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{
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"id": "10ee1bf4",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"import numpy as np\n",
"from PIL import Image\n",
"from tqdm import tqdm\n",
"from lib.gan_networks import define_G\n",
"import torchvision.transforms as transforms"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "59797ab5",
"metadata": {},
"outputs": [],
"source": [
"def __transforms2pil_resize(method):\n",
" mapper = {\n",
" transforms.InterpolationMode.BILINEAR: Image.BILINEAR,\n",
" transforms.InterpolationMode.BICUBIC: Image.BICUBIC,\n",
" transforms.InterpolationMode.NEAREST: Image.NEAREST,\n",
" transforms.InterpolationMode.LANCZOS: Image.LANCZOS,\n",
" }\n",
" return mapper[method]\n",
"\n",
"\n",
"def __scale_width(\n",
" img, target_size, crop_size, method=transforms.InterpolationMode.BICUBIC\n",
"):\n",
" method = __transforms2pil_resize(method)\n",
" ow, oh = img.size\n",
" if ow == target_size and oh >= crop_size:\n",
" return img\n",
" w = target_size\n",
" h = int(max(target_size * oh / ow, crop_size))\n",
" return img.resize((w, h), method)\n",
"\n",
"\n",
"def get_transform(load_size, crop_size, method=transforms.InterpolationMode.BICUBIC):\n",
" transform_list = [\n",
" transforms.Lambda(lambda img: __scale_width(img, load_size, crop_size, method)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
" ]\n",
" return transforms.Compose(transform_list)\n",
"\n",
"\n",
"def tensor2im(input_image, imtype=np.uint8):\n",
" \"\"\" \"Converts a Tensor array into a numpy image array.\n",
"\n",
" Parameters:\n",
" input_image (tensor) -- the input image tensor array\n",
" imtype (type) -- the desired type of the converted numpy array\n",
" \"\"\"\n",
" if not isinstance(input_image, np.ndarray):\n",
" if isinstance(input_image, torch.Tensor): # get the data from a variable\n",
" image_tensor = input_image.data\n",
" else:\n",
" return input_image\n",
" image_numpy = (\n",
" image_tensor[0].cpu().float().numpy()\n",
" ) # convert it into a numpy array\n",
" if image_numpy.shape[0] == 1: # grayscale to RGB\n",
" image_numpy = np.tile(image_numpy, (3, 1, 1))\n",
" image_numpy = (\n",
" (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0\n",
" ) # post-processing: tranpose and scaling\n",
" else: # if it is a numpy array, do nothing\n",
" image_numpy = input_image\n",
" return image_numpy.astype(imtype)\n",
"\n",
"\n",
"def create_model_and_transform(pretrained: str = None):\n",
" # Creating model\n",
" input_nc = 3\n",
" output_nc = 3\n",
" ngf = 64\n",
" netG = \"resnet_9blocks\"\n",
" norm = \"instance\"\n",
" no_dropout = True\n",
" init_type = \"normal\"\n",
" init_gain = 0.02\n",
" gpu_ids = []\n",
"\n",
" netG_A = define_G(\n",
" input_nc,\n",
" output_nc,\n",
" ngf,\n",
" netG,\n",
" norm,\n",
" not no_dropout,\n",
" init_type,\n",
" init_gain,\n",
" gpu_ids,\n",
" )\n",
" if pretrained:\n",
" chkpntA = torch.load(pretrained)\n",
" netG_A.load_state_dict(chkpntA)\n",
" netG_A.eval()\n",
"\n",
" netG_A = netG_A.cuda()\n",
"\n",
" # Creating transform\n",
" load_size = 1280\n",
" crop_size = 224\n",
" image_transforms = get_transform(load_size=load_size, crop_size=crop_size)\n",
" return netG_A, image_transforms\n",
"\n",
"\n",
"def run_inference(img_path, model, transform):\n",
" image = Image.open(img_path)\n",
" inputs = image_transforms(image).unsqueeze(0).to(\"cuda\")\n",
"\n",
" with torch.no_grad():\n",
" out = model(inputs)\n",
" out = tensor2im(out)\n",
" return Image.fromarray(out)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6fc20d26",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"initialize network with normal\n"
]
},
{
"ename": "RuntimeError",
"evalue": "Error(s) in loading state_dict for UnetGenerator:\n\tMissing key(s) in state_dict: \"model.model.0.weight\", \"model.model.0.bias\", \"model.model.1.model.1.weight\", \"model.model.1.model.1.bias\", \"model.model.1.model.3.model.1.weight\", \"model.model.1.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.3.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.3.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.5.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.5.bias\", \"model.model.1.model.3.model.3.model.3.model.5.weight\", \"model.model.1.model.3.model.3.model.3.model.5.bias\", \"model.model.1.model.3.model.3.model.5.weight\", \"model.model.1.model.3.model.3.model.5.bias\", \"model.model.1.model.3.model.5.weight\", \"model.model.1.model.3.model.5.bias\", \"model.model.1.model.5.weight\", \"model.model.1.model.5.bias\", \"model.model.3.weight\", \"model.model.3.bias\". \n\tUnexpected key(s) in state_dict: \"model.1.weight\", \"model.1.bias\", \"model.4.weight\", \"model.4.bias\", \"model.7.weight\", \"model.7.bias\", \"model.10.conv_block.1.weight\", \"model.10.conv_block.1.bias\", \"model.10.conv_block.5.weight\", \"model.10.conv_block.5.bias\", \"model.11.conv_block.1.weight\", \"model.11.conv_block.1.bias\", \"model.11.conv_block.5.weight\", \"model.11.conv_block.5.bias\", \"model.12.conv_block.1.weight\", \"model.12.conv_block.1.bias\", \"model.12.conv_block.5.weight\", \"model.12.conv_block.5.bias\", \"model.13.conv_block.1.weight\", \"model.13.conv_block.1.bias\", \"model.13.conv_block.5.weight\", \"model.13.conv_block.5.bias\", \"model.14.conv_block.1.weight\", \"model.14.conv_block.1.bias\", \"model.14.conv_block.5.weight\", \"model.14.conv_block.5.bias\", \"model.15.conv_block.1.weight\", \"model.15.conv_block.1.bias\", \"model.15.conv_block.5.weight\", \"model.15.conv_block.5.bias\", \"model.16.conv_block.1.weight\", \"model.16.conv_block.1.bias\", \"model.16.conv_block.5.weight\", \"model.16.conv_block.5.bias\", \"model.17.conv_block.1.weight\", \"model.17.conv_block.1.bias\", \"model.17.conv_block.5.weight\", \"model.17.conv_block.5.bias\", \"model.18.conv_block.1.weight\", \"model.18.conv_block.1.bias\", \"model.18.conv_block.5.weight\", \"model.18.conv_block.5.bias\", \"model.19.weight\", \"model.19.bias\", \"model.22.weight\", \"model.22.bias\", \"model.26.weight\", \"model.26.bias\". ",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m gan, image_transforms \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_model_and_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./checkpoints/clear2snowy.pth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[12], line 82\u001b[0m, in \u001b[0;36mcreate_model_and_transform\u001b[0;34m(pretrained)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pretrained:\n\u001b[1;32m 81\u001b[0m chkpntA \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(pretrained)\n\u001b[0;32m---> 82\u001b[0m \u001b[43mnetG_A\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_state_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchkpntA\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m netG_A\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 85\u001b[0m netG_A \u001b[38;5;241m=\u001b[39m netG_A\u001b[38;5;241m.\u001b[39mcuda()\n",
"File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/torch/nn/modules/module.py:2189\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m 2184\u001b[0m error_msgs\u001b[38;5;241m.\u001b[39minsert(\n\u001b[1;32m 2185\u001b[0m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing key(s) in state_dict: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2186\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m missing_keys)))\n\u001b[1;32m 2188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2189\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2190\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)))\n\u001b[1;32m 2191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",
"\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for UnetGenerator:\n\tMissing key(s) in state_dict: \"model.model.0.weight\", \"model.model.0.bias\", \"model.model.1.model.1.weight\", \"model.model.1.model.1.bias\", \"model.model.1.model.3.model.1.weight\", \"model.model.1.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.1.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.1.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.3.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.3.model.3.bias\", \"model.model.1.model.3.model.3.model.3.model.3.model.5.weight\", \"model.model.1.model.3.model.3.model.3.model.3.model.5.bias\", \"model.model.1.model.3.model.3.model.3.model.5.weight\", \"model.model.1.model.3.model.3.model.3.model.5.bias\", \"model.model.1.model.3.model.3.model.5.weight\", \"model.model.1.model.3.model.3.model.5.bias\", \"model.model.1.model.3.model.5.weight\", \"model.model.1.model.3.model.5.bias\", \"model.model.1.model.5.weight\", \"model.model.1.model.5.bias\", \"model.model.3.weight\", \"model.model.3.bias\". \n\tUnexpected key(s) in state_dict: \"model.1.weight\", \"model.1.bias\", \"model.4.weight\", \"model.4.bias\", \"model.7.weight\", \"model.7.bias\", \"model.10.conv_block.1.weight\", \"model.10.conv_block.1.bias\", \"model.10.conv_block.5.weight\", \"model.10.conv_block.5.bias\", \"model.11.conv_block.1.weight\", \"model.11.conv_block.1.bias\", \"model.11.conv_block.5.weight\", \"model.11.conv_block.5.bias\", \"model.12.conv_block.1.weight\", \"model.12.conv_block.1.bias\", \"model.12.conv_block.5.weight\", \"model.12.conv_block.5.bias\", \"model.13.conv_block.1.weight\", \"model.13.conv_block.1.bias\", \"model.13.conv_block.5.weight\", \"model.13.conv_block.5.bias\", \"model.14.conv_block.1.weight\", \"model.14.conv_block.1.bias\", \"model.14.conv_block.5.weight\", \"model.14.conv_block.5.bias\", \"model.15.conv_block.1.weight\", \"model.15.conv_block.1.bias\", \"model.15.conv_block.5.weight\", \"model.15.conv_block.5.bias\", \"model.16.conv_block.1.weight\", \"model.16.conv_block.1.bias\", \"model.16.conv_block.5.weight\", \"model.16.conv_block.5.bias\", \"model.17.conv_block.1.weight\", \"model.17.conv_block.1.bias\", \"model.17.conv_block.5.weight\", \"model.17.conv_block.5.bias\", \"model.18.conv_block.1.weight\", \"model.18.conv_block.1.bias\", \"model.18.conv_block.5.weight\", \"model.18.conv_block.5.bias\", \"model.19.weight\", \"model.19.bias\", \"model.22.weight\", \"model.22.bias\", \"model.26.weight\", \"model.26.bias\". "
]
}
],
"source": [
"gan, image_transforms = create_model_and_transform(\"./checkpoints/clear2snowy.pth\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d44ebf97",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 100/100 [00:39<00:00, 2.51it/s]\n"
]
}
],
"source": [
"image_path = os.listdir(\"./data/images\")\n",
"save_folder = \"./data/gan/snow_images\"\n",
"\n",
"for img in tqdm(image_path):\n",
" trg = os.path.join(\"./data/images\", img)\n",
" src = os.path.join(f\"./data/gan/snow_images/\", img.split(\".\")[0] + \".jpg\")\n",
" if not (os.path.exists(src)):\n",
" out = run_inference(img_path=trg, model=gan, transform=image_transforms)\n",
" out.save(src)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.14"
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},
"nbformat": 4,
"nbformat_minor": 5
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|