss=0.155\n","I0409 09:47:42.475811 139792265414528 model_lib_v2.py:682] Step 18400 per-step time 2.320s loss=0.155\n","INFO:tensorflow:Step 18500 per-step time 2.406s loss=0.239\n","I0409 09:51:29.271836 139792265414528 model_lib_v2.py:682] Step 18500 per-step time 2.406s loss=0.239\n","INFO:tensorflow:Step 18600 per-step time 2.262s loss=0.282\n","I0409 09:55:15.454497 139792265414528 model_lib_v2.py:682] Step 18600 per-step time 2.262s loss=0.282\n","INFO:tensorflow:Step 18700 per-step time 2.300s loss=0.193\n","I0409 09:59:00.907110 139792265414528 model_lib_v2.py:682] Step 18700 per-step time 2.300s loss=0.193\n","INFO:tensorflow:Step 18800 per-step time 2.199s loss=0.336\n","I0409 10:02:48.070188 139792265414528 model_lib_v2.py:682] Step 18800 per-step time 2.199s loss=0.336\n","INFO:tensorflow:Step 18900 per-step time 2.238s loss=0.178\n","I0409 10:06:34.312930 139792265414528 model_lib_v2.py:682] Step 18900 per-step time 2.238s loss=0.178\n","INFO:tensorflow:Step 19000 per-step time 2.224s loss=0.220\n","I0409 10:10:22.119791 139792265414528 model_lib_v2.py:682] Step 19000 per-step time 2.224s loss=0.220\n","INFO:tensorflow:Step 19100 per-step time 2.218s loss=0.171\n","I0409 10:14:13.666968 139792265414528 model_lib_v2.py:682] Step 19100 per-step time 2.218s loss=0.171\n","INFO:tensorflow:Step 19200 per-step time 2.289s loss=0.233\n","I0409 10:17:58.864797 139792265414528 model_lib_v2.py:682] Step 19200 per-step time 2.289s loss=0.233\n","INFO:tensorflow:Step 19300 per-step time 2.206s loss=0.185\n","I0409 10:21:43.903657 139792265414528 model_lib_v2.py:682] Step 19300 per-step time 2.206s loss=0.185\n","INFO:tensorflow:Step 19400 per-step time 2.186s loss=0.363\n","I0409 10:25:27.471174 139792265414528 model_lib_v2.py:682] Step 19400 per-step time 2.186s loss=0.363\n","INFO:tensorflow:Step 19500 per-step time 2.187s loss=0.331\n","I0409 10:29:10.388058 139792265414528 model_lib_v2.py:682] Step 19500 per-step time 2.187s loss=0.331\n","INFO:tensorflow:Step 19600 per-step time 2.194s loss=0.175\n","I0409 10:32:52.445719 139792265414528 model_lib_v2.py:682] Step 19600 per-step time 2.194s loss=0.175\n","INFO:tensorflow:Step 19700 per-step time 2.157s loss=0.204\n","I0409 10:36:34.577095 139792265414528 model_lib_v2.py:682] Step 19700 per-step time 2.157s loss=0.204\n","INFO:tensorflow:Step 19800 per-step time 2.236s loss=0.209\n","I0409 10:40:15.867336 139792265414528 model_lib_v2.py:682] Step 19800 per-step time 2.236s loss=0.209\n","INFO:tensorflow:Step 19900 per-step time 2.217s loss=0.229\n","I0409 10:43:58.504695 139792265414528 model_lib_v2.py:682] Step 19900 per-step time 2.217s loss=0.229\n","INFO:tensorflow:Step 20000 per-step time 2.205s loss=0.245\n","I0409 10:47:39.981858 139792265414528 model_lib_v2.py:682] Step 20000 per-step time 2.205s loss=0.245\n","INFO:tensorflow:Step 20100 per-step time 2.349s loss=0.197\n","I0409 10:51:25.851341 139792265414528 model_lib_v2.py:682] Step 20100 per-step time 2.349s loss=0.197\n","INFO:tensorflow:Step 20200 per-step time 2.185s loss=0.298\n","I0409 10:55:07.985585 139792265414528 model_lib_v2.py:682] Step 20200 per-step time 2.185s loss=0.298\n","INFO:tensorflow:Step 20300 per-step time 2.184s loss=0.198\n","I0409 10:58:50.149292 139792265414528 model_lib_v2.py:682] Step 20300 per-step time 2.184s loss=0.198\n","INFO:tensorflow:Step 20400 per-step time 2.156s loss=0.211\n","I0409 11:02:32.759616 139792265414528 model_lib_v2.py:682] Step 20400 per-step time 2.156s loss=0.211\n","INFO:tensorflow:Step 20500 per-step time 2.261s loss=0.254\n","I0409 11:06:16.324801 139792265414528 model_lib_v2.py:682] Step 20500 per-step time 2.261s loss=0.254\n","INFO:tensorflow:Step 20600 per-step time 2.273s loss=0.128\n","I0409 11:09:59.432785 139792265414528 model_lib_v2.py:682] Step 20600 per-step time 2.273s loss=0.128\n","INFO:tensorflow:Step 20700 per-step time 2.197s loss=0.235\n","I0409 11:13:42.250548 139792265414528 model_lib_v2.py:682] Step 20700 per-step time 2.197s loss=0.235\n","INFO:tensorflow:Step 20800 per-step time 2.220s loss=0.254\n","I0409 11:17:25.674206 139792265414528 model_lib_v2.py:682] Step 20800 per-step time 2.220s loss=0.254\n","INFO:tensorflow:Step 20900 per-step time 2.210s loss=0.262\n","I0409 11:21:09.493459 139792265414528 model_lib_v2.py:682] Step 20900 per-step time 2.210s loss=0.262\n","INFO:tensorflow:Step 21000 per-step time 2.353s loss=0.197\n","I0409 11:24:51.568389 139792265414528 model_lib_v2.py:682] Step 21000 per-step time 2.353s loss=0.197\n","INFO:tensorflow:Step 21100 per-step time 2.187s loss=0.123\n","I0409 11:28:38.629194 139792265414528 model_lib_v2.py:682] Step 21100 per-step time 2.187s loss=0.123\n","INFO:tensorflow:Step 21200 per-step time 2.268s loss=0.219\n","I0409 11:32:21.077067 139792265414528 model_lib_v2.py:682] Step 21200 per-step time 2.268s loss=0.219\n","INFO:tensorflow:Step 21300 per-step time 2.191s loss=0.161\n","I0409 11:36:04.376020 139792265414528 model_lib_v2.py:682] Step 21300 per-step time 2.191s loss=0.161\n","INFO:tensorflow:Step 21400 per-step time 2.455s loss=0.169\n","I0409 11:39:46.246728 139792265414528 model_lib_v2.py:682] Step 21400 per-step time 2.455s loss=0.169\n","INFO:tensorflow:Step 21500 per-step time 2.203s loss=0.157\n","I0409 11:43:28.721986 139792265414528 model_lib_v2.py:682] Step 21500 per-step time 2.203s loss=0.157\n","INFO:tensorflow:Step 21600 per-step time 2.193s loss=0.170\n","I0409 11:47:11.915692 139792265414528 model_lib_v2.py:682] Step 21600 per-step time 2.193s loss=0.170\n"],"name":"stdout"}]}]}