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NobuoTsukamoto/export-efficientdet-lite-tf-lite-model.ipynb

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export-efficientdet-lite-tf-lite-model.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Export EfficientDet-Lite TF-Lite Model.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"machine_shape": "hm",
"authorship_tag": "ABX9TyO3wJRZLlAT+UhDAjuJ8GHM",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/NobuoTsukamoto/c47ed552c412db4dbfac97a8568f17f0/export-efficientdet-lite-tf-lite-model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LG9qOJ4BMN2_"
},
"source": [
"MIT License\n",
"\n",
"Copyright (c) 2021 Nobuo Tsukamoto\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PnDZ7yUBMRYH"
},
"source": [
"# Mount Google drive"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c174VQJbL8vs",
"outputId": "ce91a4fe-3747-4927-d5a7-f223768e68e9"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nDzBBzVVEq_k"
},
"source": [
"# Clone repository and install dependencies"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hvsyqff0F2ku",
"outputId": "93412dae-10d8-454e-955b-d82b988914ef"
},
"source": [
"%%bash\n",
"\n",
"cd /content\n",
"git clone https://github.com/google/automl\n",
"cd automl\n",
"git checkout 17c4cac0d7601de879d9f3d57f3590c11b7af240\n",
"cd efficientdet\n",
"\n",
"pip3 install -r requirements.txt\n",
"pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI (from -r requirements.txt (line 14))\n",
" Cloning https://github.com/cocodataset/cocoapi.git to /tmp/pip-req-build-x637iazv\n",
"Collecting lxml>=4.6.1\n",
" Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
"Requirement already satisfied: absl-py>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (0.12.0)\n",
"Requirement already satisfied: matplotlib>=3.0.3 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (3.2.2)\n",
"Requirement already satisfied: numpy>=1.19.4 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (1.19.5)\n",
"Requirement already satisfied: Pillow>=6.0.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (7.1.2)\n",
"Collecting PyYAML>=5.1\n",
" Downloading https://files.pythonhosted.org/packages/7a/a5/393c087efdc78091afa2af9f1378762f9821c9c1d7a22c5753fb5ac5f97a/PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636kB)\n",
"Requirement already satisfied: six>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (1.15.0)\n",
"Requirement already satisfied: tensorflow>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 8)) (2.5.0)\n",
"Collecting tensorflow-addons>=0.12\n",
" Downloading https://files.pythonhosted.org/packages/66/4b/e893d194e626c24b3df2253066aa418f46a432fdb68250cde14bf9bb0700/tensorflow_addons-0.13.0-cp37-cp37m-manylinux2010_x86_64.whl (679kB)\n",
"Requirement already satisfied: tensorflow-hub>=0.11 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 10)) (0.12.0)\n",
"Collecting neural-structured-learning>=1.3.1\n",
" Downloading https://files.pythonhosted.org/packages/8a/23/179e6b7555000de51d9a317e9e47db84cda0180c941cfbf14775925af611/neural_structured_learning-1.3.1-py2.py3-none-any.whl (120kB)\n",
"Collecting tensorflow-model-optimization>=0.5\n",
" Downloading https://files.pythonhosted.org/packages/55/38/4fd48ea1bfcb0b6e36d949025200426fe9c3a8bfae029f0973d85518fa5a/tensorflow_model_optimization-0.5.0-py2.py3-none-any.whl (172kB)\n",
"Requirement already satisfied: Cython>=0.29.13 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 13)) (0.29.23)\n",
"Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0->-r requirements.txt (line 14)) (56.1.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.3->-r requirements.txt (line 3)) (2.8.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.3->-r requirements.txt (line 3)) (1.3.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.3->-r requirements.txt (line 3)) (2.4.7)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.3->-r requirements.txt (line 3)) (0.10.0)\n",
"Requirement already satisfied: grpcio~=1.34.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.34.1)\n",
"Requirement already satisfied: h5py~=3.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.1.0)\n",
"Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.1.0)\n",
"Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.12.4)\n",
"Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.36.2)\n",
"Requirement already satisfied: keras-nightly~=2.5.0.dev in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (2.5.0.dev2021032900)\n",
"Requirement already satisfied: tensorflow-estimator<2.6.0,>=2.5.0rc0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (2.5.0)\n",
"Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.12)\n",
"Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.3.0)\n",
"Requirement already satisfied: tensorboard~=2.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (2.5.0)\n",
"Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.6.3)\n",
"Requirement already satisfied: gast==0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.4.0)\n",
"Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.7.4.3)\n",
"Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.2.0)\n",
"Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.1.2)\n",
"Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.12.1)\n",
"Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons>=0.12->-r requirements.txt (line 9)) (2.7.1)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from neural-structured-learning>=1.3.1->-r requirements.txt (line 11)) (1.4.1)\n",
"Requirement already satisfied: attrs in /usr/local/lib/python3.7/dist-packages (from neural-structured-learning>=1.3.1->-r requirements.txt (line 11)) (21.2.0)\n",
"Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-model-optimization>=0.5->-r requirements.txt (line 12)) (0.1.6)\n",
"Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from h5py~=3.1.0->tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.5.2)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.0.1)\n",
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.8.0)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (2.23.0)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.3.4)\n",
"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.4.4)\n",
"Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.6.1)\n",
"Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.30.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (2020.12.5)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.24.3)\n",
"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (4.0.1)\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~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (1.3.0)\n",
"Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (4.2.2)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.2.8)\n",
"Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (4.7.2)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.4.1)\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~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (3.1.0)\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.5->tensorflow>=2.4.0->-r requirements.txt (line 8)) (0.4.8)\n",
"Building wheels for collected packages: pycocotools\n",
" Building wheel for pycocotools (setup.py): started\n",
" Building wheel for pycocotools (setup.py): finished with status 'done'\n",
" Created wheel for pycocotools: filename=pycocotools-2.0-cp37-cp37m-linux_x86_64.whl size=263935 sha256=86ac4db1c3b391097569635e86f47f27d2e899b9f4ccdb66d2dff2d71c75b24e\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-6q5kiwu6/wheels/90/51/41/646daf401c3bc408ff10de34ec76587a9b3ebfac8d21ca5c3a\n",
"Successfully built pycocotools\n",
"Installing collected packages: lxml, PyYAML, tensorflow-addons, neural-structured-learning, tensorflow-model-optimization, pycocotools\n",
" Found existing installation: lxml 4.2.6\n",
" Uninstalling lxml-4.2.6:\n",
" Successfully uninstalled lxml-4.2.6\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
" Found existing installation: pycocotools 2.0.2\n",
" Uninstalling pycocotools-2.0.2:\n",
" Successfully uninstalled pycocotools-2.0.2\n",
"Successfully installed PyYAML-5.4.1 lxml-4.6.3 neural-structured-learning-1.3.1 pycocotools-2.0 tensorflow-addons-0.13.0 tensorflow-model-optimization-0.5.0\n",
"Collecting git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI\n",
" Cloning https://github.com/cocodataset/cocoapi.git to /tmp/pip-req-build-pd_ccpum\n",
"Requirement already satisfied, skipping upgrade: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0) (56.1.0)\n",
"Requirement already satisfied, skipping upgrade: cython>=0.27.3 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0) (0.29.23)\n",
"Requirement already satisfied, skipping upgrade: matplotlib>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools==2.0) (3.2.2)\n",
"Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (1.3.1)\n",
"Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (0.10.0)\n",
"Requirement already satisfied, skipping upgrade: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (1.19.5)\n",
"Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (2.4.7)\n",
"Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.1.0->pycocotools==2.0) (2.8.1)\n",
"Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=2.1.0->pycocotools==2.0) (1.15.0)\n",
"Building wheels for collected packages: pycocotools\n",
" Building wheel for pycocotools (setup.py): started\n",
" Building wheel for pycocotools (setup.py): finished with status 'done'\n",
" Created wheel for pycocotools: filename=pycocotools-2.0-cp37-cp37m-linux_x86_64.whl size=263913 sha256=26b839e0d2ba69f46d72af66c6773c01df411ba5e90519776dad95ae50968837\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-rj38xnfa/wheels/90/51/41/646daf401c3bc408ff10de34ec76587a9b3ebfac8d21ca5c3a\n",
"Successfully built pycocotools\n",
"Installing collected packages: pycocotools\n",
" Found existing installation: pycocotools 2.0\n",
" Uninstalling pycocotools-2.0:\n",
" Successfully uninstalled pycocotools-2.0\n",
"Successfully installed pycocotools-2.0\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Cloning into 'automl'...\n",
"Note: checking out '17c4cac0d7601de879d9f3d57f3590c11b7af240'.\n",
"\n",
"You are in 'detached HEAD' state. You can look around, make experimental\n",
"changes and commit them, and you can discard any commits you make in this\n",
"state without impacting any branches by performing another checkout.\n",
"\n",
"If you want to create a new branch to retain commits you create, you may\n",
"do so (now or later) by using -b with the checkout command again. Example:\n",
"\n",
" git checkout -b <new-branch-name>\n",
"\n",
"HEAD is now at 17c4cac Minor fix.\n",
" Running command git clone -q https://github.com/cocodataset/cocoapi.git /tmp/pip-req-build-x637iazv\n",
" Running command git clone -q https://github.com/cocodataset/cocoapi.git /tmp/pip-req-build-pd_ccpum\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bglw3ZIZ0GGJ",
"outputId": "9557b96e-1a73-424e-e7d7-481924b0820c"
},
"source": [
"%%bash\n",
"\n",
"echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | tee /etc/apt/sources.list.d/coral-edgetpu.list\n",
"curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -\n",
"sudo apt-get -qq update\n",
"apt-get -qq install edgetpu-compiler"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\n",
"OK\n",
"Selecting previously unselected package edgetpu-compiler.\r\n",
"(Reading database ... \r(Reading database ... 5%\r(Reading database ... 10%\r(Reading database ... 15%\r(Reading database ... 20%\r(Reading database ... 25%\r(Reading database ... 30%\r(Reading database ... 35%\r(Reading database ... 40%\r(Reading database ... 45%\r(Reading database ... 50%\r(Reading database ... 55%\r(Reading database ... 60%\r(Reading database ... 65%\r(Reading database ... 70%\r(Reading database ... 75%\r(Reading database ... 80%\r(Reading database ... 85%\r(Reading database ... 90%\r(Reading database ... 95%\r(Reading database ... 100%\r(Reading database ... 160706 files and directories currently installed.)\r\n",
"Preparing to unpack .../edgetpu-compiler_15.0_amd64.deb ...\r\n",
"Unpacking edgetpu-compiler (15.0) ...\r\n",
"Setting up edgetpu-compiler (15.0) ...\r\n",
"Processing triggers for libc-bin (2.27-3ubuntu1.2) ...\r\n",
"/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\r\n",
"\r\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 2537 100 2537 0 0 93962 0 --:--:-- --:--:-- --:--:-- 93962\n",
"Warning: apt-key output should not be parsed (stdout is not a terminal)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F3_Aq1gQGKX0"
},
"source": [
"# Create COCO2017 TF-Record"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Je9hWLTb0myP",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "19e5d889-35da-4150-9761-c0775ba23747"
},
"source": [
"!mkdir /content/efficientdet-lite\n",
"%cd automl/efficientdet/"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/automl/efficientdet\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tuHHEcMxY9jh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dcac30c2-2cb8-4b6a-808b-87de3bbafcc3"
},
"source": [
"# Download coco data.\n",
"!wget http://images.cocodataset.org/zips/val2017.zip\n",
"!wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
"!unzip -qq val2017.zip\n",
"!unzip -qq annotations_trainval2017.zip\n",
"\n",
"# convert coco data to tfrecord.\n",
"!mkdir tfrecord\n",
"!PYTHONPATH=\".:$PYTHONPATH\" python dataset/create_coco_tfrecord.py \\\n",
" --image_dir=val2017 \\\n",
" --caption_annotations_file=annotations/captions_val2017.json \\\n",
" --output_file_prefix=tfrecord/val \\\n",
" --num_shards=32"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-06-02 12:15:49-- http://images.cocodataset.org/zips/val2017.zip\n",
"Resolving images.cocodataset.org (images.cocodataset.org)... 52.217.195.145\n",
"Connecting to images.cocodataset.org (images.cocodataset.org)|52.217.195.145|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 815585330 (778M) [application/zip]\n",
"Saving to: ‘val2017.zip’\n",
"\n",
"val2017.zip 100%[===================>] 777.80M 96.3MB/s in 8.2s \n",
"\n",
"2021-06-02 12:15:58 (94.9 MB/s) - ‘val2017.zip’ saved [815585330/815585330]\n",
"\n",
"--2021-06-02 12:15:58-- http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n",
"Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.141.140\n",
"Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.141.140|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 252907541 (241M) [application/zip]\n",
"Saving to: ‘annotations_trainval2017.zip’\n",
"\n",
"annotations_trainva 100%[===================>] 241.19M 95.9MB/s in 2.5s \n",
"\n",
"2021-06-02 12:16:01 (95.9 MB/s) - ‘annotations_trainval2017.zip’ saved [252907541/252907541]\n",
"\n",
"2021-06-02 12:16:15.384535: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
"I0602 12:16:17.115362 140089072838528 create_coco_tfrecord.py:285] writing to output path: tfrecord/val\n",
"I0602 12:16:17.213944 140089072838528 create_coco_tfrecord.py:237] Building caption index.\n",
"I0602 12:16:17.221894 140089072838528 create_coco_tfrecord.py:249] 0 images are missing captions.\n",
"I0602 12:16:17.302868 140089072838528 create_coco_tfrecord.py:323] On image 0 of 5000\n",
"I0602 12:16:17.407803 140089072838528 create_coco_tfrecord.py:323] On image 100 of 5000\n",
"I0602 12:16:17.504416 140089072838528 create_coco_tfrecord.py:323] On image 200 of 5000\n",
"I0602 12:16:17.599408 140089072838528 create_coco_tfrecord.py:323] On image 300 of 5000\n",
"I0602 12:16:17.703406 140089072838528 create_coco_tfrecord.py:323] On image 400 of 5000\n",
"I0602 12:16:17.789389 140089072838528 create_coco_tfrecord.py:323] On image 500 of 5000\n",
"I0602 12:16:17.887398 140089072838528 create_coco_tfrecord.py:323] On image 600 of 5000\n",
"I0602 12:16:17.977182 140089072838528 create_coco_tfrecord.py:323] On image 700 of 5000\n",
"I0602 12:16:18.077528 140089072838528 create_coco_tfrecord.py:323] On image 800 of 5000\n",
"I0602 12:16:18.168263 140089072838528 create_coco_tfrecord.py:323] On image 900 of 5000\n",
"I0602 12:16:18.263924 140089072838528 create_coco_tfrecord.py:323] On image 1000 of 5000\n",
"I0602 12:16:18.364521 140089072838528 create_coco_tfrecord.py:323] On image 1100 of 5000\n",
"I0602 12:16:18.466558 140089072838528 create_coco_tfrecord.py:323] On image 1200 of 5000\n",
"I0602 12:16:18.552939 140089072838528 create_coco_tfrecord.py:323] On image 1300 of 5000\n",
"I0602 12:16:18.632819 140089072838528 create_coco_tfrecord.py:323] On image 1400 of 5000\n",
"I0602 12:16:18.726962 140089072838528 create_coco_tfrecord.py:323] On image 1500 of 5000\n",
"I0602 12:16:18.846235 140089072838528 create_coco_tfrecord.py:323] On image 1600 of 5000\n",
"I0602 12:16:18.997060 140089072838528 create_coco_tfrecord.py:323] On image 1700 of 5000\n",
"I0602 12:16:19.122740 140089072838528 create_coco_tfrecord.py:323] On image 1800 of 5000\n",
"I0602 12:16:19.217436 140089072838528 create_coco_tfrecord.py:323] On image 1900 of 5000\n",
"I0602 12:16:19.324849 140089072838528 create_coco_tfrecord.py:323] On image 2000 of 5000\n",
"I0602 12:16:19.432494 140089072838528 create_coco_tfrecord.py:323] On image 2100 of 5000\n",
"I0602 12:16:19.528561 140089072838528 create_coco_tfrecord.py:323] On image 2200 of 5000\n",
"I0602 12:16:19.634819 140089072838528 create_coco_tfrecord.py:323] On image 2300 of 5000\n",
"I0602 12:16:19.735677 140089072838528 create_coco_tfrecord.py:323] On image 2400 of 5000\n",
"I0602 12:16:19.841954 140089072838528 create_coco_tfrecord.py:323] On image 2500 of 5000\n",
"I0602 12:16:19.938291 140089072838528 create_coco_tfrecord.py:323] On image 2600 of 5000\n",
"I0602 12:16:20.034261 140089072838528 create_coco_tfrecord.py:323] On image 2700 of 5000\n",
"I0602 12:16:20.124601 140089072838528 create_coco_tfrecord.py:323] On image 2800 of 5000\n",
"I0602 12:16:20.230741 140089072838528 create_coco_tfrecord.py:323] On image 2900 of 5000\n",
"I0602 12:16:20.329798 140089072838528 create_coco_tfrecord.py:323] On image 3000 of 5000\n",
"I0602 12:16:20.419122 140089072838528 create_coco_tfrecord.py:323] On image 3100 of 5000\n",
"I0602 12:16:20.517096 140089072838528 create_coco_tfrecord.py:323] On image 3200 of 5000\n",
"I0602 12:16:20.615928 140089072838528 create_coco_tfrecord.py:323] On image 3300 of 5000\n",
"I0602 12:16:20.714787 140089072838528 create_coco_tfrecord.py:323] On image 3400 of 5000\n",
"I0602 12:16:20.811212 140089072838528 create_coco_tfrecord.py:323] On image 3500 of 5000\n",
"I0602 12:16:20.893579 140089072838528 create_coco_tfrecord.py:323] On image 3600 of 5000\n",
"I0602 12:16:20.990887 140089072838528 create_coco_tfrecord.py:323] On image 3700 of 5000\n",
"I0602 12:16:21.086964 140089072838528 create_coco_tfrecord.py:323] On image 3800 of 5000\n",
"I0602 12:16:21.178313 140089072838528 create_coco_tfrecord.py:323] On image 3900 of 5000\n",
"I0602 12:16:21.273396 140089072838528 create_coco_tfrecord.py:323] On image 4000 of 5000\n",
"I0602 12:16:21.359884 140089072838528 create_coco_tfrecord.py:323] On image 4100 of 5000\n",
"I0602 12:16:21.472556 140089072838528 create_coco_tfrecord.py:323] On image 4200 of 5000\n",
"I0602 12:16:21.608118 140089072838528 create_coco_tfrecord.py:323] On image 4300 of 5000\n",
"I0602 12:16:21.732613 140089072838528 create_coco_tfrecord.py:323] On image 4400 of 5000\n",
"I0602 12:16:21.833691 140089072838528 create_coco_tfrecord.py:323] On image 4500 of 5000\n",
"I0602 12:16:21.925471 140089072838528 create_coco_tfrecord.py:323] On image 4600 of 5000\n",
"I0602 12:16:22.034248 140089072838528 create_coco_tfrecord.py:323] On image 4700 of 5000\n",
"I0602 12:16:22.129949 140089072838528 create_coco_tfrecord.py:323] On image 4800 of 5000\n",
"I0602 12:16:22.228182 140089072838528 create_coco_tfrecord.py:323] On image 4900 of 5000\n",
"I0602 12:16:22.377981 140089072838528 create_coco_tfrecord.py:335] Finished writing, skipped 0 annotations.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JUWbWopgLW1Z"
},
"source": [
"# Download Efficientdet-Lite Models"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yQJMBw2Yq7eS"
},
"source": [
"import os\n",
"import shutil\n",
"\n",
"import numpy as np\n",
"import cv2\n",
"import tensorflow as tf"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "YnhymSkapnig"
},
"source": [
"MODELS = [\"efficientdet-lite0\",\n",
" \"efficientdet-lite1\",\n",
" \"efficientdet-lite2\",\n",
" \"efficientdet-lite3\",\n",
" \"efficientdet-lite3x\",\n",
" \"efficientdet-lite4\"]"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NAqILfEzp7UV",
"outputId": "c06910b7-8664-4267-8982-2e1020d690e4"
},
"source": [
"for model in MODELS:\n",
" download_url = \"https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/\" + model + \".tgz\"\n",
" file_path = os.path.join('/', 'content', model + \".tgz\")\n",
" \n",
" !wget $download_url -P /content/\n",
" !tar xf $file_path -C /content/"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-06-02 12:16:25-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-lite0.tgz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.204.128, 172.217.203.128, 142.250.97.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.204.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 23972392 (23M) [application/octet-stream]\n",
"Saving to: ‘/content/efficientdet-lite0.tgz’\n",
"\n",
"efficientdet-lite0. 100%[===================>] 22.86M 56.8MB/s in 0.4s \n",
"\n",
"2021-06-02 12:16:25 (56.8 MB/s) - ‘/content/efficientdet-lite0.tgz’ saved [23972392/23972392]\n",
"\n",
"--2021-06-02 12:16:26-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-lite1.tgz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.170.128, 172.217.193.128, 172.217.204.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.170.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 31143920 (30M) [application/octet-stream]\n",
"Saving to: ‘/content/efficientdet-lite1.tgz’\n",
"\n",
"efficientdet-lite1. 100%[===================>] 29.70M 67.7MB/s in 0.4s \n",
"\n",
"2021-06-02 12:16:26 (67.7 MB/s) - ‘/content/efficientdet-lite1.tgz’ saved [31143920/31143920]\n",
"\n",
"--2021-06-02 12:16:27-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-lite2.tgz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.97.128, 108.177.12.128, 74.125.31.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.97.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 38851999 (37M) [application/octet-stream]\n",
"Saving to: ‘/content/efficientdet-lite2.tgz’\n",
"\n",
"efficientdet-lite2. 100%[===================>] 37.05M 52.3MB/s in 0.7s \n",
"\n",
"2021-06-02 12:16:28 (52.3 MB/s) - ‘/content/efficientdet-lite2.tgz’ saved [38851999/38851999]\n",
"\n",
"--2021-06-02 12:16:28-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-lite3.tgz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.216.128, 173.194.217.128, 64.233.170.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.216.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 61517308 (59M) [application/octet-stream]\n",
"Saving to: ‘/content/efficientdet-lite3.tgz’\n",
"\n",
"efficientdet-lite3. 100%[===================>] 58.67M 123MB/s in 0.5s \n",
"\n",
"2021-06-02 12:16:29 (123 MB/s) - ‘/content/efficientdet-lite3.tgz’ saved [61517308/61517308]\n",
"\n",
"--2021-06-02 12:16:30-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-lite3x.tgz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.12.128, 108.177.13.128, 74.125.31.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.12.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 68310547 (65M) [application/octet-stream]\n",
"Saving to: ‘/content/efficientdet-lite3x.tgz’\n",
"\n",
"efficientdet-lite3x 100%[===================>] 65.15M 58.9MB/s in 1.1s \n",
"\n",
"2021-06-02 12:16:31 (58.9 MB/s) - ‘/content/efficientdet-lite3x.tgz’ saved [68310547/68310547]\n",
"\n",
"--2021-06-02 12:16:32-- https://storage.googleapis.com/cloud-tpu-checkpoints/efficientdet/coco/efficientdet-lite4.tgz\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.212.128, 173.194.213.128, 173.194.214.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.212.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 111022169 (106M) [application/octet-stream]\n",
"Saving to: ‘/content/efficientdet-lite4.tgz’\n",
"\n",
"efficientdet-lite4. 100%[===================>] 105.88M 121MB/s in 0.9s \n",
"\n",
"2021-06-02 12:16:33 (121 MB/s) - ‘/content/efficientdet-lite4.tgz’ saved [111022169/111022169]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Sdwz0EoWZs4e"
},
"source": [
"# Convert models."
]
},
{
"cell_type": "code",
"metadata": {
"id": "vbdrx-osm1vC"
},
"source": [
"%%bash\n",
"\n",
"cat <<EOF > /content/tflite.yaml\n",
"tflite_max_detections: 25\n",
"EOF"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0U78mmqkPvB5",
"outputId": "335dbf3e-7396-4c8b-b93a-d157d46a6a02"
},
"source": [
"os.environ['PYTHONPATH'] = '/content/automl/efficientdet:' + os.environ['PYTHONPATH']\n",
"print(os.environ['PYTHONPATH'])"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/automl/efficientdet:/content/automl/efficientdet:/env/python\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "muXV9iSorGxa",
"outputId": "07cbc117-99a8-4a72-fd42-27ca3bfa7e31"
},
"source": [
"output_dir = \"/content/efficientdet-lite\"\n",
"\n",
"for model in MODELS:\n",
" ckpt_path = os.path.join(\"/content\", model)\n",
" saved_model_dir = os.path.join(\"/content\", \"saved_model_\" + model)\n",
"\n",
" # Export FP32\n",
" !python keras/inspector.py \\\n",
" --mode=export \\\n",
" --model_name=$model \\\n",
" --model_dir=$ckpt_path \\\n",
" --saved_model_dir=$saved_model_dir \\\n",
" --hparams=\"/content/tflite.yaml\" \\\n",
" --tflite=\"FP32\"\n",
" tflite_path = os.path.join(saved_model_dir, \"fp32.tflite\")\n",
" shutil.move(tflite_path, os.path.join(output_dir, model + \"_fp32.tflite\"))\n",
"\n",
" # Export FP16\n",
" !python keras/inspector.py \\\n",
" --mode=export \\\n",
" --model_name=$model \\\n",
" --model_dir=$ckpt_path \\\n",
" --saved_model_dir=$saved_model_dir \\\n",
" --hparams=\"/content/tflite.yaml\" \\\n",
" --tflite=\"FP16\"\n",
" tflite_path = os.path.join(saved_model_dir, \"fp16.tflite\")\n",
" shutil.move(tflite_path, os.path.join(output_dir, model + \"_fp16.tflite\"))\n",
"\n",
" # Export INT8\n",
" !python keras/inspector.py \\\n",
" --mode=export \\\n",
" --model_name=$model \\\n",
" --model_dir=$ckpt_path \\\n",
" --saved_model_dir=$saved_model_dir \\\n",
" --hparams=\"/content/tflite.yaml\" \\\n",
" --file_pattern=\"/content/automl/efficientdet/tfrecord/val-*.tfrecord\" \\\n",
" --tflite=\"INT8\"\n",
" tflite_path = os.path.join(saved_model_dir, \"int8.tflite\")\n",
" shutil.move(tflite_path, os.path.join(output_dir, model + \"_int8.tflite\"))\n",
"\n",
" # Edge TPU Model\n",
" int_model_path = os.path.join(output_dir, model + \"_int8.tflite\")\n",
" !edgetpu_compiler -s \\\n",
" -o \"/content/efficientdet-lite\" \\\n",
" $int_model_path"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1;30;43mストリーミング出力は最後の 5000 行に切り捨てられました。\u001b[0m\n",
"I0602 16:24:10.548872 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.574664 140680024610688 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:24:10.579947 140680024610688 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.615658 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:10.651488 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:10.686207 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.707946 140680024610688 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:24:10.713169 140680024610688 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.747356 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:10.783523 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:10.818829 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.845073 140680024610688 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:24:10.850931 140680024610688 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.887130 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:10.922513 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:10.960440 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:10.984843 140680024610688 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:24:10.990459 140680024610688 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.024636 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.061694 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.096195 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.119283 140680024610688 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:24:11.124607 140680024610688 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.158467 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.193166 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.232311 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.260741 140680024610688 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:24:11.267815 140680024610688 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.303683 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.340101 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.376028 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.398894 140680024610688 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:24:11.404773 140680024610688 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:11.440457 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.479620 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:11.515263 140680024610688 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:24:11.735046 140680024610688 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:24:11.735324 140680024610688 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:24:11.741052 140680024610688 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:24:11.778582 140680024610688 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:11.814717 140680024610688 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:11.839500 140680024610688 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:24:11.844915 140680024610688 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:24:11.882755 140680024610688 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:11.921310 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:11.959533 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:11.983043 140680024610688 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:24:11.988398 140680024610688 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.023450 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.062584 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.105993 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.127680 140680024610688 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:24:12.132912 140680024610688 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.173832 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.212771 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.260499 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.285691 140680024610688 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:24:12.291617 140680024610688 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.329477 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.368097 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.414116 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.437672 140680024610688 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:24:12.443336 140680024610688 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:12.481533 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:12.521450 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:12.559231 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:12.582930 140680024610688 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:24:12.588291 140680024610688 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:12.622838 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:12.661943 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:12.705857 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:12.727556 140680024610688 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:24:12.732615 140680024610688 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:12.766512 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:12.802821 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:12.848083 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:12.875132 140680024610688 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:24:12.881042 140680024610688 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:12.919091 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:12.955194 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:12.998941 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:13.020665 140680024610688 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:24:13.025960 140680024610688 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:13.062353 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:13.112160 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:13.149307 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.173214 140680024610688 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:24:13.179151 140680024610688 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.216030 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.256356 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.305326 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.328444 140680024610688 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:24:13.334477 140680024610688 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.371975 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.409202 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.456351 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.481541 140680024610688 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:24:13.487130 140680024610688 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.524208 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.560451 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.603103 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.626475 140680024610688 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:24:13.633295 140680024610688 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.668227 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.708375 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.754453 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.780866 140680024610688 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:24:13.787292 140680024610688 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.824745 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.863104 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:13.915583 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.939187 140680024610688 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:24:13.944578 140680024610688 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:13.983005 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:14.021381 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:14.059176 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.081829 140680024610688 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:24:14.087154 140680024610688 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.121473 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.156626 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.200453 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.222211 140680024610688 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:24:14.227580 140680024610688 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.262755 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.301456 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.348116 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.370423 140680024610688 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:24:14.376538 140680024610688 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.416936 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.453574 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.498594 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.520960 140680024610688 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:24:14.526434 140680024610688 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.561818 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.603994 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.647136 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.668636 140680024610688 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:24:14.673933 140680024610688 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.708985 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.743496 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.787717 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.809214 140680024610688 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:24:14.814314 140680024610688 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:14.850472 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:14.889628 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:14.927669 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:14.950562 140680024610688 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:24:14.956133 140680024610688 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:14.992462 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.030729 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.078817 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.102109 140680024610688 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:24:15.108121 140680024610688 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.146128 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.186964 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.234760 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.257702 140680024610688 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:24:15.263389 140680024610688 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.300166 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.338189 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.384020 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.411096 140680024610688 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:24:15.416627 140680024610688 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.451812 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.489493 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.534617 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.557354 140680024610688 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:24:15.563001 140680024610688 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.608201 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.646199 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.692110 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.715272 140680024610688 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:24:15.721256 140680024610688 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.757605 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.796284 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.844813 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.868479 140680024610688 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:24:15.874456 140680024610688 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:15.912446 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:15.959830 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:16.008683 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:16.030182 140680024610688 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:24:16.035744 140680024610688 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:16.071633 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:16.107352 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:16.141384 140680024610688 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:24:16.437592 140680024610688 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:24:16.437861 140680024610688 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:24:16.444139 140680024610688 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:24:16.482268 140680024610688 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:16.518480 140680024610688 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:16.542564 140680024610688 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:24:16.548387 140680024610688 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:24:16.584098 140680024610688 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:16.620435 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:16.655894 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:16.679290 140680024610688 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:24:16.684637 140680024610688 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:16.721754 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:16.758037 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:16.791971 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:16.813576 140680024610688 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:24:16.819391 140680024610688 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:16.853757 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:16.887495 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:16.922409 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:16.945349 140680024610688 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:24:16.950929 140680024610688 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:16.984971 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:17.022340 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:17.057497 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:17.081089 140680024610688 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:24:17.086129 140680024610688 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:17.122184 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:17.159523 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:17.193433 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.216052 140680024610688 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:24:17.221630 140680024610688 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.255712 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.290693 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.332838 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.355221 140680024610688 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:24:17.360754 140680024610688 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.395692 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.433065 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.471014 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.494473 140680024610688 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:24:17.500468 140680024610688 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.535915 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.573528 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.612354 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.636073 140680024610688 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:24:17.641906 140680024610688 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:17.683063 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:17.720594 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:17.756277 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:17.780281 140680024610688 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:24:17.785898 140680024610688 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:17.820807 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:17.858698 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:17.895890 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:17.923244 140680024610688 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:24:17.928745 140680024610688 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:17.969467 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.005774 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.043610 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.067337 140680024610688 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:24:18.072844 140680024610688 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.108197 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.145829 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.182873 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.211035 140680024610688 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:24:18.216354 140680024610688 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.250708 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.287038 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.322660 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.345253 140680024610688 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:24:18.350975 140680024610688 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.385136 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.421789 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.458228 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.483304 140680024610688 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:24:18.489001 140680024610688 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:18.529431 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.567349 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:18.605032 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:18.629225 140680024610688 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:24:18.635154 140680024610688 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:18.671472 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:18.711267 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:18.748432 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:18.772199 140680024610688 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:24:18.778175 140680024610688 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:18.816812 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:18.855248 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:18.892871 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:18.920031 140680024610688 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:24:18.925539 140680024610688 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:18.962149 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.002805 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.040786 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:19.065261 140680024610688 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:24:19.071142 140680024610688 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:19.108288 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.145279 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.182532 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:19.208458 140680024610688 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:24:19.214831 140680024610688 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:19.251781 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.289203 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.324492 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:19.348208 140680024610688 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:24:19.354219 140680024610688 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:19.390749 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:19.430155 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:19.466245 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.489649 140680024610688 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:24:19.495510 140680024610688 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.530164 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.565364 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.601048 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.623185 140680024610688 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:24:19.628353 140680024610688 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.662553 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.697772 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.737993 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.759291 140680024610688 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:24:19.764554 140680024610688 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.800176 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.837280 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.873028 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.896002 140680024610688 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:24:19.901628 140680024610688 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:19.941795 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:19.977548 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.015401 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.037274 140680024610688 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:24:20.043148 140680024610688 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.078861 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.113759 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.147824 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.169663 140680024610688 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:24:20.175017 140680024610688 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.209007 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.249227 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.288287 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.312217 140680024610688 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:24:20.318404 140680024610688 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.354367 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.394831 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.433265 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.457410 140680024610688 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:24:20.463942 140680024610688 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:20.501973 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.540322 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:20.577388 140680024610688 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:24:20.796002 140680024610688 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:24:20.796277 140680024610688 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:24:20.802216 140680024610688 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:24:20.840868 140680024610688 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:20.875997 140680024610688 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:20.898624 140680024610688 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:24:20.903861 140680024610688 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:24:20.940460 140680024610688 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:20.979073 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:21.023701 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.048775 140680024610688 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:24:21.054869 140680024610688 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.092285 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.129364 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.173338 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.196209 140680024610688 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:24:21.201739 140680024610688 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.239533 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.277121 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.323709 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.346242 140680024610688 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:24:21.352049 140680024610688 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.389437 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.430099 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.478409 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.506056 140680024610688 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:24:21.511835 140680024610688 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:21.554769 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:21.592354 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:21.630529 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:21.652852 140680024610688 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:24:21.658587 140680024610688 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:21.695844 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:21.737940 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:21.784459 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:21.808461 140680024610688 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:24:21.814374 140680024610688 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:21.853034 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:21.891123 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:21.937411 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:21.960464 140680024610688 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:24:21.966045 140680024610688 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:22.001330 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:22.039233 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:22.085959 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:22.110190 140680024610688 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:24:22.115837 140680024610688 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:22.159541 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:22.198276 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:22.236611 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.260129 140680024610688 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:24:22.266078 140680024610688 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.303494 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.341582 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.388606 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.411440 140680024610688 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:24:22.417253 140680024610688 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.456890 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.496683 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.544888 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.571605 140680024610688 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:24:22.578298 140680024610688 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.615329 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.653814 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.702718 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.725856 140680024610688 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:24:22.731481 140680024610688 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.771025 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.808330 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.856616 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.879840 140680024610688 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:24:22.885524 140680024610688 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:22.921675 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:22.958740 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:23.004744 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:23.029032 140680024610688 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:24:23.036127 140680024610688 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:23.080212 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:23.118629 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:23.156069 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.179782 140680024610688 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:24:23.185664 140680024610688 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.223170 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.262782 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.309788 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.332370 140680024610688 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:24:23.340327 140680024610688 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.379001 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.415864 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.460650 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.483162 140680024610688 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:24:23.488693 140680024610688 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.525805 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.562374 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.605390 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.627582 140680024610688 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:24:23.632934 140680024610688 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.672044 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.708194 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.752300 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.773720 140680024610688 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:24:23.779153 140680024610688 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.813019 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.848753 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:23.893881 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.916625 140680024610688 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:24:23.922194 140680024610688 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:23.964454 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:24.002620 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:24.046164 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.069946 140680024610688 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:24:24.075479 140680024610688 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.111431 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.148247 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.191571 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.213419 140680024610688 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:24:24.218761 140680024610688 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.259397 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.296648 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.342615 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.365523 140680024610688 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:24:24.371301 140680024610688 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.407556 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.447032 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.494265 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.517287 140680024610688 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:24:24.522889 140680024610688 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.560092 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.596836 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.641364 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.664197 140680024610688 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:24:24.669721 140680024610688 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.705914 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.746322 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.798702 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.823196 140680024610688 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:24:24.828932 140680024610688 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.867002 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.903728 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:24.950315 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:24.973174 140680024610688 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:24:24.978640 140680024610688 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:25.014685 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:25.057831 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:25.105794 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:25.128809 140680024610688 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:24:25.134597 140680024610688 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:25.172130 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:25.210481 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:25.247684 140680024610688 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:24:25.557605 140680024610688 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:24:25.558135 140680024610688 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:24:25.566587 140680024610688 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:24:25.602355 140680024610688 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:25.636268 140680024610688 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:25.659171 140680024610688 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:24:25.664746 140680024610688 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:24:25.699543 140680024610688 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:25.735348 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:25.774805 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:25.798242 140680024610688 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:24:25.804477 140680024610688 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:25.840372 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:25.886031 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:25.922434 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:25.944620 140680024610688 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:24:25.949921 140680024610688 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:25.983749 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:26.019354 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:26.057403 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:26.082001 140680024610688 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:24:26.087807 140680024610688 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:26.123552 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:26.160817 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:26.197305 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:26.221777 140680024610688 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:24:26.227626 140680024610688 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:26.264661 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:26.303200 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:26.338804 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.362980 140680024610688 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:24:26.368544 140680024610688 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.401915 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.439837 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.483512 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.507954 140680024610688 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:24:26.513832 140680024610688 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.547594 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.591584 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.628955 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.651853 140680024610688 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:24:26.657796 140680024610688 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.694318 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.731229 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.770138 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.792576 140680024610688 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:24:26.798310 140680024610688 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:26.831206 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:26.867762 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:26.901248 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:26.928382 140680024610688 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:24:26.933529 140680024610688 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:26.966915 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.003023 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.038108 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.062154 140680024610688 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:24:27.069808 140680024610688 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.107759 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.143326 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.178157 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.200711 140680024610688 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:24:27.206725 140680024610688 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.242486 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.278286 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.312659 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.335145 140680024610688 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:24:27.340719 140680024610688 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.376574 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.418833 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.455067 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.479199 140680024610688 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:24:27.484629 140680024610688 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.520639 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.557790 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.594533 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.618066 140680024610688 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:24:27.623559 140680024610688 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:27.660187 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.703452 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:27.739314 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:27.762447 140680024610688 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:24:27.769065 140680024610688 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:27.803794 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:27.840889 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:27.878990 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:27.902475 140680024610688 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:24:27.908254 140680024610688 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:27.942944 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:27.980405 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.016175 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.039267 140680024610688 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:24:28.045251 140680024610688 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.081116 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.122202 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.158682 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.182318 140680024610688 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:24:28.188308 140680024610688 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.225500 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.263087 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.304594 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.327106 140680024610688 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:24:28.332594 140680024610688 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.367859 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.403523 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.439907 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.464577 140680024610688 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:24:28.469863 140680024610688 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:28.504432 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:28.539523 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:28.575251 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:28.602432 140680024610688 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:24:28.608677 140680024610688 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:28.644029 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:28.680789 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:28.715971 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:28.739388 140680024610688 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:24:28.744712 140680024610688 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:28.781346 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:28.815553 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:28.850481 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:28.875833 140680024610688 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:24:28.882283 140680024610688 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:28.917053 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:28.953687 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:28.994766 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.018989 140680024610688 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:24:29.024531 140680024610688 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.061137 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.099327 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.137588 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.161281 140680024610688 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:24:29.166813 140680024610688 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.203835 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.242998 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.282474 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.309379 140680024610688 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:24:29.314993 140680024610688 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.350325 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.386999 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.423042 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.445131 140680024610688 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:24:29.450693 140680024610688 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.487449 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.523736 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.559208 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.589600 140680024610688 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:24:29.597770 140680024610688 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:29.638608 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.678100 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:29.718806 140680024610688 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:24:29.920484 140680024610688 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:24:29.920766 140680024610688 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:24:29.926639 140680024610688 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:24:29.966080 140680024610688 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:30.003655 140680024610688 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:30.027676 140680024610688 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:24:30.033425 140680024610688 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:24:30.072998 140680024610688 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:30.113511 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:30.149829 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.172379 140680024610688 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:24:30.178245 140680024610688 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.223318 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.263979 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.312825 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.337249 140680024610688 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:24:30.343199 140680024610688 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.381786 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.427060 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.477810 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.502150 140680024610688 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:24:30.508134 140680024610688 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.547069 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.587420 140680024610688 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.638300 140680024610688 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.661146 140680024610688 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:24:30.667061 140680024610688 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:30.703743 140680024610688 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:30.741511 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:30.777594 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:30.807281 140680024610688 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:24:30.813043 140680024610688 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:30.851444 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:30.890337 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:30.938160 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:30.961574 140680024610688 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:24:30.967568 140680024610688 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:31.006190 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:31.043726 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:31.088719 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:31.114713 140680024610688 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:24:31.121255 140680024610688 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:31.160315 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:31.197365 140680024610688 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:31.241656 140680024610688 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:31.263780 140680024610688 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:24:31.269255 140680024610688 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:31.306045 140680024610688 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:31.343055 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:31.378657 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.403298 140680024610688 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:24:31.409440 140680024610688 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.448382 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.489465 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.535433 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.558522 140680024610688 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:24:31.564768 140680024610688 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.607104 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.647005 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.695697 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.721656 140680024610688 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:24:31.727640 140680024610688 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.765430 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.808717 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.858357 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.882205 140680024610688 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:24:31.888311 140680024610688 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:31.926957 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:31.966106 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:32.015888 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:32.039232 140680024610688 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:24:32.045547 140680024610688 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:32.084631 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:32.124454 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:32.174698 140680024610688 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:32.198718 140680024610688 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:24:32.204665 140680024610688 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:32.243034 140680024610688 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:32.283798 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:32.325617 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.350167 140680024610688 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:24:32.355709 140680024610688 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.393821 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.434369 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.485319 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.510147 140680024610688 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:24:32.516340 140680024610688 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.556158 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.596746 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.646073 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.668984 140680024610688 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:24:32.674566 140680024610688 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.711453 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.748796 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.796725 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.825680 140680024610688 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:24:32.831834 140680024610688 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.869836 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.907154 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:32.952569 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:32.975011 140680024610688 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:24:32.980533 140680024610688 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:33.016468 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:33.053807 140680024610688 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:33.099328 140680024610688 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:33.127053 140680024610688 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:24:33.132384 140680024610688 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:33.170494 140680024610688 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:33.208033 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:33.244388 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.267037 140680024610688 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:24:33.272471 140680024610688 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.308803 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.346277 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.391521 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.416726 140680024610688 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:24:33.423221 140680024610688 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.461879 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.499763 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.544712 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.568608 140680024610688 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:24:33.574303 140680024610688 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.610833 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.648380 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.694091 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.724202 140680024610688 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:24:33.730125 140680024610688 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.768192 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.810033 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.858577 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.881582 140680024610688 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:24:33.887320 140680024610688 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:33.925265 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:33.963629 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.013091 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:34.037661 140680024610688 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:24:34.043374 140680024610688 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:34.082176 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.121744 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.172617 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:34.196468 140680024610688 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:24:34.202538 140680024610688 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:34.247643 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.286770 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.334679 140680024610688 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:34.358819 140680024610688 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:24:34.364879 140680024610688 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:34.402752 140680024610688 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.445715 140680024610688 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:34.484812 140680024610688 api.py:446] Project shape: (None, 20, 20, 448)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:39.936197 140680024610688 efficientnet_model.py:374] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:39.946444 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:39.954177 140680024610688 efficientnet_model.py:414] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:39.959810 140680024610688 efficientnet_model.py:374] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:24:39.968579 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:24:39.976929 140680024610688 efficientnet_model.py:414] Project shape: (None, 320, 320, 24)\n",
"I0602 16:24:40.024426 140680024610688 efficientnet_model.py:374] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.031425 140680024610688 efficientnet_model.py:390] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:40.039352 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:40.046148 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.053637 140680024610688 efficientnet_model.py:374] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:24:40.062337 140680024610688 efficientnet_model.py:390] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:24:40.071508 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:24:40.080062 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.131335 140680024610688 efficientnet_model.py:374] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.138695 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.146257 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.153789 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.160670 140680024610688 efficientnet_model.py:374] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.168549 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.177571 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.193817 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.252750 140680024610688 efficientnet_model.py:374] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.260181 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.268269 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.276176 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.283596 140680024610688 efficientnet_model.py:374] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.292129 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.301494 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.318662 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.373572 140680024610688 efficientnet_model.py:374] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.381123 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.388964 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.396347 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.404081 140680024610688 efficientnet_model.py:374] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.412927 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.422386 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.440201 140680024610688 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.497701 140680024610688 efficientnet_model.py:374] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.505263 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.513475 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:40.521190 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.529549 140680024610688 efficientnet_model.py:374] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:24:40.539489 140680024610688 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:24:40.550058 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:24:40.559540 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.616476 140680024610688 efficientnet_model.py:374] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.624035 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.632129 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.640693 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.648013 140680024610688 efficientnet_model.py:374] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.656317 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.666177 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.683853 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.740555 140680024610688 efficientnet_model.py:374] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.747296 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.755398 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.762726 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.769703 140680024610688 efficientnet_model.py:374] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.777980 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.786752 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.803021 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.862570 140680024610688 efficientnet_model.py:374] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.870388 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.878954 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.886783 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.897979 140680024610688 efficientnet_model.py:374] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.907253 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.916975 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:24:40.934326 140680024610688 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:24:40.991311 140680024610688 efficientnet_model.py:374] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:40.998471 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:41.006761 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:41.013797 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.020929 140680024610688 efficientnet_model.py:374] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:24:41.029522 140680024610688 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:24:41.039699 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:24:41.048420 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.099771 140680024610688 efficientnet_model.py:374] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.106484 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.114113 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.121606 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.128618 140680024610688 efficientnet_model.py:374] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.136700 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.147473 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.165364 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.217700 140680024610688 efficientnet_model.py:374] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.224478 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.232251 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.239677 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.247071 140680024610688 efficientnet_model.py:374] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.258537 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.268524 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.285227 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.338277 140680024610688 efficientnet_model.py:374] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.347657 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.357847 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.365364 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.372590 140680024610688 efficientnet_model.py:374] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.381889 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.390848 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.406744 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.461426 140680024610688 efficientnet_model.py:374] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.469630 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.478254 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.485834 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.493482 140680024610688 efficientnet_model.py:374] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.501973 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.511686 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.528969 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.591447 140680024610688 efficientnet_model.py:374] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.599025 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.607369 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.615561 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.623188 140680024610688 efficientnet_model.py:374] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.631925 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.641650 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.659587 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.724446 140680024610688 efficientnet_model.py:374] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.732236 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.740605 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.749052 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:41.756578 140680024610688 efficientnet_model.py:374] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:24:41.765383 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.774826 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:24:41.783680 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:41.840651 140680024610688 efficientnet_model.py:374] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.849475 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:41.857690 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:41.866284 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:41.874517 140680024610688 efficientnet_model.py:374] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:41.883171 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:41.892943 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:41.910455 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:41.968693 140680024610688 efficientnet_model.py:374] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:41.976067 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:41.984360 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:41.992719 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.000329 140680024610688 efficientnet_model.py:374] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.009174 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.018898 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.036657 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.094023 140680024610688 efficientnet_model.py:374] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:42.102147 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.110650 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.118769 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.126379 140680024610688 efficientnet_model.py:374] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.135342 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.144853 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.165700 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.222170 140680024610688 efficientnet_model.py:374] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:42.229499 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.238382 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.246191 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.254192 140680024610688 efficientnet_model.py:374] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.262680 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.273565 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.292075 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.349213 140680024610688 efficientnet_model.py:374] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:42.357841 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.368405 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.376465 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.384141 140680024610688 efficientnet_model.py:374] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.392912 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.402714 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.419831 140680024610688 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.476005 140680024610688 efficientnet_model.py:374] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:42.483140 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.491055 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:42.498483 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:42.506027 140680024610688 efficientnet_model.py:374] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:24:42.514386 140680024610688 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:24:42.523445 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:24:42.531687 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:42.590566 140680024610688 efficientnet_model.py:374] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:42.597318 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:42.604932 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:42.612157 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:42.619360 140680024610688 efficientnet_model.py:374] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:42.627380 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:42.636091 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:42.652609 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:42.706871 140680024610688 efficientnet_model.py:374] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:42.713605 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:42.722741 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:42.730654 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:42.738093 140680024610688 efficientnet_model.py:374] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.372567 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.382295 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.398700 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.452810 140680024610688 efficientnet_model.py:374] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:43.460426 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.469321 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.476949 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.483870 140680024610688 efficientnet_model.py:374] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.492216 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.501618 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.518832 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.573136 140680024610688 efficientnet_model.py:374] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:43.580373 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.588223 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.595829 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.602932 140680024610688 efficientnet_model.py:374] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.611437 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.620581 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.636912 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.691491 140680024610688 efficientnet_model.py:374] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:43.698590 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.706687 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.714998 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.722595 140680024610688 efficientnet_model.py:374] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.731737 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.741047 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.757115 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.810954 140680024610688 efficientnet_model.py:374] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:43.818421 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.826663 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.834460 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.841809 140680024610688 efficientnet_model.py:374] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.850355 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.859888 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.882537 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.941099 140680024610688 efficientnet_model.py:374] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:43.948648 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.957050 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.965303 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.972858 140680024610688 efficientnet_model.py:374] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:43.981607 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:43.991417 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:44.009192 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:24:44.067565 140680024610688 efficientnet_model.py:374] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:24:44.076581 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:44.085361 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:44.092960 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 448)\n",
"I0602 16:24:44.100526 140680024610688 efficientnet_model.py:374] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:24:44.109583 140680024610688 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:24:44.119341 140680024610688 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:24:44.128266 140680024610688 efficientnet_model.py:414] Project shape: (None, 20, 20, 448)\n",
"W0602 16:25:04.269968 140680024610688 save.py:243] Found untraced functions such as conv2d_layer_call_fn, conv2d_layer_call_and_return_conditional_losses, tpu_batch_normalization_layer_call_fn, tpu_batch_normalization_layer_call_and_return_conditional_losses, conv2d_layer_call_fn while saving (showing 5 of 2570). These functions will not be directly callable after loading.\n",
"WARNING:tensorflow:FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
"\n",
"FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
"W0602 16:25:15.724985 140680024610688 save.py:1240] FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
"\n",
"FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
"INFO:tensorflow:Assets written to: /content/saved_model_efficientdet-lite4/assets\n",
"I0602 16:25:17.143029 140680024610688 builder_impl.py:775] Assets written to: /content/saved_model_efficientdet-lite4/assets\n",
"I0602 16:25:18.856098 140680024610688 infer_lib.py:360] Model saved at /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:25:18.908366: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:25:18.909306: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
"2021-06-02 16:25:18.909475: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session\n",
"2021-06-02 16:25:18.909856: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:25:18.910705: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
"pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
"coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
"2021-06-02 16:25:18.910822: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:25:18.911635: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:25:18.912468: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
"2021-06-02 16:25:18.912537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:25:18.912555: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n",
"2021-06-02 16:25:18.912568: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n",
"2021-06-02 16:25:18.912661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:25:18.913534: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:25:18.914325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15433 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"2021-06-02 16:25:18.914729: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz\n",
"2021-06-02 16:25:19.009419: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1144] Optimization results for grappler item: graph_to_optimize\n",
" function_optimizer: Graph size after: 3626 nodes (14), 8344 edges (8), time = 39.67ms.\n",
" function_optimizer: function_optimizer did nothing. time = 2.094ms.\n",
"\n",
"I0602 16:25:22.446680 140680024610688 infer_lib.py:367] Frozen graph saved at /content/saved_model_efficientdet-lite4/efficientdet-lite4_frozen.pb\n",
"2021-06-02 16:25:53.087239: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:345] Ignored output_format.\n",
"2021-06-02 16:25:53.087298: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:348] Ignored drop_control_dependency.\n",
"2021-06-02 16:25:53.087315: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored change_concat_input_ranges.\n",
"2021-06-02 16:25:53.088357: I tensorflow/cc/saved_model/reader.cc:38] Reading SavedModel from: /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:25:53.258519: I tensorflow/cc/saved_model/reader.cc:90] Reading meta graph with tags { serve }\n",
"2021-06-02 16:25:53.258610: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:25:53.258716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:25:53.258737: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] \n",
"2021-06-02 16:25:53.953394: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.\n",
"2021-06-02 16:25:55.587544: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:25:56.244887: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 3156507 microseconds.\n",
"2021-06-02 16:25:58.125559: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2021-06-02 16:26:00.085742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:00.086757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
"pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
"coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
"2021-06-02 16:26:00.086863: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:00.087769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:00.088552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
"2021-06-02 16:26:00.088615: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:26:00.088637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n",
"2021-06-02 16:26:00.088661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n",
"2021-06-02 16:26:00.088790: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:00.089585: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:00.090392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15433 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"I0602 16:26:01.667717 140680024610688 infer_lib.py:419] TFLite is saved at /content/saved_model_efficientdet-lite4/fp16.tflite\n",
"Model are exported to /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:26:06.147100: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
"2021-06-02 16:26:07.918775: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1\n",
"2021-06-02 16:26:07.933531: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:07.934429: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
"pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
"coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
"2021-06-02 16:26:07.934484: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
"2021-06-02 16:26:07.937279: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11\n",
"2021-06-02 16:26:07.937359: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11\n",
"2021-06-02 16:26:07.939082: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10\n",
"2021-06-02 16:26:07.939428: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10\n",
"2021-06-02 16:26:07.941325: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.10\n",
"2021-06-02 16:26:07.942021: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11\n",
"2021-06-02 16:26:07.942219: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8\n",
"2021-06-02 16:26:07.942302: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:07.943165: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:07.943931: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
"I0602 16:26:07.964317 140462829037440 infer_lib.py:325] Export model without preprocessing.\n",
"2021-06-02 16:26:07.968723: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:07.969562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
"pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
"coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
"2021-06-02 16:26:07.969670: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:07.970593: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:07.971446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
"2021-06-02 16:26:07.971514: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
"2021-06-02 16:26:08.576362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:26:08.576417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n",
"2021-06-02 16:26:08.576434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n",
"2021-06-02 16:26:08.576619: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:08.577753: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:08.578728: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:26:08.579661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15433 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"I0602 16:26:08.585851 140462829037440 efficientnet_lite_builder.py:100] global_params= GlobalParams(batch_norm_momentum=0.99, batch_norm_epsilon=0.001, dropout_rate=0.3, data_format='channels_last', num_classes=1000, width_coefficient=1.4, depth_coefficient=1.8, depth_divisor=8, min_depth=None, survival_prob=0.8, relu_fn=functools.partial(<function activation_fn at 0x7fbfa1c3d170>, act_type='relu6'), batch_norm=<class 'utils.BatchNormalization'>, use_se=False, local_pooling=True, condconv_num_experts=None, clip_projection_output=False, blocks_args=['r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', 'r1_k3_s11_e6_i192_o320_se0.25'], fix_head_stem=True, grad_checkpoint=False)\n",
"I0602 16:26:08.885374 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.886360 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.887322 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.888206 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.889097 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.889949 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.890766 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.891633 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"I0602 16:26:08.893027 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.893851 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.894929 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.895706 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.896609 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.897501 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.898399 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.899219 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"I0602 16:26:08.900578 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.901486 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.902307 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.903227 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.904206 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.905054 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.906016 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.907042 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"I0602 16:26:08.908415 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.909432 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.910385 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.911335 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.912195 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.913031 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.913891 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.914784 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"I0602 16:26:08.916114 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.917118 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.918004 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.918846 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.919731 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.920655 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.921687 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.922664 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"I0602 16:26:08.924171 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.925113 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.926002 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.926999 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.927816 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.928727 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.929625 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.930639 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"I0602 16:26:08.932245 140462829037440 efficientdet_keras.py:761] fnode 0 : {'feat_level': 6, 'inputs_offsets': [3, 4]}\n",
"I0602 16:26:08.933156 140462829037440 efficientdet_keras.py:761] fnode 1 : {'feat_level': 5, 'inputs_offsets': [2, 5]}\n",
"I0602 16:26:08.934687 140462829037440 efficientdet_keras.py:761] fnode 2 : {'feat_level': 4, 'inputs_offsets': [1, 6]}\n",
"I0602 16:26:08.935650 140462829037440 efficientdet_keras.py:761] fnode 3 : {'feat_level': 3, 'inputs_offsets': [0, 7]}\n",
"I0602 16:26:08.936657 140462829037440 efficientdet_keras.py:761] fnode 4 : {'feat_level': 4, 'inputs_offsets': [1, 7, 8]}\n",
"I0602 16:26:08.937668 140462829037440 efficientdet_keras.py:761] fnode 5 : {'feat_level': 5, 'inputs_offsets': [2, 6, 9]}\n",
"I0602 16:26:08.938589 140462829037440 efficientdet_keras.py:761] fnode 6 : {'feat_level': 6, 'inputs_offsets': [3, 5, 10]}\n",
"I0602 16:26:08.939470 140462829037440 efficientdet_keras.py:761] fnode 7 : {'feat_level': 7, 'inputs_offsets': [4, 11]}\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:26:10.579806 140462829037440 api.py:446] Built stem stem : (1, 320, 320, 32)\n",
"I0602 16:26:11.926457 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:26:12.338059 140462829037440 api.py:446] Block blocks_0 input shape: (1, 320, 320, 32)\n",
"I0602 16:26:12.386336 140462829037440 api.py:446] DWConv shape: (1, 320, 320, 32)\n",
"I0602 16:26:12.431183 140462829037440 api.py:446] Project shape: (1, 320, 320, 24)\n",
"I0602 16:26:12.488373 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:12.518267 140462829037440 api.py:446] Block blocks_1 input shape: (1, 320, 320, 24)\n",
"I0602 16:26:12.561990 140462829037440 api.py:446] Expand shape: (1, 320, 320, 144)\n",
"I0602 16:26:12.614698 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 144)\n",
"I0602 16:26:12.658897 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:12.691269 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:12.722861 140462829037440 api.py:446] Block blocks_2 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:12.769427 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:12.816108 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:26:12.933060 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:12.965024 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:26:12.996350 140462829037440 api.py:446] Block blocks_3 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:13.042493 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:13.090770 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:26:13.137028 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:13.168482 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:13.199399 140462829037440 api.py:446] Block blocks_4 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:13.243995 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:13.287001 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:26:13.328486 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:13.357206 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:13.392578 140462829037440 api.py:446] Block blocks_5 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:13.438978 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:13.484498 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 192)\n",
"I0602 16:26:13.528150 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:13.558888 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:26:13.592786 140462829037440 api.py:446] Block blocks_6 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:13.642465 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:13.688544 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:26:13.736893 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:13.769213 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:13.799208 140462829037440 api.py:446] Block blocks_7 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:13.840890 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:13.887020 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:26:13.930623 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:13.962113 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:13.992598 140462829037440 api.py:446] Block blocks_8 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:14.038362 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:14.084832 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:26:14.130709 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:14.161844 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:14.194281 140462829037440 api.py:446] Block blocks_9 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:14.241363 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:14.287245 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 336)\n",
"I0602 16:26:14.331240 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.363294 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:14.395218 140462829037440 api.py:446] Block blocks_10 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.442355 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:14.495036 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:14.540764 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.571121 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:14.601390 140462829037440 api.py:446] Block blocks_11 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.644927 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:14.688621 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:14.734221 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.763621 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:26:14.795807 140462829037440 api.py:446] Block blocks_12 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.837639 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:14.881058 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:14.922882 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:14.951826 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:14.979666 140462829037440 api.py:446] Block blocks_13 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:15.030829 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:15.076800 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:15.122735 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:15.154488 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:15.183652 140462829037440 api.py:446] Block blocks_14 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:15.229626 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:15.274508 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:15.318785 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:15.350116 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:26:15.379585 140462829037440 api.py:446] Block blocks_15 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:15.427655 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:15.473688 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:15.520082 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:15.551831 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:15.580681 140462829037440 api.py:446] Block blocks_16 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:15.630183 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:15.678613 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:15.728600 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:15.761106 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:15.791834 140462829037440 api.py:446] Block blocks_17 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:15.841327 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:15.886744 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:15.929226 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:15.958952 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:26:15.987154 140462829037440 api.py:446] Block blocks_18 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.030805 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.075464 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.126579 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.160214 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:16.190148 140462829037440 api.py:446] Block blocks_19 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.234805 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.280736 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.326785 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.359321 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:16.389517 140462829037440 api.py:446] Block blocks_20 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.438534 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.492413 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.539510 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.571715 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:26:16.604259 140462829037440 api.py:446] Block blocks_21 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:16.650609 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:16.695948 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 960)\n",
"I0602 16:26:16.742171 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:16.774160 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:16.806756 140462829037440 api.py:446] Block blocks_22 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:16.853471 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:16.898291 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:16.940913 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:16.969783 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:16.997691 140462829037440 api.py:446] Block blocks_23 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.044434 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.089465 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.137925 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.170343 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:17.200351 140462829037440 api.py:446] Block blocks_24 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.245002 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.292964 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.345394 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.379603 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:17.412216 140462829037440 api.py:446] Block blocks_25 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.467064 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.519260 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.565190 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.597362 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:17.628664 140462829037440 api.py:446] Block blocks_26 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.676762 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.722000 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.766135 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.797335 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:17.832624 140462829037440 api.py:446] Block blocks_27 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.876602 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.919811 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:17.961434 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:17.991120 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:18.023728 140462829037440 api.py:446] Block blocks_28 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:18.065557 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:18.109833 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:18.154535 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:18.184806 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:18.216001 140462829037440 api.py:446] Block blocks_29 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:18.262807 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:18.308516 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:18.353334 140462829037440 api.py:446] Project shape: (1, 20, 20, 448)\n",
"I0602 16:26:22.526815 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
"W0602 16:26:22.535944 140462829037440 deprecation.py:534] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py:5049: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
"I0602 16:26:22.657784 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_0/depthwise_conv2d/depthwise_kernel:0 from efficientnet-lite4/blocks_0/depthwise_conv2d/depthwise_kernel/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.658220 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_0/tpu_batch_normalization/gamma:0 from efficientnet-lite4/blocks_0/tpu_batch_normalization/gamma/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.658566 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_0/tpu_batch_normalization/beta:0 from efficientnet-lite4/blocks_0/tpu_batch_normalization/beta/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.659046 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_0/conv2d/kernel:0 from efficientnet-lite4/blocks_0/conv2d/kernel/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.659424 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_0/tpu_batch_normalization_1/gamma:0 from efficientnet-lite4/blocks_0/tpu_batch_normalization_1/gamma/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.659793 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_0/tpu_batch_normalization_1/beta:0 from efficientnet-lite4/blocks_0/tpu_batch_normalization_1/beta/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.660204 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_1/conv2d/kernel:0 from efficientnet-lite4/blocks_1/conv2d/kernel/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.660633 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_1/tpu_batch_normalization/gamma:0 from efficientnet-lite4/blocks_1/tpu_batch_normalization/gamma/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.661017 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_1/tpu_batch_normalization/beta:0 from efficientnet-lite4/blocks_1/tpu_batch_normalization/beta/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:22.661443 140462829037440 util_keras.py:206] Init efficientnet-lite4/blocks_1/depthwise_conv2d/depthwise_kernel:0 from efficientnet-lite4/blocks_1/depthwise_conv2d/depthwise_kernel/ExponentialMovingAverage (/content/efficientdet-lite4/model)\n",
"I0602 16:26:23.577325 140462829037440 api.py:446] Built stem stem : (1, 320, 320, 32)\n",
"I0602 16:26:23.607529 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:26:23.639721 140462829037440 api.py:446] Block blocks_0 input shape: (1, 320, 320, 32)\n",
"I0602 16:26:23.681560 140462829037440 api.py:446] DWConv shape: (1, 320, 320, 32)\n",
"I0602 16:26:23.722775 140462829037440 api.py:446] Project shape: (1, 320, 320, 24)\n",
"I0602 16:26:23.754777 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:23.784620 140462829037440 api.py:446] Block blocks_1 input shape: (1, 320, 320, 24)\n",
"I0602 16:26:23.825454 140462829037440 api.py:446] Expand shape: (1, 320, 320, 144)\n",
"I0602 16:26:23.873773 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 144)\n",
"I0602 16:26:23.916787 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:23.951392 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:23.983381 140462829037440 api.py:446] Block blocks_2 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.023687 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.073691 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.116031 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.148995 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:26:24.180130 140462829037440 api.py:446] Block blocks_3 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.222445 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.265561 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.306336 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.342402 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:24.375270 140462829037440 api.py:446] Block blocks_4 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.417268 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.459101 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.502843 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.539741 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:24.573247 140462829037440 api.py:446] Block blocks_5 input shape: (1, 160, 160, 32)\n",
"I0602 16:26:24.615582 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:26:24.661041 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 192)\n",
"I0602 16:26:24.700152 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:24.732865 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:26:24.763993 140462829037440 api.py:446] Block blocks_6 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:24.802301 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:24.845348 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:26:24.889126 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:24.921060 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:24.956490 140462829037440 api.py:446] Block blocks_7 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:24.996841 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:25.040493 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:26:25.082645 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:25.115777 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:25.147757 140462829037440 api.py:446] Block blocks_8 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:25.187258 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:25.229960 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:26:25.280323 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:26:25.315290 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:25.348026 140462829037440 api.py:446] Block blocks_9 input shape: (1, 80, 80, 56)\n",
"I0602 16:26:25.389803 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:26:25.432325 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 336)\n",
"I0602 16:26:25.474369 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:25.508646 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:25.542745 140462829037440 api.py:446] Block blocks_10 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:25.584956 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:25.631422 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:25.676306 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:25.711580 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:25.748627 140462829037440 api.py:446] Block blocks_11 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:25.793637 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:25.840067 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:25.884104 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:25.919684 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:26:25.953393 140462829037440 api.py:446] Block blocks_12 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:25.995053 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.039756 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.083481 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:26.118693 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:26.151277 140462829037440 api.py:446] Block blocks_13 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:26.192779 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.238642 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.286375 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:26.318936 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:26.350431 140462829037440 api.py:446] Block blocks_14 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:26.390394 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.431187 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.473390 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:26:26.506447 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:26:26.540488 140462829037440 api.py:446] Block blocks_15 input shape: (1, 40, 40, 112)\n",
"I0602 16:26:26.588792 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.633956 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:26:26.677199 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:26.710843 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:26.743270 140462829037440 api.py:446] Block blocks_16 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:26.785400 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:26.828546 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:26.874789 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:26.908599 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:26.938971 140462829037440 api.py:446] Block blocks_17 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:26.978274 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.020624 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.067354 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.103314 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:26:27.136691 140462829037440 api.py:446] Block blocks_18 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.181303 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.226568 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.272176 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.307622 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:27.340179 140462829037440 api.py:446] Block blocks_19 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.385439 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.428695 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.474219 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.510069 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:27.544999 140462829037440 api.py:446] Block blocks_20 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.591964 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.635550 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.681574 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.715312 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:26:27.746594 140462829037440 api.py:446] Block blocks_21 input shape: (1, 40, 40, 160)\n",
"I0602 16:26:27.794331 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:26:27.839741 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 960)\n",
"I0602 16:26:27.884988 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:27.920208 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:27.953608 140462829037440 api.py:446] Block blocks_22 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:27.999884 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.043324 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.089136 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.125528 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:28.159692 140462829037440 api.py:446] Block blocks_23 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.203775 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.249573 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.293698 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.329323 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:28.363319 140462829037440 api.py:446] Block blocks_24 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.406075 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.447325 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.490571 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.524487 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:28.558108 140462829037440 api.py:446] Block blocks_25 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.612115 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.656783 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.701675 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.739404 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:28.780824 140462829037440 api.py:446] Block blocks_26 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.825792 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.871375 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:28.915612 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:28.952035 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:28.985986 140462829037440 api.py:446] Block blocks_27 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:29.029333 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:29.075633 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:29.118618 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:29.152364 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:29.186895 140462829037440 api.py:446] Block blocks_28 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:29.229960 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:29.277565 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:29.322718 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:26:29.357988 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:29.392131 140462829037440 api.py:446] Block blocks_29 input shape: (1, 20, 20, 272)\n",
"I0602 16:26:29.435272 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:26:29.481349 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:26:29.523958 140462829037440 api.py:446] Project shape: (1, 20, 20, 448)\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:26:33.820072 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:26:33.886272 140462829037440 api.py:446] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:33.919319 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:26:33.952146 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:26:33.994625 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:26:34.035861 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:26:34.070271 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:34.107831 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:26:34.149727 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:26:34.192274 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:26:34.233800 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.267982 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:34.303679 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.347897 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:34.392944 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:34.439298 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.477418 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:26:34.519965 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.564441 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:34.618139 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:34.663684 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.697841 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:34.733492 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.777382 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:34.825330 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:34.870093 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.904442 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:34.936476 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:34.976590 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:35.023783 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:26:35.064894 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.098868 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:26:35.130526 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.170329 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.217580 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.261116 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.296571 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:35.329624 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.372790 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.418861 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.464616 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.501686 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:35.536980 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.581942 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.631714 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.679666 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.716171 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:35.750764 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:35.794298 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:35.846925 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:26:35.890665 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:35.928557 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:35.962031 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.004188 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.049081 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.092403 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.130506 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:36.163484 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.206775 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.250637 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.293473 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.328892 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:26:36.362560 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.408677 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.454346 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.502449 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.539947 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:36.574853 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.620468 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.670341 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.717705 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.753660 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:36.788970 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.833789 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.880857 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:36.933162 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:36.968153 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:26:37.000736 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:37.045297 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:37.090440 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:37.133377 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.167903 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:37.199285 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.251013 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.292345 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.332971 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.367864 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:37.399082 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.449294 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.496219 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.541059 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.577166 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:26:37.612236 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.661681 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.707274 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.752086 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.786768 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:37.823369 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.865925 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.909539 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:37.951435 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:37.984780 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:38.015978 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:38.057466 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:38.100466 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:38.148086 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:38.184269 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:26:38.218024 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:38.262731 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:38.308160 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:26:38.352229 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:38.389975 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:38.426257 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:38.471192 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:38.516555 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:38.560297 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:38.593690 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:38.626134 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:38.666964 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:38.710985 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:38.759582 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:38.796083 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:38.830446 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:38.875266 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:38.920192 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:38.966248 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.002512 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:39.039720 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.083127 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.128813 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.173764 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.212922 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:39.250225 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.294467 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.338437 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.382768 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.419475 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:39.461281 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.509057 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.558413 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.604382 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.644097 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:39.678350 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.723225 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.767193 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.810224 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.843815 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:39.879590 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:39.922740 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:39.975414 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:40.019161 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:26:42.120159 140462829037440 api.py:446] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:26:42.764751 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <backbone.efficientnet_model.Head object at 0x7fbf4e0591d0>, because it is not built.\n",
"W0602 16:26:44.104279 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <backbone.efficientnet_model.Head object at 0x7fbf4e0591d0>, because it is not built.\n",
"I0602 16:26:44.141180 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:44.141409 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:26:44.143395 140462829037440 efficientnet_model.py:374] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:26:44.151622 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:26:44.158884 140462829037440 efficientnet_model.py:414] Project shape: (None, 320, 320, 24)\n",
"I0602 16:26:44.168593 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:44.170767 140462829037440 efficientnet_model.py:374] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:26:44.177947 140462829037440 efficientnet_model.py:390] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:26:44.185979 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:26:44.193325 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.206021 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:44.208056 140462829037440 efficientnet_model.py:374] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.215312 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.223382 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.231130 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.244000 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:26:44.246022 140462829037440 efficientnet_model.py:374] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.253061 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.261252 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.268969 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.281653 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:44.283807 140462829037440 efficientnet_model.py:374] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.291112 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.299277 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.306946 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.320784 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:44.322804 140462829037440 efficientnet_model.py:374] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:44.329850 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:44.517358 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:26:44.525286 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.539052 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:26:44.541431 140462829037440 efficientnet_model.py:374] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.552484 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.562740 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.571259 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.585251 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:44.587476 140462829037440 efficientnet_model.py:374] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.595413 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.604037 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.612034 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.625355 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:44.627426 140462829037440 efficientnet_model.py:374] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.634914 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.643534 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.651548 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.665091 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:44.667349 140462829037440 efficientnet_model.py:374] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:44.675686 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:44.684602 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:26:44.692382 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.706256 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:44.708450 140462829037440 efficientnet_model.py:374] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.716249 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.724976 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.733189 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.748064 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:44.751202 140462829037440 efficientnet_model.py:374] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.759152 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.769081 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.778328 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.793092 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:26:44.795774 140462829037440 efficientnet_model.py:374] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.803594 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.812538 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.821990 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.838377 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:44.840431 140462829037440 efficientnet_model.py:374] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.848284 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.857001 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.865604 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.879371 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:44.881646 140462829037440 efficientnet_model.py:374] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.889245 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.898193 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.906998 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.921301 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:26:44.923537 140462829037440 efficientnet_model.py:374] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:44.931629 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.940556 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:44.950049 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:44.964627 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:44.966942 140462829037440 efficientnet_model.py:374] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:44.974486 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:44.983525 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:44.991975 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.006134 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:45.008400 140462829037440 efficientnet_model.py:374] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.016288 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.025087 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.033724 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.048153 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:26:45.050318 140462829037440 efficientnet_model.py:374] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.058096 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.066689 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.075010 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.088177 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:45.090308 140462829037440 efficientnet_model.py:374] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.097644 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.106114 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.113935 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.127299 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:45.129597 140462829037440 efficientnet_model.py:374] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.136813 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.146383 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.156780 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.170510 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:26:45.172624 140462829037440 efficientnet_model.py:374] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:45.180448 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:45.189157 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:26:45.196960 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.210022 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:45.211972 140462829037440 efficientnet_model.py:374] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.219221 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.227504 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.235576 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.249126 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:45.251218 140462829037440 efficientnet_model.py:374] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.258568 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.266932 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.274682 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.287700 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:45.290056 140462829037440 efficientnet_model.py:374] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.297318 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.305467 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.313639 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.326804 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:45.328830 140462829037440 efficientnet_model.py:374] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.335926 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.344349 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.354075 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.369776 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:45.372079 140462829037440 efficientnet_model.py:374] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.379199 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.387440 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.395431 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.408417 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:45.410518 140462829037440 efficientnet_model.py:374] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.417963 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.425787 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.433486 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.447922 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:45.449991 140462829037440 efficientnet_model.py:374] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.457504 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.466748 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.474716 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.488027 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:45.490368 140462829037440 efficientnet_model.py:374] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:45.497945 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.506429 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:45.513887 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 448)\n",
"I0602 16:26:45.903334 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:45.903613 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:26:45.909754 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:45.917528 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:45.925115 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:26:45.933363 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:45.941189 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:45.948987 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:26:45.957050 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:45.964930 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:45.972566 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:45.980267 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:45.987883 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:45.995848 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:26:46.003679 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:46.011608 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:46.019489 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:26:46.027361 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:46.035114 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:46.043245 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:26:46.051659 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:46.060071 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:46.067993 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:26:46.075751 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:46.084070 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:46.092787 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:46.101135 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:46.109323 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:46.117162 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:46.124853 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:46.132238 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:46.490677 140462829037440 efficientnet_model.py:374] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.500038 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.509602 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.518491 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 448)\n",
"I0602 16:26:46.552284 140462829037440 efficientnet_model.py:374] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.561563 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.570959 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.587248 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.623362 140462829037440 efficientnet_model.py:374] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.634165 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.643988 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.661041 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.697798 140462829037440 efficientnet_model.py:374] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.706469 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.716122 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.732494 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.772337 140462829037440 efficientnet_model.py:374] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.784794 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.796203 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.813809 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.852983 140462829037440 efficientnet_model.py:374] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.861974 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.871040 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.887484 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.923568 140462829037440 efficientnet_model.py:374] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.931569 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.940797 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:46.957857 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:46.998004 140462829037440 efficientnet_model.py:374] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:47.006758 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:47.016794 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:47.034970 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:47.073633 140462829037440 efficientnet_model.py:374] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.082241 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.091841 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:26:47.101918 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:47.139053 140462829037440 efficientnet_model.py:374] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.148302 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.158567 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.180676 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.219485 140462829037440 efficientnet_model.py:374] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.228419 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.238785 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.256844 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.294361 140462829037440 efficientnet_model.py:374] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.304074 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.314113 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.331448 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.369757 140462829037440 efficientnet_model.py:374] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.378945 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.388209 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.405794 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.440691 140462829037440 efficientnet_model.py:374] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.449180 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.458285 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:47.475995 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.513123 140462829037440 efficientnet_model.py:374] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.521604 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.531247 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.541340 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:47.579336 140462829037440 efficientnet_model.py:374] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.589347 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.599293 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.616984 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.654966 140462829037440 efficientnet_model.py:374] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.663779 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.673788 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.692124 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.730368 140462829037440 efficientnet_model.py:374] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.739660 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.750931 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.768199 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.812780 140462829037440 efficientnet_model.py:374] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.821788 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.831763 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.849324 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.887940 140462829037440 efficientnet_model.py:374] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.897951 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.908125 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:47.925661 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:47.964135 140462829037440 efficientnet_model.py:374] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:47.973844 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:47.987447 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:26:47.996492 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:48.031527 140462829037440 efficientnet_model.py:374] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.040526 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:48.050497 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:48.068155 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.107036 140462829037440 efficientnet_model.py:374] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.115643 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:48.125948 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:48.143160 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.178507 140462829037440 efficientnet_model.py:374] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.187056 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:48.196604 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:48.213349 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.250860 140462829037440 efficientnet_model.py:374] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.259396 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.268914 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:26:48.279561 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:48.314384 140462829037440 efficientnet_model.py:374] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.322756 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.332034 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.348604 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.385565 140462829037440 efficientnet_model.py:374] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.393867 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.403620 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.420768 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.457493 140462829037440 efficientnet_model.py:374] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.466482 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.476885 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:48.496623 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.534473 140462829037440 efficientnet_model.py:374] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:26:48.543237 140462829037440 efficientnet_model.py:390] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:26:48.552758 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:26:48.561458 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:48.593962 140462829037440 efficientnet_model.py:374] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:26:48.603173 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:26:48.611770 140462829037440 efficientnet_model.py:414] Project shape: (None, 320, 320, 24)\n",
"I0602 16:26:48.945064 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:48.945374 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:26:48.951426 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:48.959939 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:48.968894 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:26:48.977736 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:48.986233 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:48.994465 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:26:49.003084 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:49.011776 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:49.020579 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:49.029291 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:49.038668 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:49.048011 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:26:49.057044 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:49.066074 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:49.074838 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:26:49.083091 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:49.093003 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:49.101383 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:26:49.110122 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:49.118503 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:49.127044 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:26:49.135139 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:49.143721 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:49.152371 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:49.160577 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:49.169042 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:49.177497 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:49.187832 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:49.196769 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:49.848536 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:49.848840 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:26:49.854350 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:49.862097 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:49.869540 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:26:49.877146 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:49.884854 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:49.892520 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:26:49.900285 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:49.907996 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:49.916035 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:49.923479 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:49.930780 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:49.938724 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:26:49.947519 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:49.954825 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:49.963350 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:26:49.970928 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:49.979003 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:49.987193 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:26:50.214346 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:50.222308 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:50.229966 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:26:50.237503 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:50.245248 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:50.252780 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:50.260385 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:50.267924 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:50.275641 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:50.283155 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:50.293599 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:50.425866 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:50.426125 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:26:50.432466 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:50.440892 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:50.449593 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:26:50.458566 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:50.468172 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:50.476566 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:26:50.485265 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:50.494206 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:50.503304 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:50.511856 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:50.520750 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:50.529508 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:26:50.538288 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:50.547077 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:50.555868 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:26:50.564757 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:50.573630 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:50.582468 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:26:50.593336 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:50.603292 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:50.612406 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:26:50.620948 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:50.630029 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:50.640039 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:50.649187 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:50.658243 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:50.667623 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:50.677184 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:50.688246 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf4e028650>, because it is not built.\n",
"W0602 16:26:50.832660 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf4e028650>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf4e028d90>, because it is not built.\n",
"W0602 16:26:50.832924 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf4e028d90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37a8f750>, because it is not built.\n",
"W0602 16:26:50.875776 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37a8f750>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37c32990>, because it is not built.\n",
"W0602 16:26:50.875996 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37c32990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37d7c990>, because it is not built.\n",
"W0602 16:26:50.877521 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37d7c990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37e2fa10>, because it is not built.\n",
"W0602 16:26:50.877727 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37e2fa10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37d142d0>, because it is not built.\n",
"W0602 16:26:50.907419 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37d142d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37be7510>, because it is not built.\n",
"W0602 16:26:50.907618 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37be7510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375e2950>, because it is not built.\n",
"W0602 16:26:50.937270 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375e2950>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37d634d0>, because it is not built.\n",
"W0602 16:26:50.937468 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37d634d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37b4d210>, because it is not built.\n",
"W0602 16:26:50.966210 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37b4d210>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37581690>, because it is not built.\n",
"W0602 16:26:50.966418 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37581690>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37577450>, because it is not built.\n",
"W0602 16:26:50.998888 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37577450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37656050>, because it is not built.\n",
"W0602 16:26:50.999123 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37656050>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37ad0690>, because it is not built.\n",
"W0602 16:26:51.000494 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37ad0690>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375bff50>, because it is not built.\n",
"W0602 16:26:51.000702 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375bff50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37db6a50>, because it is not built.\n",
"W0602 16:26:51.029577 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37db6a50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37a06d10>, because it is not built.\n",
"W0602 16:26:51.029790 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37a06d10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37b12d90>, because it is not built.\n",
"W0602 16:26:51.031182 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37b12d90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3775edd0>, because it is not built.\n",
"W0602 16:26:51.031386 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3775edd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37a97ad0>, because it is not built.\n",
"W0602 16:26:51.054983 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37a97ad0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37d7cd50>, because it is not built.\n",
"W0602 16:26:51.055214 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37d7cd50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf377f8290>, because it is not built.\n",
"W0602 16:26:51.056752 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf377f8290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37b7da10>, because it is not built.\n",
"W0602 16:26:51.056967 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37b7da10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37ad02d0>, because it is not built.\n",
"W0602 16:26:51.058359 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37ad02d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37d666d0>, because it is not built.\n",
"W0602 16:26:51.058575 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37d666d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37d1f550>, because it is not built.\n",
"W0602 16:26:51.083598 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37d1f550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37c79dd0>, because it is not built.\n",
"W0602 16:26:51.083812 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37c79dd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37518050>, because it is not built.\n",
"W0602 16:26:51.085351 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37518050>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37cacc50>, because it is not built.\n",
"W0602 16:26:51.085546 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37cacc50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37c0fe10>, because it is not built.\n",
"W0602 16:26:51.118094 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37c0fe10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3765ba10>, because it is not built.\n",
"W0602 16:26:51.118311 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3765ba10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3755f990>, because it is not built.\n",
"W0602 16:26:51.119654 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3755f990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf377d0e50>, because it is not built.\n",
"W0602 16:26:51.119839 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf377d0e50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37dca310>, because it is not built.\n",
"W0602 16:26:51.143431 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37dca310>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375f7150>, because it is not built.\n",
"W0602 16:26:51.143631 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375f7150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3777ccd0>, because it is not built.\n",
"W0602 16:26:51.145121 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3777ccd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37b2b1d0>, because it is not built.\n",
"W0602 16:26:51.145310 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37b2b1d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375f0dd0>, because it is not built.\n",
"W0602 16:26:51.169692 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375f0dd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37a06150>, because it is not built.\n",
"W0602 16:26:51.169923 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37a06150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37679050>, because it is not built.\n",
"W0602 16:26:51.171296 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37679050>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374ecc90>, because it is not built.\n",
"W0602 16:26:51.171506 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374ecc90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37b03a10>, because it is not built.\n",
"W0602 16:26:51.197102 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37b03a10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37513bd0>, because it is not built.\n",
"W0602 16:26:51.197314 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37513bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37513e10>, because it is not built.\n",
"W0602 16:26:51.199032 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37513e10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3751b4d0>, because it is not built.\n",
"W0602 16:26:51.199240 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3751b4d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37e05050>, because it is not built.\n",
"W0602 16:26:51.221930 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37e05050>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37547850>, because it is not built.\n",
"W0602 16:26:51.222127 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37547850>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37571990>, because it is not built.\n",
"W0602 16:26:51.223388 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37571990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3754a450>, because it is not built.\n",
"W0602 16:26:51.223561 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3754a450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3754a510>, because it is not built.\n",
"W0602 16:26:51.224847 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3754a510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374e3310>, because it is not built.\n",
"W0602 16:26:51.225095 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374e3310>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374e3f90>, because it is not built.\n",
"W0602 16:26:51.248150 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374e3f90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3775a7d0>, because it is not built.\n",
"W0602 16:26:51.248364 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3775a7d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37761550>, because it is not built.\n",
"W0602 16:26:51.249563 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37761550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37761890>, because it is not built.\n",
"W0602 16:26:51.249726 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37761890>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37754e90>, because it is not built.\n",
"W0602 16:26:51.250991 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37754e90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3773e590>, because it is not built.\n",
"W0602 16:26:51.251167 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3773e590>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37c68350>, because it is not built.\n",
"W0602 16:26:51.272856 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37c68350>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375b0c50>, because it is not built.\n",
"W0602 16:26:51.273076 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375b0c50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3754e610>, because it is not built.\n",
"W0602 16:26:51.274319 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3754e610>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37544e50>, because it is not built.\n",
"W0602 16:26:51.274506 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37544e50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37827710>, because it is not built.\n",
"W0602 16:26:51.275860 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37827710>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375ed550>, because it is not built.\n",
"W0602 16:26:51.276079 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf375ed550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf4e09dc90>, because it is not built.\n",
"W0602 16:26:51.300727 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf4e09dc90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37e75f90>, because it is not built.\n",
"W0602 16:26:51.300957 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37e75f90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374c8e90>, because it is not built.\n",
"W0602 16:26:51.302885 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374c8e90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374ce150>, because it is not built.\n",
"W0602 16:26:51.303112 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374ce150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37557090>, because it is not built.\n",
"W0602 16:26:51.337462 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37557090>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf377fdbd0>, because it is not built.\n",
"W0602 16:26:51.337630 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf377fdbd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374ddd50>, because it is not built.\n",
"W0602 16:26:51.339214 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374ddd50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3780f410>, because it is not built.\n",
"W0602 16:26:51.339432 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3780f410>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf377f8390>, because it is not built.\n",
"W0602 16:26:51.362430 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf377f8390>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37cd5550>, because it is not built.\n",
"W0602 16:26:51.362575 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37cd5550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf377666d0>, because it is not built.\n",
"W0602 16:26:51.363842 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf377666d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37756290>, because it is not built.\n",
"W0602 16:26:51.364052 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37756290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf379a1290>, because it is not built.\n",
"W0602 16:26:51.387543 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf379a1290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37a5c590>, because it is not built.\n",
"W0602 16:26:51.387738 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37a5c590>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37e05ad0>, because it is not built.\n",
"W0602 16:26:51.388957 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37e05ad0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3765b0d0>, because it is not built.\n",
"W0602 16:26:51.389093 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3765b0d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf378e5390>, because it is not built.\n",
"W0602 16:26:51.412553 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf378e5390>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf376b4b10>, because it is not built.\n",
"W0602 16:26:51.412695 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf376b4b10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37dc7ad0>, because it is not built.\n",
"W0602 16:26:51.414106 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37dc7ad0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37994990>, because it is not built.\n",
"W0602 16:26:51.414286 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37994990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37978a50>, because it is not built.\n",
"W0602 16:26:51.436555 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37978a50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3759d1d0>, because it is not built.\n",
"W0602 16:26:51.436772 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3759d1d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375b9e50>, because it is not built.\n",
"W0602 16:26:51.438074 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375b9e50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374cd5d0>, because it is not built.\n",
"W0602 16:26:51.438246 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374cd5d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374cda90>, because it is not built.\n",
"W0602 16:26:51.439450 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374cda90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3782d550>, because it is not built.\n",
"W0602 16:26:51.439597 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3782d550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374d4ed0>, because it is not built.\n",
"W0602 16:26:51.462892 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374d4ed0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374e5f10>, because it is not built.\n",
"W0602 16:26:51.463162 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374e5f10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374be810>, because it is not built.\n",
"W0602 16:26:51.464530 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374be810>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374bed90>, because it is not built.\n",
"W0602 16:26:51.464694 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374bed90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374f1310>, because it is not built.\n",
"W0602 16:26:51.465961 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374f1310>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374f1a50>, because it is not built.\n",
"W0602 16:26:51.466094 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374f1a50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37671d50>, because it is not built.\n",
"W0602 16:26:51.489368 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37671d50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37660150>, because it is not built.\n",
"W0602 16:26:51.489541 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37660150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374d4bd0>, because it is not built.\n",
"W0602 16:26:51.490682 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374d4bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374fa2d0>, because it is not built.\n",
"W0602 16:26:51.490856 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374fa2d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3763d590>, because it is not built.\n",
"W0602 16:26:51.492106 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3763d590>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3763df10>, because it is not built.\n",
"W0602 16:26:51.492315 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3763df10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf376672d0>, because it is not built.\n",
"W0602 16:26:51.517574 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf376672d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37667490>, because it is not built.\n",
"W0602 16:26:51.517752 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37667490>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf376699d0>, because it is not built.\n",
"W0602 16:26:51.519085 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf376699d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37489b50>, because it is not built.\n",
"W0602 16:26:51.519271 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37489b50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374b3bd0>, because it is not built.\n",
"W0602 16:26:51.550466 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374b3bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374a2a10>, because it is not built.\n",
"W0602 16:26:51.550649 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374a2a10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374a2bd0>, because it is not built.\n",
"W0602 16:26:51.551951 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374a2bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37499450>, because it is not built.\n",
"W0602 16:26:51.552121 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37499450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374985d0>, because it is not built.\n",
"W0602 16:26:51.574800 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf374985d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37498910>, because it is not built.\n",
"W0602 16:26:51.575006 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37498910>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3749cf50>, because it is not built.\n",
"W0602 16:26:51.576323 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3749cf50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374aa510>, because it is not built.\n",
"W0602 16:26:51.576522 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374aa510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375d30d0>, because it is not built.\n",
"W0602 16:26:51.599521 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf375d30d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37405f10>, because it is not built.\n",
"W0602 16:26:51.599711 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37405f10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37408b90>, because it is not built.\n",
"W0602 16:26:51.601139 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37408b90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37408cd0>, because it is not built.\n",
"W0602 16:26:51.601360 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37408cd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37418bd0>, because it is not built.\n",
"W0602 16:26:51.623830 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37418bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37425590>, because it is not built.\n",
"W0602 16:26:51.624042 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37425590>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37429c90>, because it is not built.\n",
"W0602 16:26:51.625283 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37429c90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374303d0>, because it is not built.\n",
"W0602 16:26:51.625521 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374303d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37434550>, because it is not built.\n",
"W0602 16:26:51.648653 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37434550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374aae90>, because it is not built.\n",
"W0602 16:26:51.648842 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf374aae90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373c08d0>, because it is not built.\n",
"W0602 16:26:51.650144 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373c08d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373c0bd0>, because it is not built.\n",
"W0602 16:26:51.650310 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373c0bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373c9290>, because it is not built.\n",
"W0602 16:26:51.651704 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373c9290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373c99d0>, because it is not built.\n",
"W0602 16:26:51.651882 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373c99d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373ded90>, because it is not built.\n",
"W0602 16:26:51.674737 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373ded90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373e1150>, because it is not built.\n",
"W0602 16:26:51.674923 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373e1150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373e77d0>, because it is not built.\n",
"W0602 16:26:51.676193 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373e77d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373cdb10>, because it is not built.\n",
"W0602 16:26:51.676358 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373cdb10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373d1510>, because it is not built.\n",
"W0602 16:26:51.677789 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373d1510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373e7f50>, because it is not built.\n",
"W0602 16:26:51.678040 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373e7f50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373832d0>, because it is not built.\n",
"W0602 16:26:51.703191 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373832d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37383650>, because it is not built.\n",
"W0602 16:26:51.703376 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37383650>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37388d50>, because it is not built.\n",
"W0602 16:26:51.705548 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37388d50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3738f4d0>, because it is not built.\n",
"W0602 16:26:51.705748 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3738f4d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3738f590>, because it is not built.\n",
"W0602 16:26:51.707876 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3738f590>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3738fed0>, because it is not built.\n",
"W0602 16:26:51.708163 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3738fed0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37396090>, because it is not built.\n",
"W0602 16:26:51.732831 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37396090>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373a0b10>, because it is not built.\n",
"W0602 16:26:51.733038 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373a0b10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373a3450>, because it is not built.\n",
"W0602 16:26:51.734306 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf373a3450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373a3bd0>, because it is not built.\n",
"W0602 16:26:51.734442 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf373a3bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37349810>, because it is not built.\n",
"W0602 16:26:51.764457 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37349810>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37349f90>, because it is not built.\n",
"W0602 16:26:51.764651 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37349f90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3734d490>, because it is not built.\n",
"W0602 16:26:51.766143 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3734d490>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3733d8d0>, because it is not built.\n",
"W0602 16:26:51.766333 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3733d8d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37353150>, because it is not built.\n",
"W0602 16:26:51.790530 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37353150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf377b8490>, because it is not built.\n",
"W0602 16:26:51.790782 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf377b8490>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37360250>, because it is not built.\n",
"W0602 16:26:51.792329 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37360250>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37360dd0>, because it is not built.\n",
"W0602 16:26:51.792512 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37360dd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372fced0>, because it is not built.\n",
"W0602 16:26:51.816141 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372fced0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37300490>, because it is not built.\n",
"W0602 16:26:51.816335 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37300490>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37304b10>, because it is not built.\n",
"W0602 16:26:51.817683 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37304b10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3730b290>, because it is not built.\n",
"W0602 16:26:51.817880 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3730b290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3730be50>, because it is not built.\n",
"W0602 16:26:51.840423 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3730be50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37317e50>, because it is not built.\n",
"W0602 16:26:51.840637 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37317e50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3731d750>, because it is not built.\n",
"W0602 16:26:51.842133 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3731d750>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3731dc90>, because it is not built.\n",
"W0602 16:26:51.842333 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3731dc90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3733bed0>, because it is not built.\n",
"W0602 16:26:51.869724 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3733bed0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372bf250>, because it is not built.\n",
"W0602 16:26:51.869952 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372bf250>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372c3890>, because it is not built.\n",
"W0602 16:26:51.871295 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372c3890>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372c3f90>, because it is not built.\n",
"W0602 16:26:51.871460 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372c3f90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372c74d0>, because it is not built.\n",
"W0602 16:26:51.872786 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372c74d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372fc2d0>, because it is not built.\n",
"W0602 16:26:51.872988 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372fc2d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3733b150>, because it is not built.\n",
"W0602 16:26:51.895310 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3733b150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372dce10>, because it is not built.\n",
"W0602 16:26:51.895515 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372dce10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372e02d0>, because it is not built.\n",
"W0602 16:26:51.896783 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372e02d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372e0690>, because it is not built.\n",
"W0602 16:26:51.897008 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372e0690>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372e4d90>, because it is not built.\n",
"W0602 16:26:51.898353 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372e4d90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372eb510>, because it is not built.\n",
"W0602 16:26:51.898551 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372eb510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37283850>, because it is not built.\n",
"W0602 16:26:51.924927 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37283850>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37283d90>, because it is not built.\n",
"W0602 16:26:51.925118 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37283d90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37280bd0>, because it is not built.\n",
"W0602 16:26:51.926427 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37280bd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372f2850>, because it is not built.\n",
"W0602 16:26:51.926617 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372f2850>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3728f110>, because it is not built.\n",
"W0602 16:26:51.928026 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3728f110>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3728fa90>, because it is not built.\n",
"W0602 16:26:51.928211 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3728fa90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372a5d50>, because it is not built.\n",
"W0602 16:26:51.950592 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372a5d50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372aa190>, because it is not built.\n",
"W0602 16:26:51.950796 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372aa190>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372b0490>, because it is not built.\n",
"W0602 16:26:51.952098 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372b0490>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372b0f90>, because it is not built.\n",
"W0602 16:26:51.952281 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf372b0f90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3723ddd0>, because it is not built.\n",
"W0602 16:26:51.982998 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3723ddd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37249510>, because it is not built.\n",
"W0602 16:26:51.983172 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37249510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37249610>, because it is not built.\n",
"W0602 16:26:51.984621 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37249610>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3724df50>, because it is not built.\n",
"W0602 16:26:51.984801 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3724df50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37263f10>, because it is not built.\n",
"W0602 16:26:52.008018 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37263f10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3726a450>, because it is not built.\n",
"W0602 16:26:52.008209 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3726a450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3726ea90>, because it is not built.\n",
"W0602 16:26:52.009471 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3726ea90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37274210>, because it is not built.\n",
"W0602 16:26:52.009639 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37274210>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37274b90>, because it is not built.\n",
"W0602 16:26:52.031189 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37274b90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37203d90>, because it is not built.\n",
"W0602 16:26:52.031366 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37203d90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372086d0>, because it is not built.\n",
"W0602 16:26:52.032551 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf372086d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37208a50>, because it is not built.\n",
"W0602 16:26:52.032746 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37208a50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37224d50>, because it is not built.\n",
"W0602 16:26:52.054689 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37224d50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37227150>, because it is not built.\n",
"W0602 16:26:52.054865 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37227150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3722b850>, because it is not built.\n",
"W0602 16:26:52.057196 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3722b850>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3722bf90>, because it is not built.\n",
"W0602 16:26:52.057388 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3722bf90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37233990>, because it is not built.\n",
"W0602 16:26:52.080107 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37233990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371c3790>, because it is not built.\n",
"W0602 16:26:52.080286 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371c3790>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371c84d0>, because it is not built.\n",
"W0602 16:26:52.081629 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371c84d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371c8850>, because it is not built.\n",
"W0602 16:26:52.081814 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371c8850>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371cbe90>, because it is not built.\n",
"W0602 16:26:52.083078 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371cbe90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371d2610>, because it is not built.\n",
"W0602 16:26:52.083237 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371d2610>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371e88d0>, because it is not built.\n",
"W0602 16:26:52.107761 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371e88d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371e8ad0>, because it is not built.\n",
"W0602 16:26:52.108010 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371e8ad0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371f4350>, because it is not built.\n",
"W0602 16:26:52.110289 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371f4350>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37220ed0>, because it is not built.\n",
"W0602 16:26:52.110483 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37220ed0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371d8c10>, because it is not built.\n",
"W0602 16:26:52.112493 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371d8c10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371f4ad0>, because it is not built.\n",
"W0602 16:26:52.112714 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371f4ad0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3718cdd0>, because it is not built.\n",
"W0602 16:26:52.138577 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3718cdd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37190210>, because it is not built.\n",
"W0602 16:26:52.138770 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37190210>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37195910>, because it is not built.\n",
"W0602 16:26:52.140251 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37195910>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37195e90>, because it is not built.\n",
"W0602 16:26:52.140446 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37195e90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3719b150>, because it is not built.\n",
"W0602 16:26:52.141651 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3719b150>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37188f90>, because it is not built.\n",
"W0602 16:26:52.141821 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37188f90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371a2d90>, because it is not built.\n",
"W0602 16:26:52.164597 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371a2d90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371aed50>, because it is not built.\n",
"W0602 16:26:52.164773 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371aed50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371b40d0>, because it is not built.\n",
"W0602 16:26:52.166054 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371b40d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371b4550>, because it is not built.\n",
"W0602 16:26:52.166236 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371b4550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3715a390>, because it is not built.\n",
"W0602 16:26:52.196539 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3715a390>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3715ab10>, because it is not built.\n",
"W0602 16:26:52.196714 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3715ab10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3715af90>, because it is not built.\n",
"W0602 16:26:52.197961 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3715af90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371a2550>, because it is not built.\n",
"W0602 16:26:52.198111 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371a2550>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3715fbd0>, because it is not built.\n",
"W0602 16:26:52.222558 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3715fbd0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37169350>, because it is not built.\n",
"W0602 16:26:52.222716 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37169350>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37175690>, because it is not built.\n",
"W0602 16:26:52.224020 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37175690>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371759d0>, because it is not built.\n",
"W0602 16:26:52.224210 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371759d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37111c90>, because it is not built.\n",
"W0602 16:26:52.246855 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37111c90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37115210>, because it is not built.\n",
"W0602 16:26:52.247038 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37115210>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37111710>, because it is not built.\n",
"W0602 16:26:52.248440 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37111710>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3711add0>, because it is not built.\n",
"W0602 16:26:52.248593 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3711add0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371219d0>, because it is not built.\n",
"W0602 16:26:52.271049 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf371219d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3712da90>, because it is not built.\n",
"W0602 16:26:52.271210 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3712da90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37133310>, because it is not built.\n",
"W0602 16:26:52.272459 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37133310>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371336d0>, because it is not built.\n",
"W0602 16:26:52.272632 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf371336d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370d1a50>, because it is not built.\n",
"W0602 16:26:52.294390 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370d1a50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370d1e50>, because it is not built.\n",
"W0602 16:26:52.294568 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370d1e50>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370dd450>, because it is not built.\n",
"W0602 16:26:52.295907 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370dd450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370ddb90>, because it is not built.\n",
"W0602 16:26:52.296058 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370ddb90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370e1090>, because it is not built.\n",
"W0602 16:26:52.298273 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370e1090>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370ccf90>, because it is not built.\n",
"W0602 16:26:52.298423 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370ccf90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370e5510>, because it is not built.\n",
"W0602 16:26:52.324200 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370e5510>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370f4290>, because it is not built.\n",
"W0602 16:26:52.324348 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370f4290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370f4e90>, because it is not built.\n",
"W0602 16:26:52.325515 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370f4e90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370f7290>, because it is not built.\n",
"W0602 16:26:52.325656 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370f7290>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3707c990>, because it is not built.\n",
"W0602 16:26:52.327042 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3707c990>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3707cf10>, because it is not built.\n",
"W0602 16:26:52.327204 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3707cf10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3709b450>, because it is not built.\n",
"W0602 16:26:52.349665 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3709b450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3709b5d0>, because it is not built.\n",
"W0602 16:26:52.349814 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf3709b5d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37095690>, because it is not built.\n",
"W0602 16:26:52.351146 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf37095690>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37088450>, because it is not built.\n",
"W0602 16:26:52.351304 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37088450>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3709eb10>, because it is not built.\n",
"W0602 16:26:52.352586 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf3709eb10>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370a9650>, because it is not built.\n",
"W0602 16:26:52.352747 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370a9650>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370bb910>, because it is not built.\n",
"W0602 16:26:52.375293 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370bb910>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370bbd90>, because it is not built.\n",
"W0602 16:26:52.375471 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf370bbd90>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370481d0>, because it is not built.\n",
"W0602 16:26:52.377610 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.Conv2D object at 0x7fbf370481d0>, because it is not built.\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37048b50>, because it is not built.\n",
"W0602 16:26:52.377767 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <utils.BatchNormalization object at 0x7fbf37048b50>, because it is not built.\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:26:53.116042 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:26:53.180603 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:26:53.180828 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:26:53.186246 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:26:53.220587 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:26:53.254473 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:26:53.275990 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:26:53.281434 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:26:53.314004 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:26:53.347228 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:26:53.381005 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.403334 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:26:53.408858 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.449635 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.485414 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.519974 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.541523 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:26:53.547258 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.580946 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.618252 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.651440 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.672954 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:26:53.678340 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.711283 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.749620 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.785299 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.808094 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:26:53.813712 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:26:53.847235 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:26:53.886783 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:26:53.920793 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:53.942768 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:26:53.948065 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:53.980295 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.013327 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.051485 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:54.073482 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:26:54.079162 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:54.113497 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.148686 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.182125 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:54.203155 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:26:54.208473 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:54.242667 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.277202 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.310993 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:26:54.338134 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:26:54.343424 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:26:54.375517 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:26:54.409300 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:26:54.440543 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.461811 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:26:54.467672 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.500203 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.535323 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.569676 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.591704 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:26:54.597024 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.634361 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.669796 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.704068 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.726643 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:26:54.732097 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.766813 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.801763 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.834242 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.855007 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:26:54.860118 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.892973 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.931558 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:54.966085 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:54.986752 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:26:54.991723 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:55.024475 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:55.059811 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:55.096235 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:26:55.118294 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:26:55.123552 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:26:55.155611 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:26:55.189057 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:26:55.222409 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.247885 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:26:55.253558 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.289455 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.325916 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.362330 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.384581 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:26:55.390176 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.426052 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.462696 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.498375 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.523024 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:26:55.530251 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.569590 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.608021 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.644312 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.669995 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:26:55.675829 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.712087 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.749527 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.785681 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.808474 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:26:55.813826 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.851058 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.886233 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:26:55.927975 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.949390 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:26:55.954640 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:26:55.986785 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:26:56.021259 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:26:56.055019 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.077335 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:26:56.082733 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.117382 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.157423 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.191594 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.213855 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:26:56.219609 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.253667 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.288309 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.322165 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.351654 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:26:56.356904 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.390628 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.425282 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.460590 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.482927 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:26:56.488098 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.522694 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.558705 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.591474 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.614585 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:26:56.620019 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.656307 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.692044 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.728858 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.751415 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:26:56.757026 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.789882 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.824567 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.860163 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.881795 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:26:56.887087 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:56.926340 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:56.969005 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:57.004499 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:26:57.027071 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:26:57.032479 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:26:57.069379 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:26:57.106269 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:26:57.141849 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:26:59.659016 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:26:59.765251 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"I0602 16:27:03.706242 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:27:03.706575 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:27:03.712443 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:27:03.748728 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:27:03.782166 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:27:03.802954 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:27:03.808140 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:27:03.841529 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:27:03.877943 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:27:03.912273 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:03.933120 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:27:03.938551 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:03.977513 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.017454 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.064837 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:04.088688 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:27:04.094063 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:04.132810 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.175135 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.218114 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:04.240147 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:27:04.245345 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:04.279836 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.314556 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.356354 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:04.381922 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:27:04.387648 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:04.424102 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:04.461266 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:27:04.499667 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.522270 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:27:04.527742 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.563446 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:04.602137 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:04.645768 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.667738 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:27:04.673009 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.707515 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:04.742819 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:04.792408 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.815010 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:27:04.820476 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.855695 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:04.891762 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:04.934708 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.955842 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:27:04.961354 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:04.999376 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:05.039519 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:27:05.076824 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.099517 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:27:05.105307 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.142216 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.178849 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.222202 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.243625 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:27:05.248986 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.284997 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.323837 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.370052 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.391485 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:27:05.396878 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.431309 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.466975 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.514527 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.537736 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:27:05.543228 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.579969 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.615965 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.661537 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.682853 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:27:05.688337 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.723303 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.758648 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.809240 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.832039 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:27:05.837719 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:05.875302 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.913385 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:05.950430 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:05.973327 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:27:05.981723 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.019760 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.061885 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.109328 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.133664 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:27:06.139761 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.176798 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.216630 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.264382 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.288692 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:27:06.294071 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.332013 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.370873 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.418449 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.440759 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:27:06.446978 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.485456 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.524094 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:06.944994 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:06.968239 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:27:06.974091 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:07.010922 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:07.052509 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:07.102184 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:07.126509 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:27:07.132842 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:07.171236 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:07.215714 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:27:07.254101 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.277167 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:27:07.282822 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.320212 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.357628 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.405278 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.427332 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:27:07.432796 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.468698 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.504438 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.554402 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.575990 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:27:07.581414 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.624137 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.660040 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.705968 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.728035 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:27:07.733245 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.769955 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.809033 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.854587 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.878031 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:27:07.883620 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:07.921171 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:07.959640 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.006717 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:08.029530 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:27:08.035765 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:08.074289 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.113004 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.158850 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:08.181757 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:27:08.187479 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:08.229102 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.267352 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.313656 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:08.336776 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:27:08.342553 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:08.380619 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.420116 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:08.456810 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:09.725719 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:27:10.306508 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:27:10.416233 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:12.055635 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:27:12.597759 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:27:12.712277 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:13.149971 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:27:13.725739 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:27:13.833485 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D object at 0x7fbf4e064ad0>, because it is not built.\n",
"W0602 16:27:14.181540 140462829037440 save_impl.py:77] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.pooling.GlobalAveragePooling2D object at 0x7fbf4e064ad0>, because it is not built.\n",
"2021-06-02 16:27:14.796071: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:27:15.828704 140462829037440 api.py:446] Built stem stem : (1, 320, 320, 32)\n",
"I0602 16:27:15.859902 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:27:15.892430 140462829037440 api.py:446] Block blocks_0 input shape: (1, 320, 320, 32)\n",
"I0602 16:27:15.938556 140462829037440 api.py:446] DWConv shape: (1, 320, 320, 32)\n",
"I0602 16:27:15.979841 140462829037440 api.py:446] Project shape: (1, 320, 320, 24)\n",
"I0602 16:27:16.012527 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:27:16.043811 140462829037440 api.py:446] Block blocks_1 input shape: (1, 320, 320, 24)\n",
"I0602 16:27:16.085525 140462829037440 api.py:446] Expand shape: (1, 320, 320, 144)\n",
"I0602 16:27:16.129162 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 144)\n",
"I0602 16:27:16.178260 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.212195 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:27:16.252583 140462829037440 api.py:446] Block blocks_2 input shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.293766 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.338072 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.379647 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.413545 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:27:16.445818 140462829037440 api.py:446] Block blocks_3 input shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.489267 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.537755 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.585435 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.620688 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:27:16.654534 140462829037440 api.py:446] Block blocks_4 input shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.699187 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.746999 140462829037440 api.py:446] DWConv shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.788153 140462829037440 api.py:446] Project shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.820317 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:27:16.851288 140462829037440 api.py:446] Block blocks_5 input shape: (1, 160, 160, 32)\n",
"I0602 16:27:16.893019 140462829037440 api.py:446] Expand shape: (1, 160, 160, 192)\n",
"I0602 16:27:16.934856 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 192)\n",
"I0602 16:27:16.980693 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.014644 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:27:17.046812 140462829037440 api.py:446] Block blocks_6 input shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.089269 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.136228 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.181589 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.216578 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:27:17.252342 140462829037440 api.py:446] Block blocks_7 input shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.296613 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.339491 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.382575 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.416873 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:27:17.450785 140462829037440 api.py:446] Block blocks_8 input shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.496553 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.543243 140462829037440 api.py:446] DWConv shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.585998 140462829037440 api.py:446] Project shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.622477 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:27:17.654940 140462829037440 api.py:446] Block blocks_9 input shape: (1, 80, 80, 56)\n",
"I0602 16:27:17.697175 140462829037440 api.py:446] Expand shape: (1, 80, 80, 336)\n",
"I0602 16:27:17.740725 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 336)\n",
"I0602 16:27:17.789785 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:27:17.825526 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:27:17.859158 140462829037440 api.py:446] Block blocks_10 input shape: (1, 40, 40, 112)\n",
"I0602 16:27:17.901292 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:27:17.943559 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:27:17.991505 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.026715 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:27:18.059528 140462829037440 api.py:446] Block blocks_11 input shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.100414 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.142015 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.189440 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.222973 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:27:18.255490 140462829037440 api.py:446] Block blocks_12 input shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.299425 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.341452 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.383334 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.417693 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:27:18.450626 140462829037440 api.py:446] Block blocks_13 input shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.496486 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.542104 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.587854 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.622956 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:27:18.658747 140462829037440 api.py:446] Block blocks_14 input shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.702235 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.748858 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.795100 140462829037440 api.py:446] Project shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.833071 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:27:18.869053 140462829037440 api.py:446] Block blocks_15 input shape: (1, 40, 40, 112)\n",
"I0602 16:27:18.913661 140462829037440 api.py:446] Expand shape: (1, 40, 40, 672)\n",
"I0602 16:27:18.959615 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 672)\n",
"I0602 16:27:19.001468 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.035462 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:27:19.068193 140462829037440 api.py:446] Block blocks_16 input shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.112312 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.157202 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.204457 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.238486 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:27:19.270386 140462829037440 api.py:446] Block blocks_17 input shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.315001 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.360221 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.403429 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.439596 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:27:19.475111 140462829037440 api.py:446] Block blocks_18 input shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.519458 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.565675 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.610622 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.646824 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:27:19.681445 140462829037440 api.py:446] Block blocks_19 input shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.724122 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.768537 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.813858 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.848672 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:27:19.882185 140462829037440 api.py:446] Block blocks_20 input shape: (1, 40, 40, 160)\n",
"I0602 16:27:19.925510 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:27:19.969946 140462829037440 api.py:446] DWConv shape: (1, 40, 40, 960)\n",
"I0602 16:27:20.015671 140462829037440 api.py:446] Project shape: (1, 40, 40, 160)\n",
"I0602 16:27:20.052571 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:27:20.089990 140462829037440 api.py:446] Block blocks_21 input shape: (1, 40, 40, 160)\n",
"I0602 16:27:20.132798 140462829037440 api.py:446] Expand shape: (1, 40, 40, 960)\n",
"I0602 16:27:20.175819 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 960)\n",
"I0602 16:27:20.219554 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.253294 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:27:20.290132 140462829037440 api.py:446] Block blocks_22 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.333875 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:20.379173 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:20.423555 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.460619 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:27:20.498098 140462829037440 api.py:446] Block blocks_23 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.543309 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:20.590301 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:20.635316 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.670797 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:27:20.707155 140462829037440 api.py:446] Block blocks_24 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.750577 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:20.800353 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:20.846511 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.883159 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:27:20.918273 140462829037440 api.py:446] Block blocks_25 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:20.962975 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.008634 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.053033 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.090351 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:27:21.124193 140462829037440 api.py:446] Block blocks_26 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.167237 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.212085 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.257635 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.295981 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:27:21.328940 140462829037440 api.py:446] Block blocks_27 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.370789 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.421078 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.465729 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.499325 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:27:21.533113 140462829037440 api.py:446] Block blocks_28 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.577155 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.619383 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.660985 140462829037440 api.py:446] Project shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.695730 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:27:21.727291 140462829037440 api.py:446] Block blocks_29 input shape: (1, 20, 20, 272)\n",
"I0602 16:27:21.768654 140462829037440 api.py:446] Expand shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.816013 140462829037440 api.py:446] DWConv shape: (1, 20, 20, 1632)\n",
"I0602 16:27:21.859244 140462829037440 api.py:446] Project shape: (1, 20, 20, 448)\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:27.421285 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:27:27.486263 140462829037440 api.py:446] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:27:27.513355 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:27:27.541108 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:27:27.582677 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:27:27.622098 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:27:27.652799 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:27:27.681811 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:27:27.726011 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:27:27.764716 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:27:27.802367 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:27.832520 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:27:27.861458 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:27.899121 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:27.936934 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:27.975834 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:28.009664 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:27:28.040233 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:28.081168 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:28.122921 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:28.163720 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:28.197520 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:27:28.228789 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:28.268283 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:28.312434 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:28.352187 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:28.383865 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:27:28.412842 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:28.450666 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:28.490198 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:27:28.529699 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:28.561095 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:27:28.590022 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:28.637690 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:28.679425 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:28.720202 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:28.750053 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:27:28.778513 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:28.825428 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:28.866159 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:28.907018 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:28.938653 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:27:28.969581 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:29.009413 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:29.051098 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:29.093401 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:29.130633 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:27:29.158616 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:29.195750 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:29.234971 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:27:29.273251 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.305732 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:27:29.338352 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.377296 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.418984 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.457606 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.489570 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:27:29.526978 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.568508 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.611215 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.652767 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.683722 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:27:29.713653 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.753282 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.795603 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.843241 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.876093 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:27:29.906319 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:29.946377 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:29.987791 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:30.030798 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:30.061774 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:27:30.090894 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:30.134207 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:30.173723 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:30.212721 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:30.244471 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:27:30.273260 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:30.314294 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:30.357390 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:30.395590 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.426957 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:27:30.455471 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.494126 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:30.538556 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:30.578785 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.608792 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:27:30.638047 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.682128 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:30.729039 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:30.770483 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.807301 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:27:30.838695 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.878804 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:30.922842 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:30.962643 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:30.993736 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:27:31.023128 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:31.061446 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:31.100566 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:31.140803 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:31.170308 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:27:31.199017 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:31.248417 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:31.287688 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:31.327175 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:31.360215 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:27:31.389003 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:31.428137 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:31.468625 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:27:31.507183 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:31.542495 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:27:31.573615 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:31.615258 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:31.656936 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:31.697945 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:31.735806 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:27:31.765781 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:31.804824 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:31.845230 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:31.885078 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:31.914690 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:27:31.943809 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:31.981567 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.021635 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.065607 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.101297 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:27:32.132577 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.171927 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.211688 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.253235 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.285657 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:27:32.315566 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.362375 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.405776 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.447732 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.481412 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:27:32.512277 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.556472 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.599478 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.641256 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.673199 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:27:32.704195 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.746233 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.789076 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.831782 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.866521 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:27:32.894175 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:32.931977 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:32.971169 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:33.010500 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:27:34.717996 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:27:34.826526 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:35.525701 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:27:35.591925 140462829037440 api.py:446] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:27:35.620214 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:27:35.649438 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:27:35.692246 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:27:35.732134 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:27:35.770977 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:27:35.801975 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:27:35.844054 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:27:35.887438 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:27:35.928571 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:35.960251 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:27:35.990805 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.035336 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.077854 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.129168 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.159108 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:27:36.186934 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.226797 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.270762 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.319869 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.350433 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:27:36.379791 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.421647 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.463222 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.510394 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.540992 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:27:36.574711 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:36.618579 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:36.659479 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:27:36.697614 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:36.726687 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:27:36.759003 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:36.797424 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:36.840856 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:36.890852 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:36.920761 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:27:36.948552 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:36.991711 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:37.033845 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:37.085663 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:37.118482 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:27:37.149256 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:37.191846 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:37.236195 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:37.287402 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:37.318640 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:27:37.348793 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:37.396068 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:37.443533 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:27:37.486377 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:37.518745 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:27:37.549347 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:37.592682 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:37.636466 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:37.691332 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:37.724806 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:27:37.753795 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:37.793746 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:37.834043 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:37.883027 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:37.912990 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:27:37.941859 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:37.985790 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.026004 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.073799 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:38.104238 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:27:38.136889 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:38.176862 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.218958 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.270893 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:38.302579 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:27:38.333604 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:38.375310 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.417943 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.475521 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:38.508035 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:27:38.538082 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:38.585509 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.630267 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:38.675129 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:38.710348 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:27:38.740645 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:38.784386 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:38.828042 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:38.877002 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:38.907510 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:27:38.934832 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:38.976129 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.020429 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.072046 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.104069 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:27:39.135434 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.178691 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.220957 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.272840 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.303991 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:27:39.333796 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.377717 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.419176 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.472273 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.505575 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:27:39.538457 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.583029 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.624862 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.674359 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.707406 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:27:39.737241 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:39.778460 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:39.820535 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:27:39.861455 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:39.899306 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:27:39.928782 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:39.968727 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.011149 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.060168 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.091288 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:27:40.121034 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.162451 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.205306 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.257394 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.289399 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:27:40.318655 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.360369 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.403867 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.455678 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.493183 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:27:40.523601 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.564577 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.605056 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.653220 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.688509 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:27:40.718520 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.758374 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.799837 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.847754 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.878044 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:27:40.909899 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:40.951357 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:40.994027 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:41.043750 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:41.074866 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:27:41.107731 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:41.150599 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:41.193211 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:41.244733 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:41.276610 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:27:41.311284 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:41.355461 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:41.397381 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:41.438796 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:27:43.275928 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:27:43.382072 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:44.240067 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:27:44.302924 140462829037440 api.py:446] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:27:44.330739 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:27:44.359827 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:27:44.400081 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:27:44.438575 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:27:44.471156 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:27:44.504256 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:27:44.548709 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:27:44.589516 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:27:44.629767 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:44.661015 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:27:44.692013 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:44.738857 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:44.779800 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:44.819493 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:44.850375 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:27:44.879349 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:44.923401 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:44.966416 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:45.010511 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:45.044421 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:27:45.076197 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:45.119384 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:45.162719 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:45.205287 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:45.238848 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:27:45.269529 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:45.312015 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:45.350838 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:27:45.387572 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.417836 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:27:45.445421 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.483974 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:45.525795 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:45.566168 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.598152 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:27:45.633261 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.673547 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:45.716948 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:45.757800 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.788833 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:27:45.817206 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.854432 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:45.894024 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:45.936561 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:45.968983 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:27:45.998964 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:46.039923 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:46.081078 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:27:46.122912 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.154968 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:27:46.184432 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.224344 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.266205 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.305641 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.339277 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:27:46.367518 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.404636 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.443707 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.483444 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.513713 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:27:46.549128 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.588515 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.634246 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.676468 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.707352 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:27:46.736054 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.774032 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.814568 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.854263 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.884717 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:27:46.912895 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:46.951236 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:46.991106 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:47.034574 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:47.064853 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:27:47.094419 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:47.136417 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:47.177817 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:47.215752 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.246493 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:27:47.276087 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.314463 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.357936 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.397947 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.428991 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:27:47.457664 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.497018 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.539086 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.587581 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.621714 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:27:47.652774 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.695592 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.739129 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.781192 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.814687 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:27:47.846205 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.886709 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.927436 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:47.965656 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:47.995345 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:27:48.024155 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:48.064162 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:48.104686 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:48.151499 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:48.181697 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:27:48.210086 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:48.249974 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:48.290011 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:27:48.330978 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.363177 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:27:48.394130 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.435628 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:48.479293 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:48.521225 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.560713 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:27:48.594985 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.635715 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:48.678542 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:48.719860 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.753931 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:27:48.783900 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.823894 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:48.867188 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:48.907608 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:48.940489 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:27:48.970652 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.011362 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.052965 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.095596 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.127360 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:27:49.163149 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.203040 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.246679 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.285650 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.316772 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:27:49.344542 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.383665 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.424188 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.472046 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.504406 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:27:49.534759 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.577136 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.619962 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.661653 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.696382 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:27:49.727103 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:49.769614 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.811056 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:49.850004 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:27:51.575590 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:27:51.688715 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"WARNING:tensorflow:Using a while_loop for converting ResizeBilinear\n",
"W0602 16:27:52.390090 140462829037440 pfor.py:1075] Using a while_loop for converting ResizeBilinear\n",
"I0602 16:27:52.454845 140462829037440 api.py:446] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:27:52.484223 140462829037440 api.py:446] block_0 survival_prob: 1.0\n",
"I0602 16:27:52.513279 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:27:52.555137 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:27:52.595212 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:27:52.629417 140462829037440 api.py:446] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:27:52.666073 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:27:52.708934 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:27:52.751764 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:27:52.793789 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:52.824897 140462829037440 api.py:446] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:27:52.853135 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:52.891867 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:52.931833 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:52.982495 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:53.012863 140462829037440 api.py:446] block_3 survival_prob: 0.98\n",
"I0602 16:27:53.042108 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:53.081810 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:53.123064 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:53.174233 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:53.205491 140462829037440 api.py:446] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:27:53.236067 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:53.282204 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:53.322726 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:27:53.372468 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:27:53.402590 140462829037440 api.py:446] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:27:53.431340 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:27:53.471028 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:27:53.513511 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:27:53.555586 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:53.611354 140462829037440 api.py:446] block_6 survival_prob: 0.96\n",
"I0602 16:27:53.644812 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:53.684372 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:53.725511 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:53.773246 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:53.802746 140462829037440 api.py:446] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:27:53.830843 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:53.873043 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:53.915502 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:53.965165 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:53.995140 140462829037440 api.py:446] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:27:54.025010 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:54.067687 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:54.109718 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:27:54.159523 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:27:54.189859 140462829037440 api.py:446] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:27:54.218063 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:27:54.256580 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:27:54.296092 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:27:54.334714 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.366566 140462829037440 api.py:446] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:27:54.394546 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.434079 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:54.477659 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:54.527140 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.558957 140462829037440 api.py:446] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:27:54.587891 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.629728 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:54.675790 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:54.725192 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.755669 140462829037440 api.py:446] block_12 survival_prob: 0.92\n",
"I0602 16:27:54.790464 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.831488 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:54.873303 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:54.924108 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:54.955952 140462829037440 api.py:446] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:27:54.986415 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:55.028031 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:55.075296 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:55.126978 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:55.160231 140462829037440 api.py:446] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:27:55.190569 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:55.232133 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:55.276306 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:55.327211 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:27:55.359883 140462829037440 api.py:446] block_15 survival_prob: 0.9\n",
"I0602 16:27:55.391208 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:27:55.429795 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:27:55.469628 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:27:55.508220 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:55.539156 140462829037440 api.py:446] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:27:55.567790 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:55.608678 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:55.651779 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:55.705877 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:55.736819 140462829037440 api.py:446] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:27:55.765296 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:55.804260 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:55.844131 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:55.900542 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:55.932100 140462829037440 api.py:446] block_18 survival_prob: 0.88\n",
"I0602 16:27:55.961485 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.002476 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.044091 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.094510 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.125727 140462829037440 api.py:446] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:27:56.158643 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.202245 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.245338 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.297150 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.328478 140462829037440 api.py:446] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:27:56.358377 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.401289 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.444605 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.494822 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.525208 140462829037440 api.py:446] block_21 survival_prob: 0.86\n",
"I0602 16:27:56.554194 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:27:56.594463 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:27:56.640983 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:27:56.689189 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:56.721990 140462829037440 api.py:446] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:27:56.751394 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:56.792817 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:56.833810 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:56.885024 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:56.916418 140462829037440 api.py:446] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:27:56.944646 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:56.984835 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.026390 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.074221 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.105529 140462829037440 api.py:446] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:27:57.136892 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.178636 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.221905 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.272054 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.304572 140462829037440 api.py:446] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:27:57.334037 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.373756 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.415505 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.461891 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.495672 140462829037440 api.py:446] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:27:57.524768 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.564279 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.604979 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.654685 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.690333 140462829037440 api.py:446] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:27:57.719338 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.757699 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.801800 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.851951 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.883418 140462829037440 api.py:446] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:27:57.912903 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:57.952618 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:57.992662 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:58.040931 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:27:58.069554 140462829037440 api.py:446] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:27:58.101449 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:27:58.142025 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:27:58.184349 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:27:58.224119 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:00.613178 140462829037440 postprocess.py:102] use max_reduce for pre-nms topk.\n",
"WARNING:tensorflow:Using a while_loop for converting NonMaxSuppressionV5\n",
"W0602 16:28:00.728173 140462829037440 pfor.py:1075] Using a while_loop for converting NonMaxSuppressionV5\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:03.203708 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:28:03.204023 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:28:03.209650 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:03.244856 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:03.278140 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:03.300202 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:28:03.305415 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:03.338336 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:03.372555 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:03.407310 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.428438 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:28:03.433547 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.466709 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.502292 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.544323 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.568936 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:28:03.574811 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.612981 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.650097 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.686968 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.711972 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:28:03.722069 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.759529 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.795965 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.838970 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.861035 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:28:03.866543 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:03.900869 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:03.935393 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:03.968642 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:03.992238 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:28:03.997411 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.031141 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.065968 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.100768 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.127546 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:28:04.132870 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.167624 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.202454 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.238759 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.260878 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:28:04.266435 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.299645 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.341761 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.378875 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.402638 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:28:04.408169 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:04.443798 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:04.481576 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:04.521124 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.547240 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:28:04.553122 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.589015 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:04.630314 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:04.668139 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.691858 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:28:04.697608 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.734532 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:04.772342 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:04.807899 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.835196 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:28:04.841417 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.877046 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:04.914213 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:04.951311 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:04.973706 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:28:04.979057 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:05.012256 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:05.053528 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:05.087529 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:05.109627 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:28:05.114707 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:05.149337 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:05.188177 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:05.227432 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:05.252780 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:28:05.258584 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:05.293317 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:05.332831 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:05.368129 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.389378 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:28:05.394810 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.428627 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.464382 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.499493 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.526397 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:28:05.532422 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.567229 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.608165 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.647352 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.671422 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:28:05.677119 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.712994 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.756669 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.794391 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.817685 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:28:05.823719 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.859492 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.896387 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:05.934131 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.956755 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:28:05.962187 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:05.995332 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:06.030985 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:06.066196 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:06.090125 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:28:06.095743 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:06.132613 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:06.169714 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:06.204309 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.228478 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:28:06.234257 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.269462 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.306188 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.345832 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.368485 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:28:06.374161 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.408408 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.442161 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.477589 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.499614 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:28:06.505274 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.539141 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.574275 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.609477 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.635237 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:28:06.641184 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.679432 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.718455 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.757089 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.782766 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:28:06.793368 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.830930 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.868579 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:06.905920 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.929724 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:28:06.936559 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:06.973890 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:07.012537 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:07.049884 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:07.071375 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:28:07.076821 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:07.112073 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:07.147635 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:07.183798 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:07.206074 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:28:07.211504 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:07.249895 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:07.287047 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:07.323005 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:07.543871 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:28:07.544216 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:28:07.549568 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:07.585989 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:07.621652 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:07.651814 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:28:07.657748 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:07.700263 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:07.739285 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:07.778443 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:07.802435 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:28:07.808198 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:07.845820 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:07.884179 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:07.929562 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:07.957649 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:28:07.962845 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:07.997383 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:08.031460 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:08.075350 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:08.097403 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:28:08.102689 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:08.138247 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:08.173984 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:08.217909 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:08.242543 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:28:08.250486 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:08.286244 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:08.321486 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:08.356361 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.378121 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:28:08.383237 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.417649 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.458002 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.504995 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.530555 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:28:08.536182 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.571999 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.610252 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.657102 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.679279 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:28:08.684430 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.720125 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.761582 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.807992 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.831991 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:28:08.837666 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:08.874188 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:08.910409 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:08.948121 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:08.970787 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:28:08.976060 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.012766 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.049564 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.092532 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.115053 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:28:09.120325 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.159398 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.195252 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.239619 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.261719 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:28:09.267094 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.301607 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.336508 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.380796 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.402395 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:28:09.407720 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.441773 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.481620 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.529726 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.553660 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:28:09.559526 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.597172 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.634872 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.684497 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.708027 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:28:09.713223 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:09.750918 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.790066 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:09.830990 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:09.854213 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:28:09.859885 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:09.896551 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:09.934771 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:09.981850 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.004050 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:28:10.009685 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.045613 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.082603 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.127365 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.150717 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:28:10.156065 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.194492 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.233024 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.285588 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.307421 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:28:10.312841 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.347725 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.383263 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.427251 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.449326 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:28:10.455094 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.491522 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.528551 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.580071 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.603837 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:28:10.609706 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:10.648168 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:10.685738 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:10.723315 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:10.746512 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:28:10.752451 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:10.793992 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:10.831811 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:10.877651 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:10.899750 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:28:10.904812 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:10.940028 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:10.981148 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.026530 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.049248 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:28:11.054630 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.092604 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.130281 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.179613 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.203255 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:28:11.208921 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.247514 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.286530 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.332628 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.356279 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:28:11.362575 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.400726 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.437238 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.490958 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.514240 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:28:11.520429 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.556851 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.594482 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.640830 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.664805 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:28:11.673069 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.711605 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.752275 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.799975 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.825555 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:28:11.831270 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:11.872394 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.910730 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:11.947019 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:12.245292 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:28:12.245571 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:28:12.251590 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:12.288783 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:12.322089 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:12.344807 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:28:12.350072 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:12.383970 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:12.419659 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:12.453284 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.482674 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:28:12.488454 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.525002 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.564067 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.601018 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.625659 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:28:12.631537 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.668287 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.706900 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.744760 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.769049 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:28:12.777715 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.815717 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.854795 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.892469 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.916194 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:28:12.922201 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:12.957187 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:12.994720 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:13.029832 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.052879 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:28:13.058551 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.092186 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.127712 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.162256 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.185435 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:28:13.191498 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.225599 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.260330 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.302733 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.324860 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:28:13.330560 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.366115 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.402191 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.437824 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.459942 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:28:13.466364 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:13.505125 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:13.543708 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:13.581027 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:13.604396 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:28:13.610302 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:13.648688 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:13.695166 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:13.732307 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:13.756308 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:28:13.762507 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:13.800010 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:13.838673 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:13.881230 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:13.904892 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:28:13.910684 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:13.947027 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:13.985733 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.026056 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:14.050046 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:28:14.055968 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:14.093039 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.130737 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.168056 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:14.193601 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:28:14.199602 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:14.237291 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.274503 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.312390 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:14.336611 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:28:14.342199 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:14.378925 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.420249 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:14.455671 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.479605 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:28:14.485273 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.521071 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:14.559027 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:14.601401 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.624496 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:28:14.630516 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.671064 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:14.712115 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:14.748413 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.771889 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:28:14.777780 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.817507 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:14.856149 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:14.894236 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.917582 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:28:14.923142 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:14.959698 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:15.001816 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:15.040203 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:15.063389 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:28:15.069292 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:15.104571 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:15.142527 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:15.179541 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:15.203284 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:28:15.209303 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:15.245029 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:15.280490 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:15.318411 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.340180 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:28:15.345687 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.379124 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.414772 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.449504 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.472918 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:28:15.478642 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.513781 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.549476 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.584829 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.612333 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:28:15.617952 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.654053 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.689229 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.724726 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.747096 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:28:15.752351 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.786676 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.827929 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.865586 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.890446 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:28:15.896510 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:15.933453 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:15.969204 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.008509 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:16.032046 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:28:16.037805 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:16.073543 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.110049 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.147267 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:16.170719 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:28:16.176869 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:16.212332 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.248695 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.283056 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:16.308115 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:28:16.313628 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:16.348659 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.384730 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:16.419744 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:16.641637 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:28:16.641895 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:28:16.648159 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:16.686985 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:16.725961 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:16.749694 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:28:16.755461 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:16.794290 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:16.834111 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:16.873398 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:16.899589 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:28:16.909197 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:16.950417 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:16.989851 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:17.037347 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:17.061417 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:28:17.067678 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:17.105889 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:17.151718 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:17.201224 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:17.226742 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:28:17.232821 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:17.272924 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:17.313961 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:17.359700 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:17.383579 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:28:17.390308 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:17.434448 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:17.473382 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:17.508961 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:17.531517 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:28:17.537184 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:17.573248 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:17.611378 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:17.658671 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:17.685760 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:28:17.692346 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:17.734336 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:17.775440 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:17.823693 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:17.847416 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:28:17.853485 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:17.892177 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:17.933534 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:17.981084 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:18.003472 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:28:18.009818 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:18.050768 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:18.090477 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:18.128601 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.151725 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:28:18.157199 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.194169 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.232212 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.276659 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.298852 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:28:18.304096 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.342341 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.377475 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.422035 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.442749 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:28:18.448806 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.485935 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.523876 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.570690 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.592374 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:28:18.598136 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.638748 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.676788 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.723364 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.746382 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:28:18.752390 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.790986 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.829659 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.877077 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.900363 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:28:18.906117 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:18.945738 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:18.981754 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:19.017259 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.040435 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:28:19.046218 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.081993 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.122155 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.174692 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.198369 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:28:19.204305 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.242597 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.280370 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.327126 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.349736 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:28:19.355744 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.393054 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.434118 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.482357 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.505682 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:28:19.511698 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.549739 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.587928 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.636206 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.660959 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:28:19.667504 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.705433 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.748512 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.794902 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.817995 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:28:19.823432 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:19.861078 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:19.901524 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:19.941775 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:19.965339 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:28:19.971177 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.007742 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.045042 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.091500 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.114777 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:28:20.120686 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.161639 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.199187 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.245513 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.268240 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:28:20.273461 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.309111 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.345432 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.389157 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.411566 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:28:20.416743 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.457703 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.495822 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.543242 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.566714 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:28:20.572686 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.612726 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.657416 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.703556 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.726845 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:28:20.732737 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.770175 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.808718 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.855944 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.879804 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:28:20.885509 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:20.922685 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:20.962281 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:21.006799 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:21.028022 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:28:21.033202 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:21.069287 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:21.105397 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:21.144757 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:21.432188 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:28:21.432485 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:28:21.438971 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:21.480118 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:21.515003 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:21.539406 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:28:21.544720 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:21.580539 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:21.617165 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:21.653523 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:21.682469 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:28:21.687930 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:21.726017 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:21.766051 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:21.803799 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:21.827066 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:28:21.832712 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:21.867510 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:21.903168 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:21.938155 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:21.959833 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:28:21.966217 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:22.000505 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:22.037238 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:22.076740 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:22.100483 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:28:22.106140 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:22.142101 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:22.178904 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:22.214416 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.237804 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:28:22.245013 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.284003 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.319134 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.353099 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.375176 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:28:22.381110 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.415176 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.449724 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.484658 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.506152 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:28:22.512042 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.548029 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.587749 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.624172 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.652425 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:28:22.658493 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:22.694215 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:22.730779 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:22.767469 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:22.790774 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:28:22.796776 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:22.832235 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:22.868653 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:22.905248 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:22.929259 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:28:22.935185 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:22.970726 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.013023 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.051234 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.075800 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:28:23.081640 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.117953 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.154709 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.191965 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.214743 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:28:23.221638 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.254990 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.289159 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.324596 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.346019 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:28:23.352824 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.388599 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.423357 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.457913 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.482328 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:28:23.487869 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:23.522392 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.561818 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:23.599329 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:23.622992 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:28:23.628890 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:23.665664 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:23.705616 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:23.741760 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:23.765768 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:28:23.771320 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:23.805085 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:23.840181 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:23.880456 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:23.902553 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:28:23.908169 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:23.942818 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:23.978288 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:24.017084 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:24.039233 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:28:24.044582 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:24.079368 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:24.115119 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:24.151738 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:24.181408 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:28:24.187322 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:24.223792 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:24.260576 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:24.297281 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:24.321046 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:28:24.326614 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:24.363078 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:24.399445 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:24.435893 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.461154 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:28:24.467839 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.507921 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.545879 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.582605 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.606900 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:28:24.612704 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.648432 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.689416 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.726971 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.748484 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:28:24.753977 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.791935 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.829167 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.865954 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.888606 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:28:24.894803 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:24.930122 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:24.965561 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.000260 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.030501 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:28:25.036134 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.074639 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.112820 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.150319 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.175275 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:28:25.181179 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.218025 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.253507 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.292953 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.316437 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:28:25.322758 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.358489 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.395775 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.433914 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.456682 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:28:25.463441 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:25.497654 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.533052 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:25.567830 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:25.771016 140462829037440 efficientnet_model.py:735] Built stem stem : (None, 320, 320, 32)\n",
"I0602 16:28:25.771289 140462829037440 efficientnet_model.py:756] block_0 survival_prob: 1.0\n",
"I0602 16:28:25.777165 140462829037440 api.py:446] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:25.814569 140462829037440 api.py:446] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:25.848626 140462829037440 api.py:446] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:25.874634 140462829037440 efficientnet_model.py:756] block_1 survival_prob: 0.9933333333333333\n",
"I0602 16:28:25.883464 140462829037440 api.py:446] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:25.921072 140462829037440 api.py:446] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:25.959650 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:25.997202 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.022235 140462829037440 efficientnet_model.py:756] block_2 survival_prob: 0.9866666666666667\n",
"I0602 16:28:26.028221 140462829037440 api.py:446] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.064908 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.101578 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.145980 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.169420 140462829037440 efficientnet_model.py:756] block_3 survival_prob: 0.98\n",
"I0602 16:28:26.176484 140462829037440 api.py:446] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.214071 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.253358 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.301095 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.324356 140462829037440 efficientnet_model.py:756] block_4 survival_prob: 0.9733333333333334\n",
"I0602 16:28:26.329980 140462829037440 api.py:446] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.366096 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.410491 140462829037440 api.py:446] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.456399 140462829037440 api.py:446] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.480515 140462829037440 efficientnet_model.py:756] block_5 survival_prob: 0.9666666666666667\n",
"I0602 16:28:26.486327 140462829037440 api.py:446] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:26.524052 140462829037440 api.py:446] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:26.562611 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:26.607209 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:26.631732 140462829037440 efficientnet_model.py:756] block_6 survival_prob: 0.96\n",
"I0602 16:28:26.638148 140462829037440 api.py:446] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:26.676763 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:26.717338 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:26.763111 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:26.785823 140462829037440 efficientnet_model.py:756] block_7 survival_prob: 0.9533333333333334\n",
"I0602 16:28:26.791360 140462829037440 api.py:446] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:26.828531 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:26.864648 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:26.914471 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:26.938818 140462829037440 efficientnet_model.py:756] block_8 survival_prob: 0.9466666666666667\n",
"I0602 16:28:26.944159 140462829037440 api.py:446] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:26.983508 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:27.022052 140462829037440 api.py:446] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:27.070753 140462829037440 api.py:446] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:27.093776 140462829037440 efficientnet_model.py:756] block_9 survival_prob: 0.9400000000000001\n",
"I0602 16:28:27.099642 140462829037440 api.py:446] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:27.138691 140462829037440 api.py:446] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:27.177781 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:27.215625 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.240789 140462829037440 efficientnet_model.py:756] block_10 survival_prob: 0.9333333333333333\n",
"I0602 16:28:27.246081 140462829037440 api.py:446] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.281891 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.319328 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.363995 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.386683 140462829037440 efficientnet_model.py:756] block_11 survival_prob: 0.9266666666666667\n",
"I0602 16:28:27.392064 140462829037440 api.py:446] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.428854 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.465859 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.517015 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.539433 140462829037440 efficientnet_model.py:756] block_12 survival_prob: 0.92\n",
"I0602 16:28:27.545071 140462829037440 api.py:446] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.583192 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.622184 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.670609 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.694164 140462829037440 efficientnet_model.py:756] block_13 survival_prob: 0.9133333333333333\n",
"I0602 16:28:27.699993 140462829037440 api.py:446] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.738230 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.775820 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.827068 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.848773 140462829037440 efficientnet_model.py:756] block_14 survival_prob: 0.9066666666666667\n",
"I0602 16:28:27.854128 140462829037440 api.py:446] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.890145 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.926344 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:27.972214 140462829037440 api.py:446] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:27.999525 140462829037440 efficientnet_model.py:756] block_15 survival_prob: 0.9\n",
"I0602 16:28:28.005013 140462829037440 api.py:446] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:28.044124 140462829037440 api.py:446] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:28.081511 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:28.118279 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.142080 140462829037440 efficientnet_model.py:756] block_16 survival_prob: 0.8933333333333333\n",
"I0602 16:28:28.147838 140462829037440 api.py:446] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.185852 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.227486 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.274719 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.297346 140462829037440 efficientnet_model.py:756] block_17 survival_prob: 0.8866666666666667\n",
"I0602 16:28:28.303137 140462829037440 api.py:446] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.342741 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.381694 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.430077 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.453792 140462829037440 efficientnet_model.py:756] block_18 survival_prob: 0.88\n",
"I0602 16:28:28.459623 140462829037440 api.py:446] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.500525 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.539853 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.586525 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.609989 140462829037440 efficientnet_model.py:756] block_19 survival_prob: 0.8733333333333334\n",
"I0602 16:28:28.615751 140462829037440 api.py:446] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.654529 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.692438 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.739311 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.762350 140462829037440 efficientnet_model.py:756] block_20 survival_prob: 0.8666666666666667\n",
"I0602 16:28:28.768217 140462829037440 api.py:446] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.812358 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.850563 140462829037440 api.py:446] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:28.897440 140462829037440 api.py:446] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.921426 140462829037440 efficientnet_model.py:756] block_21 survival_prob: 0.86\n",
"I0602 16:28:28.927252 140462829037440 api.py:446] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:28.967573 140462829037440 api.py:446] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:29.006088 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:29.043857 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.069467 140462829037440 efficientnet_model.py:756] block_22 survival_prob: 0.8533333333333334\n",
"I0602 16:28:29.074960 140462829037440 api.py:446] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.114840 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.154317 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.200912 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.225009 140462829037440 efficientnet_model.py:756] block_23 survival_prob: 0.8466666666666667\n",
"I0602 16:28:29.232561 140462829037440 api.py:446] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.272552 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.311730 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.360594 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.383351 140462829037440 efficientnet_model.py:756] block_24 survival_prob: 0.8400000000000001\n",
"I0602 16:28:29.389607 140462829037440 api.py:446] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.433474 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.471709 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.519286 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.542617 140462829037440 efficientnet_model.py:756] block_25 survival_prob: 0.8333333333333334\n",
"I0602 16:28:29.548529 140462829037440 api.py:446] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.586364 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.624346 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.673057 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.700415 140462829037440 efficientnet_model.py:756] block_26 survival_prob: 0.8266666666666667\n",
"I0602 16:28:29.706454 140462829037440 api.py:446] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.744886 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.782733 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.829407 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.853035 140462829037440 efficientnet_model.py:756] block_27 survival_prob: 0.8200000000000001\n",
"I0602 16:28:29.858804 140462829037440 api.py:446] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:29.896754 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.937703 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:29.987081 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:30.011265 140462829037440 efficientnet_model.py:756] block_28 survival_prob: 0.8133333333333334\n",
"I0602 16:28:30.017084 140462829037440 api.py:446] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:30.055170 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:30.096004 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:30.150238 140462829037440 api.py:446] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:30.174353 140462829037440 efficientnet_model.py:756] block_29 survival_prob: 0.8066666666666666\n",
"I0602 16:28:30.180383 140462829037440 api.py:446] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:30.218761 140462829037440 api.py:446] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:30.259086 140462829037440 api.py:446] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:30.298054 140462829037440 api.py:446] Project shape: (None, 20, 20, 448)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:35.788866 140462829037440 efficientnet_model.py:374] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:35.797541 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:35.804782 140462829037440 efficientnet_model.py:414] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:35.811051 140462829037440 efficientnet_model.py:374] Block blocks_0 input shape: (None, 320, 320, 32)\n",
"I0602 16:28:35.822191 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 320, 320, 32)\n",
"I0602 16:28:35.830991 140462829037440 efficientnet_model.py:414] Project shape: (None, 320, 320, 24)\n",
"I0602 16:28:35.881902 140462829037440 efficientnet_model.py:374] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:35.889288 140462829037440 efficientnet_model.py:390] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:35.897151 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:35.904131 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:35.911154 140462829037440 efficientnet_model.py:374] Block blocks_1 input shape: (None, 320, 320, 24)\n",
"I0602 16:28:35.919776 140462829037440 efficientnet_model.py:390] Expand shape: (None, 320, 320, 144)\n",
"I0602 16:28:35.928822 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 144)\n",
"I0602 16:28:35.936937 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:35.989104 140462829037440 efficientnet_model.py:374] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:35.996345 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.004292 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.011981 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.019623 140462829037440 efficientnet_model.py:374] Block blocks_2 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.027881 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.036938 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.053098 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.105842 140462829037440 efficientnet_model.py:374] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.113061 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.123128 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.132769 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.141913 140462829037440 efficientnet_model.py:374] Block blocks_3 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.152135 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.162493 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.180560 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.241426 140462829037440 efficientnet_model.py:374] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.248717 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.257430 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.265630 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.273454 140462829037440 efficientnet_model.py:374] Block blocks_4 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.282312 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.292011 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.309649 140462829037440 efficientnet_model.py:414] Project shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.369746 140462829037440 efficientnet_model.py:374] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.377061 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.385192 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:36.392228 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.399749 140462829037440 efficientnet_model.py:374] Block blocks_5 input shape: (None, 160, 160, 32)\n",
"I0602 16:28:36.408684 140462829037440 efficientnet_model.py:390] Expand shape: (None, 160, 160, 192)\n",
"I0602 16:28:36.418398 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 192)\n",
"I0602 16:28:36.429459 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.485835 140462829037440 efficientnet_model.py:374] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.493170 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.501166 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.509526 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.517524 140462829037440 efficientnet_model.py:374] Block blocks_6 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.525980 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.535196 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.552469 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.610451 140462829037440 efficientnet_model.py:374] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.617930 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.630398 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.640243 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.647653 140462829037440 efficientnet_model.py:374] Block blocks_7 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.657253 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.667338 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.684460 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.739639 140462829037440 efficientnet_model.py:374] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.746821 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.754798 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.762496 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.769813 140462829037440 efficientnet_model.py:374] Block blocks_8 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.778055 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.786992 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.804284 140462829037440 efficientnet_model.py:414] Project shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.858725 140462829037440 efficientnet_model.py:374] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.865491 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.873424 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:36.880941 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:36.887989 140462829037440 efficientnet_model.py:374] Block blocks_9 input shape: (None, 80, 80, 56)\n",
"I0602 16:28:36.896646 140462829037440 efficientnet_model.py:390] Expand shape: (None, 80, 80, 336)\n",
"I0602 16:28:36.906332 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 336)\n",
"I0602 16:28:36.914799 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:36.978651 140462829037440 efficientnet_model.py:374] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:36.986153 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:36.994243 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.002086 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.009498 140462829037440 efficientnet_model.py:374] Block blocks_10 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.018704 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.028799 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.046014 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.105513 140462829037440 efficientnet_model.py:374] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.112976 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.121132 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.129670 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.137799 140462829037440 efficientnet_model.py:374] Block blocks_11 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.146401 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.160232 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.177582 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.237770 140462829037440 efficientnet_model.py:374] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.245286 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.253473 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.260830 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.267869 140462829037440 efficientnet_model.py:374] Block blocks_12 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.276125 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.285049 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.301288 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.355804 140462829037440 efficientnet_model.py:374] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.363130 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.370823 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.378370 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.385405 140462829037440 efficientnet_model.py:374] Block blocks_13 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.393524 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.402572 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.419682 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.482460 140462829037440 efficientnet_model.py:374] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.489312 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.497110 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.504912 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.512460 140462829037440 efficientnet_model.py:374] Block blocks_14 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.521170 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.531431 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.548940 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.604210 140462829037440 efficientnet_model.py:374] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.611849 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.620471 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.627738 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.637725 140462829037440 efficientnet_model.py:374] Block blocks_15 input shape: (None, 40, 40, 112)\n",
"I0602 16:28:37.646651 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.656013 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 672)\n",
"I0602 16:28:37.664630 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.720255 140462829037440 efficientnet_model.py:374] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.727627 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.736395 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.744600 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.751944 140462829037440 efficientnet_model.py:374] Block blocks_16 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.759989 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.768963 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.785908 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.840922 140462829037440 efficientnet_model.py:374] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.847856 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.855620 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.863656 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.870792 140462829037440 efficientnet_model.py:374] Block blocks_17 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.879349 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.888612 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.905066 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.967293 140462829037440 efficientnet_model.py:374] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:37.975776 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.983866 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:37.992056 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:37.999700 140462829037440 efficientnet_model.py:374] Block blocks_18 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.008292 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.017708 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.035875 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.093446 140462829037440 efficientnet_model.py:374] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.101186 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.109306 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.116893 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.124384 140462829037440 efficientnet_model.py:374] Block blocks_19 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.134109 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.145520 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.163572 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.223122 140462829037440 efficientnet_model.py:374] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.230640 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.239758 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.247563 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.256920 140462829037440 efficientnet_model.py:374] Block blocks_20 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.266097 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.275500 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.292806 140462829037440 efficientnet_model.py:414] Project shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.353554 140462829037440 efficientnet_model.py:374] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.361362 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.369931 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:38.377907 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.385463 140462829037440 efficientnet_model.py:374] Block blocks_21 input shape: (None, 40, 40, 160)\n",
"I0602 16:28:38.394322 140462829037440 efficientnet_model.py:390] Expand shape: (None, 40, 40, 960)\n",
"I0602 16:28:38.403844 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 960)\n",
"I0602 16:28:38.412998 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.468438 140462829037440 efficientnet_model.py:374] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.475744 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.484013 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.491543 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.498974 140462829037440 efficientnet_model.py:374] Block blocks_22 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.507284 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.517102 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.533732 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.601514 140462829037440 efficientnet_model.py:374] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.609544 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.618425 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.626242 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.633899 140462829037440 efficientnet_model.py:374] Block blocks_23 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.643427 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.653449 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.671765 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.727773 140462829037440 efficientnet_model.py:374] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.735522 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.744191 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.751889 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.759732 140462829037440 efficientnet_model.py:374] Block blocks_24 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.768750 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.778687 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.796536 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.855594 140462829037440 efficientnet_model.py:374] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:38.863496 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.871708 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:38.881301 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.889106 140462829037440 efficientnet_model.py:374] Block blocks_25 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:38.897510 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.563537 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.581607 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.638648 140462829037440 efficientnet_model.py:374] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:39.646240 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.654553 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.662301 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.670193 140462829037440 efficientnet_model.py:374] Block blocks_26 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.678536 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.687340 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.703514 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.757405 140462829037440 efficientnet_model.py:374] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:39.764389 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.772317 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.779630 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.787194 140462829037440 efficientnet_model.py:374] Block blocks_27 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.795367 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.804360 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.821222 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.879758 140462829037440 efficientnet_model.py:374] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:39.887127 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.895468 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.903554 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.911814 140462829037440 efficientnet_model.py:374] Block blocks_28 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:39.920435 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.930061 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:39.947239 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 272)\n",
"I0602 16:28:40.004605 140462829037440 efficientnet_model.py:374] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1331: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
" warnings.warn('`layer.updates` will be removed in a future version. '\n",
"I0602 16:28:40.012040 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:40.020497 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:40.027721 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 448)\n",
"I0602 16:28:40.035153 140462829037440 efficientnet_model.py:374] Block blocks_29 input shape: (None, 20, 20, 272)\n",
"I0602 16:28:40.044175 140462829037440 efficientnet_model.py:390] Expand shape: (None, 20, 20, 1632)\n",
"I0602 16:28:40.054436 140462829037440 efficientnet_model.py:393] DWConv shape: (None, 20, 20, 1632)\n",
"I0602 16:28:40.063949 140462829037440 efficientnet_model.py:414] Project shape: (None, 20, 20, 448)\n",
"W0602 16:29:00.618458 140462829037440 save.py:243] Found untraced functions such as conv2d_layer_call_fn, conv2d_layer_call_and_return_conditional_losses, tpu_batch_normalization_layer_call_fn, tpu_batch_normalization_layer_call_and_return_conditional_losses, conv2d_layer_call_fn while saving (showing 5 of 2570). These functions will not be directly callable after loading.\n",
"WARNING:tensorflow:FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
"\n",
"FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
"W0602 16:29:12.372401 140462829037440 save.py:1240] FOR KERAS USERS: The object that you are saving contains one or more Keras models or layers. If you are loading the SavedModel with `tf.keras.models.load_model`, continue reading (otherwise, you may ignore the following instructions). Please change your code to save with `tf.keras.models.save_model` or `model.save`, and confirm that the file \"keras.metadata\" exists in the export directory. In the future, Keras will only load the SavedModels that have this file. In other words, `tf.saved_model.save` will no longer write SavedModels that can be recovered as Keras models (this will apply in TF 2.5).\n",
"\n",
"FOR DEVS: If you are overwriting _tracking_metadata in your class, this property has been used to save metadata in the SavedModel. The metadta field will be deprecated soon, so please move the metadata to a different file.\n",
"INFO:tensorflow:Assets written to: /content/saved_model_efficientdet-lite4/assets\n",
"I0602 16:29:13.793345 140462829037440 builder_impl.py:775] Assets written to: /content/saved_model_efficientdet-lite4/assets\n",
"I0602 16:29:15.459842 140462829037440 infer_lib.py:360] Model saved at /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:29:15.513179: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:15.514253: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 1\n",
"2021-06-02 16:29:15.514426: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session\n",
"2021-06-02 16:29:15.514793: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:15.515760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
"pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
"coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
"2021-06-02 16:29:15.515869: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:15.516847: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:15.517741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
"2021-06-02 16:29:15.517802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:29:15.517823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n",
"2021-06-02 16:29:15.517842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n",
"2021-06-02 16:29:15.518033: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:15.518973: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:15.519702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15433 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"2021-06-02 16:29:15.520140: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 2299995000 Hz\n",
"2021-06-02 16:29:15.612395: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1144] Optimization results for grappler item: graph_to_optimize\n",
" function_optimizer: Graph size after: 3626 nodes (14), 8344 edges (8), time = 37.375ms.\n",
" function_optimizer: function_optimizer did nothing. time = 2.138ms.\n",
"\n",
"I0602 16:29:19.038028 140462829037440 infer_lib.py:367] Frozen graph saved at /content/saved_model_efficientdet-lite4/efficientdet-lite4_frozen.pb\n",
"2021-06-02 16:29:50.967260: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:345] Ignored output_format.\n",
"2021-06-02 16:29:50.967327: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:348] Ignored drop_control_dependency.\n",
"2021-06-02 16:29:50.967353: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored change_concat_input_ranges.\n",
"2021-06-02 16:29:50.968325: I tensorflow/cc/saved_model/reader.cc:38] Reading SavedModel from: /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:29:51.130934: I tensorflow/cc/saved_model/reader.cc:90] Reading meta graph with tags { serve }\n",
"2021-06-02 16:29:51.131031: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:29:51.131136: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:29:51.131154: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] \n",
"2021-06-02 16:29:51.839157: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.\n",
"2021-06-02 16:29:53.562334: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: /content/saved_model_efficientdet-lite4\n",
"2021-06-02 16:29:54.204154: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 3235824 microseconds.\n",
"2021-06-02 16:29:56.096874: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"2021-06-02 16:29:58.078854: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:58.080147: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: \n",
"pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
"coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
"2021-06-02 16:29:58.080268: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:58.081484: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:58.082535: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0\n",
"2021-06-02 16:29:58.082616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-06-02 16:29:58.082642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264] 0 \n",
"2021-06-02 16:29:58.082664: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0: N \n",
"2021-06-02 16:29:58.082803: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:58.083912: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] 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",
"2021-06-02 16:29:58.085038: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15433 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"2021-06-02 16:29:59.685114: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
"fully_quantize: 0, inference_type: 6, input_inference_type: 3, output_inference_type: 0\n",
"W0602 18:20:17.106637 140462829037440 util.py:683] For model inputs containing unsupported operations which cannot be quantized, the `inference_input_type` attribute will default to the original type.\n",
"I0602 18:20:17.371444 140462829037440 infer_lib.py:419] TFLite is saved at /content/saved_model_efficientdet-lite4/int8.tflite\n",
"Model are exported to /content/saved_model_efficientdet-lite4\n",
"Edge TPU Compiler version 15.0.340273435\n",
"\n",
"Internal compiler error. Aborting! \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Z7jxXH1UgSyG"
},
"source": [
"%cd /content/\n",
"!tar czf /content/efficientdet-lite.tar.gz ./efficientdet-lite"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yO2_ndAZMqtU"
},
"source": [
"!cp /content/efficientdet-lite.tar.gz /content/drive/MyDrive"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pIkKF3IgSsIT"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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