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adding notebook and APT installation file
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‎apt.txt

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unzip

‎notebooks/demo.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Overview\n",
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"The default setup downloads and decompresses the model \n",
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"```bash\n",
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"curl -L \"https://www.dropbox.com/s/dlmpr7wabehq3x0/models.zip?dl=1\" > models.zip\n",
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"unzip models.zip\n",
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"sys.path.append('../')"
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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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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.autograd import Variable\n",
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"import torchvision.transforms as transforms\n",
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"import torchvision.utils as utils\n",
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"import argparse\n",
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"import time\n",
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"import numpy as np\n",
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"import cv2\n",
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"from PIL import Image\n",
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"from photo_wct import PhotoWCT\n",
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"from photo_smooth import Propagator\n",
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"#from smooth_filter import smooth_filter"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import namedtuple\n",
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"# prepare paths for model\n",
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"vgg_paths = {}\n",
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"for i in range(1,6):\n",
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" vgg_paths['vgg{}'.format(i)] = '../models/vgg_normalised_conv{}_1_mask.t7'.format(i) \n",
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" vgg_paths['decoder{}'.format(i)] = '../models/feature_invertor_conv{}_1_mask.t7'.format(i)\n",
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"\n",
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"vgg_key_list = list(vgg_paths.keys())\n",
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"vgg_arg_class = namedtuple('VggArgs', vgg_key_list)\n",
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"vgg_args = vgg_arg_class(*[vgg_paths[k] for k in vgg_key_list])"
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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": 11,
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"metadata": {},
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"outputs": [
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{
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"ename": "IOError",
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"evalue": "[Errno 2] No such file or directory: '../models/vgg_normalised_conv1_1_mask.t7'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m\u001b[0m",
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"\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
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"\u001b[0;32m<ipython-input-11-aed45ba8830c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mp_wct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPhotoWCT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvgg_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mp_pro\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPropagator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/jovyan/photo_wct.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPhotoWCT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# TODO: convert these torch models to pytorch models.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mvgg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_lua\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvgg1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mdecoder1_torch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_lua\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mvgg2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_lua\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvgg2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/srv/conda/envs/kernel/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.pyc\u001b[0m in \u001b[0;36mload_lua\u001b[0;34m(filename, **kwargs)\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0mto\u001b[0m \u001b[0;34m`\u001b[0m\u001b[0mT7Reader\u001b[0m\u001b[0;34m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 596\u001b[0m \"\"\"\n\u001b[0;32m--> 597\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 598\u001b[0m \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT7Reader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '../models/vgg_normalised_conv1_1_mask.t7'"
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]
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}
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],
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"source": [
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"# Load model\n",
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"p_wct = PhotoWCT(vgg_args)\n",
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"p_pro = Propagator()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Style and Content Images\n",
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"## Content\n",
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"![](../images/content1.png)\n",
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"## Style\n",
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"![](../images/style1.png)"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"content_image_path = \"../images/content1.png\"\n",
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"content_seg_path = []\n",
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"style_image_path = \"../images/style1.png\"\n",
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"style_seg_path = []\n",
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"output_image_path = \"../results/example1.png\""
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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": 15,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'p_wct' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m\u001b[0m",
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"\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
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"\u001b[0;32m<ipython-input-15-8e5a025d7c26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp_wct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Load image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcont_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent_image_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mstyl_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle_image_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'RGB'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mNameError\u001b[0m: name 'p_wct' is not defined"
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]
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}
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],
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"source": [
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"p_wct.cuda(0)\n",
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"\n",
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"# Load image\n",
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"cont_img = Image.open(content_image_path).convert('RGB')\n",
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"styl_img = Image.open(style_image_path).convert('RGB')\n",
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"try:\n",
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" cont_seg = Image.open(content_seg_path)\n",
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" styl_seg = Image.open(style_seg_path)\n",
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"except:\n",
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" cont_seg = []\n",
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" styl_seg = []\n",
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"\n",
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"cont_img = transforms.ToTensor()(cont_img).unsqueeze(0)\n",
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"styl_img = transforms.ToTensor()(styl_img).unsqueeze(0)\n",
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"cont_img = Variable(cont_img.cuda(0), volatile=True)\n",
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"styl_img = Variable(styl_img.cuda(0), volatile=True)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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