1222 lines
7.2 MiB
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1222 lines
7.2 MiB
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-25T06:22:19.905652Z",
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"start_time": "2021-04-25T06:22:19.902171Z"
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},
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"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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"id": "gGGJCgylfwEy"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"if not os.path.exists('ShoeV2_photo'):\n",
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" !wget https://www.dropbox.com/s/g6b6gtvmdu0h77x/ShoeV2_photo.zip\n",
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" !unzip -q ShoeV2_photo.zip\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-25T06:22:25.707794Z",
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"start_time": "2021-04-25T06:22:19.908378Z"
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},
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"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
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"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 744
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},
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"id": "6AC0roPnfwE4",
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"outputId": "3bfe139e-bcb7-4ac9-f521-b064a95dc2ab"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: torch_snippets in /home/yyr/anaconda3/lib/python3.7/site-packages (0.421)\n",
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"Requirement already satisfied: altair in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (4.1.0)\n",
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"Requirement already satisfied: pandas in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (0.24.2)\n",
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"Requirement already satisfied: loguru in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (0.5.0)\n",
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"Requirement already satisfied: tqdm in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (4.42.1)\n",
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"Requirement already satisfied: numpy in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (1.17.4)\n",
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"Requirement already satisfied: Pillow in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (6.2.2)\n",
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"Requirement already satisfied: dill in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (0.3.3)\n",
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"Requirement already satisfied: rich in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (10.1.0)\n",
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"Requirement already satisfied: ipython in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (7.19.0)\n",
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"Requirement already satisfied: matplotlib in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (3.3.3)\n",
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"Requirement already satisfied: fastcore in /home/yyr/anaconda3/lib/python3.7/site-packages (from torch_snippets) (1.3.19)\n",
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"Requirement already satisfied: toolz in /home/yyr/anaconda3/lib/python3.7/site-packages (from altair->torch_snippets) (0.10.0)\n",
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"Requirement already satisfied: jinja2 in /home/yyr/anaconda3/lib/python3.7/site-packages (from altair->torch_snippets) (2.11.1)\n",
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"Requirement already satisfied: entrypoints in /home/yyr/anaconda3/lib/python3.7/site-packages (from altair->torch_snippets) (0.3)\n",
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"Requirement already satisfied: jsonschema in /home/yyr/anaconda3/lib/python3.7/site-packages (from altair->torch_snippets) (3.2.0)\n",
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"Requirement already satisfied: python-dateutil>=2.5.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from pandas->torch_snippets) (2.8.1)\n",
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"Requirement already satisfied: pytz>=2011k in /home/yyr/anaconda3/lib/python3.7/site-packages (from pandas->torch_snippets) (2019.3)\n",
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"Requirement already satisfied: six>=1.5 in /home/yyr/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.5.0->pandas->torch_snippets) (1.15.0)\n",
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"Requirement already satisfied: packaging in /home/yyr/anaconda3/lib/python3.7/site-packages (from fastcore->torch_snippets) (20.1)\n",
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"Requirement already satisfied: pip in /home/yyr/anaconda3/lib/python3.7/site-packages (from fastcore->torch_snippets) (20.3.3)\n",
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"Requirement already satisfied: traitlets>=4.2 in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (4.3.3)\n",
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"Requirement already satisfied: pygments in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (2.8.1)\n",
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"Requirement already satisfied: jedi>=0.10 in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (0.14.1)\n",
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"Requirement already satisfied: setuptools>=18.5 in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (47.1.1)\n",
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"Requirement already satisfied: backcall in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (0.1.0)\n",
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"Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (3.0.8)\n",
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"Requirement already satisfied: pickleshare in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (0.7.5)\n",
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"Requirement already satisfied: pexpect>4.3 in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (4.8.0)\n",
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"Requirement already satisfied: decorator in /home/yyr/anaconda3/lib/python3.7/site-packages (from ipython->torch_snippets) (4.4.1)\n",
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"Requirement already satisfied: parso>=0.5.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from jedi>=0.10->ipython->torch_snippets) (0.5.2)\n",
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"Requirement already satisfied: ptyprocess>=0.5 in /home/yyr/anaconda3/lib/python3.7/site-packages (from pexpect>4.3->ipython->torch_snippets) (0.6.0)\n",
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"Requirement already satisfied: wcwidth in /home/yyr/anaconda3/lib/python3.7/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->torch_snippets) (0.1.8)\n",
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"Requirement already satisfied: ipython-genutils in /home/yyr/anaconda3/lib/python3.7/site-packages (from traitlets>=4.2->ipython->torch_snippets) (0.2.0)\n",
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"Requirement already satisfied: MarkupSafe>=0.23 in /home/yyr/anaconda3/lib/python3.7/site-packages (from jinja2->altair->torch_snippets) (1.1.1)\n",
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"Requirement already satisfied: attrs>=17.4.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from jsonschema->altair->torch_snippets) (19.3.0)\n",
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"Requirement already satisfied: pyrsistent>=0.14.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from jsonschema->altair->torch_snippets) (0.15.7)\n",
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"Requirement already satisfied: importlib-metadata in /home/yyr/anaconda3/lib/python3.7/site-packages (from jsonschema->altair->torch_snippets) (2.0.0)\n",
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"Requirement already satisfied: zipp>=0.5 in /home/yyr/anaconda3/lib/python3.7/site-packages (from importlib-metadata->jsonschema->altair->torch_snippets) (2.2.0)\n",
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"Requirement already satisfied: cycler>=0.10 in /home/yyr/anaconda3/lib/python3.7/site-packages (from matplotlib->torch_snippets) (0.10.0)\n",
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"Requirement already satisfied: kiwisolver>=1.0.1 in /home/yyr/anaconda3/lib/python3.7/site-packages (from matplotlib->torch_snippets) (1.1.0)\n",
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"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/yyr/anaconda3/lib/python3.7/site-packages (from matplotlib->torch_snippets) (2.4.6)\n",
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"Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from rich->torch_snippets) (0.9.1)\n",
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"Requirement already satisfied: typing-extensions<4.0.0,>=3.7.4 in /home/yyr/anaconda3/lib/python3.7/site-packages (from rich->torch_snippets) (3.7.4.3)\n",
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"Requirement already satisfied: colorama<0.5.0,>=0.4.0 in /home/yyr/anaconda3/lib/python3.7/site-packages (from rich->torch_snippets) (0.4.3)\n",
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"\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.1 is available.\n",
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"You should consider upgrading via the '/home/yyr/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/yyr/anaconda3/lib/python3.7/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.4) or chardet (3.0.4) doesn't match a supported version!\n",
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" RequestsDependencyWarning)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[04/25/21 11:52:25] </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f\">WARNING </span> sklearn is not found. Skipping relevant <a href=\"file:///home/yyr/anaconda3/lib/python3.7/site-packages/torch_snippets/__init__.py\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">__init__.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:<module>:13</span>\n",
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" imports from submodule `sklegos` \n",
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" Exception: No module named <span style=\"color: #008000; text-decoration-color: #008000\">'sklego'</span> \n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[2;36m[04/25/21 11:52:25]\u001b[0m\u001b[2;36m \u001b[0m\u001b[2;31mWARNING \u001b[0m sklearn is not found. Skipping relevant \u001b]8;id=1619331745.7049863-834722;file:///home/yyr/anaconda3/lib/python3.7/site-packages/torch_snippets/__init__.py\u001b\\\u001b[2m__init__.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:<module>:13\u001b[0m\n",
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" imports from submodule `sklegos` \n",
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" Exception: No module named \u001b[32m'sklego'\u001b[0m \n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"!pip install -U torch_snippets\n",
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"from torch_snippets import *\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-04-25T06:22:25.718312Z",
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"start_time": "2021-04-25T06:22:25.709314Z"
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},
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"id": "VwH3HJj-fwE7"
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},
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"outputs": [],
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"source": [
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"def detect_edges(img):\n",
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" img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
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" img_gray = cv2.bilateralFilter(img_gray, 5, 50, 50)\n",
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" img_gray_edges = cv2.Canny(img_gray, 45, 100)\n",
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" img_gray_edges = cv2.bitwise_not(img_gray_edges) # invert black/white\n",
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" img_edges = cv2.cvtColor(img_gray_edges, cv2.COLOR_GRAY2RGB)\n",
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" return img_edges\n",
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"\n",
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"IMAGE_SIZE = 256\n",
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"\n",
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"preprocess = T.Compose([\n",
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" T.Lambda(lambda x: torch.Tensor(x.copy()).permute(2, 0, 1).to(device))\n",
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"])\n",
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"\n",
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"normalize = lambda x: (x - 127.5)/127.5\n",
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"\n",
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"class ShoesData(Dataset):\n",
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" def __init__(self, items):\n",
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" self.items = items\n",
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" def __len__(self): return len(self.items)\n",
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" def __getitem__(self, ix):\n",
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" f = self.items[ix]\n",
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" try: im = read(f, 1)\n",
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" except:\n",
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" blank = preprocess(Blank(IMAGE_SIZE, IMAGE_SIZE, 3))\n",
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" return blank, blank\n",
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" edges = detect_edges(im)\n",
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" im, edges = resize(im, IMAGE_SIZE), resize(edges, IMAGE_SIZE)\n",
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" im, edges = normalize(im), normalize(edges)\n",
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" self._draw_color_circles_on_src_img(edges, im)\n",
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" im, edges = preprocess(im), preprocess(edges)\n",
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" return edges, im\n",
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"\n",
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" def _draw_color_circles_on_src_img(self, img_src, img_target):\n",
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" non_white_coords = self._get_non_white_coordinates(img_target)\n",
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" for center_y, center_x in non_white_coords:\n",
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" self._draw_color_circle_on_src_img(img_src, img_target, center_y, center_x)\n",
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"\n",
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" def _get_non_white_coordinates(self, img):\n",
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" non_white_mask = np.sum(img, axis=-1) < 2.75\n",
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" non_white_y, non_white_x = np.nonzero(non_white_mask)\n",
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" # randomly sample non-white coordinates\n",
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" n_non_white = len(non_white_y)\n",
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" n_color_points = min(n_non_white, 300)\n",
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" idxs = np.random.choice(n_non_white, n_color_points, replace=False)\n",
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" non_white_coords = list(zip(non_white_y[idxs], non_white_x[idxs]))\n",
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" return non_white_coords\n",
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"\n",
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||
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" def _draw_color_circle_on_src_img(self, img_src, img_target, center_y, center_x):\n",
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|
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" assert img_src.shape == img_target.shape, \"Image source and target must have same shape.\"\n",
|
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" y0, y1, x0, x1 = self._get_color_point_bbox_coords(center_y, center_x)\n",
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" color = np.mean(img_target[y0:y1, x0:x1], axis=(0, 1))\n",
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" img_src[y0:y1, x0:x1] = color\n",
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"\n",
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" def _get_color_point_bbox_coords(self, center_y, center_x):\n",
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" radius = 2\n",
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" y0 = max(0, center_y-radius+1)\n",
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" y1 = min(IMAGE_SIZE, center_y+radius)\n",
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" x0 = max(0, center_x-radius+1)\n",
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" x1 = min(IMAGE_SIZE, center_x+radius)\n",
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" return y0, y1, x0, x1\n",
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"\n",
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||
|
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" def choose(self): return self[randint(len(self))]"
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]
|
||
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},
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{
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||
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"cell_type": "code",
|
||
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"execution_count": 4,
|
||
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"metadata": {
|
||
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"ExecuteTime": {
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"end_time": "2021-04-25T06:22:28.203506Z",
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||
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"start_time": "2021-04-25T06:22:25.720118Z"
|
||
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},
|
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"colab": {
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||
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"base_uri": "https://localhost:8080/",
|
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"height": 161
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},
|
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"id": "iI_eZIO_fwE-",
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||
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"outputId": "264378f1-4568-4996-a13e-92ec282fbce9"
|
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},
|
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\"> </span><span style=\"color: #bfbf7f; text-decoration-color: #bfbf7f\">INFO </span> <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4381</span> files found at <a href=\"file://<ipython-input-4-2f6e4187209a>\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"><ipython-input-4-2f6e4187209a></span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:<module>:2</span>\n",
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Tensor Shape: torch.Size<span style=\"font-weight: bold\">([</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">256</span><span style=\"font-weight: bold\">])</span> Min: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">-1.000</span> Max: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.000</span> Mean: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.579</span> \n",
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"Tensor Shape: torch.Size\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m32\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m256\u001b[0m, \u001b[1;36m256\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m Min: \u001b[1;36m-1.000\u001b[0m Max: \u001b[1;36m1.000\u001b[0m Mean: \u001b[1;36m0.579\u001b[0m \n",
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}
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],
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"source": [
|
||
|
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"from sklearn.model_selection import train_test_split\n",
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||
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"train_items, val_items = train_test_split(Glob('ShoeV2_photo/*.png'), test_size=0.2, random_state=2)\n",
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||
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"trn_ds, val_ds = ShoesData(train_items), ShoesData(val_items)\n",
|
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"\n",
|
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"trn_dl = DataLoader(trn_ds, batch_size=32, shuffle=True)\n",
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"val_dl = DataLoader(val_ds, batch_size=32, shuffle=True)\n",
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||
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"\n",
|
||
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"inspect(*next(iter(trn_dl)))"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
|
||
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"ExecuteTime": {
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||
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"end_time": "2021-04-25T06:22:28.222005Z",
|
||
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"start_time": "2021-04-25T06:22:28.204697Z"
|
||
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},
|
||
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"id": "rot3Fn3ufwFA"
|
||
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},
|
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"outputs": [],
|
||
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"source": [
|
||
|
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"def weights_init_normal(m):\n",
|
||
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" classname = m.__class__.__name__\n",
|
||
|
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" if classname.find(\"Conv\") != -1:\n",
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" torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
|
||
|
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" elif classname.find(\"BatchNorm2d\") != -1:\n",
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" torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
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" torch.nn.init.constant_(m.bias.data, 0.0)\n",
|
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"\n",
|
||
|
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"class UNetDown(nn.Module):\n",
|
||
|
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" def __init__(self, in_size, out_size, normalize=True, dropout=0.0):\n",
|
||
|
|
" super(UNetDown, self).__init__()\n",
|
||
|
|
" layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]\n",
|
||
|
|
" if normalize:\n",
|
||
|
|
" layers.append(nn.InstanceNorm2d(out_size))\n",
|
||
|
|
" layers.append(nn.LeakyReLU(0.2))\n",
|
||
|
|
" if dropout:\n",
|
||
|
|
" layers.append(nn.Dropout(dropout))\n",
|
||
|
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" self.model = nn.Sequential(*layers)\n",
|
||
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"\n",
|
||
|
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" def forward(self, x):\n",
|
||
|
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" return self.model(x)\n",
|
||
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"\n",
|
||
|
|
"class UNetUp(nn.Module):\n",
|
||
|
|
" def __init__(self, in_size, out_size, dropout=0.0):\n",
|
||
|
|
" super(UNetUp, self).__init__()\n",
|
||
|
|
" layers = [\n",
|
||
|
|
" nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),\n",
|
||
|
|
" nn.InstanceNorm2d(out_size),\n",
|
||
|
|
" nn.ReLU(inplace=True),\n",
|
||
|
|
" ]\n",
|
||
|
|
" if dropout:\n",
|
||
|
|
" layers.append(nn.Dropout(dropout))\n",
|
||
|
|
"\n",
|
||
|
|
" self.model = nn.Sequential(*layers)\n",
|
||
|
|
"\n",
|
||
|
|
" def forward(self, x, skip_input):\n",
|
||
|
|
" x = self.model(x)\n",
|
||
|
|
" x = torch.cat((x, skip_input), 1)\n",
|
||
|
|
"\n",
|
||
|
|
" return x\n",
|
||
|
|
"\n",
|
||
|
|
"class GeneratorUNet(nn.Module):\n",
|
||
|
|
" def __init__(self, in_channels=3, out_channels=3):\n",
|
||
|
|
" super(GeneratorUNet, self).__init__()\n",
|
||
|
|
"\n",
|
||
|
|
" self.down1 = UNetDown(in_channels, 64, normalize=False)\n",
|
||
|
|
" self.down2 = UNetDown(64, 128)\n",
|
||
|
|
" self.down3 = UNetDown(128, 256)\n",
|
||
|
|
" self.down4 = UNetDown(256, 512, dropout=0.5)\n",
|
||
|
|
" self.down5 = UNetDown(512, 512, dropout=0.5)\n",
|
||
|
|
" self.down6 = UNetDown(512, 512, dropout=0.5)\n",
|
||
|
|
" self.down7 = UNetDown(512, 512, dropout=0.5)\n",
|
||
|
|
" self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
" self.up1 = UNetUp(512, 512, dropout=0.5)\n",
|
||
|
|
" self.up2 = UNetUp(1024, 512, dropout=0.5)\n",
|
||
|
|
" self.up3 = UNetUp(1024, 512, dropout=0.5)\n",
|
||
|
|
" self.up4 = UNetUp(1024, 512, dropout=0.5)\n",
|
||
|
|
" self.up5 = UNetUp(1024, 256)\n",
|
||
|
|
" self.up6 = UNetUp(512, 128)\n",
|
||
|
|
" self.up7 = UNetUp(256, 64)\n",
|
||
|
|
"\n",
|
||
|
|
" self.final = nn.Sequential(\n",
|
||
|
|
" nn.Upsample(scale_factor=2),\n",
|
||
|
|
" nn.ZeroPad2d((1, 0, 1, 0)),\n",
|
||
|
|
" nn.Conv2d(128, out_channels, 4, padding=1),\n",
|
||
|
|
" nn.Tanh(),\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" def forward(self, x):\n",
|
||
|
|
" d1 = self.down1(x)\n",
|
||
|
|
" d2 = self.down2(d1)\n",
|
||
|
|
" d3 = self.down3(d2)\n",
|
||
|
|
" d4 = self.down4(d3)\n",
|
||
|
|
" d5 = self.down5(d4)\n",
|
||
|
|
" d6 = self.down6(d5)\n",
|
||
|
|
" d7 = self.down7(d6)\n",
|
||
|
|
" d8 = self.down8(d7)\n",
|
||
|
|
" u1 = self.up1(d8, d7)\n",
|
||
|
|
" u2 = self.up2(u1, d6)\n",
|
||
|
|
" u3 = self.up3(u2, d5)\n",
|
||
|
|
" u4 = self.up4(u3, d4)\n",
|
||
|
|
" u5 = self.up5(u4, d3)\n",
|
||
|
|
" u6 = self.up6(u5, d2)\n",
|
||
|
|
" u7 = self.up7(u6, d1)\n",
|
||
|
|
" return self.final(u7)\n",
|
||
|
|
"\n",
|
||
|
|
"class Discriminator(nn.Module):\n",
|
||
|
|
" def __init__(self, in_channels=3):\n",
|
||
|
|
" super(Discriminator, self).__init__()\n",
|
||
|
|
"\n",
|
||
|
|
" def discriminator_block(in_filters, out_filters, normalization=True):\n",
|
||
|
|
" \"\"\"Returns downsampling layers of each discriminator block\"\"\"\n",
|
||
|
|
" layers = [nn.Conv2d(in_filters, out_filters, 4, stride=2, padding=1)]\n",
|
||
|
|
" if normalization:\n",
|
||
|
|
" layers.append(nn.InstanceNorm2d(out_filters))\n",
|
||
|
|
" layers.append(nn.LeakyReLU(0.2, inplace=True))\n",
|
||
|
|
" return layers\n",
|
||
|
|
"\n",
|
||
|
|
" self.model = nn.Sequential(\n",
|
||
|
|
" *discriminator_block(in_channels * 2, 64, normalization=False),\n",
|
||
|
|
" *discriminator_block(64, 128),\n",
|
||
|
|
" *discriminator_block(128, 256),\n",
|
||
|
|
" *discriminator_block(256, 512),\n",
|
||
|
|
" nn.ZeroPad2d((1, 0, 1, 0)),\n",
|
||
|
|
" nn.Conv2d(512, 1, 4, padding=1, bias=False)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" def forward(self, img_A, img_B):\n",
|
||
|
|
" img_input = torch.cat((img_A, img_B), 1)\n",
|
||
|
|
" return self.model(img_input)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-25T06:22:30.823685Z",
|
||
|
|
"start_time": "2021-04-25T06:22:28.223083Z"
|
||
|
|
},
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "UtSqQ1U-fwFD",
|
||
|
|
"outputId": "3f59c1bd-b184-47dc-bba4-6b051eb75a16"
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Requirement already satisfied: torch_summary in /home/yyr/anaconda3/lib/python3.7/site-packages (1.4.1)\n",
|
||
|
|
"\u001b[33mWARNING: You are using pip version 20.3.3; however, version 21.1 is available.\n",
|
||
|
|
"You should consider upgrading via the '/home/yyr/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"!pip install torch_summary\n",
|
||
|
|
"from torchsummary import summary\n",
|
||
|
|
"generator = GeneratorUNet().to(device)\n",
|
||
|
|
"discriminator = Discriminator().to(device)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-25T06:22:30.830827Z",
|
||
|
|
"start_time": "2021-04-25T06:22:30.824875Z"
|
||
|
|
},
|
||
|
|
"id": "69me3TlQfwFG"
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def discriminator_train_step(real_src, real_trg, fake_trg):\n",
|
||
|
|
" #discriminator.train()\n",
|
||
|
|
" d_optimizer.zero_grad()\n",
|
||
|
|
"\n",
|
||
|
|
" prediction_real = discriminator(real_trg, real_src)\n",
|
||
|
|
" error_real = criterion_GAN(prediction_real, torch.ones(len(real_src), 1, 16, 16).cuda())\n",
|
||
|
|
" error_real.backward()\n",
|
||
|
|
"\n",
|
||
|
|
" prediction_fake = discriminator(fake_trg.detach(), real_src)\n",
|
||
|
|
" error_fake = criterion_GAN(prediction_fake, torch.zeros(len(real_src), 1, 16, 16).cuda())\n",
|
||
|
|
" error_fake.backward()\n",
|
||
|
|
"\n",
|
||
|
|
" d_optimizer.step()\n",
|
||
|
|
"\n",
|
||
|
|
" return error_real + error_fake\n",
|
||
|
|
"\n",
|
||
|
|
"def generator_train_step(real_src, fake_trg):\n",
|
||
|
|
" #discriminator.train()\n",
|
||
|
|
" g_optimizer.zero_grad()\n",
|
||
|
|
" prediction = discriminator(fake_trg, real_src)\n",
|
||
|
|
"\n",
|
||
|
|
" loss_GAN = criterion_GAN(prediction, torch.ones(len(real_src), 1, 16, 16).cuda())\n",
|
||
|
|
" loss_pixel = criterion_pixelwise(fake_trg, real_trg)\n",
|
||
|
|
" loss_G = loss_GAN + lambda_pixel * loss_pixel\n",
|
||
|
|
"\n",
|
||
|
|
" loss_G.backward()\n",
|
||
|
|
" g_optimizer.step()\n",
|
||
|
|
" return loss_G\n",
|
||
|
|
"\n",
|
||
|
|
"denorm = T.Normalize((-1, -1, -1), (2, 2, 2))\n",
|
||
|
|
"def sample_prediction():\n",
|
||
|
|
" \"\"\"Saves a generated sample from the validation set\"\"\"\n",
|
||
|
|
" data = next(iter(val_dl))\n",
|
||
|
|
" real_src, real_trg = data\n",
|
||
|
|
" fake_trg = generator(real_src)\n",
|
||
|
|
" img_sample = torch.cat([denorm(real_src[0]), denorm(fake_trg[0]), denorm(real_trg[0])], -1)\n",
|
||
|
|
" img_sample = img_sample.detach().cpu().permute(1,2,0).numpy()\n",
|
||
|
|
" show(img_sample, title='Source::Generated::GroundTruth', sz=12)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2021-04-25T07:13:46.135098Z",
|
||
|
|
"start_time": "2021-04-25T06:22:31.006806Z"
|
||
|
|
},
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 1000
|
||
|
|
},
|
||
|
|
"id": "PHuThsmkfwFI",
|
||
|
|
"outputId": "09173470-99da-415f-e99e-e2e66ebeb9df"
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 1.000\terrD: 1.127\terrG: 20.277\t(139.10s - 13770.53s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 2.000\terrD: 0.550\terrG: 10.738\t(275.51s - 13499.84s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
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"text/plain": [
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|
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"<Figure size 864x864 with 1 Axes>"
|
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
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|
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},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 3.000\terrD: 0.498\terrG: 8.851\t(412.14s - 13325.89s remaining))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
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|
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},
|
||
|
|
"output_type": "display_data"
|
||
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},
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|
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{
|
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|
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 4.000\terrD: 0.517\terrG: 7.561\t(548.63s - 13167.20s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
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"<Figure size 864x864 with 1 Axes>"
|
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|
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
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|
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},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 5.000\terrD: 0.516\terrG: 6.858\t(685.31s - 13020.97s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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"text/plain": [
|
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|
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"<Figure size 864x864 with 1 Axes>"
|
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]
|
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},
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|
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"metadata": {
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||
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 6.000\terrD: 0.511\terrG: 6.349\t(822.47s - 12885.35s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
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|
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"metadata": {
|
||
|
|
"needs_background": "light"
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},
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|
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"output_type": "display_data"
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},
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|
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{
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|
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAD9CAYAAACbUz4yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOy9edwlRX3v//5W9XKWZ50VhkVARRFcwo2JSYwauQku0RhNNFHcosYlXKMBxT3EXLcEl4jxRjQ/3HK9LjFGwQ2NQUFAZBsYmGGA2fd59rP1UlW/P7rPec6zL/MMzGB/XgzPOd3VVXWqq7s+9V3FOUeBAgUKFChQoECBAsci1EPdgQIFChQoUKBAgQIF5kJBVgsUKFCgQIECBQocsyjIaoECBQoUKFCgQIFjFgVZLVCgQIECBQoUKHDMoiCrBQoUKFCgQIECBY5ZFGS1QIECBQoUKFCgwDGLgqwWKFCgwEMMEXmViFz3UPdjpSAizxCR3Q9SW9tF5H8+GG0VKFDgoUFBVgsUeBhCRJ4qIj8XkTERGRaR60XkyQ91v5YCEfkzEblJROoicjD//CYRkYe6b9MhIv8tIq99ENsTEblQRDaKSENE9ud9+LMHqw+LhYjUuv5ZEWl2fX/ZEuv6vIj876PV1wIFChybKMhqgQIPM4hIH3AVcDmwCjgJ+DsgWuF2vJWsb1rdFwH/BPwjcAKwHngD8DtAcLTanaMvR+13HgE+CbwFuAhYTXaP3wM8a7bCObl9SN73zrme9j9gJ/C8rmP/1tXHY3GcCxQocAygIKsFCjz8cCaAc+4rzjnjnGs6537onNsoIkpE3iMiO3Jp5RdFpB9mV912q1hF5FIR+YaIfFlExoFXicgqEblSRPaKyIiIfKvr2j8UkdtFZDSX8j5hMZ3P+/N+4E3OuW845yZchtuccy9zzkV5uVBELhORnSJyQET+RUTK3b9FRC7Kf+c+EXl1VxuLufYSEdkPXCkigyJylYgcyn/nVSJycl7+A8DvAp/KpYWfyo8/VkSuySXbW0TkxV3trxaRb4vIuIj8AnjkYm+uiJwJvAn4M+fcNfn9Nc6565xzr+oq998i8gERuR5oAGeIyG+LyM25xP1mEfnt2e51/v1SEfly/vk0EXEi8sp8zA6LyLu7ypZzqeeIiNwNLCjFn2OcZ5hD5O0+SkT+EngZ8PZ8nL/TVexJuZR5TES+KiKlxY5ngQIFjn0UZLVAgYcf7gWMiHxBRJ4tIoNd516V//s94AygB/jUEur+I+AbwADwb8CXgApwNrAO+DiAiPwa8P8BryeT/H0G+LaIhPn5T4vIp9uVTvv+W0AI/OcCffkwGTF/EvAoMuni+7rOnwD058dfA/xz11gs5tpVwCOAvyR7V16Zfz8VaJKPm3Pu3cDPgAtzaeGFIlIFrgH+bz4ufwZ8WkQel9f/z0ALOBH4i/xfBzkZfscc358J7HLO/XKB8QF4ed7/XmACuJpMKrsa+BhwtYisXkQ9bTwVeAxwHvA+ETkrP/63ZIT7kcD5wCsXWd/0cZ4TzrkryObcP+Tj/Lyu0y8mkyqfDjyBbI4XKFDgYYKCrBYo8DCDc26cjFQ44LPAoVyKt55MMvUx59wDzrka8E7gz2TxKtgbnHPfcs5ZMsL6bOANzrkR51zinLs2L/eXwGecczflUr8vkJkhPCXv45ucc2/q6nP39zXAYedc2j6fS2ZHJbN3fJqISN7GW51zw865CeCDZKSwjQR4f96v7wI14DGLvNYCf+uci3LJ5ZBz7t+dc428/AeAp88zTn8IbHfOXemcS51ztwH/DvypiGjgRcD7nHN159xdwBe6L3bO/aFz7sNzfF8D7O8un0soR0WkJSKP6Dr1eefcpnws/wDY6pz7Ut6nrwCbgW7StxD+Lh+PO4A7gCfmx18MfCAfz11khHgxmDLOS+jHdHzSObfXOTcMfIdsE1KgQIGHCQoboQIFHoZwzt1DLl0SkccCXwY+AWwAdnQV3UH2Hli/yKp3dX0+BRh2zo3MUu4RwCtF5H91HQvy9hfCELBGRLw2YXXO/TZkpIxsk72WTKJ7i0z6Wwmgu+vpJrxkqvCeRV57yDnX6pwUqZBJjZ8FtKWzvSKinXNmlt/wCOA3RWS065hHJolem3/uHsvue7IQhsgksh04507ONxxJ/lva6G5j+r1vt3vSEtruJsnt8WzXvZzfM2WcjwDT+7WYeVagQIHjBIVktUCBhzmcc5uBzwPnAHvJiFQbpwIpcACok5E4AHIJ4Nrp1XV93gWsEpGBWZrdRSZpG+j6V8mleQvhBjIp7B/NU+YwmSr+7K76+3MnnoWwmGvdtGsuIlN//6Zzrg94Wn5c5ii/C7h22u/vcc69EThENuandJU/dRH9buO/gJNF5NcXUba7X9PvfbvdPfnnKfefTEW/WOxjeb9n+rhNn4PT+zC9fIECBX4FUJDVAgUeZsgdey7qcgA6Bfhz4EbgK8BbReR0EekhU39/NZdA3guUROS5IuKTeZeHc7XjnNsHfI/MFnNQRHwRaZO4zwJvEJHflAzVvN7ehfrvnBsli17waRH5ExHplcwx7ElANS9j8zY+LiLr8t95koicv4j6l3NtLxnBHRWRVWQ2mt04QGYD3MZVwJki8vJ8XHwRebKInJVLYr8JXCoildyOdbE2njjntpDZAP8/Efn93LlJA7+9wKXfzfv0UhHxROQlwOPyvgLcTmYS4udE+E8W2yfga8A783lwMvC/FrpgDtwBnC0iT8qdpC6ddn76OBcoUOBXAAVZLVDg4YcJ4DeBm0SkTkZS7yKTDv5/ZKronwLbyJx8/heAc26MzMv8c2TStjqwUGD3l5OpnjcDB8nCKZE7/7yOzAlpBLiPLqcXybzv/2Wu7865fwD+Bng7GUE5QEbQLgF+nhe7JK/3RsmiE/yITPq5GCz12k8AZTKp7I3A96ed/yfgT3Jv+E/mdq1/QGYHu5dMTf0RJsn/hWQq9P1kUu8ruysTke+JyLvm+g78FZld6MeAYbL79PfAS8jCQ82Ac26IzJb2IjJTgrcDf+icO5wXeS+Zg9QI2Wbh/84zHtPxd2Sq/23AD8nm2JLhnLuXLBLEj4CtwPRECf8KPC63z/3WctooUKDA8QdxrtCqFChQoECBAgUKFDg2UUhWCxQoUKBAgQIFChyzKMhqgQIFChQoUKBAgWMWBVktUKBAgQIFChQocMyiIKsFChQoUKBAgQIFjlkUZLVAgQIFChQoUKDAMYuFMlgVoQIKFHgIECcRymm0pwCZmpOoQIECKw7nwDmHcwatj5fkjsJcy7SI8NBH+5m7f0etxWPidx9tTF8QHj6/V7rSCk45vsBNffiMQIECxxGsMSDCHM9tgQIFjgZywipq7ueu/Ux2r52LfU6nr7fd1812bjltLNTmXO0vVHa+Psz3uwoUmI4F5uSsk+d42T4WKPCrheJdX6DAg495nrvpa+hyCNl818x2biVI31LqmI2IL7eOAgWWAufcvHOnIKsFChyTkIKvFihwjOBXjYAt9ff+qo1PgSNDW2vQJqgLEVUoyGqBAscuivd/gQK/AnAsz+JOWN5Lot3ecq8vUGBl0Caoi9nsFGS1QIECBQoUmAcrYTu68mgTzgVKzeh7NzHu/nys/K4CD0ccqdNbQVYLFChQoECBBwFzLdgijqWu5YshzdPba3/PLp3e4MJe9HPZtC62PwUenph7Xs89X5aKgqwWKHCsYnGCkwIFCjyIWOrCvDCJczi39BBPzmUkd5aeLLGe9otmJYL/zFVH8SJ7uGFxBHQ2Kf7y5kJBVgsUOEZRcNUCBY4fLMZJZPYYoDLt76JbZHZy+FDasa5Ufwoc35g+D46MqEJBVgsUOGZRaNUKFHjosRgSupRrZiOsywlkP9X+tHtra2mTzsXUO7Wf2XVLj7fq8na7v0vX5wfTgex4wXLHZTYsZqxWsr2Z69PklJnPDnruTVERuqpAgeMQHZMyYYki1iPfwRYoUGAS0wP3L4a4LjXY/nJt+qZet/g6Vqa9KWc67We/vXj/LIwHO+fSyt7z+RJazFZ2aplJk5kidFWBAscxplL
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 7.000\terrD: 0.475\terrG: 5.913\t(962.11s - 12782.30s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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"text/plain": [
|
||
|
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"<Figure size 864x864 with 1 Axes>"
|
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]
|
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},
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|
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"metadata": {
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||
|
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 8.000\terrD: 0.492\terrG: 5.675\t(1098.87s - 12637.03s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
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},
|
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|
|
"output_type": "display_data"
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},
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{
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|
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 9.000\terrD: 0.482\terrG: 5.362\t(1235.53s - 12492.55s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
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|
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"metadata": {
|
||
|
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"needs_background": "light"
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},
|
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|
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"output_type": "display_data"
|
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},
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|
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 10.000\terrD: 0.471\terrG: 5.178\t(1378.30s - 12404.70s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
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|
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"<Figure size 864x864 with 1 Axes>"
|
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]
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},
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|
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"metadata": {
|
||
|
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"needs_background": "light"
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},
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 11.000\terrD: 0.505\terrG: 4.945\t(1529.96s - 12378.76s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
|
||
|
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"<Figure size 864x864 with 1 Axes>"
|
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|
|
]
|
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|
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},
|
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|
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"metadata": {
|
||
|
|
"needs_background": "light"
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},
|
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 12.000\terrD: 0.451\terrG: 4.828\t(1683.78s - 12347.69s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
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|
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},
|
||
|
|
"output_type": "display_data"
|
||
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},
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|
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{
|
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|
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAD9CAYAAACbUz4yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOy9d7xlV1n//37W2nufc/vUTEkjCQRISCDSlKKgAlIi8EVBELCgfv0iCF9QROFnVERRmhQBUemCBRCR8qVEWoAU0pPJTMpkWqbffk/Ze6/y+2Otc+65d+6duVNzJ+xPXpN77zm7rN0/+/N8nucR7z0VKlSoUKFChQoVKixHqPt7ABUqVKhQoUKFChUqLIaKrFaoUKFChQoVKlRYtqjIaoUKFSpUqFChQoVli4qsVqhQoUKFChUqVFi2qMhqhQoVKlSoUKFChWWLiqxWqFChQoUKFSpUWLaoyGqFChUq3M8QkV8Xkavu73GcKIjIU0Rk1yla1zYR+flTsa4KFSrcP6jIaoUKD0CIyJNE5AciMikiYyLyfRF57P09rqOBiPyKiFwjIg0R2R9/f6WIyP09tvkQkW+LyG+dwvWJiLxKRG4RkaaI7I1j+JVTNYalQkRmev45EWn1/P2rR7msj4nIX56ssVaoUGF5oiKrFSo8wCAiw8CXgPcBq4AzgT8H8hO8nuRELm/esl8PvAd4O7AeWAf8LvBEIDtZ611kLCdtO48D7wVeC7weWE04xm8GfmGhiSO5vV/u9977wc4/YAdwec9n/9IzxuW4nytUqLAMUJHVChUeeLgQwHv/Ge+99d63vPdf997fIiJKRN4sItujWvkJERmBhUO3vSFWEfkzEfmsiHxKRKaAXxeRVSLyURHZLSLjIvKFnnmfIyI3ichEVHkvXcrg43j+Anil9/6z3vtpH3Cj9/5Xvfd5nK4mIu8QkR0isk9EPiQifb3bIiKvj9u5R0R+o2cdS5n3j0RkL/BREVkpIl8SkQNxO78kImfF6d8KPBl4f1QL3x8/f5iIfCMq21tE5IU9618tIl8UkSkRuRa4YKkHV0QuBF4J/Ir3/hvx+Frv/VXe+1/vme7bIvJWEfk+0ATOF5EniMh1UXG/TkSesNCxjn//mYh8Kv7+IBHxIvJrcZ8dFJE39UzbF1XPcRHZBBxRxV9kPx9ih4jrfbCI/A7wq8Ab4n7+757JHhVV5kkR+TcRqS91f1aoUGH5oyKrFSo88HAnYEXk4yLyTBFZ2fPdr8d/TwXOBwaB9x/Fsp8LfBZYAfwL8EmgH7gYOAN4N4CIXAZ8BPjfBOXvH4Avikgtfv8BEflAZ6Hz/v4poAb81xHG8jYCMX8U8GCCuvinPd+vB0bi568A/r5nXyxl3lXAucDvEO6VH41/nwO0iPvNe/8m4HvAq6Ja+CoRGQC+AXw67pdfAT4gIhfF5f890AY2AL8Z/3URyfAbF/n7Z4Gd3vsfHWH/ALwsjn8ImAa+TFBlVwPvAr4sIquXsJwOngQ8FPg54E9F5OHx8ysIhPsC4BnAry1xefP386Lw3n+YcM79bdzPl/d8/UKCqnwecCnhHK9QocIDBBVZrVDhAQbv/RSBVHjgH4EDUcVbR1Cm3uW93+q9nwH+GPgVWXoI9ofe+y947x2BsD4T+F3v/bj3vvTefydO9zvAP3jvr4mq38cJNoSfjGN8pff+lT1j7v17DXDQe28630dldkKC3/GnRUTiOv6v937Mez8N/BWBFHZQAn8Rx/UVYAZ46BLndcAV3vs8Kpej3vvPee+bcfq3Aj9zmP30HGCb9/6j3nvjvb8R+BzwyyKigRcAf+q9b3jvbwM+3juz9/453vu3LfL3GmBv7/RRoZwQkbaInNvz1ce897fHffl04C7v/SfjmD4DbAZ6Sd+R8Odxf9wM3Aw8Mn7+QuCtcX/uJBDipWDOfj6KcczHe733u733Y8B/E15CKlSo8ABB5RGqUOEBCO/9HUR1SUQeBnwK+DtgI7C9Z9LthPvAuiUuemfP72cDY9778QWmOxf4NRF5dc9nWVz/kTAKrBGRpENYvfdPgEDKCC/ZawmK7vUym28lgO5dTi/hJYTCB5c47wHvfbv7pUg/QTX+BaCjzg6JiPbe2wW24Vzg8SIy0fNZQlCi18bfe/dl7zE5EkYJimwX3vuz4gtHGbelg951zD/2nfWeeRTr7iXJnf3ZWfaxbM+c/XwcmD+upZxnFSpUOE1QKasVKjzA4b3fDHwMeASwm0CkOjgHMMA+oEEgcQBEBXDt/MX1/L4TWCUiKxZY7U6C0rai519/VPOOhB8SVNjnHmaag4RQ/MU9yx+JSTxHwlLm9fPmeT0h/P147/0w8NPxc1lk+p3Ad+Zt/6D3/v8ABwj7/Oye6c9Zwrg7+B/gLBF5zBKm7R3X/GPfWe998fc5x58Qol8q9nBs2zN/v80/B+ePYf70FSpU+DFARVYrVHiAISb2vL4nAehs4MXA1cBngP8rIueJyCAh/P1vUYG8E6iLyLNFJCVkl9cWW4/3fg/wVYIXc6WIpCLSIXH/CPyuiDxeAgbicoeONH7v/QShesEHROSXRGRIQmLYo4CBOI2L63i3iJwRt/NMEXnGEpZ/LPMOEQjuhIisIng0e7GP4AHu4EvAhSLysrhfUhF5rIg8PCqxnwf+TET6o491qR5PvPdbCB7gfxWRp8XkJg084QizfiWO6SUikojIi4CL4lgBbiJYQtJIhH9pqWMC/h3443genAW8+kgzLIKbgYtF5FExSerP5n0/fz9XqFDhxwAVWa1Q4YGHaeDxwDUi0iCQ1NsI6uBHCKHo7wL3EpJ8Xg3gvZ8kZJn/E0FtawBHKuz+MkLoeTOwn1BOiZj889uEJKRx4G56kl4kZN9/aLG/vfd/C7wOeAOBoOwjELQ/An4QJ/ujuNyrJVQn+CZB/VwKjnbevwP6CKrs1cD/m/f9e4Bfitnw742+1qcTfLC7CWHqv2GW/L+KEELfS1C9P9q7MBH5qoj8yWJ/A79H8IW+CxgjHKe3AC8ilIc6BN77UYKX9vUEK8EbgOd47w/GSf4/QoLUOOFl4dOH2R/z8eeE0P+9wNcJ59hRw3t/J6ESxDeBu4D5jRL+Gbgo+nO/cCzrqFChwukH8b6KqlSoUKFChQoVKlRYnqiU1QoVKlSoUKFChQrLFhVZrVChQoUKFSpUqLBsUZHVChUqVKhQoUKFCssWFVmtUKFChQoVKlSosGxRkdUKFSpUqFChQoUKyxZH6mBVlQqoUOF+gLEG8YJSam4/ogoVKpwUeB/+5/HhuqvwgIOIsFwqIC2nsSwnSE9bwV5U7VYrVFiGUBJJakVUK1Q4JRCJ/1uEPyzyDK1wmmE5HcflNJZTiWMh6dXrY4UKyxQ/pvexChWWHX5cSUWFCqcKRyKwFVmtUKFChQoVFkFFVCtUOLHoXFMdguq9P+J1VpHVChWWI6T7vwoVKlSoUOEBhw5BXcoLYeVZrVBh2cJTEdYKFSpUqPBAwbEmlVVktUKFZQipeGqFCvcP5l138x+uvVnc8xWhwz2If1zsBIvtmwoVjqf6QUVWK1RYhuhc0tXtvkKF5YXeB25VeqhChVODiqxWqLAcUXlWK1Q45fCd/1WX3jGjUlQrnAxUZLVChWUKqZ6aFSqcYhyqlC4lU7kzb2eyjuA6d7bFVNjTpaCy51j6BHm/9CSaCg9sHK4RwpGus4qsVqiwDFF5VitUONVY+CF6NCRr/oPY+6XMf7pc7IsT1SPbIeQoSH+FHxd0zomqdFWFCqcp/OKNdCpUqHCi4avrrUKFU42qdFWFCg8AVBpEhQqnCF2P+ImnrEFdPfz3veuVB6RfPajHQUHrfHb6b+Ns5YNjXcLJ2Af3x2vXyT+WFVmtUGEZ4vS/jVeocJphkVD24Xx2s7MerlyTdAnr7GLm+z97f18eAc/ebT628H3Hi9tZjuv5vPfn6Ye558OxksOTuf1HN6b5L0xzIUck44tdHvM7VR0PlsdVUaFChTmoQpIVKpxE+AXi/os8kJdKVGcXMv/f7Cp75lrqSE9jzFeLT3947xc8H07/EmaHG/+xb9ti++tYUCm
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 13.000\terrD: 0.469\terrG: 4.716\t(1839.84s - 12312.80s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
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|
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"metadata": {
|
||
|
|
"needs_background": "light"
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},
|
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|
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"output_type": "display_data"
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},
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|
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 14.000\terrD: 0.477\terrG: 4.599\t(1994.97s - 12254.83s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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"text/plain": [
|
||
|
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"<Figure size 864x864 with 1 Axes>"
|
||
|
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]
|
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},
|
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|
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"metadata": {
|
||
|
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"needs_background": "light"
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},
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 15.000\terrD: 0.492\terrG: 4.436\t(2150.47s - 12186.00s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
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"metadata": {
|
||
|
|
"needs_background": "light"
|
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},
|
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|
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"output_type": "display_data"
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},
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|
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{
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|
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 16.000\terrD: 0.490\terrG: 4.362\t(2301.24s - 12081.50s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
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|
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"metadata": {
|
||
|
|
"needs_background": "light"
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|
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},
|
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 17.000\terrD: 0.519\terrG: 4.246\t(2457.18s - 11996.81s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
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"text/plain": [
|
||
|
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"<Figure size 864x864 with 1 Axes>"
|
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|
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]
|
||
|
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},
|
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|
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"metadata": {
|
||
|
|
"needs_background": "light"
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},
|
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|
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 18.000\terrD: 0.489\terrG: 4.116\t(2613.81s - 11907.36s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
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]
|
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},
|
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|
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"metadata": {
|
||
|
|
"needs_background": "light"
|
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},
|
||
|
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"output_type": "display_data"
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},
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|
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 19.000\terrD: 0.522\terrG: 4.062\t(2789.76s - 11893.21s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
|
||
|
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"<Figure size 864x864 with 1 Axes>"
|
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]
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},
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|
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"metadata": {
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||
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"EPOCH: 20.000\terrD: 0.501\terrG: 4.029\t(2966.90s - 11867.59s remaining)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x864 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"generator = GeneratorUNet().to(device)\n",
|
||
|
|
"discriminator = Discriminator().to(device)\n",
|
||
|
|
"generator.apply(weights_init_normal)\n",
|
||
|
|
"discriminator.apply(weights_init_normal)\n",
|
||
|
|
"\n",
|
||
|
|
"criterion_GAN = torch.nn.MSELoss()\n",
|
||
|
|
"criterion_pixelwise = torch.nn.L1Loss()\n",
|
||
|
|
"\n",
|
||
|
|
"lambda_pixel = 100\n",
|
||
|
|
"g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
|
||
|
|
"d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n",
|
||
|
|
"\n",
|
||
|
|
"val_dl = DataLoader(val_ds, batch_size=1, shuffle=True)\n",
|
||
|
|
"\n",
|
||
|
|
"epochs = 100\n",
|
||
|
|
"log = Report(epochs)\n",
|
||
|
|
"\n",
|
||
|
|
"for epoch in range(epochs):\n",
|
||
|
|
" N = len(trn_dl)\n",
|
||
|
|
" for bx, batch in enumerate(trn_dl):\n",
|
||
|
|
" real_src, real_trg = batch\n",
|
||
|
|
" fake_trg = generator(real_src)\n",
|
||
|
|
" \n",
|
||
|
|
" errD = discriminator_train_step(real_src, real_trg, fake_trg)\n",
|
||
|
|
" errG = generator_train_step(real_src, fake_trg)\n",
|
||
|
|
" log.record(pos=epoch+(1+bx)/N, errD=errD.item(), errG=errG.item(), end='\\r')\n",
|
||
|
|
"\n",
|
||
|
|
" log.report_avgs(epoch+1)\n",
|
||
|
|
" [sample_prediction() for _ in range(2)]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"accelerator": "GPU",
|
||
|
|
"colab": {
|
||
|
|
"collapsed_sections": [],
|
||
|
|
"name": "pix2pix.ipynb",
|
||
|
|
"provenance": []
|
||
|
|
},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|