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NobuoTsukamoto/train_deeplabv3plusmobilenetedgetpuv2_and_export_tf-lite.ipynb

Created January 10, 2022 11:08
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Train_DeeplabV3plusMobileNetEdgetpuV2_and_Export_TF-Lite.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"mount_file_id": "1ZnQqc91CJX7xIDgT2ub04Kw_RjotxFPw",
"authorship_tag": "ABX9TyOr25vy5aYpu+o8IxDSXeWq",
"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/56f3cb76232c745c33f54a8fd102cb28/train_deeplabv3plusmobilenetedgetpuv2_and_export_tf-lite.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"MIT License\n",
"\n",
"Copyright (c) 2022 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."
],
"metadata": {
"id": "W__No6fRn-Mn"
}
},
{
"cell_type": "markdown",
"source": [
"# Train DeeplabV3+ MobileNetEdgetpuV2 and Export TF-Lite model.\n",
"\n",
"This notebook contains instructions for training DeeplabV3+ MobileNetEdgetpuV2 on the PASCAL VOC 2012 dataset and exporting it to a TF-Lite model. Finally, we will perform inference with the exported TF-Lite model. \n",
"(The same procedure can be used to train AutosegEdgeTPU and export it to TF-Lite model.)"
],
"metadata": {
"id": "2HYdORnLzFgW"
}
},
{
"cell_type": "markdown",
"source": [
"# Reference\n",
"- [EdgeTPU-optimized Vision Models - TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official/projects/edgetpu/vision#edgetpu-optimized-vision-models)\n",
" - [Semantic segmentation task](https://github.com/tensorflow/models/tree/master/official/projects/edgetpu/vision#edgetpu-optimized-vision-models)"
],
"metadata": {
"id": "_8pxDVnomnF4"
}
},
{
"cell_type": "markdown",
"source": [
"# Setup"
],
"metadata": {
"id": "KvotNRGeznje"
}
},
{
"cell_type": "markdown",
"source": [
"Access the GCP SDK to download the backbone pre-trained model."
],
"metadata": {
"id": "cd5SpAY5ZObN"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import auth\n",
"auth.authenticate_user()"
],
"metadata": {
"id": "LHK4Y_AjOpkQ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Clone [tensorflow/models](https://github.com/tensorflow/models) repository and install the required packages."
],
"metadata": {
"id": "wMF3uLMhZa0y"
}
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OoHJZJ0Ps2Dt",
"outputId": "55267dba-dfb0-4577-a8d3-9a35d7135682"
},
"source": [
"%%bash\n",
"git clone https://github.com/tensorflow/models.git\n",
"cd models\n",
"git checkout 658c803879cb13db78b2339f044841b1cf47d322\n",
"pip3 install -q -r official/requirements.txt\n",
"pip3 install -q tensorflow-text-nightly\n",
"pip3 install -q tensorflow_addons\n",
"pip3 uninstall -q -y opencv-python\n",
"pip3 install -q opencv-python"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Cloning into 'models'...\n",
"Note: checking out '658c803879cb13db78b2339f044841b1cf47d322'.\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 658c80387 [bigbird] move bigbird from nlp/projects to official/projects\n",
"ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Train"
],
"metadata": {
"id": "odb_O5Zbs7iD"
}
},
{
"cell_type": "markdown",
"source": [
"## Create Pascal VOC TF-Record"
],
"metadata": {
"id": "eQ-iY_Prvou7"
}
},
{
"cell_type": "markdown",
"source": [
"You can choose TF-Record or TFDS dataset, in case of TF-Record, you can use the code in [deeplab](https://github.com/tensorflow/models/tree/master/research/deeplab/datasets). \n",
"In the case of TF-Record, you can use the code in deeplab. \n",
"Here, [download_and_convert_voc2012.sh](https://github.com/tensorflow/models/blob/master/research/deeplab/datasets/download_and_convert_voc2012.sh) generates TF-Record for the PASCAL VOC 2012 dataset."
],
"metadata": {
"id": "_SvrmAvYbaa4"
}
},
{
"cell_type": "code",
"source": [
"%cd /content/models/research/deeplab/datasets"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yypXcW5KvylT",
"outputId": "c1e95192-5702-4ad0-d2f6-88af54e904be"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/models/research/deeplab/datasets\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"It only works with TF1.x."
],
"metadata": {
"id": "1qdSGk45-yiH"
}
},
{
"cell_type": "code",
"source": [
"%tensorflow_version 1.x"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rk8g-EZHxItB",
"outputId": "d33c0795-9409-4b5b-bbcd-b0517eeb76c8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"TensorFlow 1.x selected.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!sh download_and_convert_voc2012.sh"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QPcx2f9_v6Tk",
"outputId": "71eb1e9a-7c51-4217-9eae-bcf6c180bf13"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"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",
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"Removing the color map in ground truth annotations...\n",
"Converting PASCAL VOC 2012 dataset...\n",
"WARNING:tensorflow:From /content/models/research/deeplab/datasets/build_voc2012_data.py:146: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n",
"\n",
"WARNING:tensorflow:From /content/models/research/deeplab/datasets/build_voc2012_data.py:140: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.\n",
"\n",
"W0110 07:55:21.882637 140407931357056 module_wrapper.py:139] From /content/models/research/deeplab/datasets/build_voc2012_data.py:140: The name tf.gfile.Glob is deprecated. Please use tf.io.gfile.glob instead.\n",
"\n",
"WARNING:tensorflow:From /content/models/research/deeplab/datasets/build_data.py:63: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"W0110 07:55:21.887516 140407931357056 module_wrapper.py:139] From /content/models/research/deeplab/datasets/build_data.py:63: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From /content/models/research/deeplab/datasets/build_data.py:65: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"W0110 07:55:21.894222 140407931357056 module_wrapper.py:139] From /content/models/research/deeplab/datasets/build_data.py:65: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"2022-01-10 07:55:21.896552: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
"2022-01-10 07:55:21.956731: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:21.957328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:21.968121: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:22.126421: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:22.154239: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:22.172702: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:22.328316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:22.355144: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:22.691793: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:22.692028: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.693006: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.693781: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:22.713638: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199995000 Hz\n",
"2022-01-10 07:55:22.713934: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5641cef1abc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
"2022-01-10 07:55:22.713963: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n",
"2022-01-10 07:55:22.989516: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.990351: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5641cef1ad80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2022-01-10 07:55:22.990384: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
"2022-01-10 07:55:22.990559: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.991116: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:22.991181: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:22.991203: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:22.991222: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:22.991242: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:22.991259: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:22.991276: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:22.991294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:22.991361: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.991910: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.992440: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:22.995568: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:22.996654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2022-01-10 07:55:22.996684: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2022-01-10 07:55:22.996695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2022-01-10 07:55:22.997488: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.998130: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:22.998691: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
"2022-01-10 07:55:22.998734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"2022-01-10 07:55:23.001449: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:23.001988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:23.002035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:23.002067: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:23.002090: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:23.002109: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:23.002126: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:23.002145: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:23.002164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:23.002221: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:23.002758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:23.003278: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:23.003308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2022-01-10 07:55:23.003323: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2022-01-10 07:55:23.003335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2022-01-10 07:55:23.003412: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:23.003951: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:23.004478: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"WARNING:tensorflow:From /content/models/research/deeplab/datasets/build_voc2012_data.py:111: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
"\n",
"W0110 07:55:23.005239 140407931357056 module_wrapper.py:139] From /content/models/research/deeplab/datasets/build_voc2012_data.py:111: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n",
"\n",
">> Converting image 1/1449 shard 0WARNING:tensorflow:From /content/models/research/deeplab/datasets/build_voc2012_data.py:121: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
"\n",
"W0110 07:55:23.005646 140407931357056 module_wrapper.py:139] From /content/models/research/deeplab/datasets/build_voc2012_data.py:121: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n",
"\n",
">> Converting image 363/1449 shard 0\n",
">> Converting image 726/1449 shard 1\n",
">> Converting image 1089/1449 shard 2\n",
">> Converting image 1449/1449 shard 3\n",
"2022-01-10 07:55:29.255774: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.256655: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:29.256773: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:29.256807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:29.256830: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:29.256851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:29.256871: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:29.256904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:29.256928: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:29.257025: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.257897: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.258665: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:29.258714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2022-01-10 07:55:29.258728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2022-01-10 07:55:29.258740: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2022-01-10 07:55:29.258835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.260650: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.261262: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"2022-01-10 07:55:29.264784: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.265343: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:29.265389: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:29.265409: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:29.265427: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:29.265446: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:29.265463: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:29.265479: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:29.265495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:29.265555: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.266144: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.266641: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:29.266670: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2022-01-10 07:55:29.266684: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2022-01-10 07:55:29.266696: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2022-01-10 07:55:29.266768: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.267328: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:29.267838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
">> Converting image 366/1464 shard 0\n",
">> Converting image 732/1464 shard 1\n",
">> Converting image 1098/1464 shard 2\n",
">> Converting image 1464/1464 shard 3\n",
"2022-01-10 07:55:35.653363: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.654215: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:35.654299: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:35.654321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:35.654337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:35.654354: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:35.654369: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:35.654383: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:35.654398: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:35.654475: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.655199: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.655858: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:35.655893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2022-01-10 07:55:35.655905: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2022-01-10 07:55:35.655915: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2022-01-10 07:55:35.655991: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.656708: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.657383: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"2022-01-10 07:55:35.660444: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.661233: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2022-01-10 07:55:35.661294: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2022-01-10 07:55:35.661315: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2022-01-10 07:55:35.661332: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2022-01-10 07:55:35.661349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2022-01-10 07:55:35.661366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2022-01-10 07:55:35.661382: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2022-01-10 07:55:35.661399: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2022-01-10 07:55:35.661464: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.662246: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.662972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2022-01-10 07:55:35.663003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2022-01-10 07:55:35.663022: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2022-01-10 07:55:35.663034: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2022-01-10 07:55:35.663132: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.663941: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] 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",
"2022-01-10 07:55:35.664971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
">> Converting image 729/2913 shard 0\n",
">> Converting image 1458/2913 shard 1\n",
">> Converting image 2187/2913 shard 2\n",
">> Converting image 2913/2913 shard 3\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!mv /content/models/research/deeplab/datasets/pascal_voc_seg /content/"
],
"metadata": {
"id": "gaTP-u4S0vy2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Download pre-trained model."
],
"metadata": {
"id": "6OWWcFaIwpQu"
}
},
{
"cell_type": "markdown",
"source": [
"Download the pre-trained model of the mobilenet-edgetpu-v2 backbone for fine-tuning.\n",
"- [On-device benchmarking of classification models](https://github.com/tensorflow/models/tree/master/official/projects/edgetpu/vision#on-device-benchmarking-of-classification-models)\n",
"\n",
"There is an error in the download link for Checkpoint. Please see the following issue for details.\n",
"- [Incorrect download link for Checkpoint in MobileNetEdgeTPUv2 (Classification models) #10450](https://github.com/tensorflow/models/issues/10450)"
],
"metadata": {
"id": "DvL_72T1MtdZ"
}
},
{
"cell_type": "code",
"source": [
"%cd /content/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gPHlpkDO1ZHd",
"outputId": "a361fce4-5982-47c3-db17-d2f639cf09f1"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!gsutil ls gs://tf_model_garden/models/edgetpu/checkpoint_and_tflite/vision/mobilenet-edgetpu-v2/s/mobilenet-edgetpu-v2-s.tar.gz\n",
"!gsutil copy gs://tf_model_garden/models/edgetpu/checkpoint_and_tflite/vision/mobilenet-edgetpu-v2/s/mobilenet-edgetpu-v2-s.tar.gz /content/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OPkSlE_iO3yL",
"outputId": "59dce78c-5c05-4721-c232-f3835ed5f69c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"gs://tf_model_garden/models/edgetpu/checkpoint_and_tflite/vision/mobilenet-edgetpu-v2/s/mobilenet-edgetpu-v2-s.tar.gz\n",
"Copying gs://tf_model_garden/models/edgetpu/checkpoint_and_tflite/vision/mobilenet-edgetpu-v2/s/mobilenet-edgetpu-v2-s.tar.gz...\n",
"- [1 files][ 84.3 MiB/ 84.3 MiB] \n",
"Operation completed over 1 objects/84.3 MiB. \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!tar xf mobilenet-edgetpu-v2-s.tar.gz\n",
"!cp -R /content/home/darwinn-l2l/pixel6/pretrained_checkpoints/vision/mobilenet-edgetpu-v2/s ./mobilenet-edgetpu-v2-s\n",
"!rm -rf ./home"
],
"metadata": {
"id": "VtUkj9TcsNag"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Add PYTHONPATH"
],
"metadata": {
"id": "rCxIvEm6zyVE"
}
},
{
"cell_type": "code",
"source": [
"%tensorflow_version 2.x"
],
"metadata": {
"id": "qnQ3TIxcy6K5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "26f9ba6a-0b5e-45b8-dbdc-234613ed5b35"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"TensorFlow 2.x selected.\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1yH7FWvgUqB7",
"outputId": "9d98ca73-0856-481d-9b62-310ba63de1a2"
},
"source": [
"import os\n",
"\n",
"os.environ['PYTHONPATH'] = '/content/models:' + os.environ['PYTHONPATH']\n",
"print(os.environ['PYTHONPATH'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/models:/env/python\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VwaProKfUyAq",
"outputId": "280a6100-6b18-411c-8f7b-0e4c746455b4"
},
"source": [
"%cd /content/models"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/models\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Apply patch\n",
"\n",
"Add or modify the code to train on the PASCAL VOC dataset.\n",
"- [official/projects/edgetpu/vision/configs/semantic_segmentation_config](https://github.com/tensorflow/models/blob/db2fab9906b8fcfe44948e6a300ff4ecc25853f1/official/projects/edgetpu/vision/configs/semantic_segmentation_config.py) \n",
" Add settings for training, validation TF-Record path, batch size, and other settings related to training in general. \n",
" Make changes based on [seg_deeplabv3plus_ade20k_32](https://github.com/tensorflow/models/blob/db2fab9906b8fcfe44948e6a300ff4ecc25853f1/official/projects/edgetpu/vision/configs/semantic_segmentation_config.py#L101) and add `deeplabv3plus_mobilenet_edgetpuv2_s_voc2012` experiment.\n",
" `deeplabv3plus_mobilenet_edgetpuv2_s_voc2012` is specified as argument --experiment in train.py.\n",
"- [official/projects/edgetpu/vision/tasks/semantic_segmentation.py](https://github.com/tensorflow/models/blob/db2fab9906b8fcfe44948e6a300ff4ecc25853f1/official/projects/edgetpu/vision/tasks/semantic_segmentation.py) \n",
" There is a dedicated process that makes the label an [ADE20K dataset 32 class](https://github.com/tensorflow/models/blob/db2fab9906b8fcfe44948e6a300ff4ecc25853f1/official/projects/edgetpu/vision/tasks/semantic_segmentation.py#L52-L54).\n",
" - Labels greater than 31 are set to 0.\n",
" - Set the 0 in the label to 255 and set it to ignore_label.\n",
"\n",
" Remove processing of ADEK20 32 class."
],
"metadata": {
"id": "yX843hi_tz8z"
}
},
{
"cell_type": "markdown",
"source": [
"patch semantic_segmentation_config.py"
],
"metadata": {
"id": "V97K2Wib_oDA"
}
},
{
"cell_type": "code",
"source": [
"%%bash\n",
"\n",
"cat <<EOF > /content/semantic_segmentation_config.patch\n",
"diff --git a/official/projects/edgetpu/vision/configs/semantic_segmentation_config.py b/official/projects/edgetpu/vision/configs/semantic_segmentation_config.py\n",
"index dbb5e4150..5cc6b2c46 100644\n",
"--- a/official/projects/edgetpu/vision/configs/semantic_segmentation_config.py\n",
"+++ b/official/projects/edgetpu/vision/configs/semantic_segmentation_config.py\n",
"@@ -66,6 +66,11 @@ ADE20K_TRAIN_EXAMPLES = 20210\n",
" ADE20K_VAL_EXAMPLES = 2000\n",
" ADE20K_INPUT_PATH_BASE = 'gs://**/ADE20K'\n",
" \n",
"+# VOC 2012 Dataset\n",
"+PASCAL_VOC_TRAIN_EXAMPLES = 1464\n",
"+PASCAL_VOC_VAL_EXAMPLES = 1449\n",
"+PASCAL_VOC_INPUT_PATH_BASE = '/content/pascal_voc_seg/tfrecord'\n",
"+\n",
" PRETRAINED_CKPT_PATH_BASE = 'gs://**/placeholder_for_edgetpu_models'\n",
" \n",
" BACKBONE_PRETRAINED_CHECKPOINT = {\n",
"@@ -206,6 +211,105 @@ def seg_deeplabv3plus_ade20k(backbone: str):\n",
" return config\n",
" \n",
" \n",
"+def seg_deeplabv3plus_voc(backbone: str,\n",
"+ init_backbone: bool = False\n",
"+ ) -> cfg.ExperimentConfig:\n",
"+ \"\"\"Semantic segmentation on PASCAL VOC 2021 dataset with deeplabv3+.\"\"\"\n",
"+ epochs = 50\n",
"+ train_batch_size = 32\n",
"+ eval_batch_size = 32\n",
"+ image_size = 512\n",
"+ steps_per_epoch = PASCAL_VOC_TRAIN_EXAMPLES // train_batch_size\n",
"+ aspp_dilation_rates = [5, 10, 15]\n",
"+ pretrained_checkpoint_path = BACKBONE_PRETRAINED_CHECKPOINT[\n",
"+ backbone] if init_backbone else None\n",
"+ config = cfg.ExperimentConfig(\n",
"+ task=CustomSemanticSegmentationTaskConfig(\n",
"+ model=base_cfg.SemanticSegmentationModel(\n",
"+ # Pascal VOC 2012 uses only 21 semantic classes for train/evaluation.\n",
"+ # The void (background) class is ignored in train and evaluation.\n",
"+ num_classes=21,\n",
"+ input_size=[None, None, 3],\n",
"+ backbone=Backbone(\n",
"+ type='mobilenet_edgetpu',\n",
"+ mobilenet_edgetpu=MobileNetEdgeTPU(\n",
"+ model_id=backbone,\n",
"+ pretrained_checkpoint_path=pretrained_checkpoint_path,\n",
"+ freeze_large_filters=500,\n",
"+ )),\n",
"+ decoder=decoders.Decoder(\n",
"+ type='aspp',\n",
"+ aspp=decoders.ASPP(\n",
"+ level=BACKBONE_HEADPOINT[backbone],\n",
"+ use_depthwise_convolution=True,\n",
"+ dilation_rates=aspp_dilation_rates,\n",
"+ pool_kernel_size=[256, 256],\n",
"+ num_filters=128,\n",
"+ dropout_rate=0.3,\n",
"+ )),\n",
"+ head=base_cfg.SegmentationHead(\n",
"+ level=BACKBONE_HEADPOINT[backbone],\n",
"+ num_convs=2,\n",
"+ num_filters=256,\n",
"+ use_depthwise_convolution=True,\n",
"+ feature_fusion='deeplabv3plus',\n",
"+ low_level=BACKBONE_LOWER_FEATURES[backbone],\n",
"+ low_level_num_filters=48),\n",
"+ norm_activation=common.NormActivation(\n",
"+ activation='relu',\n",
"+ norm_momentum=0.99,\n",
"+ norm_epsilon=2e-3,\n",
"+ use_sync_bn=False)),\n",
"+ train_data=base_cfg.DataConfig(\n",
"+ input_path=os.path.join(PASCAL_VOC_INPUT_PATH_BASE, 'train-*'),\n",
"+ output_size=[image_size, image_size],\n",
"+ is_training=True,\n",
"+ global_batch_size=train_batch_size),\n",
"+ validation_data=base_cfg.DataConfig(\n",
"+ input_path=os.path.join(PASCAL_VOC_INPUT_PATH_BASE, 'val-*'),\n",
"+ output_size=[image_size, image_size],\n",
"+ is_training=False,\n",
"+ global_batch_size=eval_batch_size,\n",
"+ resize_eval_groundtruth=True,\n",
"+ drop_remainder=False),\n",
"+ evaluation=base_cfg.Evaluation(report_train_mean_iou=False),\n",
"+ ),\n",
"+ trainer=cfg.TrainerConfig(\n",
"+ steps_per_loop=steps_per_epoch,\n",
"+ summary_interval=steps_per_epoch,\n",
"+ checkpoint_interval=steps_per_epoch,\n",
"+ train_steps=epochs * steps_per_epoch,\n",
"+ validation_steps=PASCAL_VOC_VAL_EXAMPLES // eval_batch_size,\n",
"+ validation_interval=steps_per_epoch,\n",
"+ optimizer_config=optimization.OptimizationConfig({\n",
"+ 'optimizer': {\n",
"+ 'type': 'adam',\n",
"+ },\n",
"+ 'learning_rate': {\n",
"+ 'type': 'polynomial',\n",
"+ 'polynomial': {\n",
"+ 'initial_learning_rate': 0.0001,\n",
"+ 'decay_steps': epochs * steps_per_epoch,\n",
"+ 'end_learning_rate': 0.0,\n",
"+ 'power': 0.9\n",
"+ }\n",
"+ },\n",
"+ 'warmup': {\n",
"+ 'type': 'linear',\n",
"+ 'linear': {\n",
"+ 'warmup_steps': 4 * steps_per_epoch,\n",
"+ 'warmup_learning_rate': 0\n",
"+ }\n",
"+ }\n",
"+ })),\n",
"+ restrictions=[\n",
"+ 'task.train_data.is_training != None',\n",
"+ 'task.validation_data.is_training != None'\n",
"+ ])\n",
"+\n",
"+ return config\n",
"+\n",
"+\n",
" # Experiment configs for 32 output classes\n",
" @exp_factory.register_config_factory(\n",
" 'deeplabv3plus_mobilenet_edgetpuv2_m_ade20k_32')\n",
"@@ -245,3 +349,10 @@ def deeplabv3plus_mobilenet_edgetpuv2_s_ade20k() -> cfg.ExperimentConfig:\n",
" def deeplabv3plus_mobilenet_edgetpuv2_xs_ade20k() -> cfg.ExperimentConfig:\n",
" config = seg_deeplabv3plus_ade20k('mobilenet_edgetpu_v2_xs')\n",
" return config\n",
"+\n",
"+\n",
"+# Experiment configs for Pascal VOC 21 output classes\n",
"+@exp_factory.register_config_factory(\n",
"+ 'deeplabv3plus_mobilenet_edgetpuv2_s_voc2012')\n",
"+def deeplabv3plus_mobilenet_edgetpuv2_s_voc2012() -> cfg.ExperimentConfig:\n",
"+ return seg_deeplabv3plus_voc('mobilenet_edgetpu_v2_s')\n",
"EOF"
],
"metadata": {
"id": "MUATzeyKuLl3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!patch -p1 < /content/semantic_segmentation_config.patch"
],
"metadata": {
"id": "ZlHscG2kunx3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2d3ae6cd-451c-496c-b903-ea3539960312"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"patching file official/projects/edgetpu/vision/configs/semantic_segmentation_config.py\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Patch semantic_segmentation.py"
],
"metadata": {
"id": "N_3X2KYsLk_B"
}
},
{
"cell_type": "code",
"source": [
"%%bash\n",
"\n",
"cat <<EOF > /content/semantic_segmentation.patch\n",
"diff --git a/official/projects/edgetpu/vision/tasks/semantic_segmentation.py b/official/projects/edgetpu/vision/tasks/semantic_segmentation.py\n",
"index 28477f1bd..4e9c8e381 100644\n",
"--- a/official/projects/edgetpu/vision/tasks/semantic_segmentation.py\n",
"+++ b/official/projects/edgetpu/vision/tasks/semantic_segmentation.py\n",
"@@ -37,7 +37,7 @@ from official.vision.beta.tasks import semantic_segmentation\n",
" class ClassMappingParser(segmentation_input.Parser):\n",
" \"\"\"Same parser but maps classes max_class+1... to class 0.\"\"\"\n",
" \n",
"- max_class = 31\n",
"+ # max_class = 31\n",
" \n",
" def _prepare_image_and_label(self, data):\n",
" \"\"\"Prepare normalized image and label.\"\"\"\n",
"@@ -49,9 +49,9 @@ class ClassMappingParser(segmentation_input.Parser):\n",
" image = tf.reshape(image, (height, width, 3))\n",
" \n",
" label = tf.reshape(label, (1, height, width))\n",
"- label = tf.where(\n",
"- tf.math.greater(label, self.max_class), tf.zeros_like(label), label)\n",
"- label = tf.where(tf.math.equal(label, 0), tf.ones_like(label)*255, label)\n",
"+ # label = tf.where(\n",
"+ # tf.math.greater(label, self.max_class), tf.zeros_like(label), label)\n",
"+ # label = tf.where(tf.math.equal(label, 0), tf.ones_like(label)*255, label)\n",
" label = tf.cast(label, tf.float32)\n",
" # Normalizes image with mean and std pixel values.\n",
" image = preprocess_ops.normalize_image(\n",
"EOF"
],
"metadata": {
"id": "UoMbHxDN8A4j"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!patch -p1 < /content/semantic_segmentation.patch"
],
"metadata": {
"id": "IqxyWjLS8HH_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "6cac92f0-70e3-49aa-dc00-8ee002b40914"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"patching file official/projects/edgetpu/vision/tasks/semantic_segmentation.py\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Generate config file\n",
"\n",
"Generate a config file to override the parameters. Here, you can specify the number of GPUs and the path to the pre-trained model."
],
"metadata": {
"id": "hNVE0v_YLrZJ"
}
},
{
"cell_type": "code",
"source": [
"%%bash\n",
"\n",
"cat <<EOF > /content/param_overrides.json\n",
"{\n",
" 'runtime': {\n",
" 'num_gpus': 1, \n",
" },\n",
" 'task': {\n",
" 'model': {\n",
" 'backbone': {\n",
" 'mobilenet_edgetpu': {\n",
" 'pretrained_checkpoint_path': '/content/mobilenet-edgetpu-v2-s/ckpt-171600',\n",
" },\n",
" },\n",
" },\n",
" },\n",
"}\n",
"EOF"
],
"metadata": {
"id": "ic_Jd0HLmbEs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Train"
],
"metadata": {
"id": "ATEmrbLRtIRk"
}
},
{
"cell_type": "code",
"source": [
"!rm -rf /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012"
],
"metadata": {
"id": "ODwRQPu7FFF-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!python3 /content/models/official/projects/edgetpu/vision/train.py \\\n",
" --mode=\"train_and_eval\" \\\n",
" --model_dir=\"/content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012\" \\\n",
" --experiment=\"deeplabv3plus_mobilenet_edgetpuv2_s_voc2012\" \\\n",
" --config_file=\"/content/param_overrides.json\""
],
"metadata": {
"id": "LHFzS2PrjK1t",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "73a5a764-3a51-4770-bf69-d95f0c36bbdb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"WARNING:absl:The list/tuple don't match the value dictionaries provided. Thus, the list/tuple is determined by the type annotation and values provided. This is error-prone.\n",
"I0110 07:56:01.642769 140678257067904 train_utils.py:336] Final experiment parameters:\n",
"{'runtime': {'all_reduce_alg': None,\n",
" 'batchnorm_spatial_persistent': False,\n",
" 'dataset_num_private_threads': None,\n",
" 'default_shard_dim': -1,\n",
" 'distribution_strategy': 'mirrored',\n",
" 'enable_xla': False,\n",
" 'gpu_thread_mode': None,\n",
" 'loss_scale': None,\n",
" 'mixed_precision_dtype': None,\n",
" 'num_cores_per_replica': 1,\n",
" 'num_gpus': 1,\n",
" 'num_packs': 1,\n",
" 'per_gpu_thread_count': 0,\n",
" 'run_eagerly': False,\n",
" 'task_index': -1,\n",
" 'tpu': None,\n",
" 'tpu_enable_xla_dynamic_padder': None,\n",
" 'worker_hosts': None},\n",
" 'task': {'eval_input_partition_dims': [],\n",
" 'evaluation': {'report_per_class_iou': True,\n",
" 'report_train_mean_iou': False},\n",
" 'init_checkpoint': None,\n",
" 'init_checkpoint_modules': 'all',\n",
" 'losses': {'class_weights': [],\n",
" 'ignore_label': 255,\n",
" 'l2_weight_decay': 0.0,\n",
" 'label_smoothing': 0.0,\n",
" 'loss_weight': 1.0,\n",
" 'top_k_percent_pixels': 1.0,\n",
" 'use_groundtruth_dimension': True},\n",
" 'model': {'backbone': {'mobilenet_edgetpu': {'freeze_large_filters': 500,\n",
" 'model_id': 'mobilenet_edgetpu_v2_s',\n",
" 'pretrained_checkpoint_path': '/content/mobilenet-edgetpu-v2-s/ckpt-171600'},\n",
" 'type': 'mobilenet_edgetpu'},\n",
" 'decoder': {'aspp': {'dilation_rates': [5, 10, 15],\n",
" 'dropout_rate': 0.3,\n",
" 'level': 4,\n",
" 'num_filters': 128,\n",
" 'output_tensor': False,\n",
" 'pool_kernel_size': [256, 256],\n",
" 'spp_layer_version': 'v1',\n",
" 'use_depthwise_convolution': True},\n",
" 'type': 'aspp'},\n",
" 'head': {'decoder_max_level': None,\n",
" 'decoder_min_level': None,\n",
" 'feature_fusion': 'deeplabv3plus',\n",
" 'level': 4,\n",
" 'low_level': 3,\n",
" 'low_level_num_filters': 48,\n",
" 'num_convs': 2,\n",
" 'num_filters': 256,\n",
" 'prediction_kernel_size': 1,\n",
" 'upsample_factor': 1,\n",
" 'use_depthwise_convolution': True},\n",
" 'input_size': [None, None, 3],\n",
" 'mask_scoring_head': None,\n",
" 'max_level': 6,\n",
" 'min_level': 3,\n",
" 'norm_activation': {'activation': 'relu',\n",
" 'norm_epsilon': 0.002,\n",
" 'norm_momentum': 0.99,\n",
" 'use_sync_bn': False},\n",
" 'num_classes': 21},\n",
" 'name': None,\n",
" 'train_data': {'aug_policy': None,\n",
" 'aug_rand_hflip': True,\n",
" 'aug_scale_max': 1.0,\n",
" 'aug_scale_min': 1.0,\n",
" 'block_length': 1,\n",
" 'cache': False,\n",
" 'crop_size': [],\n",
" 'cycle_length': 10,\n",
" 'decoder': {'simple_decoder': {'mask_binarize_threshold': None,\n",
" 'regenerate_source_id': False},\n",
" 'type': 'simple_decoder'},\n",
" 'deterministic': None,\n",
" 'drop_remainder': True,\n",
" 'dtype': 'float32',\n",
" 'enable_tf_data_service': False,\n",
" 'file_type': 'tfrecord',\n",
" 'global_batch_size': 32,\n",
" 'groundtruth_padded_size': [],\n",
" 'input_path': '/content/pascal_voc_seg/tfrecord/train-*',\n",
" 'is_training': True,\n",
" 'output_size': [512, 512],\n",
" 'preserve_aspect_ratio': True,\n",
" 'resize_eval_groundtruth': True,\n",
" 'seed': None,\n",
" 'sharding': True,\n",
" 'shuffle_buffer_size': 1000,\n",
" 'tf_data_service_address': None,\n",
" 'tf_data_service_job_name': None,\n",
" 'tfds_as_supervised': False,\n",
" 'tfds_data_dir': '',\n",
" 'tfds_name': '',\n",
" 'tfds_skip_decoding_feature': '',\n",
" 'tfds_split': ''},\n",
" 'train_input_partition_dims': [],\n",
" 'validation_data': {'aug_policy': None,\n",
" 'aug_rand_hflip': True,\n",
" 'aug_scale_max': 1.0,\n",
" 'aug_scale_min': 1.0,\n",
" 'block_length': 1,\n",
" 'cache': False,\n",
" 'crop_size': [],\n",
" 'cycle_length': 10,\n",
" 'decoder': {'simple_decoder': {'mask_binarize_threshold': None,\n",
" 'regenerate_source_id': False},\n",
" 'type': 'simple_decoder'},\n",
" 'deterministic': None,\n",
" 'drop_remainder': False,\n",
" 'dtype': 'float32',\n",
" 'enable_tf_data_service': False,\n",
" 'file_type': 'tfrecord',\n",
" 'global_batch_size': 32,\n",
" 'groundtruth_padded_size': [],\n",
" 'input_path': '/content/pascal_voc_seg/tfrecord/val-*',\n",
" 'is_training': False,\n",
" 'output_size': [512, 512],\n",
" 'preserve_aspect_ratio': True,\n",
" 'resize_eval_groundtruth': True,\n",
" 'seed': None,\n",
" 'sharding': True,\n",
" 'shuffle_buffer_size': 1000,\n",
" 'tf_data_service_address': None,\n",
" 'tf_data_service_job_name': None,\n",
" 'tfds_as_supervised': False,\n",
" 'tfds_data_dir': '',\n",
" 'tfds_name': '',\n",
" 'tfds_skip_decoding_feature': '',\n",
" 'tfds_split': ''}},\n",
" 'trainer': {'allow_tpu_summary': False,\n",
" 'best_checkpoint_eval_metric': '',\n",
" 'best_checkpoint_export_subdir': '',\n",
" 'best_checkpoint_metric_comp': 'higher',\n",
" 'checkpoint_interval': 45,\n",
" 'continuous_eval_timeout': 3600,\n",
" 'eval_tf_function': True,\n",
" 'eval_tf_while_loop': False,\n",
" 'loss_upper_bound': 1000000.0,\n",
" 'max_to_keep': 5,\n",
" 'optimizer_config': {'ema': None,\n",
" 'learning_rate': {'polynomial': {'cycle': False,\n",
" 'decay_steps': 2250,\n",
" 'end_learning_rate': 0.0,\n",
" 'initial_learning_rate': 0.0001,\n",
" 'name': 'PolynomialDecay',\n",
" 'offset': 0,\n",
" 'power': 0.9},\n",
" 'type': 'polynomial'},\n",
" 'optimizer': {'adam': {'amsgrad': False,\n",
" 'beta_1': 0.9,\n",
" 'beta_2': 0.999,\n",
" 'clipnorm': None,\n",
" 'clipvalue': None,\n",
" 'epsilon': 1e-07,\n",
" 'global_clipnorm': None,\n",
" 'name': 'Adam'},\n",
" 'type': 'adam'},\n",
" 'warmup': {'linear': {'name': 'linear',\n",
" 'warmup_learning_rate': 0,\n",
" 'warmup_steps': 180},\n",
" 'type': 'linear'}},\n",
" 'recovery_begin_steps': 0,\n",
" 'recovery_max_trials': 0,\n",
" 'steps_per_loop': 45,\n",
" 'summary_interval': 45,\n",
" 'train_steps': 2250,\n",
" 'train_tf_function': True,\n",
" 'train_tf_while_loop': True,\n",
" 'validation_interval': 45,\n",
" 'validation_steps': 45,\n",
" 'validation_summary_subdir': 'validation'}}\n",
"I0110 07:56:01.643216 140678257067904 train_utils.py:347] Saving experiment configuration to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/params.yaml\n",
"2022-01-10 07:56:03.972089: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
"INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
"I0110 07:56:04.002645 140678257067904 mirrored_strategy.py:376] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
"I0110 07:56:04.014735 140678257067904 train_utils.py:220] Running default trainer.\n",
"I0110 07:56:04.024068 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.146021 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.159602 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.162497 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.163387 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.164837 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.168260 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.178916 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.179035 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=24 output=24\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.190505 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.191385 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.193017 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.193891 140678257067904 cross_device_ops.py:621] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
"I0110 07:56:04.206753 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=24 output=24\n",
"I0110 07:56:04.206859 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=48 output=48\n",
"I0110 07:56:04.264511 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=48 output=48\n",
"I0110 07:56:04.264631 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=48 output=48\n",
"I0110 07:56:04.511335 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=48 output=48\n",
"I0110 07:56:04.511502 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.566555 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.566685 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.706489 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.706673 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.773430 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.773605 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.905163 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=64 output=64\n",
"I0110 07:56:04.905343 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:04.966774 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:04.966956 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:05.057472 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:05.057654 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:05.149955 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:05.150133 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:05.237086 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=128 output=128\n",
"I0110 07:56:05.237229 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.315848 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.316004 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.398354 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.398490 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.627026 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.627200 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.724294 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=160 output=160\n",
"I0110 07:56:05.724457 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:05.805863 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:05.806013 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:05.891486 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:05.891624 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:05.978572 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:05.978718 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:06.061044 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=192 output=192\n",
"I0110 07:56:06.061220 140678257067904 mobilenet_edgetpu_v2_model_blocks.py:665] round_filter input=256 output=256\n",
"I0110 07:56:06.143997 140678257067904 mobilenet_edgetpu_v2_model.py:111] Building model mobilenet_edgetpu_v2 with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=(None, None), dropout_rate=0.2, stem_base_filters=64, stem_kernel_size=5, top_base_filters=1280, blocks=(BlockConfig(input_filters=64, output_filters=24, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='full', group_size=None), BlockConfig(input_filters=24, output_filters=48, kernel_size=3, num_repeat=1, expand_ratio=8, strides=(2, 2), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='full', group_size=None), BlockConfig(input_filters=48, output_filters=48, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='group', group_size=None), BlockConfig(input_filters=48, output_filters=64, kernel_size=3, num_repeat=1, expand_ratio=8, strides=(2, 2), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='full', group_size=None), BlockConfig(input_filters=64, output_filters=64, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='group', group_size=None), BlockConfig(input_filters=64, output_filters=64, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='full', group_size=None), BlockConfig(input_filters=64, output_filters=64, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='group', group_size=None), BlockConfig(input_filters=64, output_filters=128, kernel_size=3, num_repeat=1, expand_ratio=8, strides=(2, 2), se_ratio=None, id_skip=True, fused_expand=True, fused_project=False, conv_type='full', group_size=None), BlockConfig(input_filters=128, output_filters=128, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=128, output_filters=128, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=128, output_filters=128, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=128, output_filters=160, kernel_size=3, num_repeat=1, expand_ratio=8, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=160, output_filters=160, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=160, output_filters=160, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=160, output_filters=160, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=160, output_filters=192, kernel_size=3, num_repeat=1, expand_ratio=8, strides=(2, 2), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=192, output_filters=192, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=192, output_filters=192, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=192, output_filters=192, kernel_size=3, num_repeat=1, expand_ratio=4, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None), BlockConfig(input_filters=192, output_filters=256, kernel_size=3, num_repeat=1, expand_ratio=8, strides=(1, 1), se_ratio=None, id_skip=True, fused_expand=False, fused_project=False, conv_type='depthwise', group_size=None)), activation='relu', batch_norm='tpu', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.1, depth_divisor=8, min_depth=None, use_se=False, input_channels=3, num_classes=1001, model_name='mobilenet_edgetpu_v2', rescale_input=False, data_format='channels_last', dtype='bfloat16', group_base_size=64, backbone_only=True, features_as_dict=True)\n",
"I0110 07:56:06.970495 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_7/block_0/fused_conv2d\n",
"I0110 07:56:06.970793 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_7/block_0/fused_bn\n",
"I0110 07:56:06.970970 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_7/block_0/fused_activation\n",
"I0110 07:56:06.971209 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_8/block_0/expand_conv2d\n",
"I0110 07:56:06.971350 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_8/block_0/expand_bn\n",
"I0110 07:56:06.971496 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_8/block_0/expand_activation\n",
"I0110 07:56:06.971640 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_8/block_0/depthwise_conv2d\n",
"I0110 07:56:06.971786 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_8/block_0/depthwise_bn\n",
"I0110 07:56:06.971931 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_8/block_0/depthwise_activation\n",
"I0110 07:56:06.972215 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_9/block_0/expand_conv2d\n",
"I0110 07:56:06.972358 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_9/block_0/expand_bn\n",
"I0110 07:56:06.972502 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_9/block_0/expand_activation\n",
"I0110 07:56:06.972642 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_9/block_0/depthwise_conv2d\n",
"I0110 07:56:06.972784 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_9/block_0/depthwise_bn\n",
"I0110 07:56:06.972926 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_9/block_0/depthwise_activation\n",
"I0110 07:56:06.973210 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_10/block_0/expand_conv2d\n",
"I0110 07:56:06.973347 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_10/block_0/expand_bn\n",
"I0110 07:56:06.973488 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_10/block_0/expand_activation\n",
"I0110 07:56:06.973627 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_10/block_0/depthwise_conv2d\n",
"I0110 07:56:06.973768 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_10/block_0/depthwise_bn\n",
"I0110 07:56:06.973919 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_10/block_0/depthwise_activation\n",
"I0110 07:56:06.974206 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_11/block_0/expand_conv2d\n",
"I0110 07:56:06.974348 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_11/block_0/expand_bn\n",
"I0110 07:56:06.974490 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_11/block_0/expand_activation\n",
"I0110 07:56:06.974628 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_11/block_0/depthwise_conv2d\n",
"I0110 07:56:06.974769 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_11/block_0/depthwise_bn\n",
"I0110 07:56:06.974910 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_11/block_0/depthwise_activation\n",
"I0110 07:56:06.975142 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_12/block_0/expand_conv2d\n",
"I0110 07:56:06.975277 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_12/block_0/expand_bn\n",
"I0110 07:56:06.975416 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_12/block_0/expand_activation\n",
"I0110 07:56:06.975556 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_12/block_0/depthwise_conv2d\n",
"I0110 07:56:06.975694 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_12/block_0/depthwise_bn\n",
"I0110 07:56:06.975832 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_12/block_0/depthwise_activation\n",
"I0110 07:56:06.976120 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_13/block_0/expand_conv2d\n",
"I0110 07:56:06.976257 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_13/block_0/expand_bn\n",
"I0110 07:56:06.976397 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_13/block_0/expand_activation\n",
"I0110 07:56:06.976535 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_13/block_0/depthwise_conv2d\n",
"I0110 07:56:06.976678 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_13/block_0/depthwise_bn\n",
"I0110 07:56:06.976819 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_13/block_0/depthwise_activation\n",
"I0110 07:56:06.977108 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_14/block_0/expand_conv2d\n",
"I0110 07:56:06.977244 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_14/block_0/expand_bn\n",
"I0110 07:56:06.977379 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_14/block_0/expand_activation\n",
"I0110 07:56:06.977517 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_14/block_0/depthwise_conv2d\n",
"I0110 07:56:06.977656 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_14/block_0/depthwise_bn\n",
"I0110 07:56:06.977797 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_14/block_0/depthwise_activation\n",
"I0110 07:56:06.978076 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_15/block_0/expand_conv2d\n",
"I0110 07:56:06.978215 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_15/block_0/expand_bn\n",
"I0110 07:56:06.978346 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_15/block_0/expand_activation\n",
"I0110 07:56:06.978497 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_15/block_0/depthwise_conv2d\n",
"I0110 07:56:06.978636 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_15/block_0/depthwise_bn\n",
"I0110 07:56:06.978777 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_15/block_0/depthwise_activation\n",
"I0110 07:56:06.978995 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_16/block_0/expand_conv2d\n",
"I0110 07:56:06.979141 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_16/block_0/expand_bn\n",
"I0110 07:56:06.979278 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_16/block_0/expand_activation\n",
"I0110 07:56:06.979422 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_16/block_0/depthwise_conv2d\n",
"I0110 07:56:06.979564 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_16/block_0/depthwise_bn\n",
"I0110 07:56:06.979703 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_16/block_0/depthwise_activation\n",
"I0110 07:56:06.979969 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_17/block_0/expand_conv2d\n",
"I0110 07:56:06.980114 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_17/block_0/expand_bn\n",
"I0110 07:56:06.980252 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_17/block_0/expand_activation\n",
"I0110 07:56:06.980395 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_17/block_0/depthwise_conv2d\n",
"I0110 07:56:06.980538 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_17/block_0/depthwise_bn\n",
"I0110 07:56:06.980680 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_17/block_0/depthwise_activation\n",
"I0110 07:56:06.980948 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_18/block_0/expand_conv2d\n",
"I0110 07:56:06.981088 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_18/block_0/expand_bn\n",
"I0110 07:56:06.981219 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_18/block_0/expand_activation\n",
"I0110 07:56:06.981353 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_18/block_0/depthwise_conv2d\n",
"I0110 07:56:06.981499 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_18/block_0/depthwise_bn\n",
"I0110 07:56:06.981639 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_18/block_0/depthwise_activation\n",
"I0110 07:56:06.981903 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_19/block_0/expand_conv2d\n",
"I0110 07:56:06.982036 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_19/block_0/expand_bn\n",
"I0110 07:56:06.982179 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_19/block_0/expand_activation\n",
"I0110 07:56:06.982316 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_19/block_0/depthwise_conv2d\n",
"I0110 07:56:06.982462 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_19/block_0/depthwise_bn\n",
"I0110 07:56:06.982605 140678257067904 mobilenet_edgetpu.py:57] Freezing layer: stack_19/block_0/depthwise_activation\n",
"I0110 07:56:08.404924 140678257067904 controller.py:391] restoring or initializing model...\n",
"restoring or initializing model...\n",
"I0110 07:56:08.405159 140678257067904 controller.py:397] initialized model.\n",
"initialized model.\n",
"I0110 07:56:08.405247 140678257067904 train_lib.py:110] Starts to execute mode: train_and_eval\n",
"I0110 07:56:08.406329 140678257067904 controller.py:236] train | step: 0 | training until step 45...\n",
"train | step: 0 | training until step 45...\n",
"2022-01-10 07:56:24.607324: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 33554432 exceeds 10% of free system memory.\n",
"2022-01-10 07:56:25.917676: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 33554432 exceeds 10% of free system memory.\n",
"2022-01-10 07:56:25.936398: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 33554432 exceeds 10% of free system memory.\n",
"2022-01-10 07:56:25.989590: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 33554432 exceeds 10% of free system memory.\n",
"2022-01-10 07:56:26.008103: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 33554432 exceeds 10% of free system memory.\n",
"I0110 07:57:26.926922 140678257067904 controller.py:457] train | step: 45 | steps/sec: 0.6 | output: {'learning_rate': 2.3192579e-05, 'training_loss': 3.095288}\n",
"train | step: 45 | steps/sec: 0.6 | output: {'learning_rate': 2.3192579e-05, 'training_loss': 3.095288}\n",
"I0110 07:57:27.396764 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-45.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-45.\n",
"I0110 07:57:27.397956 140678257067904 controller.py:277] eval | step: 45 | running 45 steps of evaluation...\n",
" eval | step: 45 | running 45 steps of evaluation...\n",
"I0110 07:58:02.092620 140678257067904 controller.py:290] eval | step: 45 | eval time: 34.7 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.1407158}\n",
" eval | step: 45 | eval time: 34.7 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.1407158}\n",
"I0110 07:58:02.129292 140678257067904 controller.py:236] train | step: 45 | training until step 90...\n",
"train | step: 45 | training until step 90...\n",
"I0110 07:58:52.448599 140678257067904 controller.py:457] train | step: 90 | steps/sec: 0.5 | output: {'learning_rate': 4.6385157e-05, 'training_loss': 2.7944796}\n",
"train | step: 90 | steps/sec: 0.5 | output: {'learning_rate': 4.6385157e-05, 'training_loss': 2.7944796}\n",
"I0110 07:58:52.911514 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-90.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-90.\n",
"I0110 07:58:52.912735 140678257067904 controller.py:277] eval | step: 90 | running 45 steps of evaluation...\n",
" eval | step: 90 | running 45 steps of evaluation...\n",
"I0110 07:59:22.983808 140678257067904 controller.py:290] eval | step: 90 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.1328886}\n",
" eval | step: 90 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.1328886}\n",
"I0110 07:59:23.017642 140678257067904 controller.py:236] train | step: 90 | training until step 135...\n",
"train | step: 90 | training until step 135...\n",
"I0110 08:00:13.054105 140678257067904 controller.py:457] train | step: 135 | steps/sec: 0.6 | output: {'learning_rate': 6.9577734e-05, 'training_loss': 1.8967547}\n",
"train | step: 135 | steps/sec: 0.6 | output: {'learning_rate': 6.9577734e-05, 'training_loss': 1.8967547}\n",
"I0110 08:00:13.771066 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-135.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-135.\n",
"I0110 08:00:13.772291 140678257067904 controller.py:277] eval | step: 135 | running 45 steps of evaluation...\n",
" eval | step: 135 | running 45 steps of evaluation...\n",
"I0110 08:00:44.259231 140678257067904 controller.py:290] eval | step: 135 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.1063864}\n",
" eval | step: 135 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.1063864}\n",
"I0110 08:00:44.292304 140678257067904 controller.py:236] train | step: 135 | training until step 180...\n",
"train | step: 135 | training until step 180...\n",
"I0110 08:01:34.411818 140678257067904 controller.py:457] train | step: 180 | steps/sec: 0.6 | output: {'learning_rate': 9.2770315e-05, 'training_loss': 1.3087547}\n",
"train | step: 180 | steps/sec: 0.6 | output: {'learning_rate': 9.2770315e-05, 'training_loss': 1.3087547}\n",
"I0110 08:01:34.915520 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-180.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-180.\n",
"I0110 08:01:34.916653 140678257067904 controller.py:277] eval | step: 180 | running 45 steps of evaluation...\n",
" eval | step: 180 | running 45 steps of evaluation...\n",
"I0110 08:02:04.967975 140678257067904 controller.py:290] eval | step: 180 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.058222}\n",
" eval | step: 180 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.058222}\n",
"I0110 08:02:05.000659 140678257067904 controller.py:236] train | step: 180 | training until step 225...\n",
"train | step: 180 | training until step 225...\n",
"I0110 08:02:55.427690 140678257067904 controller.py:457] train | step: 225 | steps/sec: 0.6 | output: {'learning_rate': 9.0953254e-05, 'training_loss': 1.0054822}\n",
"train | step: 225 | steps/sec: 0.6 | output: {'learning_rate': 9.0953254e-05, 'training_loss': 1.0054822}\n",
"I0110 08:02:55.884682 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-225.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-225.\n",
"I0110 08:02:55.885791 140678257067904 controller.py:277] eval | step: 225 | running 45 steps of evaluation...\n",
" eval | step: 225 | running 45 steps of evaluation...\n",
"I0110 08:03:25.975976 140678257067904 controller.py:290] eval | step: 225 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.023324}\n",
" eval | step: 225 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 3.023324}\n",
"I0110 08:03:26.008984 140678257067904 controller.py:236] train | step: 225 | training until step 270...\n",
"train | step: 225 | training until step 270...\n",
"I0110 08:04:16.174890 140678257067904 controller.py:457] train | step: 270 | steps/sec: 0.6 | output: {'learning_rate': 8.9132154e-05, 'training_loss': 0.8362479}\n",
"train | step: 270 | steps/sec: 0.6 | output: {'learning_rate': 8.9132154e-05, 'training_loss': 0.8362479}\n",
"I0110 08:04:16.648662 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-270.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-270.\n",
"I0110 08:04:16.649823 140678257067904 controller.py:277] eval | step: 270 | running 45 steps of evaluation...\n",
" eval | step: 270 | running 45 steps of evaluation...\n",
"I0110 08:04:46.984287 140678257067904 controller.py:290] eval | step: 270 | eval time: 30.3 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 2.999992}\n",
" eval | step: 270 | eval time: 30.3 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 2.999992}\n",
"I0110 08:04:47.019725 140678257067904 controller.py:236] train | step: 270 | training until step 315...\n",
"train | step: 270 | training until step 315...\n",
"I0110 08:05:37.577206 140678257067904 controller.py:457] train | step: 315 | steps/sec: 0.6 | output: {'learning_rate': 8.73069e-05, 'training_loss': 0.75002015}\n",
"train | step: 315 | steps/sec: 0.6 | output: {'learning_rate': 8.73069e-05, 'training_loss': 0.75002015}\n",
"I0110 08:05:38.056327 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-315.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-315.\n",
"I0110 08:05:38.057494 140678257067904 controller.py:277] eval | step: 315 | running 45 steps of evaluation...\n",
" eval | step: 315 | running 45 steps of evaluation...\n",
"I0110 08:06:08.795488 140678257067904 controller.py:290] eval | step: 315 | eval time: 30.7 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 2.96427}\n",
" eval | step: 315 | eval time: 30.7 sec | output: \n",
" {'iou/0': 0.7332444,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.0,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.0349164,\n",
" 'validation_loss': 2.96427}\n",
"I0110 08:06:08.844897 140678257067904 controller.py:236] train | step: 315 | training until step 360...\n",
"train | step: 315 | training until step 360...\n",
"I0110 08:06:58.695339 140678257067904 controller.py:457] train | step: 360 | steps/sec: 0.6 | output: {'learning_rate': 8.547741e-05, 'training_loss': 0.6655324}\n",
"train | step: 360 | steps/sec: 0.6 | output: {'learning_rate': 8.547741e-05, 'training_loss': 0.6655324}\n",
"I0110 08:06:59.166555 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-360.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-360.\n",
"I0110 08:06:59.168107 140678257067904 controller.py:277] eval | step: 360 | running 45 steps of evaluation...\n",
" eval | step: 360 | running 45 steps of evaluation...\n",
"I0110 08:07:29.372377 140678257067904 controller.py:290] eval | step: 360 | eval time: 30.2 sec | output: \n",
" {'iou/0': 0.7404991,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0072591123,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.18040141,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0012792757,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0038182146,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.044440813,\n",
" 'validation_loss': 2.823228}\n",
" eval | step: 360 | eval time: 30.2 sec | output: \n",
" {'iou/0': 0.7404991,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.0072591123,\n",
" 'iou/13': 0.0,\n",
" 'iou/14': 0.0,\n",
" 'iou/15': 0.18040141,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.0,\n",
" 'iou/18': 0.0,\n",
" 'iou/19': 0.0012792757,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.0,\n",
" 'iou/7': 0.0038182146,\n",
" 'iou/8': 0.0,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.044440813,\n",
" 'validation_loss': 2.823228}\n",
"I0110 08:07:29.404524 140678257067904 controller.py:236] train | step: 360 | training until step 405...\n",
"train | step: 360 | training until step 405...\n",
"I0110 08:08:19.570186 140678257067904 controller.py:457] train | step: 405 | steps/sec: 0.6 | output: {'learning_rate': 8.364355e-05, 'training_loss': 0.63190734}\n",
"train | step: 405 | steps/sec: 0.6 | output: {'learning_rate': 8.364355e-05, 'training_loss': 0.63190734}\n",
"I0110 08:08:20.041932 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-405.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-405.\n",
"I0110 08:08:20.043186 140678257067904 controller.py:277] eval | step: 405 | running 45 steps of evaluation...\n",
" eval | step: 405 | running 45 steps of evaluation...\n",
"I0110 08:08:50.318282 140678257067904 controller.py:290] eval | step: 405 | eval time: 30.3 sec | output: \n",
" {'iou/0': 0.8323656,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.43403426,\n",
" 'iou/13': 0.034618657,\n",
" 'iou/14': 0.28678963,\n",
" 'iou/15': 0.5949965,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.14908294,\n",
" 'iou/18': 0.15806817,\n",
" 'iou/19': 0.54669756,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.3144621,\n",
" 'iou/7': 0.49264154,\n",
" 'iou/8': 0.39231676,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.20171778,\n",
" 'validation_loss': 2.5376556}\n",
" eval | step: 405 | eval time: 30.3 sec | output: \n",
" {'iou/0': 0.8323656,\n",
" 'iou/1': 0.0,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.0,\n",
" 'iou/12': 0.43403426,\n",
" 'iou/13': 0.034618657,\n",
" 'iou/14': 0.28678963,\n",
" 'iou/15': 0.5949965,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.14908294,\n",
" 'iou/18': 0.15806817,\n",
" 'iou/19': 0.54669756,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.0,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.3144621,\n",
" 'iou/7': 0.49264154,\n",
" 'iou/8': 0.39231676,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.20171778,\n",
" 'validation_loss': 2.5376556}\n",
"I0110 08:08:50.351179 140678257067904 controller.py:236] train | step: 405 | training until step 450...\n",
"train | step: 405 | training until step 450...\n",
"I0110 08:09:40.648167 140678257067904 controller.py:457] train | step: 450 | steps/sec: 0.6 | output: {'learning_rate': 8.180521e-05, 'training_loss': 0.56720436}\n",
"train | step: 450 | steps/sec: 0.6 | output: {'learning_rate': 8.180521e-05, 'training_loss': 0.56720436}\n",
"I0110 08:09:41.117933 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-450.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-450.\n",
"I0110 08:09:41.119047 140678257067904 controller.py:277] eval | step: 450 | running 45 steps of evaluation...\n",
" eval | step: 450 | running 45 steps of evaluation...\n",
"I0110 08:10:11.687395 140678257067904 controller.py:290] eval | step: 450 | eval time: 30.6 sec | output: \n",
" {'iou/0': 0.8855553,\n",
" 'iou/1': 3.1109296e-06,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.050414823,\n",
" 'iou/12': 0.6131538,\n",
" 'iou/13': 0.27952594,\n",
" 'iou/14': 0.5932948,\n",
" 'iou/15': 0.63927823,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.27211532,\n",
" 'iou/18': 0.1607323,\n",
" 'iou/19': 0.49587673,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.43092665,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.6548882,\n",
" 'iou/7': 0.6208063,\n",
" 'iou/8': 0.687641,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.30401012,\n",
" 'validation_loss': 2.0715725}\n",
" eval | step: 450 | eval time: 30.6 sec | output: \n",
" {'iou/0': 0.8855553,\n",
" 'iou/1': 3.1109296e-06,\n",
" 'iou/10': 0.0,\n",
" 'iou/11': 0.050414823,\n",
" 'iou/12': 0.6131538,\n",
" 'iou/13': 0.27952594,\n",
" 'iou/14': 0.5932948,\n",
" 'iou/15': 0.63927823,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.27211532,\n",
" 'iou/18': 0.1607323,\n",
" 'iou/19': 0.49587673,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.43092665,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.6548882,\n",
" 'iou/7': 0.6208063,\n",
" 'iou/8': 0.687641,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.30401012,\n",
" 'validation_loss': 2.0715725}\n",
"I0110 08:10:11.719256 140678257067904 controller.py:236] train | step: 450 | training until step 495...\n",
"train | step: 450 | training until step 495...\n",
"I0110 08:11:01.709595 140678257067904 controller.py:457] train | step: 495 | steps/sec: 0.6 | output: {'learning_rate': 7.996227e-05, 'training_loss': 0.5319116}\n",
"train | step: 495 | steps/sec: 0.6 | output: {'learning_rate': 7.996227e-05, 'training_loss': 0.5319116}\n",
"I0110 08:11:02.174753 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-495.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-495.\n",
"I0110 08:11:02.175920 140678257067904 controller.py:277] eval | step: 495 | running 45 steps of evaluation...\n",
" eval | step: 495 | running 45 steps of evaluation...\n",
"I0110 08:11:33.455466 140678257067904 controller.py:290] eval | step: 495 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.90064985,\n",
" 'iou/1': 0.48442075,\n",
" 'iou/10': 0.00030099085,\n",
" 'iou/11': 0.30683753,\n",
" 'iou/12': 0.69083333,\n",
" 'iou/13': 0.3488199,\n",
" 'iou/14': 0.6352257,\n",
" 'iou/15': 0.7244202,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.09118251,\n",
" 'iou/18': 0.28529724,\n",
" 'iou/19': 0.69081986,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.43997273,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.8211834,\n",
" 'iou/7': 0.7465597,\n",
" 'iou/8': 0.774286,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.37813383,\n",
" 'validation_loss': 1.5683619}\n",
" eval | step: 495 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.90064985,\n",
" 'iou/1': 0.48442075,\n",
" 'iou/10': 0.00030099085,\n",
" 'iou/11': 0.30683753,\n",
" 'iou/12': 0.69083333,\n",
" 'iou/13': 0.3488199,\n",
" 'iou/14': 0.6352257,\n",
" 'iou/15': 0.7244202,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.09118251,\n",
" 'iou/18': 0.28529724,\n",
" 'iou/19': 0.69081986,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.43997273,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.8211834,\n",
" 'iou/7': 0.7465597,\n",
" 'iou/8': 0.774286,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.37813383,\n",
" 'validation_loss': 1.5683619}\n",
"I0110 08:11:33.488515 140678257067904 controller.py:236] train | step: 495 | training until step 540...\n",
"train | step: 495 | training until step 540...\n",
"I0110 08:12:23.516649 140678257067904 controller.py:457] train | step: 540 | steps/sec: 0.6 | output: {'learning_rate': 7.811459e-05, 'training_loss': 0.49242252}\n",
"train | step: 540 | steps/sec: 0.6 | output: {'learning_rate': 7.811459e-05, 'training_loss': 0.49242252}\n",
"I0110 08:12:23.997570 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-540.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-540.\n",
"I0110 08:12:23.998723 140678257067904 controller.py:277] eval | step: 540 | running 45 steps of evaluation...\n",
" eval | step: 540 | running 45 steps of evaluation...\n",
"I0110 08:12:54.842720 140678257067904 controller.py:290] eval | step: 540 | eval time: 30.8 sec | output: \n",
" {'iou/0': 0.9050902,\n",
" 'iou/1': 0.48474428,\n",
" 'iou/10': 0.000359442,\n",
" 'iou/11': 0.31804064,\n",
" 'iou/12': 0.64931333,\n",
" 'iou/13': 0.34992245,\n",
" 'iou/14': 0.64587754,\n",
" 'iou/15': 0.75100374,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.21119882,\n",
" 'iou/18': 0.32426685,\n",
" 'iou/19': 0.70662457,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.49997225,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.81108695,\n",
" 'iou/7': 0.7381674,\n",
" 'iou/8': 0.79880375,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.39021295,\n",
" 'validation_loss': 1.1251657}\n",
" eval | step: 540 | eval time: 30.8 sec | output: \n",
" {'iou/0': 0.9050902,\n",
" 'iou/1': 0.48474428,\n",
" 'iou/10': 0.000359442,\n",
" 'iou/11': 0.31804064,\n",
" 'iou/12': 0.64931333,\n",
" 'iou/13': 0.34992245,\n",
" 'iou/14': 0.64587754,\n",
" 'iou/15': 0.75100374,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.21119882,\n",
" 'iou/18': 0.32426685,\n",
" 'iou/19': 0.70662457,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.49997225,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.81108695,\n",
" 'iou/7': 0.7381674,\n",
" 'iou/8': 0.79880375,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.39021295,\n",
" 'validation_loss': 1.1251657}\n",
"I0110 08:12:54.876602 140678257067904 controller.py:236] train | step: 540 | training until step 585...\n",
"train | step: 540 | training until step 585...\n",
"I0110 08:13:45.148301 140678257067904 controller.py:457] train | step: 585 | steps/sec: 0.6 | output: {'learning_rate': 7.626206e-05, 'training_loss': 0.46140096}\n",
"train | step: 585 | steps/sec: 0.6 | output: {'learning_rate': 7.626206e-05, 'training_loss': 0.46140096}\n",
"I0110 08:13:45.611674 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-585.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-585.\n",
"I0110 08:13:45.612914 140678257067904 controller.py:277] eval | step: 585 | running 45 steps of evaluation...\n",
" eval | step: 585 | running 45 steps of evaluation...\n",
"I0110 08:14:16.847315 140678257067904 controller.py:290] eval | step: 585 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.9107924,\n",
" 'iou/1': 0.545916,\n",
" 'iou/10': 0.028783673,\n",
" 'iou/11': 0.34046063,\n",
" 'iou/12': 0.7242705,\n",
" 'iou/13': 0.39716962,\n",
" 'iou/14': 0.6664711,\n",
" 'iou/15': 0.76556766,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.34716478,\n",
" 'iou/18': 0.3591653,\n",
" 'iou/19': 0.7223331,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.5776266,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.8363216,\n",
" 'iou/7': 0.77146983,\n",
" 'iou/8': 0.7978593,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.41863677,\n",
" 'validation_loss': 0.838823}\n",
" eval | step: 585 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.9107924,\n",
" 'iou/1': 0.545916,\n",
" 'iou/10': 0.028783673,\n",
" 'iou/11': 0.34046063,\n",
" 'iou/12': 0.7242705,\n",
" 'iou/13': 0.39716962,\n",
" 'iou/14': 0.6664711,\n",
" 'iou/15': 0.76556766,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.34716478,\n",
" 'iou/18': 0.3591653,\n",
" 'iou/19': 0.7223331,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.0,\n",
" 'iou/3': 0.5776266,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.8363216,\n",
" 'iou/7': 0.77146983,\n",
" 'iou/8': 0.7978593,\n",
" 'iou/9': 0.0,\n",
" 'mean_iou': 0.41863677,\n",
" 'validation_loss': 0.838823}\n",
"I0110 08:14:16.882191 140678257067904 controller.py:236] train | step: 585 | training until step 630...\n",
"train | step: 585 | training until step 630...\n",
"I0110 08:15:07.234329 140678257067904 controller.py:457] train | step: 630 | steps/sec: 0.5 | output: {'learning_rate': 7.44045e-05, 'training_loss': 0.43301}\n",
"train | step: 630 | steps/sec: 0.5 | output: {'learning_rate': 7.44045e-05, 'training_loss': 0.43301}\n",
"I0110 08:15:07.693620 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-630.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-630.\n",
"I0110 08:15:07.695123 140678257067904 controller.py:277] eval | step: 630 | running 45 steps of evaluation...\n",
" eval | step: 630 | running 45 steps of evaluation...\n",
"I0110 08:15:38.427744 140678257067904 controller.py:290] eval | step: 630 | eval time: 30.7 sec | output: \n",
" {'iou/0': 0.9116546,\n",
" 'iou/1': 0.5325819,\n",
" 'iou/10': 0.062431265,\n",
" 'iou/11': 0.36532593,\n",
" 'iou/12': 0.71684206,\n",
" 'iou/13': 0.40323788,\n",
" 'iou/14': 0.68293566,\n",
" 'iou/15': 0.7771147,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.316984,\n",
" 'iou/18': 0.36553368,\n",
" 'iou/19': 0.73913586,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.10149655,\n",
" 'iou/3': 0.5841178,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.8353494,\n",
" 'iou/7': 0.7666744,\n",
" 'iou/8': 0.7929727,\n",
" 'iou/9': 0.0051897885,\n",
" 'mean_iou': 0.4266466,\n",
" 'validation_loss': 0.6769598}\n",
" eval | step: 630 | eval time: 30.7 sec | output: \n",
" {'iou/0': 0.9116546,\n",
" 'iou/1': 0.5325819,\n",
" 'iou/10': 0.062431265,\n",
" 'iou/11': 0.36532593,\n",
" 'iou/12': 0.71684206,\n",
" 'iou/13': 0.40323788,\n",
" 'iou/14': 0.68293566,\n",
" 'iou/15': 0.7771147,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.316984,\n",
" 'iou/18': 0.36553368,\n",
" 'iou/19': 0.73913586,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.10149655,\n",
" 'iou/3': 0.5841178,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0,\n",
" 'iou/6': 0.8353494,\n",
" 'iou/7': 0.7666744,\n",
" 'iou/8': 0.7929727,\n",
" 'iou/9': 0.0051897885,\n",
" 'mean_iou': 0.4266466,\n",
" 'validation_loss': 0.6769598}\n",
"I0110 08:15:38.460453 140678257067904 controller.py:236] train | step: 630 | training until step 675...\n",
"train | step: 630 | training until step 675...\n",
"I0110 08:16:28.568226 140678257067904 controller.py:457] train | step: 675 | steps/sec: 0.6 | output: {'learning_rate': 7.2541785e-05, 'training_loss': 0.39058843}\n",
"train | step: 675 | steps/sec: 0.6 | output: {'learning_rate': 7.2541785e-05, 'training_loss': 0.39058843}\n",
"I0110 08:16:29.048429 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-675.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-675.\n",
"I0110 08:16:29.049621 140678257067904 controller.py:277] eval | step: 675 | running 45 steps of evaluation...\n",
" eval | step: 675 | running 45 steps of evaluation...\n",
"I0110 08:16:59.567401 140678257067904 controller.py:290] eval | step: 675 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.9183424,\n",
" 'iou/1': 0.52886885,\n",
" 'iou/10': 0.0027221711,\n",
" 'iou/11': 0.35976118,\n",
" 'iou/12': 0.6707339,\n",
" 'iou/13': 0.37600324,\n",
" 'iou/14': 0.6675318,\n",
" 'iou/15': 0.7853089,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.48376465,\n",
" 'iou/18': 0.36525124,\n",
" 'iou/19': 0.7494944,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.5755536,\n",
" 'iou/3': 0.7191806,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.00022449192,\n",
" 'iou/6': 0.8360645,\n",
" 'iou/7': 0.7837744,\n",
" 'iou/8': 0.794693,\n",
" 'iou/9': 0.0033994454,\n",
" 'mean_iou': 0.4581273,\n",
" 'validation_loss': 0.56583583}\n",
" eval | step: 675 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.9183424,\n",
" 'iou/1': 0.52886885,\n",
" 'iou/10': 0.0027221711,\n",
" 'iou/11': 0.35976118,\n",
" 'iou/12': 0.6707339,\n",
" 'iou/13': 0.37600324,\n",
" 'iou/14': 0.6675318,\n",
" 'iou/15': 0.7853089,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.48376465,\n",
" 'iou/18': 0.36525124,\n",
" 'iou/19': 0.7494944,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.5755536,\n",
" 'iou/3': 0.7191806,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.00022449192,\n",
" 'iou/6': 0.8360645,\n",
" 'iou/7': 0.7837744,\n",
" 'iou/8': 0.794693,\n",
" 'iou/9': 0.0033994454,\n",
" 'mean_iou': 0.4581273,\n",
" 'validation_loss': 0.56583583}\n",
"I0110 08:16:59.601721 140678257067904 controller.py:236] train | step: 675 | training until step 720...\n",
"train | step: 675 | training until step 720...\n",
"I0110 08:17:49.684173 140678257067904 controller.py:457] train | step: 720 | steps/sec: 0.6 | output: {'learning_rate': 7.0673734e-05, 'training_loss': 0.38783163}\n",
"train | step: 720 | steps/sec: 0.6 | output: {'learning_rate': 7.0673734e-05, 'training_loss': 0.38783163}\n",
"I0110 08:17:50.146304 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-720.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-720.\n",
"I0110 08:17:50.147457 140678257067904 controller.py:277] eval | step: 720 | running 45 steps of evaluation...\n",
" eval | step: 720 | running 45 steps of evaluation...\n",
"I0110 08:18:20.288827 140678257067904 controller.py:290] eval | step: 720 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.9198817,\n",
" 'iou/1': 0.53257394,\n",
" 'iou/10': 0.1627025,\n",
" 'iou/11': 0.376328,\n",
" 'iou/12': 0.7440758,\n",
" 'iou/13': 0.41754934,\n",
" 'iou/14': 0.68120927,\n",
" 'iou/15': 0.78656256,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.45999348,\n",
" 'iou/18': 0.38496268,\n",
" 'iou/19': 0.74844253,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.5998785,\n",
" 'iou/3': 0.68320084,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0959279,\n",
" 'iou/6': 0.84265584,\n",
" 'iou/7': 0.7897449,\n",
" 'iou/8': 0.8168343,\n",
" 'iou/9': 0.008802929,\n",
" 'mean_iou': 0.4786346,\n",
" 'validation_loss': 0.51274127}\n",
" eval | step: 720 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.9198817,\n",
" 'iou/1': 0.53257394,\n",
" 'iou/10': 0.1627025,\n",
" 'iou/11': 0.376328,\n",
" 'iou/12': 0.7440758,\n",
" 'iou/13': 0.41754934,\n",
" 'iou/14': 0.68120927,\n",
" 'iou/15': 0.78656256,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.45999348,\n",
" 'iou/18': 0.38496268,\n",
" 'iou/19': 0.74844253,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.5998785,\n",
" 'iou/3': 0.68320084,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.0959279,\n",
" 'iou/6': 0.84265584,\n",
" 'iou/7': 0.7897449,\n",
" 'iou/8': 0.8168343,\n",
" 'iou/9': 0.008802929,\n",
" 'mean_iou': 0.4786346,\n",
" 'validation_loss': 0.51274127}\n",
"I0110 08:18:20.321485 140678257067904 controller.py:236] train | step: 720 | training until step 765...\n",
"train | step: 720 | training until step 765...\n",
"I0110 08:19:10.365483 140678257067904 controller.py:457] train | step: 765 | steps/sec: 0.6 | output: {'learning_rate': 6.880017e-05, 'training_loss': 0.35282964}\n",
"train | step: 765 | steps/sec: 0.6 | output: {'learning_rate': 6.880017e-05, 'training_loss': 0.35282964}\n",
"I0110 08:19:10.850516 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-765.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-765.\n",
"I0110 08:19:10.851867 140678257067904 controller.py:277] eval | step: 765 | running 45 steps of evaluation...\n",
" eval | step: 765 | running 45 steps of evaluation...\n",
"I0110 08:19:42.283723 140678257067904 controller.py:290] eval | step: 765 | eval time: 31.4 sec | output: \n",
" {'iou/0': 0.92027146,\n",
" 'iou/1': 0.5370898,\n",
" 'iou/10': 0.048352316,\n",
" 'iou/11': 0.41504782,\n",
" 'iou/12': 0.71925175,\n",
" 'iou/13': 0.41219074,\n",
" 'iou/14': 0.6664862,\n",
" 'iou/15': 0.78995454,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.56340367,\n",
" 'iou/18': 0.37411934,\n",
" 'iou/19': 0.7287201,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.6172857,\n",
" 'iou/3': 0.8034988,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.43744245,\n",
" 'iou/6': 0.84156466,\n",
" 'iou/7': 0.7963559,\n",
" 'iou/8': 0.78470886,\n",
" 'iou/9': 0.00444512,\n",
" 'mean_iou': 0.49810427,\n",
" 'validation_loss': 0.48147735}\n",
" eval | step: 765 | eval time: 31.4 sec | output: \n",
" {'iou/0': 0.92027146,\n",
" 'iou/1': 0.5370898,\n",
" 'iou/10': 0.048352316,\n",
" 'iou/11': 0.41504782,\n",
" 'iou/12': 0.71925175,\n",
" 'iou/13': 0.41219074,\n",
" 'iou/14': 0.6664862,\n",
" 'iou/15': 0.78995454,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.56340367,\n",
" 'iou/18': 0.37411934,\n",
" 'iou/19': 0.7287201,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.6172857,\n",
" 'iou/3': 0.8034988,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.43744245,\n",
" 'iou/6': 0.84156466,\n",
" 'iou/7': 0.7963559,\n",
" 'iou/8': 0.78470886,\n",
" 'iou/9': 0.00444512,\n",
" 'mean_iou': 0.49810427,\n",
" 'validation_loss': 0.48147735}\n",
"I0110 08:19:42.322826 140678257067904 controller.py:236] train | step: 765 | training until step 810...\n",
"train | step: 765 | training until step 810...\n",
"I0110 08:20:32.571315 140678257067904 controller.py:457] train | step: 810 | steps/sec: 0.5 | output: {'learning_rate': 6.6920926e-05, 'training_loss': 0.33315444}\n",
"train | step: 810 | steps/sec: 0.5 | output: {'learning_rate': 6.6920926e-05, 'training_loss': 0.33315444}\n",
"I0110 08:20:33.053930 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-810.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-810.\n",
"I0110 08:20:33.055139 140678257067904 controller.py:277] eval | step: 810 | running 45 steps of evaluation...\n",
" eval | step: 810 | running 45 steps of evaluation...\n",
"I0110 08:21:04.226520 140678257067904 controller.py:290] eval | step: 810 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.92319787,\n",
" 'iou/1': 0.5374477,\n",
" 'iou/10': 0.046590734,\n",
" 'iou/11': 0.46501872,\n",
" 'iou/12': 0.7229139,\n",
" 'iou/13': 0.41710836,\n",
" 'iou/14': 0.6548459,\n",
" 'iou/15': 0.7959898,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.5744683,\n",
" 'iou/18': 0.3906065,\n",
" 'iou/19': 0.7556293,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.61969465,\n",
" 'iou/3': 0.8266961,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.61917907,\n",
" 'iou/6': 0.8322394,\n",
" 'iou/7': 0.7860155,\n",
" 'iou/8': 0.77921754,\n",
" 'iou/9': 0.043373447,\n",
" 'mean_iou': 0.5138206,\n",
" 'validation_loss': 0.4531264}\n",
" eval | step: 810 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.92319787,\n",
" 'iou/1': 0.5374477,\n",
" 'iou/10': 0.046590734,\n",
" 'iou/11': 0.46501872,\n",
" 'iou/12': 0.7229139,\n",
" 'iou/13': 0.41710836,\n",
" 'iou/14': 0.6548459,\n",
" 'iou/15': 0.7959898,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.5744683,\n",
" 'iou/18': 0.3906065,\n",
" 'iou/19': 0.7556293,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.61969465,\n",
" 'iou/3': 0.8266961,\n",
" 'iou/4': 0.0,\n",
" 'iou/5': 0.61917907,\n",
" 'iou/6': 0.8322394,\n",
" 'iou/7': 0.7860155,\n",
" 'iou/8': 0.77921754,\n",
" 'iou/9': 0.043373447,\n",
" 'mean_iou': 0.5138206,\n",
" 'validation_loss': 0.4531264}\n",
"I0110 08:21:04.260008 140678257067904 controller.py:236] train | step: 810 | training until step 855...\n",
"train | step: 810 | training until step 855...\n",
"I0110 08:21:54.341487 140678257067904 controller.py:457] train | step: 855 | steps/sec: 0.6 | output: {'learning_rate': 6.5035805e-05, 'training_loss': 0.32748395}\n",
"train | step: 855 | steps/sec: 0.6 | output: {'learning_rate': 6.5035805e-05, 'training_loss': 0.32748395}\n",
"I0110 08:21:55.040354 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-855.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-855.\n",
"I0110 08:21:55.041536 140678257067904 controller.py:277] eval | step: 855 | running 45 steps of evaluation...\n",
" eval | step: 855 | running 45 steps of evaluation...\n",
"I0110 08:22:25.881691 140678257067904 controller.py:290] eval | step: 855 | eval time: 30.8 sec | output: \n",
" {'iou/0': 0.9227111,\n",
" 'iou/1': 0.53836447,\n",
" 'iou/10': 0.21215075,\n",
" 'iou/11': 0.47563413,\n",
" 'iou/12': 0.7579652,\n",
" 'iou/13': 0.4350067,\n",
" 'iou/14': 0.6901653,\n",
" 'iou/15': 0.7936741,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.5718342,\n",
" 'iou/18': 0.40540874,\n",
" 'iou/19': 0.74071306,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.6205383,\n",
" 'iou/3': 0.83999836,\n",
" 'iou/4': 1.4241039e-06,\n",
" 'iou/5': 0.5972334,\n",
" 'iou/6': 0.83164066,\n",
" 'iou/7': 0.7931974,\n",
" 'iou/8': 0.8232223,\n",
" 'iou/9': 0.07171669,\n",
" 'mean_iou': 0.5295798,\n",
" 'validation_loss': 0.4328378}\n",
" eval | step: 855 | eval time: 30.8 sec | output: \n",
" {'iou/0': 0.9227111,\n",
" 'iou/1': 0.53836447,\n",
" 'iou/10': 0.21215075,\n",
" 'iou/11': 0.47563413,\n",
" 'iou/12': 0.7579652,\n",
" 'iou/13': 0.4350067,\n",
" 'iou/14': 0.6901653,\n",
" 'iou/15': 0.7936741,\n",
" 'iou/16': 0.0,\n",
" 'iou/17': 0.5718342,\n",
" 'iou/18': 0.40540874,\n",
" 'iou/19': 0.74071306,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.6205383,\n",
" 'iou/3': 0.83999836,\n",
" 'iou/4': 1.4241039e-06,\n",
" 'iou/5': 0.5972334,\n",
" 'iou/6': 0.83164066,\n",
" 'iou/7': 0.7931974,\n",
" 'iou/8': 0.8232223,\n",
" 'iou/9': 0.07171669,\n",
" 'mean_iou': 0.5295798,\n",
" 'validation_loss': 0.4328378}\n",
"I0110 08:22:25.914669 140678257067904 controller.py:236] train | step: 855 | training until step 900...\n",
"train | step: 855 | training until step 900...\n",
"I0110 08:23:16.055338 140678257067904 controller.py:457] train | step: 900 | steps/sec: 0.6 | output: {'learning_rate': 6.314459e-05, 'training_loss': 0.31190714}\n",
"train | step: 900 | steps/sec: 0.6 | output: {'learning_rate': 6.314459e-05, 'training_loss': 0.31190714}\n",
"I0110 08:23:16.512428 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-900.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-900.\n",
"I0110 08:23:16.513622 140678257067904 controller.py:277] eval | step: 900 | running 45 steps of evaluation...\n",
" eval | step: 900 | running 45 steps of evaluation...\n",
"I0110 08:23:47.657757 140678257067904 controller.py:290] eval | step: 900 | eval time: 31.1 sec | output: \n",
" {'iou/0': 0.9240004,\n",
" 'iou/1': 0.58483547,\n",
" 'iou/10': 0.19239771,\n",
" 'iou/11': 0.4726559,\n",
" 'iou/12': 0.76338434,\n",
" 'iou/13': 0.44316944,\n",
" 'iou/14': 0.653964,\n",
" 'iou/15': 0.78979194,\n",
" 'iou/16': 0.0036863093,\n",
" 'iou/17': 0.54950774,\n",
" 'iou/18': 0.41877037,\n",
" 'iou/19': 0.75122935,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.63891196,\n",
" 'iou/3': 0.8315963,\n",
" 'iou/4': 0.12633152,\n",
" 'iou/5': 0.61772364,\n",
" 'iou/6': 0.85020983,\n",
" 'iou/7': 0.8003236,\n",
" 'iou/8': 0.8257985,\n",
" 'iou/9': 0.06690598,\n",
" 'mean_iou': 0.5383426,\n",
" 'validation_loss': 0.4233214}\n",
" eval | step: 900 | eval time: 31.1 sec | output: \n",
" {'iou/0': 0.9240004,\n",
" 'iou/1': 0.58483547,\n",
" 'iou/10': 0.19239771,\n",
" 'iou/11': 0.4726559,\n",
" 'iou/12': 0.76338434,\n",
" 'iou/13': 0.44316944,\n",
" 'iou/14': 0.653964,\n",
" 'iou/15': 0.78979194,\n",
" 'iou/16': 0.0036863093,\n",
" 'iou/17': 0.54950774,\n",
" 'iou/18': 0.41877037,\n",
" 'iou/19': 0.75122935,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.63891196,\n",
" 'iou/3': 0.8315963,\n",
" 'iou/4': 0.12633152,\n",
" 'iou/5': 0.61772364,\n",
" 'iou/6': 0.85020983,\n",
" 'iou/7': 0.8003236,\n",
" 'iou/8': 0.8257985,\n",
" 'iou/9': 0.06690598,\n",
" 'mean_iou': 0.5383426,\n",
" 'validation_loss': 0.4233214}\n",
"I0110 08:23:47.692159 140678257067904 controller.py:236] train | step: 900 | training until step 945...\n",
"train | step: 900 | training until step 945...\n",
"I0110 08:24:37.744755 140678257067904 controller.py:457] train | step: 945 | steps/sec: 0.6 | output: {'learning_rate': 6.1247054e-05, 'training_loss': 0.30468065}\n",
"train | step: 945 | steps/sec: 0.6 | output: {'learning_rate': 6.1247054e-05, 'training_loss': 0.30468065}\n",
"I0110 08:24:38.206959 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-945.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-945.\n",
"I0110 08:24:38.208156 140678257067904 controller.py:277] eval | step: 945 | running 45 steps of evaluation...\n",
" eval | step: 945 | running 45 steps of evaluation...\n",
"I0110 08:25:09.374938 140678257067904 controller.py:290] eval | step: 945 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.92378736,\n",
" 'iou/1': 0.58966655,\n",
" 'iou/10': 0.22599617,\n",
" 'iou/11': 0.49061137,\n",
" 'iou/12': 0.7675211,\n",
" 'iou/13': 0.45461372,\n",
" 'iou/14': 0.6856207,\n",
" 'iou/15': 0.796819,\n",
" 'iou/16': 0.047615644,\n",
" 'iou/17': 0.57802004,\n",
" 'iou/18': 0.4375715,\n",
" 'iou/19': 0.7648492,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.61429673,\n",
" 'iou/3': 0.84445095,\n",
" 'iou/4': 0.15508837,\n",
" 'iou/5': 0.6296048,\n",
" 'iou/6': 0.8585276,\n",
" 'iou/7': 0.80236846,\n",
" 'iou/8': 0.8211116,\n",
" 'iou/9': 0.1464235,\n",
" 'mean_iou': 0.5540269,\n",
" 'validation_loss': 0.41443244}\n",
" eval | step: 945 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.92378736,\n",
" 'iou/1': 0.58966655,\n",
" 'iou/10': 0.22599617,\n",
" 'iou/11': 0.49061137,\n",
" 'iou/12': 0.7675211,\n",
" 'iou/13': 0.45461372,\n",
" 'iou/14': 0.6856207,\n",
" 'iou/15': 0.796819,\n",
" 'iou/16': 0.047615644,\n",
" 'iou/17': 0.57802004,\n",
" 'iou/18': 0.4375715,\n",
" 'iou/19': 0.7648492,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.61429673,\n",
" 'iou/3': 0.84445095,\n",
" 'iou/4': 0.15508837,\n",
" 'iou/5': 0.6296048,\n",
" 'iou/6': 0.8585276,\n",
" 'iou/7': 0.80236846,\n",
" 'iou/8': 0.8211116,\n",
" 'iou/9': 0.1464235,\n",
" 'mean_iou': 0.5540269,\n",
" 'validation_loss': 0.41443244}\n",
"I0110 08:25:09.408777 140678257067904 controller.py:236] train | step: 945 | training until step 990...\n",
"train | step: 945 | training until step 990...\n",
"I0110 08:25:59.897287 140678257067904 controller.py:457] train | step: 990 | steps/sec: 0.5 | output: {'learning_rate': 5.934296e-05, 'training_loss': 0.2907947}\n",
"train | step: 990 | steps/sec: 0.5 | output: {'learning_rate': 5.934296e-05, 'training_loss': 0.2907947}\n",
"I0110 08:26:00.363543 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-990.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-990.\n",
"I0110 08:26:00.364763 140678257067904 controller.py:277] eval | step: 990 | running 45 steps of evaluation...\n",
" eval | step: 990 | running 45 steps of evaluation...\n",
"I0110 08:26:31.301623 140678257067904 controller.py:290] eval | step: 990 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.9230869,\n",
" 'iou/1': 0.7402816,\n",
" 'iou/10': 0.19237942,\n",
" 'iou/11': 0.49530262,\n",
" 'iou/12': 0.74391794,\n",
" 'iou/13': 0.45956215,\n",
" 'iou/14': 0.6961246,\n",
" 'iou/15': 0.7998104,\n",
" 'iou/16': 0.14924213,\n",
" 'iou/17': 0.6009276,\n",
" 'iou/18': 0.41662493,\n",
" 'iou/19': 0.7605144,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.63816494,\n",
" 'iou/3': 0.8472196,\n",
" 'iou/4': 0.50101936,\n",
" 'iou/5': 0.6439679,\n",
" 'iou/6': 0.8456089,\n",
" 'iou/7': 0.77511823,\n",
" 'iou/8': 0.80677944,\n",
" 'iou/9': 0.12853704,\n",
" 'mean_iou': 0.5792472,\n",
" 'validation_loss': 0.41135746}\n",
" eval | step: 990 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.9230869,\n",
" 'iou/1': 0.7402816,\n",
" 'iou/10': 0.19237942,\n",
" 'iou/11': 0.49530262,\n",
" 'iou/12': 0.74391794,\n",
" 'iou/13': 0.45956215,\n",
" 'iou/14': 0.6961246,\n",
" 'iou/15': 0.7998104,\n",
" 'iou/16': 0.14924213,\n",
" 'iou/17': 0.6009276,\n",
" 'iou/18': 0.41662493,\n",
" 'iou/19': 0.7605144,\n",
" 'iou/2': 0.0,\n",
" 'iou/20': 0.63816494,\n",
" 'iou/3': 0.8472196,\n",
" 'iou/4': 0.50101936,\n",
" 'iou/5': 0.6439679,\n",
" 'iou/6': 0.8456089,\n",
" 'iou/7': 0.77511823,\n",
" 'iou/8': 0.80677944,\n",
" 'iou/9': 0.12853704,\n",
" 'mean_iou': 0.5792472,\n",
" 'validation_loss': 0.41135746}\n",
"I0110 08:26:31.334724 140678257067904 controller.py:236] train | step: 990 | training until step 1035...\n",
"train | step: 990 | training until step 1035...\n",
"I0110 08:27:21.743919 140678257067904 controller.py:457] train | step: 1035 | steps/sec: 0.5 | output: {'learning_rate': 5.7432055e-05, 'training_loss': 0.27723396}\n",
"train | step: 1035 | steps/sec: 0.5 | output: {'learning_rate': 5.7432055e-05, 'training_loss': 0.27723396}\n",
"I0110 08:27:22.218595 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1035.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1035.\n",
"I0110 08:27:22.219804 140678257067904 controller.py:277] eval | step: 1035 | running 45 steps of evaluation...\n",
" eval | step: 1035 | running 45 steps of evaluation...\n",
"I0110 08:27:53.506133 140678257067904 controller.py:290] eval | step: 1035 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.92454463,\n",
" 'iou/1': 0.77220565,\n",
" 'iou/10': 0.29610795,\n",
" 'iou/11': 0.5184365,\n",
" 'iou/12': 0.733315,\n",
" 'iou/13': 0.48029423,\n",
" 'iou/14': 0.7040934,\n",
" 'iou/15': 0.79988384,\n",
" 'iou/16': 0.25765446,\n",
" 'iou/17': 0.6276598,\n",
" 'iou/18': 0.41851875,\n",
" 'iou/19': 0.7452729,\n",
" 'iou/2': 6.7570276e-05,\n",
" 'iou/20': 0.65486574,\n",
" 'iou/3': 0.83704823,\n",
" 'iou/4': 0.5697312,\n",
" 'iou/5': 0.6681175,\n",
" 'iou/6': 0.83258545,\n",
" 'iou/7': 0.79651856,\n",
" 'iou/8': 0.8049578,\n",
" 'iou/9': 0.13721608,\n",
" 'mean_iou': 0.59900457,\n",
" 'validation_loss': 0.40554863}\n",
" eval | step: 1035 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.92454463,\n",
" 'iou/1': 0.77220565,\n",
" 'iou/10': 0.29610795,\n",
" 'iou/11': 0.5184365,\n",
" 'iou/12': 0.733315,\n",
" 'iou/13': 0.48029423,\n",
" 'iou/14': 0.7040934,\n",
" 'iou/15': 0.79988384,\n",
" 'iou/16': 0.25765446,\n",
" 'iou/17': 0.6276598,\n",
" 'iou/18': 0.41851875,\n",
" 'iou/19': 0.7452729,\n",
" 'iou/2': 6.7570276e-05,\n",
" 'iou/20': 0.65486574,\n",
" 'iou/3': 0.83704823,\n",
" 'iou/4': 0.5697312,\n",
" 'iou/5': 0.6681175,\n",
" 'iou/6': 0.83258545,\n",
" 'iou/7': 0.79651856,\n",
" 'iou/8': 0.8049578,\n",
" 'iou/9': 0.13721608,\n",
" 'mean_iou': 0.59900457,\n",
" 'validation_loss': 0.40554863}\n",
"I0110 08:27:53.570279 140678257067904 controller.py:236] train | step: 1035 | training until step 1080...\n",
"train | step: 1035 | training until step 1080...\n",
"I0110 08:28:43.230939 140678257067904 controller.py:457] train | step: 1080 | steps/sec: 0.6 | output: {'learning_rate': 5.551406e-05, 'training_loss': 0.26833478}\n",
"train | step: 1080 | steps/sec: 0.6 | output: {'learning_rate': 5.551406e-05, 'training_loss': 0.26833478}\n",
"I0110 08:28:43.697542 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1080.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1080.\n",
"I0110 08:28:43.698673 140678257067904 controller.py:277] eval | step: 1080 | running 45 steps of evaluation...\n",
" eval | step: 1080 | running 45 steps of evaluation...\n",
"I0110 08:29:14.637501 140678257067904 controller.py:290] eval | step: 1080 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.9254576,\n",
" 'iou/1': 0.78913367,\n",
" 'iou/10': 0.29177245,\n",
" 'iou/11': 0.5151305,\n",
" 'iou/12': 0.75709313,\n",
" 'iou/13': 0.4790751,\n",
" 'iou/14': 0.69744056,\n",
" 'iou/15': 0.8037278,\n",
" 'iou/16': 0.3873403,\n",
" 'iou/17': 0.6279328,\n",
" 'iou/18': 0.43119103,\n",
" 'iou/19': 0.7599927,\n",
" 'iou/2': 0.015826702,\n",
" 'iou/20': 0.6443736,\n",
" 'iou/3': 0.8514763,\n",
" 'iou/4': 0.5926746,\n",
" 'iou/5': 0.6474192,\n",
" 'iou/6': 0.8565463,\n",
" 'iou/7': 0.79864156,\n",
" 'iou/8': 0.8155619,\n",
" 'iou/9': 0.19336617,\n",
" 'mean_iou': 0.61338925,\n",
" 'validation_loss': 0.39531055}\n",
" eval | step: 1080 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.9254576,\n",
" 'iou/1': 0.78913367,\n",
" 'iou/10': 0.29177245,\n",
" 'iou/11': 0.5151305,\n",
" 'iou/12': 0.75709313,\n",
" 'iou/13': 0.4790751,\n",
" 'iou/14': 0.69744056,\n",
" 'iou/15': 0.8037278,\n",
" 'iou/16': 0.3873403,\n",
" 'iou/17': 0.6279328,\n",
" 'iou/18': 0.43119103,\n",
" 'iou/19': 0.7599927,\n",
" 'iou/2': 0.015826702,\n",
" 'iou/20': 0.6443736,\n",
" 'iou/3': 0.8514763,\n",
" 'iou/4': 0.5926746,\n",
" 'iou/5': 0.6474192,\n",
" 'iou/6': 0.8565463,\n",
" 'iou/7': 0.79864156,\n",
" 'iou/8': 0.8155619,\n",
" 'iou/9': 0.19336617,\n",
" 'mean_iou': 0.61338925,\n",
" 'validation_loss': 0.39531055}\n",
"I0110 08:29:14.669558 140678257067904 controller.py:236] train | step: 1080 | training until step 1125...\n",
"train | step: 1080 | training until step 1125...\n",
"I0110 08:30:04.937004 140678257067904 controller.py:457] train | step: 1125 | steps/sec: 0.6 | output: {'learning_rate': 5.3588676e-05, 'training_loss': 0.25775716}\n",
"train | step: 1125 | steps/sec: 0.6 | output: {'learning_rate': 5.3588676e-05, 'training_loss': 0.25775716}\n",
"I0110 08:30:05.408488 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1125.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1125.\n",
"I0110 08:30:05.410164 140678257067904 controller.py:277] eval | step: 1125 | running 45 steps of evaluation...\n",
" eval | step: 1125 | running 45 steps of evaluation...\n",
"I0110 08:30:36.436576 140678257067904 controller.py:290] eval | step: 1125 | eval time: 31.0 sec | output: \n",
" {'iou/0': 0.9245155,\n",
" 'iou/1': 0.79151237,\n",
" 'iou/10': 0.34997258,\n",
" 'iou/11': 0.5222257,\n",
" 'iou/12': 0.74813604,\n",
" 'iou/13': 0.50240815,\n",
" 'iou/14': 0.70802194,\n",
" 'iou/15': 0.7976451,\n",
" 'iou/16': 0.43480152,\n",
" 'iou/17': 0.6264194,\n",
" 'iou/18': 0.4311376,\n",
" 'iou/19': 0.75249344,\n",
" 'iou/2': 0.067010894,\n",
" 'iou/20': 0.64145404,\n",
" 'iou/3': 0.85548437,\n",
" 'iou/4': 0.58678335,\n",
" 'iou/5': 0.65020674,\n",
" 'iou/6': 0.84340227,\n",
" 'iou/7': 0.80168134,\n",
" 'iou/8': 0.82644796,\n",
" 'iou/9': 0.19620559,\n",
" 'mean_iou': 0.6218079,\n",
" 'validation_loss': 0.39167085}\n",
" eval | step: 1125 | eval time: 31.0 sec | output: \n",
" {'iou/0': 0.9245155,\n",
" 'iou/1': 0.79151237,\n",
" 'iou/10': 0.34997258,\n",
" 'iou/11': 0.5222257,\n",
" 'iou/12': 0.74813604,\n",
" 'iou/13': 0.50240815,\n",
" 'iou/14': 0.70802194,\n",
" 'iou/15': 0.7976451,\n",
" 'iou/16': 0.43480152,\n",
" 'iou/17': 0.6264194,\n",
" 'iou/18': 0.4311376,\n",
" 'iou/19': 0.75249344,\n",
" 'iou/2': 0.067010894,\n",
" 'iou/20': 0.64145404,\n",
" 'iou/3': 0.85548437,\n",
" 'iou/4': 0.58678335,\n",
" 'iou/5': 0.65020674,\n",
" 'iou/6': 0.84340227,\n",
" 'iou/7': 0.80168134,\n",
" 'iou/8': 0.82644796,\n",
" 'iou/9': 0.19620559,\n",
" 'mean_iou': 0.6218079,\n",
" 'validation_loss': 0.39167085}\n",
"I0110 08:30:36.468782 140678257067904 controller.py:236] train | step: 1125 | training until step 1170...\n",
"train | step: 1125 | training until step 1170...\n",
"I0110 08:31:26.384536 140678257067904 controller.py:457] train | step: 1170 | steps/sec: 0.6 | output: {'learning_rate': 5.1655566e-05, 'training_loss': 0.24573137}\n",
"train | step: 1170 | steps/sec: 0.6 | output: {'learning_rate': 5.1655566e-05, 'training_loss': 0.24573137}\n",
"I0110 08:31:26.840966 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1170.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1170.\n",
"I0110 08:31:26.842867 140678257067904 controller.py:277] eval | step: 1170 | running 45 steps of evaluation...\n",
" eval | step: 1170 | running 45 steps of evaluation...\n",
"I0110 08:31:58.250824 140678257067904 controller.py:290] eval | step: 1170 | eval time: 31.4 sec | output: \n",
" {'iou/0': 0.9278167,\n",
" 'iou/1': 0.8001687,\n",
" 'iou/10': 0.32259333,\n",
" 'iou/11': 0.5223475,\n",
" 'iou/12': 0.7669622,\n",
" 'iou/13': 0.49850115,\n",
" 'iou/14': 0.6784839,\n",
" 'iou/15': 0.80215913,\n",
" 'iou/16': 0.46128327,\n",
" 'iou/17': 0.658675,\n",
" 'iou/18': 0.45477435,\n",
" 'iou/19': 0.7751987,\n",
" 'iou/2': 0.19515912,\n",
" 'iou/20': 0.64059097,\n",
" 'iou/3': 0.8517133,\n",
" 'iou/4': 0.59431523,\n",
" 'iou/5': 0.65126884,\n",
" 'iou/6': 0.8542542,\n",
" 'iou/7': 0.80442226,\n",
" 'iou/8': 0.8267295,\n",
" 'iou/9': 0.22351143,\n",
" 'mean_iou': 0.63385373,\n",
" 'validation_loss': 0.3797599}\n",
" eval | step: 1170 | eval time: 31.4 sec | output: \n",
" {'iou/0': 0.9278167,\n",
" 'iou/1': 0.8001687,\n",
" 'iou/10': 0.32259333,\n",
" 'iou/11': 0.5223475,\n",
" 'iou/12': 0.7669622,\n",
" 'iou/13': 0.49850115,\n",
" 'iou/14': 0.6784839,\n",
" 'iou/15': 0.80215913,\n",
" 'iou/16': 0.46128327,\n",
" 'iou/17': 0.658675,\n",
" 'iou/18': 0.45477435,\n",
" 'iou/19': 0.7751987,\n",
" 'iou/2': 0.19515912,\n",
" 'iou/20': 0.64059097,\n",
" 'iou/3': 0.8517133,\n",
" 'iou/4': 0.59431523,\n",
" 'iou/5': 0.65126884,\n",
" 'iou/6': 0.8542542,\n",
" 'iou/7': 0.80442226,\n",
" 'iou/8': 0.8267295,\n",
" 'iou/9': 0.22351143,\n",
" 'mean_iou': 0.63385373,\n",
" 'validation_loss': 0.3797599}\n",
"I0110 08:31:58.283087 140678257067904 controller.py:236] train | step: 1170 | training until step 1215...\n",
"train | step: 1170 | training until step 1215...\n",
"I0110 08:32:48.399827 140678257067904 controller.py:457] train | step: 1215 | steps/sec: 0.5 | output: {'learning_rate': 4.971438e-05, 'training_loss': 0.24412759}\n",
"train | step: 1215 | steps/sec: 0.5 | output: {'learning_rate': 4.971438e-05, 'training_loss': 0.24412759}\n",
"I0110 08:32:48.862317 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1215.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1215.\n",
"I0110 08:32:48.863511 140678257067904 controller.py:277] eval | step: 1215 | running 45 steps of evaluation...\n",
" eval | step: 1215 | running 45 steps of evaluation...\n",
"I0110 08:33:20.033390 140678257067904 controller.py:290] eval | step: 1215 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.92725456,\n",
" 'iou/1': 0.79810387,\n",
" 'iou/10': 0.47476098,\n",
" 'iou/11': 0.50313413,\n",
" 'iou/12': 0.7290233,\n",
" 'iou/13': 0.524863,\n",
" 'iou/14': 0.6965183,\n",
" 'iou/15': 0.80513483,\n",
" 'iou/16': 0.44788316,\n",
" 'iou/17': 0.65355015,\n",
" 'iou/18': 0.43373844,\n",
" 'iou/19': 0.7786365,\n",
" 'iou/2': 0.33688283,\n",
" 'iou/20': 0.6359713,\n",
" 'iou/3': 0.8593423,\n",
" 'iou/4': 0.58898413,\n",
" 'iou/5': 0.66214913,\n",
" 'iou/6': 0.86048913,\n",
" 'iou/7': 0.79830265,\n",
" 'iou/8': 0.7959471,\n",
" 'iou/9': 0.23752996,\n",
" 'mean_iou': 0.6451524,\n",
" 'validation_loss': 0.3800226}\n",
" eval | step: 1215 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.92725456,\n",
" 'iou/1': 0.79810387,\n",
" 'iou/10': 0.47476098,\n",
" 'iou/11': 0.50313413,\n",
" 'iou/12': 0.7290233,\n",
" 'iou/13': 0.524863,\n",
" 'iou/14': 0.6965183,\n",
" 'iou/15': 0.80513483,\n",
" 'iou/16': 0.44788316,\n",
" 'iou/17': 0.65355015,\n",
" 'iou/18': 0.43373844,\n",
" 'iou/19': 0.7786365,\n",
" 'iou/2': 0.33688283,\n",
" 'iou/20': 0.6359713,\n",
" 'iou/3': 0.8593423,\n",
" 'iou/4': 0.58898413,\n",
" 'iou/5': 0.66214913,\n",
" 'iou/6': 0.86048913,\n",
" 'iou/7': 0.79830265,\n",
" 'iou/8': 0.7959471,\n",
" 'iou/9': 0.23752996,\n",
" 'mean_iou': 0.6451524,\n",
" 'validation_loss': 0.3800226}\n",
"I0110 08:33:20.065583 140678257067904 controller.py:236] train | step: 1215 | training until step 1260...\n",
"train | step: 1215 | training until step 1260...\n",
"I0110 08:34:10.402987 140678257067904 controller.py:457] train | step: 1260 | steps/sec: 0.5 | output: {'learning_rate': 4.7764737e-05, 'training_loss': 0.22985744}\n",
"train | step: 1260 | steps/sec: 0.5 | output: {'learning_rate': 4.7764737e-05, 'training_loss': 0.22985744}\n",
"I0110 08:34:10.897103 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1260.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1260.\n",
"I0110 08:34:10.898294 140678257067904 controller.py:277] eval | step: 1260 | running 45 steps of evaluation...\n",
" eval | step: 1260 | running 45 steps of evaluation...\n",
"I0110 08:34:42.691668 140678257067904 controller.py:290] eval | step: 1260 | eval time: 31.8 sec | output: \n",
" {'iou/0': 0.92760265,\n",
" 'iou/1': 0.80756485,\n",
" 'iou/10': 0.5010928,\n",
" 'iou/11': 0.5117292,\n",
" 'iou/12': 0.7522951,\n",
" 'iou/13': 0.52926236,\n",
" 'iou/14': 0.7167403,\n",
" 'iou/15': 0.79920095,\n",
" 'iou/16': 0.4414572,\n",
" 'iou/17': 0.6490282,\n",
" 'iou/18': 0.42894965,\n",
" 'iou/19': 0.7711001,\n",
" 'iou/2': 0.2732911,\n",
" 'iou/20': 0.64145356,\n",
" 'iou/3': 0.851226,\n",
" 'iou/4': 0.59306175,\n",
" 'iou/5': 0.6600265,\n",
" 'iou/6': 0.8575642,\n",
" 'iou/7': 0.7994206,\n",
" 'iou/8': 0.8211515,\n",
" 'iou/9': 0.24611217,\n",
" 'mean_iou': 0.64663476,\n",
" 'validation_loss': 0.3782482}\n",
" eval | step: 1260 | eval time: 31.8 sec | output: \n",
" {'iou/0': 0.92760265,\n",
" 'iou/1': 0.80756485,\n",
" 'iou/10': 0.5010928,\n",
" 'iou/11': 0.5117292,\n",
" 'iou/12': 0.7522951,\n",
" 'iou/13': 0.52926236,\n",
" 'iou/14': 0.7167403,\n",
" 'iou/15': 0.79920095,\n",
" 'iou/16': 0.4414572,\n",
" 'iou/17': 0.6490282,\n",
" 'iou/18': 0.42894965,\n",
" 'iou/19': 0.7711001,\n",
" 'iou/2': 0.2732911,\n",
" 'iou/20': 0.64145356,\n",
" 'iou/3': 0.851226,\n",
" 'iou/4': 0.59306175,\n",
" 'iou/5': 0.6600265,\n",
" 'iou/6': 0.8575642,\n",
" 'iou/7': 0.7994206,\n",
" 'iou/8': 0.8211515,\n",
" 'iou/9': 0.24611217,\n",
" 'mean_iou': 0.64663476,\n",
" 'validation_loss': 0.3782482}\n",
"I0110 08:34:42.726941 140678257067904 controller.py:236] train | step: 1260 | training until step 1305...\n",
"train | step: 1260 | training until step 1305...\n",
"I0110 08:35:32.674492 140678257067904 controller.py:457] train | step: 1305 | steps/sec: 0.5 | output: {'learning_rate': 4.580621e-05, 'training_loss': 0.23151031}\n",
"train | step: 1305 | steps/sec: 0.5 | output: {'learning_rate': 4.580621e-05, 'training_loss': 0.23151031}\n",
"I0110 08:35:33.163119 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1305.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1305.\n",
"I0110 08:35:33.164443 140678257067904 controller.py:277] eval | step: 1305 | running 45 steps of evaluation...\n",
" eval | step: 1305 | running 45 steps of evaluation...\n",
"I0110 08:36:04.505995 140678257067904 controller.py:290] eval | step: 1305 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.9283738,\n",
" 'iou/1': 0.80850214,\n",
" 'iou/10': 0.52499145,\n",
" 'iou/11': 0.52566266,\n",
" 'iou/12': 0.75490487,\n",
" 'iou/13': 0.537579,\n",
" 'iou/14': 0.73345053,\n",
" 'iou/15': 0.8036683,\n",
" 'iou/16': 0.45781425,\n",
" 'iou/17': 0.6667556,\n",
" 'iou/18': 0.4419953,\n",
" 'iou/19': 0.7764658,\n",
" 'iou/2': 0.40449616,\n",
" 'iou/20': 0.649114,\n",
" 'iou/3': 0.8608717,\n",
" 'iou/4': 0.598254,\n",
" 'iou/5': 0.6593011,\n",
" 'iou/6': 0.8631406,\n",
" 'iou/7': 0.798533,\n",
" 'iou/8': 0.81247765,\n",
" 'iou/9': 0.2434353,\n",
" 'mean_iou': 0.65951365,\n",
" 'validation_loss': 0.37105742}\n",
" eval | step: 1305 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.9283738,\n",
" 'iou/1': 0.80850214,\n",
" 'iou/10': 0.52499145,\n",
" 'iou/11': 0.52566266,\n",
" 'iou/12': 0.75490487,\n",
" 'iou/13': 0.537579,\n",
" 'iou/14': 0.73345053,\n",
" 'iou/15': 0.8036683,\n",
" 'iou/16': 0.45781425,\n",
" 'iou/17': 0.6667556,\n",
" 'iou/18': 0.4419953,\n",
" 'iou/19': 0.7764658,\n",
" 'iou/2': 0.40449616,\n",
" 'iou/20': 0.649114,\n",
" 'iou/3': 0.8608717,\n",
" 'iou/4': 0.598254,\n",
" 'iou/5': 0.6593011,\n",
" 'iou/6': 0.8631406,\n",
" 'iou/7': 0.798533,\n",
" 'iou/8': 0.81247765,\n",
" 'iou/9': 0.2434353,\n",
" 'mean_iou': 0.65951365,\n",
" 'validation_loss': 0.37105742}\n",
"I0110 08:36:04.540087 140678257067904 controller.py:236] train | step: 1305 | training until step 1350...\n",
"train | step: 1305 | training until step 1350...\n",
"I0110 08:36:55.179453 140678257067904 controller.py:457] train | step: 1350 | steps/sec: 0.5 | output: {'learning_rate': 4.383833e-05, 'training_loss': 0.2276832}\n",
"train | step: 1350 | steps/sec: 0.5 | output: {'learning_rate': 4.383833e-05, 'training_loss': 0.2276832}\n",
"I0110 08:36:55.644763 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1350.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1350.\n",
"I0110 08:36:55.646029 140678257067904 controller.py:277] eval | step: 1350 | running 45 steps of evaluation...\n",
" eval | step: 1350 | running 45 steps of evaluation...\n",
"I0110 08:37:26.743151 140678257067904 controller.py:290] eval | step: 1350 | eval time: 31.1 sec | output: \n",
" {'iou/0': 0.92792076,\n",
" 'iou/1': 0.8100698,\n",
" 'iou/10': 0.52928734,\n",
" 'iou/11': 0.5296004,\n",
" 'iou/12': 0.76499945,\n",
" 'iou/13': 0.57381576,\n",
" 'iou/14': 0.72779864,\n",
" 'iou/15': 0.79777527,\n",
" 'iou/16': 0.46096152,\n",
" 'iou/17': 0.69336385,\n",
" 'iou/18': 0.44812375,\n",
" 'iou/19': 0.7672999,\n",
" 'iou/2': 0.43492907,\n",
" 'iou/20': 0.653837,\n",
" 'iou/3': 0.8648942,\n",
" 'iou/4': 0.5968084,\n",
" 'iou/5': 0.65441513,\n",
" 'iou/6': 0.8596268,\n",
" 'iou/7': 0.7944246,\n",
" 'iou/8': 0.822845,\n",
" 'iou/9': 0.2533331,\n",
" 'mean_iou': 0.6650538,\n",
" 'validation_loss': 0.3678605}\n",
" eval | step: 1350 | eval time: 31.1 sec | output: \n",
" {'iou/0': 0.92792076,\n",
" 'iou/1': 0.8100698,\n",
" 'iou/10': 0.52928734,\n",
" 'iou/11': 0.5296004,\n",
" 'iou/12': 0.76499945,\n",
" 'iou/13': 0.57381576,\n",
" 'iou/14': 0.72779864,\n",
" 'iou/15': 0.79777527,\n",
" 'iou/16': 0.46096152,\n",
" 'iou/17': 0.69336385,\n",
" 'iou/18': 0.44812375,\n",
" 'iou/19': 0.7672999,\n",
" 'iou/2': 0.43492907,\n",
" 'iou/20': 0.653837,\n",
" 'iou/3': 0.8648942,\n",
" 'iou/4': 0.5968084,\n",
" 'iou/5': 0.65441513,\n",
" 'iou/6': 0.8596268,\n",
" 'iou/7': 0.7944246,\n",
" 'iou/8': 0.822845,\n",
" 'iou/9': 0.2533331,\n",
" 'mean_iou': 0.6650538,\n",
" 'validation_loss': 0.3678605}\n",
"I0110 08:37:26.776697 140678257067904 controller.py:236] train | step: 1350 | training until step 1395...\n",
"train | step: 1350 | training until step 1395...\n",
"I0110 08:38:16.545912 140678257067904 controller.py:457] train | step: 1395 | steps/sec: 0.6 | output: {'learning_rate': 4.1860578e-05, 'training_loss': 0.21750636}\n",
"train | step: 1395 | steps/sec: 0.6 | output: {'learning_rate': 4.1860578e-05, 'training_loss': 0.21750636}\n",
"I0110 08:38:17.029767 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1395.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1395.\n",
"I0110 08:38:17.030960 140678257067904 controller.py:277] eval | step: 1395 | running 45 steps of evaluation...\n",
" eval | step: 1395 | running 45 steps of evaluation...\n",
"I0110 08:38:47.547201 140678257067904 controller.py:290] eval | step: 1395 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.9275929,\n",
" 'iou/1': 0.8141944,\n",
" 'iou/10': 0.52584976,\n",
" 'iou/11': 0.53252983,\n",
" 'iou/12': 0.75283587,\n",
" 'iou/13': 0.5698263,\n",
" 'iou/14': 0.7357535,\n",
" 'iou/15': 0.80099654,\n",
" 'iou/16': 0.45714745,\n",
" 'iou/17': 0.68202156,\n",
" 'iou/18': 0.44674477,\n",
" 'iou/19': 0.7682362,\n",
" 'iou/2': 0.45159557,\n",
" 'iou/20': 0.6390785,\n",
" 'iou/3': 0.8650681,\n",
" 'iou/4': 0.5976354,\n",
" 'iou/5': 0.6523835,\n",
" 'iou/6': 0.85914606,\n",
" 'iou/7': 0.79294044,\n",
" 'iou/8': 0.8150119,\n",
" 'iou/9': 0.25242808,\n",
" 'mean_iou': 0.6637627,\n",
" 'validation_loss': 0.36964023}\n",
" eval | step: 1395 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.9275929,\n",
" 'iou/1': 0.8141944,\n",
" 'iou/10': 0.52584976,\n",
" 'iou/11': 0.53252983,\n",
" 'iou/12': 0.75283587,\n",
" 'iou/13': 0.5698263,\n",
" 'iou/14': 0.7357535,\n",
" 'iou/15': 0.80099654,\n",
" 'iou/16': 0.45714745,\n",
" 'iou/17': 0.68202156,\n",
" 'iou/18': 0.44674477,\n",
" 'iou/19': 0.7682362,\n",
" 'iou/2': 0.45159557,\n",
" 'iou/20': 0.6390785,\n",
" 'iou/3': 0.8650681,\n",
" 'iou/4': 0.5976354,\n",
" 'iou/5': 0.6523835,\n",
" 'iou/6': 0.85914606,\n",
" 'iou/7': 0.79294044,\n",
" 'iou/8': 0.8150119,\n",
" 'iou/9': 0.25242808,\n",
" 'mean_iou': 0.6637627,\n",
" 'validation_loss': 0.36964023}\n",
"I0110 08:38:47.578667 140678257067904 controller.py:236] train | step: 1395 | training until step 1440...\n",
"train | step: 1395 | training until step 1440...\n",
"I0110 08:39:37.825137 140678257067904 controller.py:457] train | step: 1440 | steps/sec: 0.6 | output: {'learning_rate': 3.987239e-05, 'training_loss': 0.2216701}\n",
"train | step: 1440 | steps/sec: 0.6 | output: {'learning_rate': 3.987239e-05, 'training_loss': 0.2216701}\n",
"I0110 08:39:38.297419 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1440.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1440.\n",
"I0110 08:39:38.298686 140678257067904 controller.py:277] eval | step: 1440 | running 45 steps of evaluation...\n",
" eval | step: 1440 | running 45 steps of evaluation...\n",
"I0110 08:40:09.485890 140678257067904 controller.py:290] eval | step: 1440 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.9286059,\n",
" 'iou/1': 0.81363666,\n",
" 'iou/10': 0.5284853,\n",
" 'iou/11': 0.5318089,\n",
" 'iou/12': 0.7490796,\n",
" 'iou/13': 0.57136947,\n",
" 'iou/14': 0.732552,\n",
" 'iou/15': 0.80617416,\n",
" 'iou/16': 0.45672026,\n",
" 'iou/17': 0.67242247,\n",
" 'iou/18': 0.4141471,\n",
" 'iou/19': 0.77752316,\n",
" 'iou/2': 0.42756453,\n",
" 'iou/20': 0.6410497,\n",
" 'iou/3': 0.8652268,\n",
" 'iou/4': 0.6024614,\n",
" 'iou/5': 0.65822196,\n",
" 'iou/6': 0.8589954,\n",
" 'iou/7': 0.7940089,\n",
" 'iou/8': 0.80781883,\n",
" 'iou/9': 0.24969278,\n",
" 'mean_iou': 0.66131264,\n",
" 'validation_loss': 0.36609098}\n",
" eval | step: 1440 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.9286059,\n",
" 'iou/1': 0.81363666,\n",
" 'iou/10': 0.5284853,\n",
" 'iou/11': 0.5318089,\n",
" 'iou/12': 0.7490796,\n",
" 'iou/13': 0.57136947,\n",
" 'iou/14': 0.732552,\n",
" 'iou/15': 0.80617416,\n",
" 'iou/16': 0.45672026,\n",
" 'iou/17': 0.67242247,\n",
" 'iou/18': 0.4141471,\n",
" 'iou/19': 0.77752316,\n",
" 'iou/2': 0.42756453,\n",
" 'iou/20': 0.6410497,\n",
" 'iou/3': 0.8652268,\n",
" 'iou/4': 0.6024614,\n",
" 'iou/5': 0.65822196,\n",
" 'iou/6': 0.8589954,\n",
" 'iou/7': 0.7940089,\n",
" 'iou/8': 0.80781883,\n",
" 'iou/9': 0.24969278,\n",
" 'mean_iou': 0.66131264,\n",
" 'validation_loss': 0.36609098}\n",
"I0110 08:40:09.518593 140678257067904 controller.py:236] train | step: 1440 | training until step 1485...\n",
"train | step: 1440 | training until step 1485...\n",
"I0110 08:40:59.994710 140678257067904 controller.py:457] train | step: 1485 | steps/sec: 0.5 | output: {'learning_rate': 3.7873113e-05, 'training_loss': 0.2097194}\n",
"train | step: 1485 | steps/sec: 0.5 | output: {'learning_rate': 3.7873113e-05, 'training_loss': 0.2097194}\n",
"I0110 08:41:00.470365 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1485.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1485.\n",
"I0110 08:41:00.471511 140678257067904 controller.py:277] eval | step: 1485 | running 45 steps of evaluation...\n",
" eval | step: 1485 | running 45 steps of evaluation...\n",
"I0110 08:41:32.237684 140678257067904 controller.py:290] eval | step: 1485 | eval time: 31.8 sec | output: \n",
" {'iou/0': 0.92855436,\n",
" 'iou/1': 0.81344557,\n",
" 'iou/10': 0.5390062,\n",
" 'iou/11': 0.5257104,\n",
" 'iou/12': 0.783331,\n",
" 'iou/13': 0.5741588,\n",
" 'iou/14': 0.73610616,\n",
" 'iou/15': 0.80363816,\n",
" 'iou/16': 0.45788628,\n",
" 'iou/17': 0.68007755,\n",
" 'iou/18': 0.4265539,\n",
" 'iou/19': 0.76637655,\n",
" 'iou/2': 0.4560577,\n",
" 'iou/20': 0.6490481,\n",
" 'iou/3': 0.8632242,\n",
" 'iou/4': 0.5986748,\n",
" 'iou/5': 0.65289724,\n",
" 'iou/6': 0.85701114,\n",
" 'iou/7': 0.8006746,\n",
" 'iou/8': 0.83043945,\n",
" 'iou/9': 0.25654247,\n",
" 'mean_iou': 0.6666388,\n",
" 'validation_loss': 0.3650753}\n",
" eval | step: 1485 | eval time: 31.8 sec | output: \n",
" {'iou/0': 0.92855436,\n",
" 'iou/1': 0.81344557,\n",
" 'iou/10': 0.5390062,\n",
" 'iou/11': 0.5257104,\n",
" 'iou/12': 0.783331,\n",
" 'iou/13': 0.5741588,\n",
" 'iou/14': 0.73610616,\n",
" 'iou/15': 0.80363816,\n",
" 'iou/16': 0.45788628,\n",
" 'iou/17': 0.68007755,\n",
" 'iou/18': 0.4265539,\n",
" 'iou/19': 0.76637655,\n",
" 'iou/2': 0.4560577,\n",
" 'iou/20': 0.6490481,\n",
" 'iou/3': 0.8632242,\n",
" 'iou/4': 0.5986748,\n",
" 'iou/5': 0.65289724,\n",
" 'iou/6': 0.85701114,\n",
" 'iou/7': 0.8006746,\n",
" 'iou/8': 0.83043945,\n",
" 'iou/9': 0.25654247,\n",
" 'mean_iou': 0.6666388,\n",
" 'validation_loss': 0.3650753}\n",
"I0110 08:41:32.269181 140678257067904 controller.py:236] train | step: 1485 | training until step 1530...\n",
"train | step: 1485 | training until step 1530...\n",
"I0110 08:42:22.560542 140678257067904 controller.py:457] train | step: 1530 | steps/sec: 0.5 | output: {'learning_rate': 3.586204e-05, 'training_loss': 0.20831405}\n",
"train | step: 1530 | steps/sec: 0.5 | output: {'learning_rate': 3.586204e-05, 'training_loss': 0.20831405}\n",
"I0110 08:42:23.056023 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1530.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1530.\n",
"I0110 08:42:23.057324 140678257067904 controller.py:277] eval | step: 1530 | running 45 steps of evaluation...\n",
" eval | step: 1530 | running 45 steps of evaluation...\n",
"I0110 08:42:53.668784 140678257067904 controller.py:290] eval | step: 1530 | eval time: 30.6 sec | output: \n",
" {'iou/0': 0.92909324,\n",
" 'iou/1': 0.8158297,\n",
" 'iou/10': 0.56804305,\n",
" 'iou/11': 0.5319861,\n",
" 'iou/12': 0.7583937,\n",
" 'iou/13': 0.5807165,\n",
" 'iou/14': 0.7317145,\n",
" 'iou/15': 0.80629665,\n",
" 'iou/16': 0.4633754,\n",
" 'iou/17': 0.6812741,\n",
" 'iou/18': 0.4207367,\n",
" 'iou/19': 0.77066797,\n",
" 'iou/2': 0.46143082,\n",
" 'iou/20': 0.6427769,\n",
" 'iou/3': 0.8689982,\n",
" 'iou/4': 0.60063624,\n",
" 'iou/5': 0.66786426,\n",
" 'iou/6': 0.86125225,\n",
" 'iou/7': 0.80102706,\n",
" 'iou/8': 0.81774074,\n",
" 'iou/9': 0.251789,\n",
" 'mean_iou': 0.6681735,\n",
" 'validation_loss': 0.36370066}\n",
" eval | step: 1530 | eval time: 30.6 sec | output: \n",
" {'iou/0': 0.92909324,\n",
" 'iou/1': 0.8158297,\n",
" 'iou/10': 0.56804305,\n",
" 'iou/11': 0.5319861,\n",
" 'iou/12': 0.7583937,\n",
" 'iou/13': 0.5807165,\n",
" 'iou/14': 0.7317145,\n",
" 'iou/15': 0.80629665,\n",
" 'iou/16': 0.4633754,\n",
" 'iou/17': 0.6812741,\n",
" 'iou/18': 0.4207367,\n",
" 'iou/19': 0.77066797,\n",
" 'iou/2': 0.46143082,\n",
" 'iou/20': 0.6427769,\n",
" 'iou/3': 0.8689982,\n",
" 'iou/4': 0.60063624,\n",
" 'iou/5': 0.66786426,\n",
" 'iou/6': 0.86125225,\n",
" 'iou/7': 0.80102706,\n",
" 'iou/8': 0.81774074,\n",
" 'iou/9': 0.251789,\n",
" 'mean_iou': 0.6681735,\n",
" 'validation_loss': 0.36370066}\n",
"I0110 08:42:53.704123 140678257067904 controller.py:236] train | step: 1530 | training until step 1575...\n",
"train | step: 1530 | training until step 1575...\n",
"I0110 08:43:44.254873 140678257067904 controller.py:457] train | step: 1575 | steps/sec: 0.6 | output: {'learning_rate': 3.3838347e-05, 'training_loss': 0.2044727}\n",
"train | step: 1575 | steps/sec: 0.6 | output: {'learning_rate': 3.3838347e-05, 'training_loss': 0.2044727}\n",
"I0110 08:43:44.712526 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1575.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1575.\n",
"I0110 08:43:44.713660 140678257067904 controller.py:277] eval | step: 1575 | running 45 steps of evaluation...\n",
" eval | step: 1575 | running 45 steps of evaluation...\n",
"I0110 08:44:16.033668 140678257067904 controller.py:290] eval | step: 1575 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.92930555,\n",
" 'iou/1': 0.81044585,\n",
" 'iou/10': 0.5186797,\n",
" 'iou/11': 0.5285135,\n",
" 'iou/12': 0.7597186,\n",
" 'iou/13': 0.5752905,\n",
" 'iou/14': 0.74584675,\n",
" 'iou/15': 0.80687636,\n",
" 'iou/16': 0.46132147,\n",
" 'iou/17': 0.6805967,\n",
" 'iou/18': 0.40436658,\n",
" 'iou/19': 0.7796377,\n",
" 'iou/2': 0.47124448,\n",
" 'iou/20': 0.64421076,\n",
" 'iou/3': 0.868804,\n",
" 'iou/4': 0.60165054,\n",
" 'iou/5': 0.66821676,\n",
" 'iou/6': 0.86291534,\n",
" 'iou/7': 0.8047803,\n",
" 'iou/8': 0.8230313,\n",
" 'iou/9': 0.26826575,\n",
" 'mean_iou': 0.6673199,\n",
" 'validation_loss': 0.36291948}\n",
" eval | step: 1575 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.92930555,\n",
" 'iou/1': 0.81044585,\n",
" 'iou/10': 0.5186797,\n",
" 'iou/11': 0.5285135,\n",
" 'iou/12': 0.7597186,\n",
" 'iou/13': 0.5752905,\n",
" 'iou/14': 0.74584675,\n",
" 'iou/15': 0.80687636,\n",
" 'iou/16': 0.46132147,\n",
" 'iou/17': 0.6805967,\n",
" 'iou/18': 0.40436658,\n",
" 'iou/19': 0.7796377,\n",
" 'iou/2': 0.47124448,\n",
" 'iou/20': 0.64421076,\n",
" 'iou/3': 0.868804,\n",
" 'iou/4': 0.60165054,\n",
" 'iou/5': 0.66821676,\n",
" 'iou/6': 0.86291534,\n",
" 'iou/7': 0.8047803,\n",
" 'iou/8': 0.8230313,\n",
" 'iou/9': 0.26826575,\n",
" 'mean_iou': 0.6673199,\n",
" 'validation_loss': 0.36291948}\n",
"I0110 08:44:16.066101 140678257067904 controller.py:236] train | step: 1575 | training until step 1620...\n",
"train | step: 1575 | training until step 1620...\n",
"I0110 08:45:06.250605 140678257067904 controller.py:457] train | step: 1620 | steps/sec: 0.5 | output: {'learning_rate': 3.1801104e-05, 'training_loss': 0.19872649}\n",
"train | step: 1620 | steps/sec: 0.5 | output: {'learning_rate': 3.1801104e-05, 'training_loss': 0.19872649}\n",
"I0110 08:45:06.987092 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1620.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1620.\n",
"I0110 08:45:06.988418 140678257067904 controller.py:277] eval | step: 1620 | running 45 steps of evaluation...\n",
" eval | step: 1620 | running 45 steps of evaluation...\n",
"I0110 08:45:37.980437 140678257067904 controller.py:290] eval | step: 1620 | eval time: 31.0 sec | output: \n",
" {'iou/0': 0.9292853,\n",
" 'iou/1': 0.81048244,\n",
" 'iou/10': 0.5190843,\n",
" 'iou/11': 0.5355792,\n",
" 'iou/12': 0.760535,\n",
" 'iou/13': 0.5795876,\n",
" 'iou/14': 0.7498174,\n",
" 'iou/15': 0.8076759,\n",
" 'iou/16': 0.47739127,\n",
" 'iou/17': 0.6835878,\n",
" 'iou/18': 0.4200787,\n",
" 'iou/19': 0.7649707,\n",
" 'iou/2': 0.46004623,\n",
" 'iou/20': 0.6428955,\n",
" 'iou/3': 0.8646098,\n",
" 'iou/4': 0.60254,\n",
" 'iou/5': 0.67554885,\n",
" 'iou/6': 0.85715795,\n",
" 'iou/7': 0.80536747,\n",
" 'iou/8': 0.8250903,\n",
" 'iou/9': 0.25413778,\n",
" 'mean_iou': 0.6678795,\n",
" 'validation_loss': 0.36331138}\n",
" eval | step: 1620 | eval time: 31.0 sec | output: \n",
" {'iou/0': 0.9292853,\n",
" 'iou/1': 0.81048244,\n",
" 'iou/10': 0.5190843,\n",
" 'iou/11': 0.5355792,\n",
" 'iou/12': 0.760535,\n",
" 'iou/13': 0.5795876,\n",
" 'iou/14': 0.7498174,\n",
" 'iou/15': 0.8076759,\n",
" 'iou/16': 0.47739127,\n",
" 'iou/17': 0.6835878,\n",
" 'iou/18': 0.4200787,\n",
" 'iou/19': 0.7649707,\n",
" 'iou/2': 0.46004623,\n",
" 'iou/20': 0.6428955,\n",
" 'iou/3': 0.8646098,\n",
" 'iou/4': 0.60254,\n",
" 'iou/5': 0.67554885,\n",
" 'iou/6': 0.85715795,\n",
" 'iou/7': 0.80536747,\n",
" 'iou/8': 0.8250903,\n",
" 'iou/9': 0.25413778,\n",
" 'mean_iou': 0.6678795,\n",
" 'validation_loss': 0.36331138}\n",
"I0110 08:45:38.013730 140678257067904 controller.py:236] train | step: 1620 | training until step 1665...\n",
"train | step: 1620 | training until step 1665...\n",
"I0110 08:46:28.204798 140678257067904 controller.py:457] train | step: 1665 | steps/sec: 0.5 | output: {'learning_rate': 2.9749246e-05, 'training_loss': 0.20666489}\n",
"train | step: 1665 | steps/sec: 0.5 | output: {'learning_rate': 2.9749246e-05, 'training_loss': 0.20666489}\n",
"I0110 08:46:28.683523 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1665.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1665.\n",
"I0110 08:46:28.684799 140678257067904 controller.py:277] eval | step: 1665 | running 45 steps of evaluation...\n",
" eval | step: 1665 | running 45 steps of evaluation...\n",
"I0110 08:46:59.967679 140678257067904 controller.py:290] eval | step: 1665 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.9291142,\n",
" 'iou/1': 0.8112726,\n",
" 'iou/10': 0.5655368,\n",
" 'iou/11': 0.52933925,\n",
" 'iou/12': 0.7266466,\n",
" 'iou/13': 0.5905855,\n",
" 'iou/14': 0.7411469,\n",
" 'iou/15': 0.8068681,\n",
" 'iou/16': 0.47498116,\n",
" 'iou/17': 0.68648285,\n",
" 'iou/18': 0.42245072,\n",
" 'iou/19': 0.77231705,\n",
" 'iou/2': 0.46920812,\n",
" 'iou/20': 0.6452734,\n",
" 'iou/3': 0.86135614,\n",
" 'iou/4': 0.60686904,\n",
" 'iou/5': 0.6806402,\n",
" 'iou/6': 0.85538095,\n",
" 'iou/7': 0.8029113,\n",
" 'iou/8': 0.8001322,\n",
" 'iou/9': 0.2532494,\n",
" 'mean_iou': 0.66817915,\n",
" 'validation_loss': 0.36522812}\n",
" eval | step: 1665 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.9291142,\n",
" 'iou/1': 0.8112726,\n",
" 'iou/10': 0.5655368,\n",
" 'iou/11': 0.52933925,\n",
" 'iou/12': 0.7266466,\n",
" 'iou/13': 0.5905855,\n",
" 'iou/14': 0.7411469,\n",
" 'iou/15': 0.8068681,\n",
" 'iou/16': 0.47498116,\n",
" 'iou/17': 0.68648285,\n",
" 'iou/18': 0.42245072,\n",
" 'iou/19': 0.77231705,\n",
" 'iou/2': 0.46920812,\n",
" 'iou/20': 0.6452734,\n",
" 'iou/3': 0.86135614,\n",
" 'iou/4': 0.60686904,\n",
" 'iou/5': 0.6806402,\n",
" 'iou/6': 0.85538095,\n",
" 'iou/7': 0.8029113,\n",
" 'iou/8': 0.8001322,\n",
" 'iou/9': 0.2532494,\n",
" 'mean_iou': 0.66817915,\n",
" 'validation_loss': 0.36522812}\n",
"I0110 08:47:00.001066 140678257067904 controller.py:236] train | step: 1665 | training until step 1710...\n",
"train | step: 1665 | training until step 1710...\n",
"I0110 08:47:50.037953 140678257067904 controller.py:457] train | step: 1710 | steps/sec: 0.5 | output: {'learning_rate': 2.7681535e-05, 'training_loss': 0.1921477}\n",
"train | step: 1710 | steps/sec: 0.5 | output: {'learning_rate': 2.7681535e-05, 'training_loss': 0.1921477}\n",
"I0110 08:47:50.492503 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1710.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1710.\n",
"I0110 08:47:50.493615 140678257067904 controller.py:277] eval | step: 1710 | running 45 steps of evaluation...\n",
" eval | step: 1710 | running 45 steps of evaluation...\n",
"I0110 08:48:22.023386 140678257067904 controller.py:290] eval | step: 1710 | eval time: 31.5 sec | output: \n",
" {'iou/0': 0.9291489,\n",
" 'iou/1': 0.81243616,\n",
" 'iou/10': 0.55403876,\n",
" 'iou/11': 0.54453915,\n",
" 'iou/12': 0.7551557,\n",
" 'iou/13': 0.5873874,\n",
" 'iou/14': 0.7324411,\n",
" 'iou/15': 0.806522,\n",
" 'iou/16': 0.47606975,\n",
" 'iou/17': 0.6820965,\n",
" 'iou/18': 0.41810358,\n",
" 'iou/19': 0.78020644,\n",
" 'iou/2': 0.47359917,\n",
" 'iou/20': 0.6455099,\n",
" 'iou/3': 0.85943294,\n",
" 'iou/4': 0.6102657,\n",
" 'iou/5': 0.66661865,\n",
" 'iou/6': 0.86242914,\n",
" 'iou/7': 0.80707633,\n",
" 'iou/8': 0.8289488,\n",
" 'iou/9': 0.26651832,\n",
" 'mean_iou': 0.67135924,\n",
" 'validation_loss': 0.35967985}\n",
" eval | step: 1710 | eval time: 31.5 sec | output: \n",
" {'iou/0': 0.9291489,\n",
" 'iou/1': 0.81243616,\n",
" 'iou/10': 0.55403876,\n",
" 'iou/11': 0.54453915,\n",
" 'iou/12': 0.7551557,\n",
" 'iou/13': 0.5873874,\n",
" 'iou/14': 0.7324411,\n",
" 'iou/15': 0.806522,\n",
" 'iou/16': 0.47606975,\n",
" 'iou/17': 0.6820965,\n",
" 'iou/18': 0.41810358,\n",
" 'iou/19': 0.78020644,\n",
" 'iou/2': 0.47359917,\n",
" 'iou/20': 0.6455099,\n",
" 'iou/3': 0.85943294,\n",
" 'iou/4': 0.6102657,\n",
" 'iou/5': 0.66661865,\n",
" 'iou/6': 0.86242914,\n",
" 'iou/7': 0.80707633,\n",
" 'iou/8': 0.8289488,\n",
" 'iou/9': 0.26651832,\n",
" 'mean_iou': 0.67135924,\n",
" 'validation_loss': 0.35967985}\n",
"I0110 08:48:22.055651 140678257067904 controller.py:236] train | step: 1710 | training until step 1755...\n",
"train | step: 1710 | training until step 1755...\n",
"I0110 08:49:11.937827 140678257067904 controller.py:457] train | step: 1755 | steps/sec: 0.5 | output: {'learning_rate': 2.559649e-05, 'training_loss': 0.19828546}\n",
"train | step: 1755 | steps/sec: 0.5 | output: {'learning_rate': 2.559649e-05, 'training_loss': 0.19828546}\n",
"I0110 08:49:12.402358 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1755.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1755.\n",
"I0110 08:49:12.403516 140678257067904 controller.py:277] eval | step: 1755 | running 45 steps of evaluation...\n",
" eval | step: 1755 | running 45 steps of evaluation...\n",
"I0110 08:49:43.299194 140678257067904 controller.py:290] eval | step: 1755 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.9299015,\n",
" 'iou/1': 0.8146485,\n",
" 'iou/10': 0.5295246,\n",
" 'iou/11': 0.5387655,\n",
" 'iou/12': 0.7504779,\n",
" 'iou/13': 0.5844143,\n",
" 'iou/14': 0.74691516,\n",
" 'iou/15': 0.8085688,\n",
" 'iou/16': 0.47193235,\n",
" 'iou/17': 0.68075883,\n",
" 'iou/18': 0.4358782,\n",
" 'iou/19': 0.77837676,\n",
" 'iou/2': 0.49099857,\n",
" 'iou/20': 0.6434493,\n",
" 'iou/3': 0.8694334,\n",
" 'iou/4': 0.6083377,\n",
" 'iou/5': 0.6727972,\n",
" 'iou/6': 0.86368245,\n",
" 'iou/7': 0.8048036,\n",
" 'iou/8': 0.8174637,\n",
" 'iou/9': 0.26629752,\n",
" 'mean_iou': 0.6717822,\n",
" 'validation_loss': 0.35955316}\n",
" eval | step: 1755 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.9299015,\n",
" 'iou/1': 0.8146485,\n",
" 'iou/10': 0.5295246,\n",
" 'iou/11': 0.5387655,\n",
" 'iou/12': 0.7504779,\n",
" 'iou/13': 0.5844143,\n",
" 'iou/14': 0.74691516,\n",
" 'iou/15': 0.8085688,\n",
" 'iou/16': 0.47193235,\n",
" 'iou/17': 0.68075883,\n",
" 'iou/18': 0.4358782,\n",
" 'iou/19': 0.77837676,\n",
" 'iou/2': 0.49099857,\n",
" 'iou/20': 0.6434493,\n",
" 'iou/3': 0.8694334,\n",
" 'iou/4': 0.6083377,\n",
" 'iou/5': 0.6727972,\n",
" 'iou/6': 0.86368245,\n",
" 'iou/7': 0.8048036,\n",
" 'iou/8': 0.8174637,\n",
" 'iou/9': 0.26629752,\n",
" 'mean_iou': 0.6717822,\n",
" 'validation_loss': 0.35955316}\n",
"I0110 08:49:43.331491 140678257067904 controller.py:236] train | step: 1755 | training until step 1800...\n",
"train | step: 1755 | training until step 1800...\n",
"I0110 08:50:33.675846 140678257067904 controller.py:457] train | step: 1800 | steps/sec: 0.6 | output: {'learning_rate': 2.3492375e-05, 'training_loss': 0.19684535}\n",
"train | step: 1800 | steps/sec: 0.6 | output: {'learning_rate': 2.3492375e-05, 'training_loss': 0.19684535}\n",
"I0110 08:50:34.143383 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1800.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1800.\n",
"I0110 08:50:34.144540 140678257067904 controller.py:277] eval | step: 1800 | running 45 steps of evaluation...\n",
" eval | step: 1800 | running 45 steps of evaluation...\n",
"I0110 08:51:05.429208 140678257067904 controller.py:290] eval | step: 1800 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.9300853,\n",
" 'iou/1': 0.8141351,\n",
" 'iou/10': 0.5621116,\n",
" 'iou/11': 0.5425815,\n",
" 'iou/12': 0.7440795,\n",
" 'iou/13': 0.5944028,\n",
" 'iou/14': 0.74655175,\n",
" 'iou/15': 0.8095806,\n",
" 'iou/16': 0.47106156,\n",
" 'iou/17': 0.6872694,\n",
" 'iou/18': 0.43090138,\n",
" 'iou/19': 0.7723348,\n",
" 'iou/2': 0.49002475,\n",
" 'iou/20': 0.6461099,\n",
" 'iou/3': 0.8698584,\n",
" 'iou/4': 0.60452485,\n",
" 'iou/5': 0.67549324,\n",
" 'iou/6': 0.85985726,\n",
" 'iou/7': 0.7986084,\n",
" 'iou/8': 0.8114938,\n",
" 'iou/9': 0.26613763,\n",
" 'mean_iou': 0.672724,\n",
" 'validation_loss': 0.36069658}\n",
" eval | step: 1800 | eval time: 31.3 sec | output: \n",
" {'iou/0': 0.9300853,\n",
" 'iou/1': 0.8141351,\n",
" 'iou/10': 0.5621116,\n",
" 'iou/11': 0.5425815,\n",
" 'iou/12': 0.7440795,\n",
" 'iou/13': 0.5944028,\n",
" 'iou/14': 0.74655175,\n",
" 'iou/15': 0.8095806,\n",
" 'iou/16': 0.47106156,\n",
" 'iou/17': 0.6872694,\n",
" 'iou/18': 0.43090138,\n",
" 'iou/19': 0.7723348,\n",
" 'iou/2': 0.49002475,\n",
" 'iou/20': 0.6461099,\n",
" 'iou/3': 0.8698584,\n",
" 'iou/4': 0.60452485,\n",
" 'iou/5': 0.67549324,\n",
" 'iou/6': 0.85985726,\n",
" 'iou/7': 0.7986084,\n",
" 'iou/8': 0.8114938,\n",
" 'iou/9': 0.26613763,\n",
" 'mean_iou': 0.672724,\n",
" 'validation_loss': 0.36069658}\n",
"I0110 08:51:05.461502 140678257067904 controller.py:236] train | step: 1800 | training until step 1845...\n",
"train | step: 1800 | training until step 1845...\n",
"I0110 08:51:55.960572 140678257067904 controller.py:457] train | step: 1845 | steps/sec: 0.5 | output: {'learning_rate': 2.1367086e-05, 'training_loss': 0.1889659}\n",
"train | step: 1845 | steps/sec: 0.5 | output: {'learning_rate': 2.1367086e-05, 'training_loss': 0.1889659}\n",
"I0110 08:51:56.442789 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1845.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1845.\n",
"I0110 08:51:56.444003 140678257067904 controller.py:277] eval | step: 1845 | running 45 steps of evaluation...\n",
" eval | step: 1845 | running 45 steps of evaluation...\n",
"I0110 08:52:27.356860 140678257067904 controller.py:290] eval | step: 1845 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.92974013,\n",
" 'iou/1': 0.81484,\n",
" 'iou/10': 0.52739406,\n",
" 'iou/11': 0.5339889,\n",
" 'iou/12': 0.7357759,\n",
" 'iou/13': 0.5852773,\n",
" 'iou/14': 0.7457619,\n",
" 'iou/15': 0.8067699,\n",
" 'iou/16': 0.46205533,\n",
" 'iou/17': 0.6943027,\n",
" 'iou/18': 0.42521495,\n",
" 'iou/19': 0.77101284,\n",
" 'iou/2': 0.49108705,\n",
" 'iou/20': 0.6485598,\n",
" 'iou/3': 0.8635628,\n",
" 'iou/4': 0.60370356,\n",
" 'iou/5': 0.67722774,\n",
" 'iou/6': 0.8567875,\n",
" 'iou/7': 0.80375004,\n",
" 'iou/8': 0.8031512,\n",
" 'iou/9': 0.26736853,\n",
" 'mean_iou': 0.6689206,\n",
" 'validation_loss': 0.36604035}\n",
" eval | step: 1845 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.92974013,\n",
" 'iou/1': 0.81484,\n",
" 'iou/10': 0.52739406,\n",
" 'iou/11': 0.5339889,\n",
" 'iou/12': 0.7357759,\n",
" 'iou/13': 0.5852773,\n",
" 'iou/14': 0.7457619,\n",
" 'iou/15': 0.8067699,\n",
" 'iou/16': 0.46205533,\n",
" 'iou/17': 0.6943027,\n",
" 'iou/18': 0.42521495,\n",
" 'iou/19': 0.77101284,\n",
" 'iou/2': 0.49108705,\n",
" 'iou/20': 0.6485598,\n",
" 'iou/3': 0.8635628,\n",
" 'iou/4': 0.60370356,\n",
" 'iou/5': 0.67722774,\n",
" 'iou/6': 0.8567875,\n",
" 'iou/7': 0.80375004,\n",
" 'iou/8': 0.8031512,\n",
" 'iou/9': 0.26736853,\n",
" 'mean_iou': 0.6689206,\n",
" 'validation_loss': 0.36604035}\n",
"I0110 08:52:27.390789 140678257067904 controller.py:236] train | step: 1845 | training until step 1890...\n",
"train | step: 1845 | training until step 1890...\n",
"I0110 08:53:17.246736 140678257067904 controller.py:457] train | step: 1890 | steps/sec: 0.6 | output: {'learning_rate': 1.9217994e-05, 'training_loss': 0.19459508}\n",
"train | step: 1890 | steps/sec: 0.6 | output: {'learning_rate': 1.9217994e-05, 'training_loss': 0.19459508}\n",
"I0110 08:53:17.737254 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1890.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1890.\n",
"I0110 08:53:17.738616 140678257067904 controller.py:277] eval | step: 1890 | running 45 steps of evaluation...\n",
" eval | step: 1890 | running 45 steps of evaluation...\n",
"I0110 08:53:48.234227 140678257067904 controller.py:290] eval | step: 1890 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.9296158,\n",
" 'iou/1': 0.81479704,\n",
" 'iou/10': 0.5792989,\n",
" 'iou/11': 0.53014594,\n",
" 'iou/12': 0.7382084,\n",
" 'iou/13': 0.59289306,\n",
" 'iou/14': 0.7469085,\n",
" 'iou/15': 0.8088193,\n",
" 'iou/16': 0.47364965,\n",
" 'iou/17': 0.69531846,\n",
" 'iou/18': 0.4305654,\n",
" 'iou/19': 0.781546,\n",
" 'iou/2': 0.495035,\n",
" 'iou/20': 0.6440982,\n",
" 'iou/3': 0.86137974,\n",
" 'iou/4': 0.6057702,\n",
" 'iou/5': 0.67344534,\n",
" 'iou/6': 0.863562,\n",
" 'iou/7': 0.80393463,\n",
" 'iou/8': 0.8071301,\n",
" 'iou/9': 0.2708486,\n",
" 'mean_iou': 0.6736653,\n",
" 'validation_loss': 0.36179495}\n",
" eval | step: 1890 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.9296158,\n",
" 'iou/1': 0.81479704,\n",
" 'iou/10': 0.5792989,\n",
" 'iou/11': 0.53014594,\n",
" 'iou/12': 0.7382084,\n",
" 'iou/13': 0.59289306,\n",
" 'iou/14': 0.7469085,\n",
" 'iou/15': 0.8088193,\n",
" 'iou/16': 0.47364965,\n",
" 'iou/17': 0.69531846,\n",
" 'iou/18': 0.4305654,\n",
" 'iou/19': 0.781546,\n",
" 'iou/2': 0.495035,\n",
" 'iou/20': 0.6440982,\n",
" 'iou/3': 0.86137974,\n",
" 'iou/4': 0.6057702,\n",
" 'iou/5': 0.67344534,\n",
" 'iou/6': 0.863562,\n",
" 'iou/7': 0.80393463,\n",
" 'iou/8': 0.8071301,\n",
" 'iou/9': 0.2708486,\n",
" 'mean_iou': 0.6736653,\n",
" 'validation_loss': 0.36179495}\n",
"I0110 08:53:48.266741 140678257067904 controller.py:236] train | step: 1890 | training until step 1935...\n",
"train | step: 1890 | training until step 1935...\n",
"I0110 08:54:38.457001 140678257067904 controller.py:457] train | step: 1935 | steps/sec: 0.6 | output: {'learning_rate': 1.704179e-05, 'training_loss': 0.18857741}\n",
"train | step: 1935 | steps/sec: 0.6 | output: {'learning_rate': 1.704179e-05, 'training_loss': 0.18857741}\n",
"I0110 08:54:38.911677 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1935.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1935.\n",
"I0110 08:54:38.912914 140678257067904 controller.py:277] eval | step: 1935 | running 45 steps of evaluation...\n",
" eval | step: 1935 | running 45 steps of evaluation...\n",
"I0110 08:55:10.017705 140678257067904 controller.py:290] eval | step: 1935 | eval time: 31.1 sec | output: \n",
" {'iou/0': 0.9301198,\n",
" 'iou/1': 0.8215841,\n",
" 'iou/10': 0.5653151,\n",
" 'iou/11': 0.54076564,\n",
" 'iou/12': 0.74394894,\n",
" 'iou/13': 0.596069,\n",
" 'iou/14': 0.7532291,\n",
" 'iou/15': 0.80873424,\n",
" 'iou/16': 0.47641864,\n",
" 'iou/17': 0.68989336,\n",
" 'iou/18': 0.42616186,\n",
" 'iou/19': 0.7799486,\n",
" 'iou/2': 0.49586073,\n",
" 'iou/20': 0.64660925,\n",
" 'iou/3': 0.8662006,\n",
" 'iou/4': 0.60480845,\n",
" 'iou/5': 0.6769079,\n",
" 'iou/6': 0.8597978,\n",
" 'iou/7': 0.79995024,\n",
" 'iou/8': 0.8103445,\n",
" 'iou/9': 0.27137983,\n",
" 'mean_iou': 0.6744784,\n",
" 'validation_loss': 0.36102894}\n",
" eval | step: 1935 | eval time: 31.1 sec | output: \n",
" {'iou/0': 0.9301198,\n",
" 'iou/1': 0.8215841,\n",
" 'iou/10': 0.5653151,\n",
" 'iou/11': 0.54076564,\n",
" 'iou/12': 0.74394894,\n",
" 'iou/13': 0.596069,\n",
" 'iou/14': 0.7532291,\n",
" 'iou/15': 0.80873424,\n",
" 'iou/16': 0.47641864,\n",
" 'iou/17': 0.68989336,\n",
" 'iou/18': 0.42616186,\n",
" 'iou/19': 0.7799486,\n",
" 'iou/2': 0.49586073,\n",
" 'iou/20': 0.64660925,\n",
" 'iou/3': 0.8662006,\n",
" 'iou/4': 0.60480845,\n",
" 'iou/5': 0.6769079,\n",
" 'iou/6': 0.8597978,\n",
" 'iou/7': 0.79995024,\n",
" 'iou/8': 0.8103445,\n",
" 'iou/9': 0.27137983,\n",
" 'mean_iou': 0.6744784,\n",
" 'validation_loss': 0.36102894}\n",
"I0110 08:55:10.050958 140678257067904 controller.py:236] train | step: 1935 | training until step 1980...\n",
"train | step: 1935 | training until step 1980...\n",
"I0110 08:56:00.349736 140678257067904 controller.py:457] train | step: 1980 | steps/sec: 0.5 | output: {'learning_rate': 1.4834168e-05, 'training_loss': 0.18639724}\n",
"train | step: 1980 | steps/sec: 0.5 | output: {'learning_rate': 1.4834168e-05, 'training_loss': 0.18639724}\n",
"I0110 08:56:00.808816 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1980.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-1980.\n",
"I0110 08:56:00.810017 140678257067904 controller.py:277] eval | step: 1980 | running 45 steps of evaluation...\n",
" eval | step: 1980 | running 45 steps of evaluation...\n",
"I0110 08:56:31.239988 140678257067904 controller.py:290] eval | step: 1980 | eval time: 30.4 sec | output: \n",
" {'iou/0': 0.9300513,\n",
" 'iou/1': 0.81914616,\n",
" 'iou/10': 0.56936044,\n",
" 'iou/11': 0.5412331,\n",
" 'iou/12': 0.75782025,\n",
" 'iou/13': 0.6021211,\n",
" 'iou/14': 0.75780004,\n",
" 'iou/15': 0.80753666,\n",
" 'iou/16': 0.47653982,\n",
" 'iou/17': 0.69312453,\n",
" 'iou/18': 0.42333207,\n",
" 'iou/19': 0.76841384,\n",
" 'iou/2': 0.49700966,\n",
" 'iou/20': 0.64990765,\n",
" 'iou/3': 0.8666844,\n",
" 'iou/4': 0.6042173,\n",
" 'iou/5': 0.6742604,\n",
" 'iou/6': 0.8570492,\n",
" 'iou/7': 0.8023468,\n",
" 'iou/8': 0.8231332,\n",
" 'iou/9': 0.26676667,\n",
" 'mean_iou': 0.67561215,\n",
" 'validation_loss': 0.36105695}\n",
" eval | step: 1980 | eval time: 30.4 sec | output: \n",
" {'iou/0': 0.9300513,\n",
" 'iou/1': 0.81914616,\n",
" 'iou/10': 0.56936044,\n",
" 'iou/11': 0.5412331,\n",
" 'iou/12': 0.75782025,\n",
" 'iou/13': 0.6021211,\n",
" 'iou/14': 0.75780004,\n",
" 'iou/15': 0.80753666,\n",
" 'iou/16': 0.47653982,\n",
" 'iou/17': 0.69312453,\n",
" 'iou/18': 0.42333207,\n",
" 'iou/19': 0.76841384,\n",
" 'iou/2': 0.49700966,\n",
" 'iou/20': 0.64990765,\n",
" 'iou/3': 0.8666844,\n",
" 'iou/4': 0.6042173,\n",
" 'iou/5': 0.6742604,\n",
" 'iou/6': 0.8570492,\n",
" 'iou/7': 0.8023468,\n",
" 'iou/8': 0.8231332,\n",
" 'iou/9': 0.26676667,\n",
" 'mean_iou': 0.67561215,\n",
" 'validation_loss': 0.36105695}\n",
"I0110 08:56:31.272420 140678257067904 controller.py:236] train | step: 1980 | training until step 2025...\n",
"train | step: 1980 | training until step 2025...\n",
"I0110 08:57:21.233271 140678257067904 controller.py:457] train | step: 2025 | steps/sec: 0.6 | output: {'learning_rate': 1.2589259e-05, 'training_loss': 0.18853217}\n",
"train | step: 2025 | steps/sec: 0.6 | output: {'learning_rate': 1.2589259e-05, 'training_loss': 0.18853217}\n",
"I0110 08:57:21.697147 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2025.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2025.\n",
"I0110 08:57:21.698346 140678257067904 controller.py:277] eval | step: 2025 | running 45 steps of evaluation...\n",
" eval | step: 2025 | running 45 steps of evaluation...\n",
"I0110 08:57:52.150892 140678257067904 controller.py:290] eval | step: 2025 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.93053114,\n",
" 'iou/1': 0.8210465,\n",
" 'iou/10': 0.57279086,\n",
" 'iou/11': 0.5362907,\n",
" 'iou/12': 0.7510732,\n",
" 'iou/13': 0.6048374,\n",
" 'iou/14': 0.75472265,\n",
" 'iou/15': 0.8080253,\n",
" 'iou/16': 0.47718355,\n",
" 'iou/17': 0.69036496,\n",
" 'iou/18': 0.4316649,\n",
" 'iou/19': 0.78003156,\n",
" 'iou/2': 0.4955439,\n",
" 'iou/20': 0.6506391,\n",
" 'iou/3': 0.8716078,\n",
" 'iou/4': 0.60787374,\n",
" 'iou/5': 0.6778033,\n",
" 'iou/6': 0.86607724,\n",
" 'iou/7': 0.8046598,\n",
" 'iou/8': 0.8157803,\n",
" 'iou/9': 0.26669395,\n",
" 'mean_iou': 0.6769163,\n",
" 'validation_loss': 0.35859698}\n",
" eval | step: 2025 | eval time: 30.5 sec | output: \n",
" {'iou/0': 0.93053114,\n",
" 'iou/1': 0.8210465,\n",
" 'iou/10': 0.57279086,\n",
" 'iou/11': 0.5362907,\n",
" 'iou/12': 0.7510732,\n",
" 'iou/13': 0.6048374,\n",
" 'iou/14': 0.75472265,\n",
" 'iou/15': 0.8080253,\n",
" 'iou/16': 0.47718355,\n",
" 'iou/17': 0.69036496,\n",
" 'iou/18': 0.4316649,\n",
" 'iou/19': 0.78003156,\n",
" 'iou/2': 0.4955439,\n",
" 'iou/20': 0.6506391,\n",
" 'iou/3': 0.8716078,\n",
" 'iou/4': 0.60787374,\n",
" 'iou/5': 0.6778033,\n",
" 'iou/6': 0.86607724,\n",
" 'iou/7': 0.8046598,\n",
" 'iou/8': 0.8157803,\n",
" 'iou/9': 0.26669395,\n",
" 'mean_iou': 0.6769163,\n",
" 'validation_loss': 0.35859698}\n",
"I0110 08:57:52.186310 140678257067904 controller.py:236] train | step: 2025 | training until step 2070...\n",
"train | step: 2025 | training until step 2070...\n",
"I0110 08:58:42.452509 140678257067904 controller.py:457] train | step: 2070 | steps/sec: 0.6 | output: {'learning_rate': 1.0298664e-05, 'training_loss': 0.18540694}\n",
"train | step: 2070 | steps/sec: 0.6 | output: {'learning_rate': 1.0298664e-05, 'training_loss': 0.18540694}\n",
"I0110 08:58:42.903616 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2070.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2070.\n",
"I0110 08:58:42.904722 140678257067904 controller.py:277] eval | step: 2070 | running 45 steps of evaluation...\n",
" eval | step: 2070 | running 45 steps of evaluation...\n",
"I0110 08:59:13.206641 140678257067904 controller.py:290] eval | step: 2070 | eval time: 30.3 sec | output: \n",
" {'iou/0': 0.9307624,\n",
" 'iou/1': 0.821278,\n",
" 'iou/10': 0.56706804,\n",
" 'iou/11': 0.5325528,\n",
" 'iou/12': 0.75325626,\n",
" 'iou/13': 0.6049621,\n",
" 'iou/14': 0.7567836,\n",
" 'iou/15': 0.8087872,\n",
" 'iou/16': 0.4752412,\n",
" 'iou/17': 0.6958772,\n",
" 'iou/18': 0.42757517,\n",
" 'iou/19': 0.78389984,\n",
" 'iou/2': 0.49981526,\n",
" 'iou/20': 0.6495531,\n",
" 'iou/3': 0.8691845,\n",
" 'iou/4': 0.60741085,\n",
" 'iou/5': 0.6777887,\n",
" 'iou/6': 0.8646602,\n",
" 'iou/7': 0.8063429,\n",
" 'iou/8': 0.8181624,\n",
" 'iou/9': 0.26739526,\n",
" 'mean_iou': 0.6770646,\n",
" 'validation_loss': 0.35809118}\n",
" eval | step: 2070 | eval time: 30.3 sec | output: \n",
" {'iou/0': 0.9307624,\n",
" 'iou/1': 0.821278,\n",
" 'iou/10': 0.56706804,\n",
" 'iou/11': 0.5325528,\n",
" 'iou/12': 0.75325626,\n",
" 'iou/13': 0.6049621,\n",
" 'iou/14': 0.7567836,\n",
" 'iou/15': 0.8087872,\n",
" 'iou/16': 0.4752412,\n",
" 'iou/17': 0.6958772,\n",
" 'iou/18': 0.42757517,\n",
" 'iou/19': 0.78389984,\n",
" 'iou/2': 0.49981526,\n",
" 'iou/20': 0.6495531,\n",
" 'iou/3': 0.8691845,\n",
" 'iou/4': 0.60741085,\n",
" 'iou/5': 0.6777887,\n",
" 'iou/6': 0.8646602,\n",
" 'iou/7': 0.8063429,\n",
" 'iou/8': 0.8181624,\n",
" 'iou/9': 0.26739526,\n",
" 'mean_iou': 0.6770646,\n",
" 'validation_loss': 0.35809118}\n",
"I0110 08:59:13.239900 140678257067904 controller.py:236] train | step: 2070 | training until step 2115...\n",
"train | step: 2070 | training until step 2115...\n",
"I0110 09:00:03.278507 140678257067904 controller.py:457] train | step: 2115 | steps/sec: 0.6 | output: {'learning_rate': 7.949434e-06, 'training_loss': 0.18795098}\n",
"train | step: 2115 | steps/sec: 0.6 | output: {'learning_rate': 7.949434e-06, 'training_loss': 0.18795098}\n",
"I0110 09:00:03.765398 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2115.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2115.\n",
"I0110 09:00:03.766532 140678257067904 controller.py:277] eval | step: 2115 | running 45 steps of evaluation...\n",
" eval | step: 2115 | running 45 steps of evaluation...\n",
"I0110 09:00:34.217006 140678257067904 controller.py:290] eval | step: 2115 | eval time: 30.4 sec | output: \n",
" {'iou/0': 0.9305273,\n",
" 'iou/1': 0.8217553,\n",
" 'iou/10': 0.580306,\n",
" 'iou/11': 0.537275,\n",
" 'iou/12': 0.7589284,\n",
" 'iou/13': 0.6048603,\n",
" 'iou/14': 0.75703293,\n",
" 'iou/15': 0.80866593,\n",
" 'iou/16': 0.48170036,\n",
" 'iou/17': 0.6959347,\n",
" 'iou/18': 0.4295522,\n",
" 'iou/19': 0.78313386,\n",
" 'iou/2': 0.49658677,\n",
" 'iou/20': 0.6475653,\n",
" 'iou/3': 0.8672409,\n",
" 'iou/4': 0.60875404,\n",
" 'iou/5': 0.6756781,\n",
" 'iou/6': 0.8646738,\n",
" 'iou/7': 0.8079146,\n",
" 'iou/8': 0.82179207,\n",
" 'iou/9': 0.27256468,\n",
" 'mean_iou': 0.67868775,\n",
" 'validation_loss': 0.35752094}\n",
" eval | step: 2115 | eval time: 30.4 sec | output: \n",
" {'iou/0': 0.9305273,\n",
" 'iou/1': 0.8217553,\n",
" 'iou/10': 0.580306,\n",
" 'iou/11': 0.537275,\n",
" 'iou/12': 0.7589284,\n",
" 'iou/13': 0.6048603,\n",
" 'iou/14': 0.75703293,\n",
" 'iou/15': 0.80866593,\n",
" 'iou/16': 0.48170036,\n",
" 'iou/17': 0.6959347,\n",
" 'iou/18': 0.4295522,\n",
" 'iou/19': 0.78313386,\n",
" 'iou/2': 0.49658677,\n",
" 'iou/20': 0.6475653,\n",
" 'iou/3': 0.8672409,\n",
" 'iou/4': 0.60875404,\n",
" 'iou/5': 0.6756781,\n",
" 'iou/6': 0.8646738,\n",
" 'iou/7': 0.8079146,\n",
" 'iou/8': 0.82179207,\n",
" 'iou/9': 0.27256468,\n",
" 'mean_iou': 0.67868775,\n",
" 'validation_loss': 0.35752094}\n",
"I0110 09:00:34.251919 140678257067904 controller.py:236] train | step: 2115 | training until step 2160...\n",
"train | step: 2115 | training until step 2160...\n",
"I0110 09:01:24.322998 140678257067904 controller.py:457] train | step: 2160 | steps/sec: 0.6 | output: {'learning_rate': 5.518921e-06, 'training_loss': 0.18346585}\n",
"train | step: 2160 | steps/sec: 0.6 | output: {'learning_rate': 5.518921e-06, 'training_loss': 0.18346585}\n",
"I0110 09:01:24.783570 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2160.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2160.\n",
"I0110 09:01:24.784728 140678257067904 controller.py:277] eval | step: 2160 | running 45 steps of evaluation...\n",
" eval | step: 2160 | running 45 steps of evaluation...\n",
"I0110 09:01:54.866816 140678257067904 controller.py:290] eval | step: 2160 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.9307569,\n",
" 'iou/1': 0.8206349,\n",
" 'iou/10': 0.57371616,\n",
" 'iou/11': 0.5338653,\n",
" 'iou/12': 0.7604656,\n",
" 'iou/13': 0.60473627,\n",
" 'iou/14': 0.75747436,\n",
" 'iou/15': 0.8086358,\n",
" 'iou/16': 0.47497746,\n",
" 'iou/17': 0.69614327,\n",
" 'iou/18': 0.42707142,\n",
" 'iou/19': 0.7825252,\n",
" 'iou/2': 0.5006538,\n",
" 'iou/20': 0.6503308,\n",
" 'iou/3': 0.86811906,\n",
" 'iou/4': 0.60676324,\n",
" 'iou/5': 0.6737245,\n",
" 'iou/6': 0.8640625,\n",
" 'iou/7': 0.8040477,\n",
" 'iou/8': 0.8219055,\n",
" 'iou/9': 0.27408093,\n",
" 'mean_iou': 0.67784244,\n",
" 'validation_loss': 0.35747287}\n",
" eval | step: 2160 | eval time: 30.1 sec | output: \n",
" {'iou/0': 0.9307569,\n",
" 'iou/1': 0.8206349,\n",
" 'iou/10': 0.57371616,\n",
" 'iou/11': 0.5338653,\n",
" 'iou/12': 0.7604656,\n",
" 'iou/13': 0.60473627,\n",
" 'iou/14': 0.75747436,\n",
" 'iou/15': 0.8086358,\n",
" 'iou/16': 0.47497746,\n",
" 'iou/17': 0.69614327,\n",
" 'iou/18': 0.42707142,\n",
" 'iou/19': 0.7825252,\n",
" 'iou/2': 0.5006538,\n",
" 'iou/20': 0.6503308,\n",
" 'iou/3': 0.86811906,\n",
" 'iou/4': 0.60676324,\n",
" 'iou/5': 0.6737245,\n",
" 'iou/6': 0.8640625,\n",
" 'iou/7': 0.8040477,\n",
" 'iou/8': 0.8219055,\n",
" 'iou/9': 0.27408093,\n",
" 'mean_iou': 0.67784244,\n",
" 'validation_loss': 0.35747287}\n",
"I0110 09:01:54.900092 140678257067904 controller.py:236] train | step: 2160 | training until step 2205...\n",
"train | step: 2160 | training until step 2205...\n",
"I0110 09:02:44.462013 140678257067904 controller.py:457] train | step: 2205 | steps/sec: 0.6 | output: {'learning_rate': 2.9575128e-06, 'training_loss': 0.18650979}\n",
"train | step: 2205 | steps/sec: 0.6 | output: {'learning_rate': 2.9575128e-06, 'training_loss': 0.18650979}\n",
"I0110 09:02:44.913124 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2205.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2205.\n",
"I0110 09:02:44.914244 140678257067904 controller.py:277] eval | step: 2205 | running 45 steps of evaluation...\n",
" eval | step: 2205 | running 45 steps of evaluation...\n",
"I0110 09:03:15.828936 140678257067904 controller.py:290] eval | step: 2205 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.93084997,\n",
" 'iou/1': 0.8211984,\n",
" 'iou/10': 0.57152873,\n",
" 'iou/11': 0.5351485,\n",
" 'iou/12': 0.76075,\n",
" 'iou/13': 0.605255,\n",
" 'iou/14': 0.7584754,\n",
" 'iou/15': 0.8088599,\n",
" 'iou/16': 0.4744896,\n",
" 'iou/17': 0.6972386,\n",
" 'iou/18': 0.42640233,\n",
" 'iou/19': 0.7809329,\n",
" 'iou/2': 0.50176287,\n",
" 'iou/20': 0.6509739,\n",
" 'iou/3': 0.86851597,\n",
" 'iou/4': 0.6078056,\n",
" 'iou/5': 0.67510146,\n",
" 'iou/6': 0.8634989,\n",
" 'iou/7': 0.8048458,\n",
" 'iou/8': 0.8242906,\n",
" 'iou/9': 0.27161765,\n",
" 'mean_iou': 0.6780734,\n",
" 'validation_loss': 0.35724026}\n",
" eval | step: 2205 | eval time: 30.9 sec | output: \n",
" {'iou/0': 0.93084997,\n",
" 'iou/1': 0.8211984,\n",
" 'iou/10': 0.57152873,\n",
" 'iou/11': 0.5351485,\n",
" 'iou/12': 0.76075,\n",
" 'iou/13': 0.605255,\n",
" 'iou/14': 0.7584754,\n",
" 'iou/15': 0.8088599,\n",
" 'iou/16': 0.4744896,\n",
" 'iou/17': 0.6972386,\n",
" 'iou/18': 0.42640233,\n",
" 'iou/19': 0.7809329,\n",
" 'iou/2': 0.50176287,\n",
" 'iou/20': 0.6509739,\n",
" 'iou/3': 0.86851597,\n",
" 'iou/4': 0.6078056,\n",
" 'iou/5': 0.67510146,\n",
" 'iou/6': 0.8634989,\n",
" 'iou/7': 0.8048458,\n",
" 'iou/8': 0.8242906,\n",
" 'iou/9': 0.27161765,\n",
" 'mean_iou': 0.6780734,\n",
" 'validation_loss': 0.35724026}\n",
"I0110 09:03:15.861507 140678257067904 controller.py:236] train | step: 2205 | training until step 2250...\n",
"train | step: 2205 | training until step 2250...\n",
"I0110 09:04:06.045882 140678257067904 controller.py:457] train | step: 2250 | steps/sec: 0.6 | output: {'learning_rate': 0.0, 'training_loss': 0.18220524}\n",
"train | step: 2250 | steps/sec: 0.6 | output: {'learning_rate': 0.0, 'training_loss': 0.18220524}\n",
"I0110 09:04:06.504820 140678257067904 controller.py:486] saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2250.\n",
"saved checkpoint to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/ckpt-2250.\n",
"I0110 09:04:06.505900 140678257067904 controller.py:277] eval | step: 2250 | running 45 steps of evaluation...\n",
" eval | step: 2250 | running 45 steps of evaluation...\n",
"I0110 09:04:37.679387 140678257067904 controller.py:290] eval | step: 2250 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.9308169,\n",
" 'iou/1': 0.81995404,\n",
" 'iou/10': 0.57013047,\n",
" 'iou/11': 0.5331078,\n",
" 'iou/12': 0.76125324,\n",
" 'iou/13': 0.6047389,\n",
" 'iou/14': 0.75819975,\n",
" 'iou/15': 0.8089952,\n",
" 'iou/16': 0.47662112,\n",
" 'iou/17': 0.69701266,\n",
" 'iou/18': 0.42446283,\n",
" 'iou/19': 0.77982134,\n",
" 'iou/2': 0.50232404,\n",
" 'iou/20': 0.6509815,\n",
" 'iou/3': 0.86739725,\n",
" 'iou/4': 0.60666263,\n",
" 'iou/5': 0.6750802,\n",
" 'iou/6': 0.8633021,\n",
" 'iou/7': 0.8046794,\n",
" 'iou/8': 0.8264023,\n",
" 'iou/9': 0.27187103,\n",
" 'mean_iou': 0.67780066,\n",
" 'validation_loss': 0.35719055}\n",
" eval | step: 2250 | eval time: 31.2 sec | output: \n",
" {'iou/0': 0.9308169,\n",
" 'iou/1': 0.81995404,\n",
" 'iou/10': 0.57013047,\n",
" 'iou/11': 0.5331078,\n",
" 'iou/12': 0.76125324,\n",
" 'iou/13': 0.6047389,\n",
" 'iou/14': 0.75819975,\n",
" 'iou/15': 0.8089952,\n",
" 'iou/16': 0.47662112,\n",
" 'iou/17': 0.69701266,\n",
" 'iou/18': 0.42446283,\n",
" 'iou/19': 0.77982134,\n",
" 'iou/2': 0.50232404,\n",
" 'iou/20': 0.6509815,\n",
" 'iou/3': 0.86739725,\n",
" 'iou/4': 0.60666263,\n",
" 'iou/5': 0.6750802,\n",
" 'iou/6': 0.8633021,\n",
" 'iou/7': 0.8046794,\n",
" 'iou/8': 0.8264023,\n",
" 'iou/9': 0.27187103,\n",
" 'mean_iou': 0.67780066,\n",
" 'validation_loss': 0.35719055}\n",
"I0110 09:04:37.721222 140678257067904 train_lib.py:138] Number of trainable params in model: 4.810213 Millions.\n",
"I0110 09:04:37.721560 140678257067904 train_utils.py:514] Failed to count model FLOPs with error get_concrete_function() argument after ** must be a mapping, not NoneType, because the build() methods in keras layers were not called. This is probably because the model was not feed any input, e.g., the max train step already reached before this run.\n",
"I0110 09:04:37.722177 140678257067904 train_utils.py:356] Saving gin configurations to /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012/operative_config.train_and_eval.gin\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).global_step\n",
"W0110 09:04:38.049778 140678257067904 util.py:182] Unresolved object in checkpoint: (root).global_step\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer\n",
"W0110 09:04:38.049996 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-101\n",
"W0110 09:04:38.050126 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-101\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-102\n",
"W0110 09:04:38.050201 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-102\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer-179\n",
"W0110 09:04:38.050267 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer-179\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer-180\n",
"W0110 09:04:38.050330 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer-180\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer-181\n",
"W0110 09:04:38.050392 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer-181\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-103\n",
"W0110 09:04:38.050459 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-103\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer-183\n",
"W0110 09:04:38.050547 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer-183\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer-184\n",
"W0110 09:04:38.050613 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer-184\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer-185\n",
"W0110 09:04:38.050679 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer-185\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer\n",
"W0110 09:04:38.050745 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-101.kernel\n",
"W0110 09:04:38.050852 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-101.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-102.axis\n",
"W0110 09:04:38.050927 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-102.axis\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-102.gamma\n",
"W0110 09:04:38.051004 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-102.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-102.beta\n",
"W0110 09:04:38.051074 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-102.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-102.moving_mean\n",
"W0110 09:04:38.051137 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-102.moving_mean\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-102.moving_variance\n",
"W0110 09:04:38.051199 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-102.moving_variance\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-103.kernel\n",
"W0110 09:04:38.051261 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-103.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).model.layer_with_weights-103.bias\n",
"W0110 09:04:38.051323 140678257067904 util.py:182] Unresolved object in checkpoint: (root).model.layer_with_weights-103.bias\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer.iter\n",
"W0110 09:04:38.051384 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer.iter\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer.decay\n",
"W0110 09:04:38.051445 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer.decay\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer.momentum\n",
"W0110 09:04:38.051506 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer.momentum\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer.rho\n",
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"W0110 09:04:38.259561 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer's state 'average' for (root).model.layer_with_weights-22.batch_norm_layer.3.gamma\n",
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"W0110 09:04:38.259643 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer's state 'average' for (root).model.layer_with_weights-22.batch_norm_layer.3.beta\n",
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"W0110 09:04:38.259725 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer's state 'average' for (root).model.layer_with_weights-22.batch_norm_layer.3.moving_mean\n",
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"W0110 09:04:38.259807 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer's state 'average' for (root).model.layer_with_weights-22.batch_norm_layer.3.moving_variance\n",
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"W0110 09:04:38.259887 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-0.kernel\n",
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"W0110 09:04:38.259969 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-1.gamma\n",
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"W0110 09:04:38.260050 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-1.beta\n",
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"W0110 09:04:38.260152 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-2.kernel\n",
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"W0110 09:04:38.260231 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-3.gamma\n",
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"W0110 09:04:38.260312 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-3.beta\n",
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"W0110 09:04:38.260401 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-4.kernel\n",
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"W0110 09:04:38.260486 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-5.gamma\n",
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"W0110 09:04:38.260647 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-6.kernel\n",
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"W0110 09:04:38.260887 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-8.kernel_0\n",
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"W0110 09:04:38.260968 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-8.kernel_1\n",
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"W0110 09:04:38.261047 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-8.kernel_2\n",
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"W0110 09:04:38.261132 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-9.kernel\n",
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"W0110 09:04:38.261691 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-15.kernel_0\n",
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"W0110 09:04:38.261753 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-15.kernel_1\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-15.kernel_3\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-16.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-17.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-18.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-20.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-21.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-21.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_0\n",
"W0110 09:04:38.262710 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_0\n",
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"W0110 09:04:38.262789 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_1\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_2\n",
"W0110 09:04:38.262868 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_2\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_3\n",
"W0110 09:04:38.262948 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.kernel_3\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-23.kernel\n",
"W0110 09:04:38.263028 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-23.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-24.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-24.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-25.kernel\n",
"W0110 09:04:38.263284 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-25.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-26.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-26.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-27.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-28.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-28.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-29.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-30.gamma\n",
"W0110 09:04:38.263854 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-30.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-30.beta\n",
"W0110 09:04:38.263935 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-30.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-31.depthwise_kernel\n",
"W0110 09:04:38.264016 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-31.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-32.gamma\n",
"W0110 09:04:38.264111 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-32.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-32.beta\n",
"W0110 09:04:38.264193 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-32.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-33.kernel\n",
"W0110 09:04:38.264272 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-33.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-34.gamma\n",
"W0110 09:04:38.264370 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-34.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-34.beta\n",
"W0110 09:04:38.264461 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-34.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-35.kernel\n",
"W0110 09:04:38.264543 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-35.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-36.gamma\n",
"W0110 09:04:38.264624 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-36.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-36.beta\n",
"W0110 09:04:38.264704 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-36.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-37.depthwise_kernel\n",
"W0110 09:04:38.264784 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-37.depthwise_kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-62.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-64.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-65.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-66.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-68.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-68.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-69.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-70.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-70.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-71.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-72.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-72.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-74.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-74.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-75.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-76.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-76.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-77.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-78.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-78.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-79.depthwise_kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-80.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-80.beta\n",
"W0110 09:04:38.270016 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-80.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-81.kernel\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-82.gamma\n",
"W0110 09:04:38.270185 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-82.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-82.beta\n",
"W0110 09:04:38.270262 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-82.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-83.kernel\n",
"W0110 09:04:38.270337 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-83.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-84.gamma\n",
"W0110 09:04:38.270427 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-84.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.0.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.0.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.1.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.1.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.2.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.2.beta\n",
"W0110 09:04:38.275745 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.2.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.3.gamma\n",
"W0110 09:04:38.275831 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.3.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.3.beta\n",
"W0110 09:04:38.275921 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'rms' for (root).model.layer_with_weights-22.batch_norm_layer.3.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-0.kernel\n",
"W0110 09:04:38.276187 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-0.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-1.gamma\n",
"W0110 09:04:38.276304 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-1.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-1.beta\n",
"W0110 09:04:38.276409 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-1.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-2.kernel\n",
"W0110 09:04:38.276500 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-2.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-3.gamma\n",
"W0110 09:04:38.276764 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-3.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-3.beta\n",
"W0110 09:04:38.276868 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-3.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-4.kernel\n",
"W0110 09:04:38.276961 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-4.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-5.gamma\n",
"W0110 09:04:38.277220 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-5.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-5.beta\n",
"W0110 09:04:38.277334 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-5.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-6.kernel\n",
"W0110 09:04:38.277440 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-6.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-7.gamma\n",
"W0110 09:04:38.277529 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-7.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-7.beta\n",
"W0110 09:04:38.277621 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-7.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.kernel_0\n",
"W0110 09:04:38.277705 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.kernel_0\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.kernel_1\n",
"W0110 09:04:38.277790 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.kernel_1\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.kernel_2\n",
"W0110 09:04:38.277879 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.kernel_2\n",
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"W0110 09:04:38.277967 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-9.kernel\n",
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"W0110 09:04:38.278066 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-10.gamma\n",
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"W0110 09:04:38.278161 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-10.beta\n",
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"W0110 09:04:38.278246 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-11.kernel\n",
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"W0110 09:04:38.278334 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-12.gamma\n",
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"W0110 09:04:38.278626 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-12.beta\n",
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"W0110 09:04:38.278730 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-13.kernel\n",
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"W0110 09:04:38.278997 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-14.gamma\n",
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"W0110 09:04:38.279343 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-14.beta\n",
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"W0110 09:04:38.279482 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.kernel_0\n",
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"W0110 09:04:38.279665 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.kernel_1\n",
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"W0110 09:04:38.279760 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.kernel_2\n",
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"W0110 09:04:38.279844 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.kernel_3\n",
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"W0110 09:04:38.279925 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-16.kernel\n",
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"W0110 09:04:38.280006 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-17.gamma\n",
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"W0110 09:04:38.280100 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-17.beta\n",
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"W0110 09:04:38.280182 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-18.kernel\n",
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"W0110 09:04:38.280258 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-19.gamma\n",
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"W0110 09:04:38.280336 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-19.beta\n",
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"W0110 09:04:38.280582 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-21.beta\n",
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"W0110 09:04:38.280659 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.kernel_0\n",
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"W0110 09:04:38.280736 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.kernel_1\n",
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"W0110 09:04:38.280812 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.kernel_2\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.kernel_3\n",
"W0110 09:04:38.280887 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.kernel_3\n",
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"W0110 09:04:38.280965 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-23.kernel\n",
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"W0110 09:04:38.281134 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-24.beta\n",
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"W0110 09:04:38.281210 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-25.kernel\n",
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"W0110 09:04:38.281287 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-26.gamma\n",
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"W0110 09:04:38.281364 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-26.beta\n",
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"W0110 09:04:38.281450 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-27.kernel\n",
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"W0110 09:04:38.281527 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-28.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-28.beta\n",
"W0110 09:04:38.281603 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-28.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-29.kernel\n",
"W0110 09:04:38.281679 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-29.kernel\n",
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"W0110 09:04:38.281755 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-30.gamma\n",
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"W0110 09:04:38.281832 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-30.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-31.depthwise_kernel\n",
"W0110 09:04:38.281909 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-31.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-32.gamma\n",
"W0110 09:04:38.281986 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-32.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-32.beta\n",
"W0110 09:04:38.282074 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-32.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-33.kernel\n",
"W0110 09:04:38.282154 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-33.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-34.gamma\n",
"W0110 09:04:38.282230 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-34.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-34.beta\n",
"W0110 09:04:38.282306 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-34.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-35.kernel\n",
"W0110 09:04:38.282381 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-35.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-36.gamma\n",
"W0110 09:04:38.282468 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-36.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-36.beta\n",
"W0110 09:04:38.282545 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-36.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-37.depthwise_kernel\n",
"W0110 09:04:38.282620 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-37.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-38.gamma\n",
"W0110 09:04:38.282696 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-38.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-38.beta\n",
"W0110 09:04:38.282771 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-38.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-39.kernel\n",
"W0110 09:04:38.282847 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-39.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-40.gamma\n",
"W0110 09:04:38.282929 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-40.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-40.beta\n",
"W0110 09:04:38.283006 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-40.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-41.kernel\n",
"W0110 09:04:38.283093 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-41.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-42.gamma\n",
"W0110 09:04:38.283172 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-42.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-42.beta\n",
"W0110 09:04:38.283248 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-42.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-43.depthwise_kernel\n",
"W0110 09:04:38.283323 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-43.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-44.gamma\n",
"W0110 09:04:38.283407 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-44.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-44.beta\n",
"W0110 09:04:38.283486 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-44.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-45.kernel\n",
"W0110 09:04:38.283562 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-45.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-46.gamma\n",
"W0110 09:04:38.283637 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-46.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-46.beta\n",
"W0110 09:04:38.283713 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-46.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-47.kernel\n",
"W0110 09:04:38.283790 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-47.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-48.gamma\n",
"W0110 09:04:38.283866 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-48.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-48.beta\n",
"W0110 09:04:38.283943 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-48.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-49.depthwise_kernel\n",
"W0110 09:04:38.381045 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-49.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-50.gamma\n",
"W0110 09:04:38.381178 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-50.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-50.beta\n",
"W0110 09:04:38.381247 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-50.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-51.kernel\n",
"W0110 09:04:38.381309 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-51.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-52.gamma\n",
"W0110 09:04:38.381364 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-52.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-52.beta\n",
"W0110 09:04:38.381439 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-52.beta\n",
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"W0110 09:04:38.389197 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-78.beta\n",
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"W0110 09:04:38.390079 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-80.beta\n",
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"W0110 09:04:38.391076 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-83.kernel\n",
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"W0110 09:04:38.391692 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-85.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-86.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-87.kernel\n",
"W0110 09:04:38.392225 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-87.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-88.gamma\n",
"W0110 09:04:38.392305 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-88.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-88.beta\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-89.kernel\n",
"W0110 09:04:38.392456 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-89.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-90.gamma\n",
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"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-90.beta\n",
"W0110 09:04:38.393133 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-90.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-91.depthwise_kernel\n",
"W0110 09:04:38.393219 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-91.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-92.gamma\n",
"W0110 09:04:38.393289 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-92.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-92.beta\n",
"W0110 09:04:38.393347 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-92.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-93.kernel\n",
"W0110 09:04:38.393412 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-93.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-94.gamma\n",
"W0110 09:04:38.393469 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-94.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-94.beta\n",
"W0110 09:04:38.393532 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-94.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-95.kernel\n",
"W0110 09:04:38.393596 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-95.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-96.gamma\n",
"W0110 09:04:38.393661 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-96.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-96.beta\n",
"W0110 09:04:38.393717 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-96.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-97.depthwise_kernel\n",
"W0110 09:04:38.393772 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-97.depthwise_kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-98.gamma\n",
"W0110 09:04:38.393831 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-98.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-98.beta\n",
"W0110 09:04:38.393916 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-98.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-99.kernel\n",
"W0110 09:04:38.393989 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-99.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-100.gamma\n",
"W0110 09:04:38.394079 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-100.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-100.beta\n",
"W0110 09:04:38.394151 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-100.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-101.kernel\n",
"W0110 09:04:38.394214 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-101.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-102.gamma\n",
"W0110 09:04:38.394275 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-102.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-102.beta\n",
"W0110 09:04:38.394336 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-102.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-103.kernel\n",
"W0110 09:04:38.394402 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-103.kernel\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-103.bias\n",
"W0110 09:04:38.394464 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-103.bias\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.0.gamma\n",
"W0110 09:04:38.394524 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.0.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.0.beta\n",
"W0110 09:04:38.394585 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.0.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.1.gamma\n",
"W0110 09:04:38.394645 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.1.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.1.beta\n",
"W0110 09:04:38.394706 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.1.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.2.gamma\n",
"W0110 09:04:38.394766 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.2.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.2.beta\n",
"W0110 09:04:38.394825 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-8.batch_norm_layer.2.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.0.gamma\n",
"W0110 09:04:38.394885 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.0.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.0.beta\n",
"W0110 09:04:38.394946 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.0.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.1.gamma\n",
"W0110 09:04:38.395006 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.1.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.1.beta\n",
"W0110 09:04:38.395075 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.1.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.2.gamma\n",
"W0110 09:04:38.395138 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.2.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.2.beta\n",
"W0110 09:04:38.395198 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.2.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.3.gamma\n",
"W0110 09:04:38.395258 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.3.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.3.beta\n",
"W0110 09:04:38.395318 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-15.batch_norm_layer.3.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.0.gamma\n",
"W0110 09:04:38.395378 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.0.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.0.beta\n",
"W0110 09:04:38.395447 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.0.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.1.gamma\n",
"W0110 09:04:38.395507 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.1.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.1.beta\n",
"W0110 09:04:38.395568 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.1.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.2.gamma\n",
"W0110 09:04:38.395627 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.2.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.2.beta\n",
"W0110 09:04:38.395687 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.2.beta\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.3.gamma\n",
"W0110 09:04:38.395747 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.3.gamma\n",
"WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.3.beta\n",
"W0110 09:04:38.492411 140678257067904 util.py:182] Unresolved object in checkpoint: (root).optimizer.base_optimizer's state 'momentum' for (root).model.layer_with_weights-22.batch_norm_layer.3.beta\n",
"WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
"W0110 09:04:38.492531 140678257067904 util.py:190] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!ls /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012"
],
"metadata": {
"id": "2UNjL80r-Ick",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b11565c6-a885-4ade-9560-842c67bda3c3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"checkpoint\t\t ckpt-2205.index\n",
"ckpt-2070.data-00000-of-00001 ckpt-2250.data-00000-of-00001\n",
"ckpt-2070.index\t\t ckpt-2250.index\n",
"ckpt-2115.data-00000-of-00001 operative_config.train_and_eval.gin\n",
"ckpt-2115.index\t\t params.yaml\n",
"ckpt-2160.data-00000-of-00001 train\n",
"ckpt-2160.index\t\t validation\n",
"ckpt-2205.data-00000-of-00001\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Display TensorBoard"
],
"metadata": {
"id": "sq5uuQRysBa3"
}
},
{
"cell_type": "code",
"source": [
"%load_ext tensorboard"
],
"metadata": {
"id": "_yY8w5sI3Mvs"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%tensorboard --logdir /content/train_deeplabv3plus_mobilenet_edgetpuv2_s_voc2012"
],
"metadata": {
"id": "8lBVp_r3RTI_",
"colab": {
"resources": {
"https://localhost:6006/?tensorboardColab=true": {
"data": 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