style_transfer_dataset.ipynb

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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob, os\n",
    "from PIL import Image\n",
    "from torch.utils.data import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImageDataset(Dataset):\n",
    "    def __init__(self, root, transform=None, mode='train'):\n",
    "        self.transform = transform\n",
    "        self.files_A = sorted(glob.glob(os.path.join(root, '{}A'.format(mode)) + '/*.*'))\n",
    "        self.files_B = sorted(glob.glob(os.path.join(root, '{}B'.format(mode)) + '/*.*'))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        item_A = self.transform(Image.open(self.files_A[index % len(self.files_A)]))\n",
    "        item_B = self.transform(Image.open(self.files_B[index % len(self.files_B)]))\n",
    "        return {'A': item_A, 'B': item_B}\n",
    "\n",
    "    def __len__(self):\n",
    "        return max(len(self.files_A), len(self.files_B))"
   ]
  }
 ],
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