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Created October 31, 2022 09:34
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [],
"mount_file_id": "1H-f9kwlvWRLtZb_0eqoV596OzhDUkevb",
"authorship_tag": "ABX9TyNdu3myL+HkNhxR9b7ZNeKH",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"gpuClass": "standard",
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/IzumiSatoshi/5db9add58fd109b39592b7895a87d449/fspbt_org.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Few-Shot-Patch-Based-Training\n",
"Just running this repo : https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training \n",
"Advance reading is recommended.\n",
"And there is cool fork : https://github.com/nicolai256/Few-Shot-Patch-Based-Training\n",
"You can probably get good result than this notebook. \n",
"\n",
"## You need to prepare these 3 dir in project dir \n",
"```\n",
"projectName/\n",
" ┝ processName_gen\n",
" └ input_filtered\n",
" └ processName_train\n",
" ├ input_filtered\n",
" └ output\n",
"```\n",
"**processName_gen/input_filtered** : Put validation images. It is used to monitor the progress of learning. \n",
"**processName_train/input_filtered** : Put original keyframe images. \n",
"**processName_train/output** : Put generated keyframe images by stable diffusion's i2i or something. \n",
"\n",
"Note that _train/input_filtered image names correspond to _train/output image names. Also, all images should be the same size."
],
"metadata": {
"id": "MxNKC1Pll4uU"
}
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2En6jFxIhWOC",
"outputId": "35e767df-841c-49af-b95d-b0e26cf0296c"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yAhr8k7M4iOM",
"outputId": "eb1cc10d-6bdb-45bc-fced-e14a9e2564fe"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'Few-Shot-Patch-Based-Training'...\n",
"remote: Enumerating objects: 440, done.\u001b[K\n",
"remote: Counting objects: 100% (38/38), done.\u001b[K\n",
"remote: Compressing objects: 100% (20/20), done.\u001b[K\n",
"remote: Total 440 (delta 26), reused 18 (delta 18), pack-reused 402\u001b[K\n",
"Receiving objects: 100% (440/440), 11.63 MiB | 25.34 MiB/s, done.\n",
"Resolving deltas: 100% (104/104), done.\n"
]
}
],
"source": [
"!git clone https://github.com/OndrejTexler/Few-Shot-Patch-Based-Training.git"
]
},
{
"cell_type": "code",
"source": [
"# You may see some errors but somehow working in my case\n",
"!pip install numpy==1.19.1\n",
"!pip install opencv-python==4.4.0.40\n",
"!pip install Pillow==7.2.0\n",
"!pip install PyYAML==5.3.1\n",
"!pip install scikit-image==0.17.2\n",
"!pip install scipy==1.5.2\n",
"!pip install tensorflow==1.15.3\n",
"!pip install torch==1.6.0\n",
"!pip install torchvision==0.7.0"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "FyaKXx_4hdak",
"outputId": "8c09e540-2482-4278-c57f-6527b1e581a7"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting numpy==1.19.1\n",
" Downloading numpy-1.19.1-cp37-cp37m-manylinux2010_x86_64.whl (14.5 MB)\n",
"\u001b[K |████████████████████████████████| 14.5 MB 10.5 MB/s \n",
"\u001b[?25hInstalling collected packages: numpy\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.21.6\n",
" Uninstalling numpy-1.21.6:\n",
" Successfully uninstalled numpy-1.21.6\n",
"\u001b[31mERROR: 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",
"xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.19.1 which is incompatible.\n",
"tensorflow 2.9.2 requires numpy>=1.20, but you have numpy 1.19.1 which is incompatible.\n",
"jaxlib 0.3.22+cuda11.cudnn805 requires numpy>=1.20, but you have numpy 1.19.1 which is incompatible.\n",
"jax 0.3.23 requires numpy>=1.20, but you have numpy 1.19.1 which is incompatible.\n",
"cupy-cuda11x 11.0.0 requires numpy<1.26,>=1.20, but you have numpy 1.19.1 which is incompatible.\n",
"cmdstanpy 1.0.7 requires numpy>=1.21, but you have numpy 1.19.1 which is incompatible.\u001b[0m\n",
"Successfully installed numpy-1.19.1\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"numpy"
]
}
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting opencv-python==4.4.0.40\n",
" Downloading opencv_python-4.4.0.40-cp37-cp37m-manylinux2014_x86_64.whl (49.4 MB)\n",
"\u001b[K |████████████████████████████████| 49.4 MB 155 kB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python==4.4.0.40) (1.19.1)\n",
"Installing collected packages: opencv-python\n",
" Attempting uninstall: opencv-python\n",
" Found existing installation: opencv-python 4.6.0.66\n",
" Uninstalling opencv-python-4.6.0.66:\n",
" Successfully uninstalled opencv-python-4.6.0.66\n",
"Successfully installed opencv-python-4.4.0.40\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting Pillow==7.2.0\n",
" Downloading Pillow-7.2.0-cp37-cp37m-manylinux1_x86_64.whl (2.2 MB)\n",
"\u001b[K |████████████████████████████████| 2.2 MB 14.9 MB/s \n",
"\u001b[?25hInstalling collected packages: Pillow\n",
" Attempting uninstall: Pillow\n",
" Found existing installation: Pillow 7.1.2\n",
" Uninstalling Pillow-7.1.2:\n",
" Successfully uninstalled Pillow-7.1.2\n",
"Successfully installed Pillow-7.2.0\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"PIL"
]
}
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting PyYAML==5.3.1\n",
" Downloading PyYAML-5.3.1.tar.gz (269 kB)\n",
"\u001b[K |████████████████████████████████| 269 kB 14.9 MB/s \n",
"\u001b[?25hBuilding wheels for collected packages: PyYAML\n",
" Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for PyYAML: filename=PyYAML-5.3.1-cp37-cp37m-linux_x86_64.whl size=44636 sha256=d134dc0f5044db7c9f0147356b0cad01597b1a293c60b190596a127210550532\n",
" Stored in directory: /root/.cache/pip/wheels/5e/03/1e/e1e954795d6f35dfc7b637fe2277bff021303bd9570ecea653\n",
"Successfully built PyYAML\n",
"Installing collected packages: PyYAML\n",
" Attempting uninstall: PyYAML\n",
" Found existing installation: PyYAML 6.0\n",
" Uninstalling PyYAML-6.0:\n",
" Successfully uninstalled PyYAML-6.0\n",
"Successfully installed PyYAML-5.3.1\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting scikit-image==0.17.2\n",
" Downloading scikit_image-0.17.2-cp37-cp37m-manylinux1_x86_64.whl (12.5 MB)\n",
"\u001b[K |████████████████████████████████| 12.5 MB 16.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (1.3.0)\n",
"Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (2021.11.2)\n",
"Requirement already satisfied: scipy>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (1.7.3)\n",
"Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (2.9.0)\n",
"Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (7.2.0)\n",
"Requirement already satisfied: numpy>=1.15.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (1.19.1)\n",
"Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (3.2.2)\n",
"Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image==0.17.2) (2.6.3)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.17.2) (3.0.9)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.17.2) (1.4.4)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.17.2) (0.11.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image==0.17.2) (2.8.2)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image==0.17.2) (4.1.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image==0.17.2) (1.15.0)\n",
"Installing collected packages: scikit-image\n",
" Attempting uninstall: scikit-image\n",
" Found existing installation: scikit-image 0.18.3\n",
" Uninstalling scikit-image-0.18.3:\n",
" Successfully uninstalled scikit-image-0.18.3\n",
"Successfully installed scikit-image-0.17.2\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting scipy==1.5.2\n",
" Downloading scipy-1.5.2-cp37-cp37m-manylinux1_x86_64.whl (25.9 MB)\n",
"\u001b[K |████████████████████████████████| 25.9 MB 1.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from scipy==1.5.2) (1.19.1)\n",
"Installing collected packages: scipy\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.7.3\n",
" Uninstalling scipy-1.7.3:\n",
" Successfully uninstalled scipy-1.7.3\n",
"\u001b[31mERROR: 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",
"xarray-einstats 0.2.2 requires numpy>=1.21, but you have numpy 1.19.1 which is incompatible.\n",
"jaxlib 0.3.22+cuda11.cudnn805 requires numpy>=1.20, but you have numpy 1.19.1 which is incompatible.\n",
"jax 0.3.23 requires numpy>=1.20, but you have numpy 1.19.1 which is incompatible.\u001b[0m\n",
"Successfully installed scipy-1.5.2\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting tensorflow==1.15.3\n",
" Downloading tensorflow-1.15.3-cp37-cp37m-manylinux2010_x86_64.whl (110.5 MB)\n",
"\u001b[K |████████████████████████████████| 110.5 MB 26 kB/s \n",
"\u001b[?25hCollecting keras-applications>=1.0.8\n",
" Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
"\u001b[K |████████████████████████████████| 50 kB 8.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (3.17.3)\n",
"Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (1.14.1)\n",
"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (0.37.1)\n",
"Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (0.8.1)\n",
"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (1.1.2)\n",
"Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (1.50.0)\n",
"Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (1.19.1)\n",
"Collecting tensorboard<1.16.0,>=1.15.0\n",
" Downloading tensorboard-1.15.0-py3-none-any.whl (3.8 MB)\n",
"\u001b[K |████████████████████████████████| 3.8 MB 58.8 MB/s \n",
"\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (2.0.1)\n",
"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (1.3.0)\n",
"Collecting gast==0.2.2\n",
" Downloading gast-0.2.2.tar.gz (10 kB)\n",
"Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (3.3.0)\n",
"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (1.15.0)\n",
"Collecting tensorflow-estimator==1.15.1\n",
" Downloading tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503 kB)\n",
"\u001b[K |████████████████████████████████| 503 kB 60.0 MB/s \n",
"\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.3) (0.2.0)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15.3) (3.1.0)\n",
"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3) (57.4.0)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3) (1.0.1)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3) (3.4.1)\n",
"Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3) (4.13.0)\n",
"Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3) (4.1.1)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.3) (3.9.0)\n",
"Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.8->tensorflow==1.15.3) (1.5.2)\n",
"Building wheels for collected packages: gast\n",
" Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=20afcaabbeeceeac6cb30b706cb3aca20ec0dac39f299dd6fc3e7c4e72559bc3\n",
" Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\n",
"Successfully built gast\n",
"Installing collected packages: tensorflow-estimator, tensorboard, keras-applications, gast, tensorflow\n",
" Attempting uninstall: tensorflow-estimator\n",
" Found existing installation: tensorflow-estimator 2.9.0\n",
" Uninstalling tensorflow-estimator-2.9.0:\n",
" Successfully uninstalled tensorflow-estimator-2.9.0\n",
" Attempting uninstall: tensorboard\n",
" Found existing installation: tensorboard 2.9.1\n",
" Uninstalling tensorboard-2.9.1:\n",
" Successfully uninstalled tensorboard-2.9.1\n",
" Attempting uninstall: gast\n",
" Found existing installation: gast 0.4.0\n",
" Uninstalling gast-0.4.0:\n",
" Successfully uninstalled gast-0.4.0\n",
" Attempting uninstall: tensorflow\n",
" Found existing installation: tensorflow 2.9.2\n",
" Uninstalling tensorflow-2.9.2:\n",
" Successfully uninstalled tensorflow-2.9.2\n",
"\u001b[31mERROR: 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",
"tensorflow-probability 0.16.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\n",
"kapre 0.3.7 requires tensorflow>=2.0.0, but you have tensorflow 1.15.3 which is incompatible.\u001b[0m\n",
"Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-1.15.0 tensorflow-1.15.3 tensorflow-estimator-1.15.1\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting torch==1.6.0\n",
" Downloading torch-1.6.0-cp37-cp37m-manylinux1_x86_64.whl (748.8 MB)\n",
"\u001b[K |████████████████████████████████| 748.8 MB 16 kB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==1.6.0) (1.19.1)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from torch==1.6.0) (0.16.0)\n",
"Installing collected packages: torch\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 1.12.1+cu113\n",
" Uninstalling torch-1.12.1+cu113:\n",
" Successfully uninstalled torch-1.12.1+cu113\n",
"\u001b[31mERROR: 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",
"torchvision 0.13.1+cu113 requires torch==1.12.1, but you have torch 1.6.0 which is incompatible.\n",
"torchtext 0.13.1 requires torch==1.12.1, but you have torch 1.6.0 which is incompatible.\n",
"torchaudio 0.12.1+cu113 requires torch==1.12.1, but you have torch 1.6.0 which is incompatible.\n",
"fastai 2.7.9 requires torch<1.14,>=1.7, but you have torch 1.6.0 which is incompatible.\u001b[0m\n",
"Successfully installed torch-1.6.0\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting torchvision==0.7.0\n",
" Downloading torchvision-0.7.0-cp37-cp37m-manylinux1_x86_64.whl (5.9 MB)\n",
"\u001b[K |████████████████████████████████| 5.9 MB 14.9 MB/s \n",
"\u001b[?25hRequirement already satisfied: torch==1.6.0 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.7.0) (1.6.0)\n",
"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.7.0) (7.2.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==0.7.0) (1.19.1)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from torch==1.6.0->torchvision==0.7.0) (0.16.0)\n",
"Installing collected packages: torchvision\n",
" Attempting uninstall: torchvision\n",
" Found existing installation: torchvision 0.13.1+cu113\n",
" Uninstalling torchvision-0.13.1+cu113:\n",
" Successfully uninstalled torchvision-0.13.1+cu113\n",
"\u001b[31mERROR: 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",
"fastai 2.7.9 requires torch<1.14,>=1.7, but you have torch 1.6.0 which is incompatible.\n",
"fastai 2.7.9 requires torchvision>=0.8.2, but you have torchvision 0.7.0 which is incompatible.\u001b[0m\n",
"Successfully installed torchvision-0.7.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"%cd ./Few-Shot-Patch-Based-Training/"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IWTfM8_o67jC",
"outputId": "32554e05-7954-40e1-c253-db8f1d043b5b"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/Few-Shot-Patch-Based-Training\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@markdown images size\n",
"height = 512 #@param{type:\"number\"}\n",
"width = 512 #@param{type:\"number\"}\n",
"#@markdown project_dir path and processName\n",
"project_dir = \"/content/drive/MyDrive/tell_your_world/3\" # @param{type:\"string\"}\n",
"process_name = \"Miku\" # @param{type:\"string\"}"
],
"metadata": {
"id": "4kTRKVajnchD"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# create blank masks whitch is needed for training\n",
"import cv2\n",
"import numpy as np\n",
"import os\n",
"train_dir = f\"{project_dir}/{process_name}_train\"\n",
"train_input_filtered_dir = f\"{train_dir}/input_filtered\"\n",
"train_mask_dir = f\"{train_dir}/mask\"\n",
"train_output_dir = f\"{train_dir}/output\"\n",
"\n",
"gen_dir = f\"{project_dir}/{process_name}_gen\"\n",
"gen_input_filtered_dir = f\"{gen_dir}/input_filtered\"\n",
"gen_mask_dir = f\"{gen_dir}/mask\"\n",
"gen_output_dir = f\"{gen_dir}/output\"\n",
"\n",
"os.makedirs(train_mask_dir, exist_ok=True)\n",
"os.makedirs(gen_mask_dir, exist_ok=True)\n",
"\n",
"blank = np.zeros((height, width, 3))\n",
"blank += 255 #white\n",
"\n",
"train_input_filtered_list = os.listdir(train_input_filtered_dir)\n",
"for img in train_input_filtered_list:\n",
" mask_img_path = f\"{train_mask_dir}/{img}\"\n",
" print(mask_img_path)\n",
" cv2.imwrite(mask_img_path, blank)\n",
"\n",
"gen_input_filtered_list = os.listdir(gen_input_filtered_dir)\n",
"for img in gen_input_filtered_list:\n",
" mask_img_path = f\"{gen_mask_dir}/{img}\"\n",
" print(mask_img_path)\n",
" cv2.imwrite(mask_img_path, blank)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pDLdP3gOm_5m",
"outputId": "b744c94f-c1cb-458d-c414-ae304a1dfad7"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/006.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/001.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/051.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/026.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/021.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/056.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/046.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/011.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/076.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/091.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/061.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/066.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/106.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/071.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/081.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/086.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/111.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/101.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/161.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/121.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/166.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/116.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/146.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/156.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/151.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/181.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/176.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_train/mask/171.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/017.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/029.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/110.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/074.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/116.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/080.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/152.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/146.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/188.png\n",
"/content/drive/MyDrive/tell_your_world/3/Miku_gen/mask/182.png\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## training\n",
"It will take at least 30 min. \n",
"Check test result in project/processName_gen/res_p and stop if you satisfied with results. \n",
"Your checkpoints will be saved at project/processName_train/logs_reference_P."
],
"metadata": {
"id": "zuT_wRKk8WXh"
}
},
{
"cell_type": "code",
"source": [
"!python train.py --config \"_config/reference_P.yaml\" --data_root {train_dir} --log_interval 2000 --log_folder logs_reference_P"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y0QwRtHk8uyF",
"outputId": "0aa33bea-2ec0-43db-bfa1-4c391d9c1e85"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'type': 'DatasetPatches_M', 'dir_pre': '/content/drive/MyDrive/tell_your_world/3/Miku_train/input_filtered', 'dir_post': '/content/drive/MyDrive/tell_your_world/3/Miku_train/output', 'dir_mask': '/content/drive/MyDrive/tell_your_world/3/Miku_train/mask', 'patch_size': 32, 'device': 'cuda:0', 'dir_x1': None, 'dir_x2': None, 'dir_x3': None, 'dir_x4': None, 'dir_x5': None, 'dir_x6': None, 'dir_x7': None, 'dir_x8': None, 'dir_x9': None}\n",
"Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n",
"100% 548M/548M [00:06<00:00, 90.1MB/s]\n",
"### \n",
"[1] totalLossOnEbsynth: 0.0000\n",
"WARNING:tensorflow:From /content/Few-Shot-Patch-Based-Training/logger.py:13: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
"\n",
"[1] [discriminator_loss] 0.0005 [g_adv_loss] 0.0004 [g_image_loss] 0.0003 [g_perc_loss] 0.0006 [generator_loss] 0.0052. Took 6.517916202545166\n",
"Eval of batch: 1 took 6.306636095046997\n",
"Batch num: 100, totally elapsed 15.860011100769043\n",
"Batch num: 200, totally elapsed 25.415923595428467\n",
"Batch num: 300, totally elapsed 35.24431395530701\n",
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"Batch num: 600, totally elapsed 64.3191590309143\n",
"Batch num: 700, totally elapsed 74.02737069129944\n",
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"Batch num: 900, totally elapsed 93.73515367507935\n",
"Batch num: 1000, totally elapsed 103.71409559249878\n",
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"Batch num: 1900, totally elapsed 192.74144864082336\n",
"Batch num: 2000, totally elapsed 202.60708045959473\n",
"### \n",
"[2000] totalLossOnEbsynth: 0.0000\n",
"[2000] [discriminator_loss] 0.4189 [g_adv_loss] 0.3689 [g_image_loss] 0.1116 [g_perc_loss] 0.3657 [generator_loss] 2.8250. Took 204.80102443695068\n",
"Eval of batch: 2000 took 2.444024085998535\n",
"Batch num: 2100, totally elapsed 214.90102767944336\n",
"Batch num: 2200, totally elapsed 224.77874565124512\n",
"Batch num: 2300, totally elapsed 234.68202590942383\n",
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"Batch num: 2900, totally elapsed 295.36971521377563\n",
"Batch num: 3000, totally elapsed 305.25508069992065\n",
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"Batch num: 3200, totally elapsed 325.12700510025024\n",
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"Batch num: 3900, totally elapsed 394.69032049179077\n",
"Batch num: 4000, totally elapsed 404.5750813484192\n",
"### \n",
"[4000] totalLossOnEbsynth: 0.0000\n",
"[4000] [discriminator_loss] 0.3993 [g_adv_loss] 0.3947 [g_image_loss] 0.0993 [g_perc_loss] 0.3519 [generator_loss] 2.7060. Took 406.7096354961395\n",
"Eval of batch: 4000 took 2.384157657623291\n",
"Batch num: 4100, totally elapsed 416.8132312297821\n",
"Batch num: 4200, totally elapsed 426.7067093849182\n",
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"Batch num: 6000, totally elapsed 605.8036139011383\n",
"### \n",
"[6000] totalLossOnEbsynth: 0.0000\n",
"[6000] [discriminator_loss] 0.3975 [g_adv_loss] 0.3957 [g_image_loss] 0.0945 [g_perc_loss] 0.3434 [generator_loss] 2.6367. Took 607.8776659965515\n",
"Eval of batch: 6000 took 2.327298164367676\n",
"Batch num: 6100, totally elapsed 618.1476917266846\n",
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"Batch num: 7900, totally elapsed 798.3331758975983\n",
"Batch num: 8000, totally elapsed 808.2556490898132\n",
"### \n",
"[8000] totalLossOnEbsynth: 0.0000\n",
"[8000] [discriminator_loss] 0.3980 [g_adv_loss] 0.3949 [g_image_loss] 0.0917 [g_perc_loss] 0.3368 [generator_loss] 2.5852. Took 810.3424053192139\n",
"Eval of batch: 8000 took 2.337718963623047\n",
"Batch num: 8100, totally elapsed 820.4587299823761\n",
"Batch num: 8200, totally elapsed 830.3905606269836\n",
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"Batch num: 10000, totally elapsed 1010.1874301433563\n",
"### \n",
"[10000] totalLossOnEbsynth: 0.0000\n",
"[10000] [discriminator_loss] 0.3986 [g_adv_loss] 0.3942 [g_image_loss] 0.0886 [g_perc_loss] 0.3315 [generator_loss] 2.5405. Took 1012.3586032390594\n",
"Eval of batch: 10000 took 2.4206297397613525\n",
"Batch num: 10100, totally elapsed 1022.4911930561066\n",
"Batch num: 10200, totally elapsed 1032.3474595546722\n",
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"Batch num: 11900, totally elapsed 1200.9378623962402\n",
"Batch num: 12000, totally elapsed 1210.7754912376404\n",
"### \n",
"[12000] totalLossOnEbsynth: 0.0000\n",
"[12000] [discriminator_loss] 0.4009 [g_adv_loss] 0.3903 [g_image_loss] 0.0856 [g_perc_loss] 0.3190 [generator_loss] 2.4515. Took 1212.9549691677094\n",
"Eval of batch: 12000 took 2.4369399547576904\n",
"Batch num: 12100, totally elapsed 1223.0407269001007\n",
"Batch num: 12200, totally elapsed 1232.8654673099518\n",
"Batch num: 12300, totally elapsed 1242.7192676067352\n",
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"Batch num: 14000, totally elapsed 1411.8768668174744\n",
"### \n",
"[14000] totalLossOnEbsynth: 0.0000\n",
"[14000] [discriminator_loss] 0.4029 [g_adv_loss] 0.3879 [g_image_loss] 0.0833 [g_perc_loss] 0.3128 [generator_loss] 2.4042. Took 1414.172494649887\n",
"Eval of batch: 14000 took 2.5571141242980957\n",
"Batch num: 14100, totally elapsed 1424.285615682602\n",
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"Batch num: 15900, totally elapsed 1602.4525213241577\n",
"Batch num: 16000, totally elapsed 1612.4418885707855\n",
"### \n",
"[16000] totalLossOnEbsynth: 0.0000\n",
"[16000] [discriminator_loss] 0.4044 [g_adv_loss] 0.3862 [g_image_loss] 0.0817 [g_perc_loss] 0.3081 [generator_loss] 2.3684. Took 1614.73548412323\n",
"Eval of batch: 16000 took 2.6287429332733154\n",
"Batch num: 16100, totally elapsed 1624.911402463913\n",
"Batch num: 16200, totally elapsed 1634.8350205421448\n",
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"Batch num: 16700, totally elapsed 1685.0751554965973\n",
"Batch num: 16800, totally elapsed 1695.270174741745\n",
"Batch num: 16900, totally elapsed 1705.1027088165283\n",
"Batch num: 17000, totally elapsed 1714.9319789409637\n",
"Batch num: 17100, totally elapsed 1724.8163187503815\n",
"Batch num: 17200, totally elapsed 1734.8691844940186\n",
"Batch num: 17300, totally elapsed 1744.7078948020935\n",
"Batch num: 17400, totally elapsed 1754.5986695289612\n",
"Batch num: 17500, totally elapsed 1764.4753029346466\n",
"Batch num: 17600, totally elapsed 1774.3591256141663\n",
"Batch num: 17700, totally elapsed 1784.1956300735474\n",
"Batch num: 17800, totally elapsed 1794.0450944900513\n",
"Batch num: 17900, totally elapsed 1803.8995959758759\n",
"Batch num: 18000, totally elapsed 1813.8220481872559\n",
"### \n",
"[18000] totalLossOnEbsynth: 0.0000\n",
"[18000] [discriminator_loss] 0.4076 [g_adv_loss] 0.3822 [g_image_loss] 0.0805 [g_perc_loss] 0.3055 [generator_loss] 2.3460. Took 1815.9278662204742\n",
"Eval of batch: 18000 took 2.415041923522949\n",
"Batch num: 18100, totally elapsed 1826.0606172084808\n",
"Batch num: 18200, totally elapsed 1835.889500617981\n",
"Batch num: 18300, totally elapsed 1845.727575302124\n",
"Batch num: 18400, totally elapsed 1855.5795426368713\n",
"Batch num: 18500, totally elapsed 1865.4650371074677\n",
"Batch num: 18600, totally elapsed 1875.305186033249\n",
"Batch num: 18700, totally elapsed 1885.1453914642334\n",
"Batch num: 18800, totally elapsed 1895.4092977046967\n",
"Batch num: 18900, totally elapsed 1905.8725681304932\n",
"Batch num: 19000, totally elapsed 1915.7416152954102\n",
"Batch num: 19100, totally elapsed 1925.5754516124725\n",
"Batch num: 19200, totally elapsed 1935.451744556427\n",
"Batch num: 19300, totally elapsed 1945.2862794399261\n",
"Batch num: 19400, totally elapsed 1955.1260313987732\n",
"Batch num: 19500, totally elapsed 1964.9426386356354\n",
"Batch num: 19600, totally elapsed 1974.7383284568787\n",
"Batch num: 19700, totally elapsed 1984.6198115348816\n",
"Batch num: 19800, totally elapsed 1994.4627075195312\n",
"Batch num: 19900, totally elapsed 2004.2951233386993\n",
"Batch num: 20000, totally elapsed 2014.1198658943176\n",
"### \n",
"[20000] totalLossOnEbsynth: 0.0000\n",
"[20000] [discriminator_loss] 0.4101 [g_adv_loss] 0.3788 [g_image_loss] 0.0776 [g_perc_loss] 0.2924 [generator_loss] 2.2540. Took 2016.2736291885376\n",
"Eval of batch: 20000 took 2.465571641921997\n",
"Batch num: 20100, totally elapsed 2026.4733574390411\n",
"Batch num: 20200, totally elapsed 2036.3227479457855\n",
"Batch num: 20300, totally elapsed 2046.15096449852\n",
"Batch num: 20400, totally elapsed 2055.98819231987\n",
"Batch num: 20500, totally elapsed 2065.8143479824066\n",
"Batch num: 20600, totally elapsed 2075.669774532318\n",
"Batch num: 20700, totally elapsed 2085.490693092346\n",
"Batch num: 20800, totally elapsed 2095.3053357601166\n",
"Batch num: 20900, totally elapsed 2105.182357311249\n",
"Batch num: 21000, totally elapsed 2115.0188932418823\n",
"Batch num: 21100, totally elapsed 2124.8529822826385\n",
"Batch num: 21200, totally elapsed 2134.6751339435577\n",
"Batch num: 21300, totally elapsed 2144.5245978832245\n",
"Batch num: 21400, totally elapsed 2154.3462398052216\n",
"Batch num: 21500, totally elapsed 2164.1661772727966\n",
"Batch num: 21600, totally elapsed 2174.159272670746\n",
"Batch num: 21700, totally elapsed 2184.790803194046\n",
"Batch num: 21800, totally elapsed 2194.820755004883\n",
"Batch num: 21900, totally elapsed 2204.7346017360687\n",
"Batch num: 22000, totally elapsed 2214.69060754776\n",
"### \n",
"[22000] totalLossOnEbsynth: 0.0000\n",
"[22000] [discriminator_loss] 0.4140 [g_adv_loss] 0.3738 [g_image_loss] 0.0768 [g_perc_loss] 0.2878 [generator_loss] 2.2206. Took 2216.8121695518494\n",
"Eval of batch: 22000 took 2.440885305404663\n",
"Batch num: 22100, totally elapsed 2226.9951112270355\n",
"Batch num: 22200, totally elapsed 2237.0215888023376\n",
"Batch num: 22300, totally elapsed 2246.911426305771\n",
"Batch num: 22400, totally elapsed 2256.7907660007477\n",
"Batch num: 22500, totally elapsed 2266.639649629593\n",
"Batch num: 22600, totally elapsed 2276.4820017814636\n",
"Batch num: 22700, totally elapsed 2286.3750851154327\n",
"Batch num: 22800, totally elapsed 2296.313318490982\n",
"Batch num: 22900, totally elapsed 2306.262621164322\n",
"Batch num: 23000, totally elapsed 2316.1989979743958\n",
"Batch num: 23100, totally elapsed 2326.0633590221405\n",
"Batch num: 23200, totally elapsed 2335.8907215595245\n",
"Batch num: 23300, totally elapsed 2345.762372493744\n",
"Batch num: 23400, totally elapsed 2355.630115509033\n",
"Batch num: 23500, totally elapsed 2365.459573030472\n",
"Batch num: 23600, totally elapsed 2375.2823729515076\n",
"Batch num: 23700, totally elapsed 2385.146441936493\n",
"Batch num: 23800, totally elapsed 2395.083131790161\n",
"Batch num: 23900, totally elapsed 2405.051654100418\n",
"Batch num: 24000, totally elapsed 2414.863485097885\n",
"### \n",
"[24000] totalLossOnEbsynth: 0.0000\n",
"[24000] [discriminator_loss] 0.4132 [g_adv_loss] 0.3747 [g_image_loss] 0.0759 [g_perc_loss] 0.2859 [generator_loss] 2.2059. Took 2416.960685491562\n",
"Eval of batch: 24000 took 2.4463067054748535\n",
"Batch num: 24100, totally elapsed 2427.189384698868\n",
"Batch num: 24200, totally elapsed 2437.786125421524\n",
"Batch num: 24300, totally elapsed 2447.9786880016327\n",
"Batch num: 24400, totally elapsed 2457.910779953003\n",
"Batch num: 24500, totally elapsed 2467.780484199524\n",
"Batch num: 24600, totally elapsed 2477.6281328201294\n",
"Batch num: 24700, totally elapsed 2487.456510066986\n",
"Batch num: 24800, totally elapsed 2497.4143850803375\n",
"Batch num: 24900, totally elapsed 2507.2383103370667\n",
"Batch num: 25000, totally elapsed 2517.058742761612\n",
"Batch num: 25100, totally elapsed 2526.9340040683746\n",
"Batch num: 25200, totally elapsed 2536.781378030777\n",
"Batch num: 25300, totally elapsed 2546.6628165245056\n",
"Batch num: 25400, totally elapsed 2556.6736912727356\n",
"Batch num: 25500, totally elapsed 2566.610030889511\n",
"Batch num: 25600, totally elapsed 2576.540682077408\n",
"Batch num: 25700, totally elapsed 2586.4676587581635\n",
"Batch num: 25800, totally elapsed 2596.3853685855865\n",
"Batch num: 25900, totally elapsed 2606.22158408165\n",
"Batch num: 26000, totally elapsed 2616.0358572006226\n",
"### \n",
"[26000] totalLossOnEbsynth: 0.0000\n",
"[26000] [discriminator_loss] 0.4166 [g_adv_loss] 0.3711 [g_image_loss] 0.0746 [g_perc_loss] 0.2798 [generator_loss] 2.1625. Took 2618.1567273139954\n",
"Eval of batch: 26000 took 2.432030439376831\n",
"Batch num: 26100, totally elapsed 2628.2713718414307\n",
"Batch num: 26200, totally elapsed 2638.1011798381805\n",
"Batch num: 26300, totally elapsed 2647.972184896469\n",
"Batch num: 26400, totally elapsed 2657.786290168762\n",
"Batch num: 26500, totally elapsed 2667.6148800849915\n",
"Batch num: 26600, totally elapsed 2677.429120540619\n",
"Batch num: 26700, totally elapsed 2687.2551460266113\n",
"Batch num: 26800, totally elapsed 2697.1178874969482\n",
"Batch num: 26900, totally elapsed 2707.0702397823334\n",
"Batch num: 27000, totally elapsed 2717.2852160930634\n",
"Batch num: 27100, totally elapsed 2727.9247138500214\n",
"Batch num: 27200, totally elapsed 2737.7310016155243\n",
"Batch num: 27300, totally elapsed 2747.5682649612427\n",
"Batch num: 27400, totally elapsed 2757.463299512863\n",
"Batch num: 27500, totally elapsed 2767.279320001602\n",
"Batch num: 27600, totally elapsed 2777.107898712158\n",
"Batch num: 27700, totally elapsed 2786.9094462394714\n",
"Batch num: 27800, totally elapsed 2796.718763113022\n",
"Batch num: 27900, totally elapsed 2806.517930984497\n",
"Batch num: 28000, totally elapsed 2816.325943470001\n",
"### \n",
"[28000] totalLossOnEbsynth: 0.0000\n",
"[28000] [discriminator_loss] 0.4171 [g_adv_loss] 0.3696 [g_image_loss] 0.0737 [g_perc_loss] 0.2759 [generator_loss] 2.1350. Took 2818.444890975952\n",
"Eval of batch: 28000 took 2.464775800704956\n",
"Batch num: 28100, totally elapsed 2828.67080283165\n",
"Batch num: 28200, totally elapsed 2838.5441341400146\n",
"Batch num: 28300, totally elapsed 2848.428414583206\n",
"Batch num: 28400, totally elapsed 2858.2524564266205\n",
"Batch num: 28500, totally elapsed 2868.216289281845\n",
"Batch num: 28600, totally elapsed 2878.1362493038177\n",
"Batch num: 28700, totally elapsed 2888.0047554969788\n",
"Batch num: 28800, totally elapsed 2897.8693902492523\n",
"Batch num: 28900, totally elapsed 2907.711842775345\n",
"Batch num: 29000, totally elapsed 2917.6174206733704\n",
"Batch num: 29100, totally elapsed 2927.4499044418335\n",
"Batch num: 29200, totally elapsed 2937.302142381668\n",
"Batch num: 29300, totally elapsed 2947.142507314682\n",
"Batch num: 29400, totally elapsed 2957.0256695747375\n",
"Batch num: 29500, totally elapsed 2966.8725781440735\n",
"Batch num: 29600, totally elapsed 2977.2867958545685\n",
"Batch num: 29700, totally elapsed 2987.7168641090393\n",
"Batch num: 29800, totally elapsed 2997.6852054595947\n",
"Batch num: 29900, totally elapsed 3007.6141834259033\n",
"Batch num: 30000, totally elapsed 3017.4141244888306\n",
"### \n",
"[30000] totalLossOnEbsynth: 0.0000\n",
"[30000] [discriminator_loss] 0.4201 [g_adv_loss] 0.3657 [g_image_loss] 0.0726 [g_perc_loss] 0.2709 [generator_loss] 2.0988. Took 3019.5217232704163\n",
"Eval of batch: 30000 took 2.467773914337158\n",
"Batch num: 30100, totally elapsed 3029.700844526291\n",
"Batch num: 30200, totally elapsed 3039.5345511436462\n",
"Batch num: 30300, totally elapsed 3049.3410062789917\n",
"Batch num: 30400, totally elapsed 3059.1745870113373\n",
"Batch num: 30500, totally elapsed 3069.0002641677856\n",
"Batch num: 30600, totally elapsed 3078.8901562690735\n",
"Batch num: 30700, totally elapsed 3088.7011563777924\n",
"Batch num: 30800, totally elapsed 3098.500760793686\n",
"Batch num: 30900, totally elapsed 3108.3199286460876\n",
"Batch num: 31000, totally elapsed 3118.136905670166\n",
"Batch num: 31100, totally elapsed 3128.1083800792694\n",
"Batch num: 31200, totally elapsed 3138.042204618454\n",
"Batch num: 31300, totally elapsed 3147.947022676468\n",
"Batch num: 31400, totally elapsed 3157.7924621105194\n",
"Batch num: 31500, totally elapsed 3167.6020629405975\n",
"Batch num: 31600, totally elapsed 3177.4605717658997\n",
"Batch num: 31700, totally elapsed 3187.32142329216\n",
"Batch num: 31800, totally elapsed 3197.158322572708\n",
"Batch num: 31900, totally elapsed 3207.0248312950134\n",
"Batch num: 32000, totally elapsed 3216.811379671097\n",
"### \n",
"[32000] totalLossOnEbsynth: 0.0000\n",
"[32000] [discriminator_loss] 0.4213 [g_adv_loss] 0.3628 [g_image_loss] 0.0712 [g_perc_loss] 0.2626 [generator_loss] 2.0415. Took 3218.9128210544586\n",
"Eval of batch: 32000 took 2.430328845977783\n",
"Batch num: 32100, totally elapsed 3229.06268286705\n",
"Batch num: 32200, totally elapsed 3238.901010274887\n",
"Batch num: 32300, totally elapsed 3248.7915325164795\n",
"Batch num: 32400, totally elapsed 3259.695948123932\n",
"Batch num: 32500, totally elapsed 3270.8409399986267\n",
"Batch num: 32600, totally elapsed 3280.7442648410797\n",
"Batch num: 32700, totally elapsed 3290.680929660797\n",
"Batch num: 32800, totally elapsed 3300.50869345665\n",
"Batch num: 32900, totally elapsed 3310.3566110134125\n",
"Batch num: 33000, totally elapsed 3320.2852103710175\n",
"Batch num: 33100, totally elapsed 3330.0922005176544\n",
"Batch num: 33200, totally elapsed 3339.9093816280365\n",
"Batch num: 33300, totally elapsed 3349.720226049423\n",
"Batch num: 33400, totally elapsed 3359.6744117736816\n",
"Batch num: 33500, totally elapsed 3369.5060925483704\n",
"Batch num: 33600, totally elapsed 3379.327118396759\n",
"Batch num: 33700, totally elapsed 3389.1581287384033\n",
"Batch num: 33800, totally elapsed 3398.9809126853943\n",
"Batch num: 33900, totally elapsed 3408.8073773384094\n",
"Batch num: 34000, totally elapsed 3418.616870880127\n",
"### \n",
"[34000] totalLossOnEbsynth: 0.0000\n",
"[34000] [discriminator_loss] 0.4247 [g_adv_loss] 0.3594 [g_image_loss] 0.0703 [g_perc_loss] 0.2596 [generator_loss] 2.0184. Took 3420.779714822769\n",
"Eval of batch: 34000 took 2.4704737663269043\n",
"Batch num: 34100, totally elapsed 3430.8969106674194\n",
"Batch num: 34200, totally elapsed 3440.7303309440613\n",
"Batch num: 34300, totally elapsed 3450.5877227783203\n",
"Batch num: 34400, totally elapsed 3460.4190695285797\n",
"Batch num: 34500, totally elapsed 3470.289863348007\n",
"Batch num: 34600, totally elapsed 3480.119461774826\n",
"Batch num: 34700, totally elapsed 3489.9415929317474\n",
"Batch num: 34800, totally elapsed 3499.769547224045\n",
"Batch num: 34900, totally elapsed 3509.600593805313\n",
"Batch num: 35000, totally elapsed 3519.41167140007\n",
"Batch num: 35100, totally elapsed 3529.2001333236694\n",
"Batch num: 35200, totally elapsed 3539.1811892986298\n",
"Batch num: 35300, totally elapsed 3549.681322336197\n",
"Batch num: 35400, totally elapsed 3559.5787768363953\n",
"Batch num: 35500, totally elapsed 3569.402172803879\n",
"Batch num: 35600, totally elapsed 3579.2441267967224\n",
"Batch num: 35700, totally elapsed 3589.0557658672333\n",
"Batch num: 35800, totally elapsed 3598.8896572589874\n",
"Batch num: 35900, totally elapsed 3608.7371575832367\n",
"Batch num: 36000, totally elapsed 3618.56027674675\n",
"### \n",
"[36000] totalLossOnEbsynth: 0.0000\n",
"[36000] [discriminator_loss] 0.4245 [g_adv_loss] 0.3590 [g_image_loss] 0.0698 [g_perc_loss] 0.2567 [generator_loss] 1.9987. Took 3620.7050063610077\n",
"Eval of batch: 36000 took 2.4607670307159424\n",
"Batch num: 36100, totally elapsed 3630.8298687934875\n",
"Batch num: 36200, totally elapsed 3640.618828058243\n",
"Batch num: 36300, totally elapsed 3650.4267189502716\n",
"Batch num: 36400, totally elapsed 3660.2499816417694\n",
"Batch num: 36500, totally elapsed 3670.0509078502655\n",
"Batch num: 36600, totally elapsed 3679.8645539283752\n",
"Batch num: 36700, totally elapsed 3689.7148463726044\n",
"Batch num: 36800, totally elapsed 3699.4968745708466\n",
"Batch num: 36900, totally elapsed 3709.351906776428\n",
"Batch num: 37000, totally elapsed 3719.1665008068085\n",
"Batch num: 37100, totally elapsed 3728.9733839035034\n",
"Batch num: 37200, totally elapsed 3738.821915626526\n",
"Batch num: 37300, totally elapsed 3748.638895750046\n",
"Batch num: 37400, totally elapsed 3758.453070640564\n",
"Batch num: 37500, totally elapsed 3768.254495859146\n",
"Batch num: 37600, totally elapsed 3778.0645909309387\n",
"Batch num: 37700, totally elapsed 3788.402983903885\n",
"Batch num: 37800, totally elapsed 3798.7252333164215\n",
"Batch num: 37900, totally elapsed 3808.5075962543488\n",
"Batch num: 38000, totally elapsed 3818.3293027877808\n",
"### \n",
"[38000] totalLossOnEbsynth: 0.0000\n",
"[38000] [discriminator_loss] 0.4274 [g_adv_loss] 0.3551 [g_image_loss] 0.0689 [g_perc_loss] 0.2519 [generator_loss] 1.9647. Took 3820.480068206787\n",
"Eval of batch: 38000 took 2.469454288482666\n",
"Batch num: 38100, totally elapsed 3830.6005351543427\n",
"Batch num: 38200, totally elapsed 3840.4242448806763\n",
"Batch num: 38300, totally elapsed 3850.194483757019\n",
"Batch num: 38400, totally elapsed 3860.0069403648376\n",
"Batch num: 38500, totally elapsed 3869.8431057929993\n",
"Batch num: 38600, totally elapsed 3879.6448583602905\n",
"Batch num: 38700, totally elapsed 3889.48127412796\n",
"Batch num: 38800, totally elapsed 3899.283410549164\n",
"Batch num: 38900, totally elapsed 3909.102781534195\n",
"Batch num: 39000, totally elapsed 3918.928179502487\n",
"Batch num: 39100, totally elapsed 3928.766419649124\n",
"Batch num: 39200, totally elapsed 3938.5863847732544\n",
"Batch num: 39300, totally elapsed 3948.4038598537445\n",
"Batch num: 39400, totally elapsed 3958.180634021759\n",
"Batch num: 39500, totally elapsed 3968.001063108444\n",
"Batch num: 39600, totally elapsed 3977.823803424835\n",
"Batch num: 39700, totally elapsed 3987.6461560726166\n",
"Batch num: 39800, totally elapsed 3997.6371762752533\n",
"Batch num: 39900, totally elapsed 4007.4416213035583\n",
"Batch num: 40000, totally elapsed 4017.2413024902344\n",
"### \n",
"[40000] totalLossOnEbsynth: 0.0000\n",
"[40000] [discriminator_loss] 0.4289 [g_adv_loss] 0.3536 [g_image_loss] 0.0685 [g_perc_loss] 0.2476 [generator_loss] 1.9365. Took 4019.393046617508\n",
"Eval of batch: 40000 took 2.472273826599121\n",
"Batch num: 40100, totally elapsed 4029.492274045944\n",
"Batch num: 40200, totally elapsed 4039.389631986618\n",
"Batch num: 40300, totally elapsed 4049.1907913684845\n",
"Batch num: 40400, totally elapsed 4059.002611875534\n",
"Batch num: 40500, totally elapsed 4069.365531682968\n",
"Batch num: 40600, totally elapsed 4079.6723585128784\n",
"Batch num: 40700, totally elapsed 4089.462467432022\n",
"Batch num: 40800, totally elapsed 4099.231997728348\n",
"Batch num: 40900, totally elapsed 4109.027760505676\n",
"Batch num: 41000, totally elapsed 4118.829762220383\n",
"Batch num: 41100, totally elapsed 4128.62596988678\n",
"Batch num: 41200, totally elapsed 4138.459014892578\n",
"Batch num: 41300, totally elapsed 4148.2960596084595\n",
"Batch num: 41400, totally elapsed 4158.120906591415\n",
"Batch num: 41500, totally elapsed 4167.91814994812\n",
"Batch num: 41600, totally elapsed 4177.741580963135\n",
"Batch num: 41700, totally elapsed 4187.549211502075\n",
"Batch num: 41800, totally elapsed 4197.366897583008\n",
"Batch num: 41900, totally elapsed 4207.212929725647\n",
"Batch num: 42000, totally elapsed 4217.0721645355225\n",
"### \n",
"[42000] totalLossOnEbsynth: 0.0000\n",
"[42000] [discriminator_loss] 0.4305 [g_adv_loss] 0.3505 [g_image_loss] 0.0672 [g_perc_loss] 0.2418 [generator_loss] 1.8948. Took 4219.2734117507935\n",
"Eval of batch: 42000 took 2.5107412338256836\n",
"Batch num: 42100, totally elapsed 4229.385268449783\n",
"Batch num: 42200, totally elapsed 4239.188582420349\n",
"Batch num: 42300, totally elapsed 4248.971903562546\n",
"Batch num: 42400, totally elapsed 4258.966473579407\n",
"Batch num: 42500, totally elapsed 4268.812895774841\n",
"Batch num: 42600, totally elapsed 4278.6303191185\n",
"Batch num: 42700, totally elapsed 4288.433993339539\n",
"Batch num: 42800, totally elapsed 4298.255055189133\n",
"Batch num: 42900, totally elapsed 4308.064113140106\n",
"Batch num: 43000, totally elapsed 4317.897703886032\n",
"Batch num: 43100, totally elapsed 4327.682731628418\n",
"Batch num: 43200, totally elapsed 4337.542146682739\n",
"Batch num: 43300, totally elapsed 4347.856190919876\n",
"Batch num: 43400, totally elapsed 4358.208554267883\n",
"Batch num: 43500, totally elapsed 4368.015465974808\n",
"Batch num: 43600, totally elapsed 4377.814529657364\n",
"Batch num: 43700, totally elapsed 4387.61024928093\n",
"Batch num: 43800, totally elapsed 4397.40927362442\n",
"Batch num: 43900, totally elapsed 4407.27272772789\n",
"Batch num: 44000, totally elapsed 4417.1361401081085\n",
"### \n",
"[44000] totalLossOnEbsynth: 0.0000\n",
"[44000] [discriminator_loss] 0.4315 [g_adv_loss] 0.3492 [g_image_loss] 0.0665 [g_perc_loss] 0.2395 [generator_loss] 1.8780. Took 4419.24751830101\n",
"Eval of batch: 44000 took 2.422485113143921\n",
"Batch num: 44100, totally elapsed 4429.357949733734\n",
"Batch num: 44200, totally elapsed 4439.213006258011\n",
"Batch num: 44300, totally elapsed 4449.001255750656\n",
"Batch num: 44400, totally elapsed 4458.795275211334\n",
"Batch num: 44500, totally elapsed 4468.625674009323\n",
"Batch num: 44600, totally elapsed 4478.449679136276\n",
"Batch num: 44700, totally elapsed 4488.2308230400085\n",
"Batch num: 44800, totally elapsed 4498.033332824707\n",
"Batch num: 44900, totally elapsed 4507.831226110458\n",
"Batch num: 45000, totally elapsed 4517.656393766403\n",
"Batch num: 45100, totally elapsed 4527.464559793472\n",
"Batch num: 45200, totally elapsed 4537.246941566467\n",
"Batch num: 45300, totally elapsed 4547.042198896408\n",
"Batch num: 45400, totally elapsed 4556.841461420059\n",
"Batch num: 45500, totally elapsed 4566.65459895134\n",
"Batch num: 45600, totally elapsed 4576.781780719757\n",
"Batch num: 45700, totally elapsed 4586.929583311081\n",
"Batch num: 45800, totally elapsed 4597.045116901398\n",
"Batch num: 45900, totally elapsed 4606.826860904694\n",
"Batch num: 46000, totally elapsed 4616.596259832382\n",
"### \n",
"[46000] totalLossOnEbsynth: 0.0000\n",
"[46000] [discriminator_loss] 0.4328 [g_adv_loss] 0.3472 [g_image_loss] 0.0658 [g_perc_loss] 0.2353 [generator_loss] 1.8488. Took 4618.7498524188995\n",
"Eval of batch: 46000 took 2.46342134475708\n",
"Batch num: 46100, totally elapsed 4628.831183433533\n",
"Batch num: 46200, totally elapsed 4638.656513214111\n",
"Batch num: 46300, totally elapsed 4648.437903404236\n",
"Batch num: 46400, totally elapsed 4658.236577033997\n",
"Batch num: 46500, totally elapsed 4668.025000333786\n",
"Batch num: 46600, totally elapsed 4677.831622362137\n",
"Batch num: 46700, totally elapsed 4687.641821861267\n",
"Batch num: 46800, totally elapsed 4697.430096626282\n",
"Batch num: 46900, totally elapsed 4707.27951169014\n",
"Batch num: 47000, totally elapsed 4717.110889911652\n",
"Batch num: 47100, totally elapsed 4726.900093793869\n",
"Batch num: 47200, totally elapsed 4736.703029632568\n",
"Batch num: 47300, totally elapsed 4746.499087095261\n",
"Batch num: 47400, totally elapsed 4756.320437431335\n",
"Batch num: 47500, totally elapsed 4766.13729763031\n",
"Batch num: 47600, totally elapsed 4775.978013277054\n",
"Batch num: 47700, totally elapsed 4785.839224815369\n",
"Batch num: 47800, totally elapsed 4795.630359649658\n",
"Batch num: 47900, totally elapsed 4805.471980333328\n",
"Batch num: 48000, totally elapsed 4815.724821567535\n",
"### \n",
"[48000] totalLossOnEbsynth: 0.0000\n",
"[48000] [discriminator_loss] 0.4351 [g_adv_loss] 0.3448 [g_image_loss] 0.0652 [g_perc_loss] 0.2323 [generator_loss] 1.8268. Took 4818.194292545319\n",
"Eval of batch: 48000 took 2.9324591159820557\n",
"Batch num: 48100, totally elapsed 4828.837398052216\n",
"Batch num: 48200, totally elapsed 4838.661880493164\n",
"Batch num: 48300, totally elapsed 4848.454022884369\n",
"Batch num: 48400, totally elapsed 4858.255030870438\n",
"Batch num: 48500, totally elapsed 4868.041792631149\n",
"Batch num: 48600, totally elapsed 4877.8382132053375\n",
"Batch num: 48700, totally elapsed 4887.622608423233\n",
"Batch num: 48800, totally elapsed 4897.410399436951\n",
"Batch num: 48900, totally elapsed 4907.19319319725\n",
"Batch num: 49000, totally elapsed 4916.983993053436\n",
"Batch num: 49100, totally elapsed 4926.752141237259\n",
"Batch num: 49200, totally elapsed 4936.516060113907\n",
"Batch num: 49300, totally elapsed 4946.301300048828\n",
"Batch num: 49400, totally elapsed 4956.1200432777405\n",
"Batch num: 49500, totally elapsed 4965.930772781372\n",
"Batch num: 49600, totally elapsed 4975.742431402206\n",
"Batch num: 49700, totally elapsed 4985.59596323967\n",
"Batch num: 49800, totally elapsed 4995.4077570438385\n",
"Batch num: 49900, totally elapsed 5005.248277425766\n",
"Batch num: 50000, totally elapsed 5015.036473274231\n",
"### \n",
"[50000] totalLossOnEbsynth: 0.0000\n",
"[50000] [discriminator_loss] 0.4359 [g_adv_loss] 0.3430 [g_image_loss] 0.0647 [g_perc_loss] 0.2282 [generator_loss] 1.7997. Took 5017.162667989731\n",
"Eval of batch: 50000 took 2.432823657989502\n",
"Batch num: 50100, totally elapsed 5027.2627811431885\n",
"Batch num: 50200, totally elapsed 5037.074534654617\n",
"Batch num: 50300, totally elapsed 5047.377979755402\n",
"Batch num: 50400, totally elapsed 5057.468791723251\n",
"Batch num: 50500, totally elapsed 5067.473701477051\n",
"Batch num: 50600, totally elapsed 5077.26628780365\n",
"Batch num: 50700, totally elapsed 5087.057331323624\n",
"Batch num: 50800, totally elapsed 5096.878610134125\n",
"Batch num: 50900, totally elapsed 5106.702671289444\n",
"Batch num: 51000, totally elapsed 5116.504338979721\n",
"Batch num: 51100, totally elapsed 5126.326645851135\n",
"Batch num: 51200, totally elapsed 5136.1396741867065\n",
"Batch num: 51300, totally elapsed 5145.932847499847\n",
"Batch num: 51400, totally elapsed 5155.766111373901\n",
"Batch num: 51500, totally elapsed 5165.538451433182\n",
"Batch num: 51600, totally elapsed 5175.358617782593\n",
"Batch num: 51700, totally elapsed 5185.134854316711\n",
"Batch num: 51800, totally elapsed 5194.9520611763\n",
"Batch num: 51900, totally elapsed 5204.739928007126\n",
"Batch num: 52000, totally elapsed 5214.518585205078\n",
"### \n",
"[52000] totalLossOnEbsynth: 0.0000\n",
"[52000] [discriminator_loss] 0.4373 [g_adv_loss] 0.3407 [g_image_loss] 0.0639 [g_perc_loss] 0.2241 [generator_loss] 1.7709. Took 5216.726027727127\n",
"Eval of batch: 52000 took 2.531583547592163\n",
"Batch num: 52100, totally elapsed 5226.827446222305\n",
"Batch num: 52200, totally elapsed 5236.629896402359\n",
"Batch num: 52300, totally elapsed 5246.467666149139\n",
"Batch num: 52400, totally elapsed 5256.306070804596\n",
"Batch num: 52500, totally elapsed 5266.185570240021\n",
"Batch num: 52600, totally elapsed 5276.36740398407\n",
"Batch num: 52700, totally elapsed 5286.440764665604\n",
"Batch num: 52800, totally elapsed 5296.6612668037415\n",
"Batch num: 52900, totally elapsed 5306.485636949539\n",
"Batch num: 53000, totally elapsed 5316.41002702713\n",
"Batch num: 53100, totally elapsed 5326.219408035278\n",
"Batch num: 53200, totally elapsed 5336.005883216858\n",
"Batch num: 53300, totally elapsed 5345.818922996521\n",
"Batch num: 53400, totally elapsed 5355.622505664825\n",
"Batch num: 53500, totally elapsed 5365.419422388077\n",
"Batch num: 53600, totally elapsed 5375.195680856705\n",
"Batch num: 53700, totally elapsed 5384.992548942566\n",
"Batch num: 53800, totally elapsed 5394.825209856033\n",
"Batch num: 53900, totally elapsed 5404.669491529465\n",
"Batch num: 54000, totally elapsed 5414.493144273758\n",
"### \n",
"[54000] totalLossOnEbsynth: 0.0000\n",
"[54000] [discriminator_loss] 0.4389 [g_adv_loss] 0.3395 [g_image_loss] 0.0634 [g_perc_loss] 0.2227 [generator_loss] 1.7594. Took 5416.666664838791\n",
"Eval of batch: 54000 took 2.4802377223968506\n",
"Batch num: 54100, totally elapsed 5426.7839012146\n",
"Batch num: 54200, totally elapsed 5436.567806005478\n",
"Batch num: 54300, totally elapsed 5446.347341060638\n",
"Batch num: 54400, totally elapsed 5456.151207447052\n",
"Batch num: 54500, totally elapsed 5465.940123081207\n",
"Batch num: 54600, totally elapsed 5475.764328241348\n",
"Batch num: 54700, totally elapsed 5485.569463729858\n",
"Batch num: 54800, totally elapsed 5495.3989198207855\n",
"Batch num: 54900, totally elapsed 5505.401293039322\n",
"Batch num: 55000, totally elapsed 5515.432999134064\n",
"Batch num: 55100, totally elapsed 5525.716208457947\n",
"Batch num: 55200, totally elapsed 5535.545946598053\n",
"Batch num: 55300, totally elapsed 5545.3303236961365\n",
"Batch num: 55400, totally elapsed 5555.16086435318\n",
"Batch num: 55500, totally elapsed 5564.952041864395\n",
"Batch num: 55600, totally elapsed 5574.724107027054\n",
"Batch num: 55700, totally elapsed 5584.507682800293\n",
"Batch num: 55800, totally elapsed 5594.35661315918\n",
"Batch num: 55900, totally elapsed 5604.135808944702\n",
"Batch num: 56000, totally elapsed 5613.965427875519\n",
"### \n",
"[56000] totalLossOnEbsynth: 0.0000\n",
"[56000] [discriminator_loss] 0.4394 [g_adv_loss] 0.3381 [g_image_loss] 0.0628 [g_perc_loss] 0.2192 [generator_loss] 1.7355. Took 5616.14327454567\n",
"Eval of batch: 56000 took 2.4870827198028564\n",
"Batch num: 56100, totally elapsed 5626.247679233551\n",
"Batch num: 56200, totally elapsed 5636.045228242874\n",
"Batch num: 56300, totally elapsed 5645.86577129364\n",
"Batch num: 56400, totally elapsed 5655.652329683304\n",
"Batch num: 56500, totally elapsed 5665.482451200485\n",
"Batch num: 56600, totally elapsed 5675.33153629303\n",
"Batch num: 56700, totally elapsed 5685.151159286499\n",
"Batch num: 56800, totally elapsed 5694.9468631744385\n",
"Batch num: 56900, totally elapsed 5704.743568658829\n",
"Batch num: 57000, totally elapsed 5714.542425870895\n",
"Batch num: 57100, totally elapsed 5724.311002969742\n",
"Batch num: 57200, totally elapsed 5734.463696241379\n",
"Batch num: 57300, totally elapsed 5744.458547115326\n",
"Batch num: 57400, totally elapsed 5754.730397224426\n",
"Batch num: 57500, totally elapsed 5764.519234657288\n",
"Batch num: 57600, totally elapsed 5774.290914773941\n",
"Batch num: 57700, totally elapsed 5784.046639442444\n",
"Batch num: 57800, totally elapsed 5793.88191819191\n",
"Batch num: 57900, totally elapsed 5803.68504858017\n",
"Batch num: 58000, totally elapsed 5813.503917694092\n",
"### \n",
"[58000] totalLossOnEbsynth: 0.0000\n",
"[58000] [discriminator_loss] 0.4392 [g_adv_loss] 0.3383 [g_image_loss] 0.0625 [g_perc_loss] 0.2167 [generator_loss] 1.7195. Took 5815.65963435173\n",
"Eval of batch: 58000 took 2.4651851654052734\n",
"Batch num: 58100, totally elapsed 5825.789473056793\n",
"Batch num: 58200, totally elapsed 5835.6254370212555\n",
"Batch num: 58300, totally elapsed 5845.4090321063995\n",
"Batch num: 58400, totally elapsed 5855.225843667984\n",
"Batch num: 58500, totally elapsed 5865.026282787323\n",
"Batch num: 58600, totally elapsed 5874.842795848846\n",
"Batch num: 58700, totally elapsed 5884.625504493713\n",
"Batch num: 58800, totally elapsed 5894.4271647930145\n",
"Batch num: 58900, totally elapsed 5904.219175815582\n",
"Batch num: 59000, totally elapsed 5914.095318555832\n",
"Batch num: 59100, totally elapsed 5923.880935192108\n",
"Batch num: 59200, totally elapsed 5933.703191518784\n",
"Batch num: 59300, totally elapsed 5943.492196083069\n",
"Batch num: 59400, totally elapsed 5953.30605840683\n",
"Batch num: 59500, totally elapsed 5963.560983419418\n",
"Batch num: 59600, totally elapsed 5973.356400728226\n",
"Batch num: 59700, totally elapsed 5983.556867599487\n",
"Batch num: 59800, totally elapsed 5993.468925237656\n",
"Batch num: 59900, totally elapsed 6003.250297546387\n",
"Batch num: 60000, totally elapsed 6013.078956127167\n",
"### \n",
"[60000] totalLossOnEbsynth: 0.0000\n",
"[60000] [discriminator_loss] 0.4417 [g_adv_loss] 0.3358 [g_image_loss] 0.0617 [g_perc_loss] 0.2101 [generator_loss] 1.6753. Took 6015.2917737960815\n",
"Eval of batch: 60000 took 2.5288846492767334\n",
"Batch num: 60100, totally elapsed 6025.409059762955\n",
"Batch num: 60200, totally elapsed 6035.208334445953\n",
"Batch num: 60300, totally elapsed 6045.025723934174\n",
"Batch num: 60400, totally elapsed 6054.819159030914\n",
"Batch num: 60500, totally elapsed 6064.625987291336\n",
"Batch num: 60600, totally elapsed 6074.432827949524\n",
"Batch num: 60700, totally elapsed 6084.207659959793\n",
"Batch num: 60800, totally elapsed 6093.996474027634\n",
"Batch num: 60900, totally elapsed 6103.812500476837\n",
"Batch num: 61000, totally elapsed 6113.6129586696625\n",
"Batch num: 61100, totally elapsed 6123.398867368698\n",
"Batch num: 61200, totally elapsed 6133.178556680679\n",
"Batch num: 61300, totally elapsed 6142.971213817596\n",
"Batch num: 61400, totally elapsed 6152.7666015625\n",
"Batch num: 61500, totally elapsed 6162.538907527924\n",
"Batch num: 61600, totally elapsed 6172.324579000473\n",
"Batch num: 61700, totally elapsed 6182.103179216385\n",
"Batch num: 61800, totally elapsed 6192.377503871918\n",
"Batch num: 61900, totally elapsed 6202.180931091309\n",
"Batch num: 62000, totally elapsed 6212.204025268555\n",
"### \n",
"[62000] totalLossOnEbsynth: 0.0000\n",
"[62000] [discriminator_loss] 0.4430 [g_adv_loss] 0.3339 [g_image_loss] 0.0615 [g_perc_loss] 0.2095 [generator_loss] 1.6702. Took 6214.808812379837\n",
"Eval of batch: 62000 took 3.0891449451446533\n",
"Batch num: 62100, totally elapsed 6225.078835010529\n",
"Batch num: 62200, totally elapsed 6234.90473484993\n",
"Batch num: 62300, totally elapsed 6244.673888683319\n",
"Batch num: 62400, totally elapsed 6254.469566822052\n",
"Batch num: 62500, totally elapsed 6264.287671327591\n",
"Batch num: 62600, totally elapsed 6274.092273712158\n",
"Batch num: 62700, totally elapsed 6283.863711357117\n",
"Batch num: 62800, totally elapsed 6293.634742975235\n",
"Batch num: 62900, totally elapsed 6303.483061313629\n",
"Batch num: 63000, totally elapsed 6313.285356521606\n",
"Batch num: 63100, totally elapsed 6323.030967473984\n",
"Batch num: 63200, totally elapsed 6332.787911891937\n",
"Batch num: 63300, totally elapsed 6342.623788356781\n",
"Batch num: 63400, totally elapsed 6352.4075791835785\n",
"Batch num: 63500, totally elapsed 6362.243637561798\n",
"Batch num: 63600, totally elapsed 6372.041348457336\n",
"Batch num: 63700, totally elapsed 6381.823472499847\n",
"Batch num: 63800, totally elapsed 6391.601076364517\n",
"Batch num: 63900, totally elapsed 6401.403341054916\n",
"Batch num: 64000, totally elapsed 6411.198344707489\n",
"### \n",
"[64000] totalLossOnEbsynth: 0.0000\n",
"[64000] [discriminator_loss] 0.4454 [g_adv_loss] 0.3329 [g_image_loss] 0.0610 [g_perc_loss] 0.2082 [generator_loss] 1.6600. Took 6413.337418079376\n",
"Eval of batch: 64000 took 2.495438814163208\n",
"Batch num: 64100, totally elapsed 6423.964576482773\n",
"Batch num: 64200, totally elapsed 6433.789111852646\n",
"Batch num: 64300, totally elapsed 6443.935859680176\n",
"Batch num: 64400, totally elapsed 6453.869919061661\n",
"Batch num: 64500, totally elapsed 6463.666590213776\n",
"Batch num: 64600, totally elapsed 6473.482182264328\n",
"Batch num: 64700, totally elapsed 6483.2896292209625\n",
"Batch num: 64800, totally elapsed 6493.089367866516\n",
"Batch num: 64900, totally elapsed 6502.884281635284\n",
"Batch num: 65000, totally elapsed 6512.718975782394\n",
"Batch num: 65100, totally elapsed 6522.522303104401\n",
"Batch num: 65200, totally elapsed 6532.290442466736\n",
"Batch num: 65300, totally elapsed 6542.077561616898\n",
"Batch num: 65400, totally elapsed 6551.894202470779\n",
"Batch num: 65500, totally elapsed 6561.675875902176\n",
"Batch num: 65600, totally elapsed 6571.455168962479\n",
"Batch num: 65700, totally elapsed 6581.256386518478\n",
"Batch num: 65800, totally elapsed 6591.031674623489\n",
"Batch num: 65900, totally elapsed 6600.801218748093\n",
"Batch num: 66000, totally elapsed 6610.591273784637\n",
"### \n",
"[66000] totalLossOnEbsynth: 0.0000\n",
"[66000] [discriminator_loss] 0.4444 [g_adv_loss] 0.3328 [g_image_loss] 0.0609 [g_perc_loss] 0.2064 [generator_loss] 1.6485. Took 6612.70387005806\n",
"Eval of batch: 66000 took 2.41800594329834\n",
"Batch num: 66100, totally elapsed 6622.778607130051\n",
"Batch num: 66200, totally elapsed 6632.567827224731\n",
"Batch num: 66300, totally elapsed 6642.433663606644\n",
"Batch num: 66400, totally elapsed 6652.619780063629\n",
"Batch num: 66500, totally elapsed 6662.386095285416\n",
"Batch num: 66600, totally elapsed 6672.255127668381\n",
"Batch num: 66700, totally elapsed 6682.537937879562\n",
"Batch num: 66800, totally elapsed 6692.397887229919\n",
"Batch num: 66900, totally elapsed 6702.184777259827\n",
"Batch num: 67000, totally elapsed 6711.99657201767\n",
"Batch num: 67100, totally elapsed 6721.787394762039\n",
"Batch num: 67200, totally elapsed 6731.605154752731\n",
"Batch num: 67300, totally elapsed 6741.398442029953\n",
"Batch num: 67400, totally elapsed 6751.191324234009\n",
"Batch num: 67500, totally elapsed 6760.995062351227\n",
"Batch num: 67600, totally elapsed 6770.805474996567\n",
"Batch num: 67700, totally elapsed 6780.62898850441\n",
"Batch num: 67800, totally elapsed 6790.413285493851\n",
"Batch num: 67900, totally elapsed 6800.228466749191\n",
"Batch num: 68000, totally elapsed 6810.00314617157\n",
"### \n",
"[68000] totalLossOnEbsynth: 0.0000\n",
"[68000] [discriminator_loss] 0.4470 [g_adv_loss] 0.3313 [g_image_loss] 0.0596 [g_perc_loss] 0.1991 [generator_loss] 1.5987. Took 6812.127818107605\n",
"Eval of batch: 68000 took 2.434063673019409\n",
"Batch num: 68100, totally elapsed 6822.248172283173\n",
"Batch num: 68200, totally elapsed 6832.030705690384\n",
"Batch num: 68300, totally elapsed 6841.81874704361\n",
"Batch num: 68400, totally elapsed 6851.600392580032\n",
"Batch num: 68500, totally elapsed 6861.391239643097\n",
"Batch num: 68600, totally elapsed 6871.1928453445435\n",
"Batch num: 68700, totally elapsed 6881.452370166779\n",
"Batch num: 68800, totally elapsed 6891.214351177216\n",
"Batch num: 68900, totally elapsed 6901.032327413559\n",
"Batch num: 69000, totally elapsed 6911.26726603508\n",
"Batch num: 69100, totally elapsed 6921.154995918274\n",
"Batch num: 69200, totally elapsed 6930.9503009319305\n",
"Batch num: 69300, totally elapsed 6940.7262551784515\n",
"Batch num: 69400, totally elapsed 6950.538934469223\n",
"Batch num: 69500, totally elapsed 6960.421230554581\n",
"Batch num: 69600, totally elapsed 6970.185884237289\n",
"Batch num: 69700, totally elapsed 6979.957455873489\n",
"Batch num: 69800, totally elapsed 6989.779239416122\n",
"Batch num: 69900, totally elapsed 6999.599164009094\n",
"Batch num: 70000, totally elapsed 7009.378574848175\n",
"### \n",
"[70000] totalLossOnEbsynth: 0.0000\n",
"[70000] [discriminator_loss] 0.4463 [g_adv_loss] 0.3311 [g_image_loss] 0.0597 [g_perc_loss] 0.1992 [generator_loss] 1.5994. Took 7011.52357840538\n",
"Eval of batch: 70000 took 2.44673228263855\n",
"Batch num: 70100, totally elapsed 7021.594683408737\n",
"Batch num: 70200, totally elapsed 7031.374869585037\n",
"Batch num: 70300, totally elapsed 7041.186985731125\n",
"Batch num: 70400, totally elapsed 7051.0192930698395\n",
"Batch num: 70500, totally elapsed 7060.804489612579\n",
"Batch num: 70600, totally elapsed 7070.60360789299\n",
"Batch num: 70700, totally elapsed 7080.428535223007\n",
"Batch num: 70800, totally elapsed 7090.187797546387\n",
"Batch num: 70900, totally elapsed 7099.9694147109985\n",
"Batch num: 71000, totally elapsed 7110.217581987381\n",
"Batch num: 71100, totally elapsed 7120.009219169617\n",
"Batch num: 71200, totally elapsed 7129.780326843262\n",
"Batch num: 71300, totally elapsed 7139.973022937775\n",
"Batch num: 71400, totally elapsed 7149.908832073212\n",
"Batch num: 71500, totally elapsed 7159.661710977554\n",
"Batch num: 71600, totally elapsed 7169.411510229111\n",
"Batch num: 71700, totally elapsed 7179.190581560135\n",
"Batch num: 71800, totally elapsed 7188.975366592407\n",
"Batch num: 71900, totally elapsed 7198.7623834609985\n",
"Batch num: 72000, totally elapsed 7208.553202867508\n",
"### \n",
"[72000] totalLossOnEbsynth: 0.0000\n",
"[72000] [discriminator_loss] 0.4499 [g_adv_loss] 0.3298 [g_image_loss] 0.0595 [g_perc_loss] 0.1970 [generator_loss] 1.5846. Took 7210.668068408966\n",
"Eval of batch: 72000 took 2.4271178245544434\n",
"Batch num: 72100, totally elapsed 7220.827872991562\n",
"Batch num: 72200, totally elapsed 7230.5900065898895\n",
"Batch num: 72300, totally elapsed 7240.35919213295\n",
"Batch num: 72400, totally elapsed 7250.131542444229\n",
"Batch num: 72500, totally elapsed 7259.898780345917\n",
"Batch num: 72600, totally elapsed 7269.687106847763\n",
"Batch num: 72700, totally elapsed 7279.470879077911\n",
"Batch num: 72800, totally elapsed 7289.237717628479\n",
"Batch num: 72900, totally elapsed 7299.0102207660675\n",
"Batch num: 73000, totally elapsed 7308.800205230713\n",
"Batch num: 73100, totally elapsed 7318.60372543335\n",
"Batch num: 73200, totally elapsed 7328.392449617386\n",
"Batch num: 73300, totally elapsed 7338.589257240295\n",
"Batch num: 73400, totally elapsed 7348.384701728821\n",
"Batch num: 73500, totally elapsed 7358.209103107452\n",
"Batch num: 73600, totally elapsed 7368.417989253998\n",
"Batch num: 73700, totally elapsed 7378.217224359512\n",
"Batch num: 73800, totally elapsed 7388.018933773041\n",
"Batch num: 73900, totally elapsed 7397.807194948196\n",
"Batch num: 74000, totally elapsed 7407.56262087822\n",
"### \n",
"[74000] totalLossOnEbsynth: 0.0000\n",
"[74000] [discriminator_loss] 0.4486 [g_adv_loss] 0.3263 [g_image_loss] 0.0588 [g_perc_loss] 0.1935 [generator_loss] 1.5595. Took 7409.693073749542\n",
"Eval of batch: 74000 took 2.4374783039093018\n",
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"### \n",
"[76000] totalLossOnEbsynth: 0.0000\n",
"[76000] [discriminator_loss] 0.4528 [g_adv_loss] 0.3238 [g_image_loss] 0.0581 [g_perc_loss] 0.1893 [generator_loss] 1.5304. Took 7608.73827791214\n",
"Eval of batch: 76000 took 2.4498863220214844\n",
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"### \n",
"[78000] totalLossOnEbsynth: 0.0000\n",
"[78000] [discriminator_loss] 0.4519 [g_adv_loss] 0.3240 [g_image_loss] 0.0580 [g_perc_loss] 0.1896 [generator_loss] 1.5315. Took 7807.657914876938\n",
"Eval of batch: 78000 took 2.4679620265960693\n",
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"### \n",
"[80000] totalLossOnEbsynth: 0.0000\n",
"[80000] [discriminator_loss] 0.4541 [g_adv_loss] 0.3213 [g_image_loss] 0.0578 [g_perc_loss] 0.1869 [generator_loss] 1.5132. Took 8006.667387962341\n",
"Eval of batch: 80000 took 2.476036787033081\n",
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"### \n",
"[82000] totalLossOnEbsynth: 0.0000\n",
"[82000] [discriminator_loss] 0.4542 [g_adv_loss] 0.3206 [g_image_loss] 0.0574 [g_perc_loss] 0.1854 [generator_loss] 1.5022. Took 8205.749531269073\n",
"Eval of batch: 82000 took 2.4752323627471924\n",
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"### \n",
"[84000] totalLossOnEbsynth: 0.0000\n",
"[84000] [discriminator_loss] 0.4541 [g_adv_loss] 0.3187 [g_image_loss] 0.0572 [g_perc_loss] 0.1830 [generator_loss] 1.4861. Took 8405.02466416359\n",
"Eval of batch: 84000 took 2.447042465209961\n",
"Batch num: 84100, totally elapsed 8415.12938785553\n",
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"### \n",
"[86000] totalLossOnEbsynth: 0.0000\n",
"[86000] [discriminator_loss] 0.4548 [g_adv_loss] 0.3207 [g_image_loss] 0.0565 [g_perc_loss] 0.1787 [generator_loss] 1.4583. Took 8604.345239639282\n",
"Eval of batch: 86000 took 2.4757933616638184\n",
"Batch num: 86100, totally elapsed 8614.458328008652\n",
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"Traceback (most recent call last):\n",
" File \"train.py\", line 130, in <module>\n",
" trainer.train(generator, discriminator, int(config['trainer']['epochs']), args.data_root, args_config, 0)\n",
" File \"/content/Few-Shot-Patch-Based-Training/trainers.py\", line 139, in train\n",
" current_log = {key: value.item() for key, value in six.iteritems(locals()) if\n",
" File \"/content/Few-Shot-Patch-Based-Training/trainers.py\", line 140, in <dictcomp>\n",
" 'loss' in key and isinstance(value, Variable)}\n",
"KeyboardInterrupt\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Generate whole video frames"
],
"metadata": {
"id": "PRbPGTSiBxrI"
}
},
{
"cell_type": "code",
"source": [
"pth_path = \"/content/drive/MyDrive/tell_your_world/3/Miku_train/logs_reference_P/model_00043.pth\"#@param{type:\"string\"}\n",
"output_path = f\"{gen_dir}/whole_video_output\"\n",
"!python generate.py --checkpoint {pth_path} --data_root {gen_dir} --dir_input whole_video_input --outdir {output_path} --device \"cuda:0\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T_f8cohp_gII",
"outputId": "590770a3-688b-4f74-a9f4-267a62ca8630"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Generating took 23.697771787643433\n"
]
}
]
}
]
}
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