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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "_qsogBHiKtzF",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"datasets 2.4.0 requires dill<0.3.6, but you have dill 0.3.7 which is incompatible.\n",
"awscli 1.25.91 requires botocore==1.27.90, but you have botocore 1.31.17 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -qq hub\n",
"!pip install -qq flask"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "E8nHybN3KDIq",
"tags": []
},
"outputs": [],
"source": [
"import torch\n",
"import deeplake\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import transforms\n",
"import torch.nn as nn\n",
"from network import Style_Transfer_Network, Encoder\n",
"from utils import save_img\n",
"import torchvision"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rnAFLCiIKqkM",
"outputId": "81b8f1c3-3974-4ee3-a284-99186c1502c7",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"|"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Opening dataset in read-only mode as you don't have write permissions.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"-"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/activeloop/wiki-art\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"-"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hub://activeloop/wiki-art loaded successfully.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Opening dataset in read-only mode as you don't have write permissions.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\\"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/activeloop/coco-test\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\\"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hub://activeloop/coco-test loaded successfully.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" "
]
}
],
"source": [
"reshape_size = 512\n",
"crop_size = 256\n",
"def any_to_rgb(img):\n",
" return img.convert('RGB')\n",
"preprocess = transforms.Compose([\n",
" transforms.Lambda(any_to_rgb),\n",
" transforms.ToTensor(),\n",
" transforms.Resize(reshape_size),\n",
" transforms.RandomCrop(crop_size),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
" ])\n",
"wiki_art_dataset = deeplake.load('hub://activeloop/wiki-art')\n",
"coco_dataset = deeplake.load('hub://activeloop/coco-test')\n",
"\n",
"style_data_loader = wiki_art_dataset.pytorch(batch_size = 8, num_workers = 0,\n",
" transform = {'images': preprocess, 'labels': None}, shuffle = True, decode_method = {'images':'pil'})\n",
"\n",
"cnt_data_loader = coco_dataset.pytorch(batch_size = 8, num_workers = 0,\n",
" transform = {'images': preprocess}, shuffle = True, decode_method = {'images': 'pil'})\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "XKqi9mMyoNUy",
"tags": []
},
"outputs": [],
"source": [
"mse_loss = nn.MSELoss(reduction = 'mean')\n",
"def content_loss(source, target):\n",
" cnt_loss = mse_loss(source, target)\n",
" return cnt_loss\n",
"\n",
"def style_loss(features, targets):\n",
" loss = 0\n",
" for feature, target in zip(features, targets):\n",
" B, C, H, W = feature.shape\n",
" feature_std, feature_mean = torch.std_mean(feature.view(B, C, -1), dim = 2)\n",
" target_std, target_mean = torch.std_mean(target.view(B, C, -1), dim = 2)\n",
" loss += mse_loss(feature_std, target_std) + mse_loss(feature_mean, target_mean)\n",
" return loss * 1. / len(features)\n",
"\"\"\"\n",
"def style_loss(features, targets, weights=None):\n",
" if weights is None:\n",
" weights = [1/len(features)] * len(features)\n",
" \n",
" loss = 0\n",
" for feature, target, weight in zip(features, targets, weights):\n",
" b, c, h, w = feature.size()\n",
" feature_std, feature_mean = torch.std_mean(feature.view(b, c, -1), dim=2)\n",
" target_std, target_mean = torch.std_mean(target.view(b, c, -1), dim=2)\n",
" loss += (mse_loss(feature_std, target_std) + mse_loss(feature_mean, target_mean))*weight\n",
" return loss\n",
"\"\"\"\n",
"def total_variational_loss(images):\n",
" loss = 0.0\n",
" B = images.shape[0]\n",
" vertical_up = images[:,:,:-1]\n",
" vertical_down = images[:,:,1:]\n",
"\n",
" horizontal_up = images[:,:,:,:-1]\n",
" horizontal_down = images[:,:,:,1:]\n",
"\n",
" loss = ((vertical_up - vertical_down) ** 2).sum() + \\\n",
" ((horizontal_up - horizontal_down) ** 2).sum()\n",
"\n",
" return loss * 1.0 / B"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "JAeuZ2Sq6E-0",
"tags": []
},
"outputs": [],
"source": [
"if torch.cuda.is_available():\n",
" device = \"cuda\"\n",
"else: device = \"cpu\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"style_transfer_network = Style_Transfer_Network().to(device)\n",
"check_point = torch.load(\"/notebooks/Style_transfer_with_ADAin/check_point.pth\", map_location = 'cuda')\n",
"style_transfer_network.load_state_dict(check_point['state_dict'])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def denormalize():\n",
" # out = (x - mean) / std\n",
" MEAN = [0.485, 0.456, 0.406]\n",
" STD = [0.229, 0.224, 0.225]\n",
" MEAN = [-mean/std for mean, std in zip(MEAN, STD)]\n",
" STD = [1/std for std in STD]\n",
" return transforms.Normalize(mean=MEAN, std=STD)\n",
"\n",
"def save_img(tensor, path):\n",
" denormalizer = denormalize() \n",
" if tensor.is_cuda:\n",
" tensor = tensor.cpu()\n",
" tensor = torchvision.utils.make_grid(tensor)\n",
" torchvision.utils.save_image(denormalizer(tensor).clamp_(0.0, 1.0), path) \n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1Y-JrlNquBwn",
"outputId": "31d5fe14-5315-40cd-8946-99c34ff41726",
"tags": []
},
"outputs": [],
"source": [
"def train_network(iteration, loss_weight = [1.0, 100.0, 0.001], check_iter = 1, test_iter = 10):\n",
" for param in style_transfer_network.encoder.parameters():\n",
" # freeze parameter in the encoder network\n",
" param.requires_grad = False\n",
" optimizer = torch.optim.Adam(style_transfer_network.decoder.parameters(), lr = 1e-6)\n",
"\n",
" encoder_net = Encoder().to(device)\n",
" for param in encoder_net.parameters():\n",
" param.requires_grad = False\n",
" for i in range(iteration):\n",
" content_imgs = next(iter(cnt_data_loader))['images'].to(device)\n",
" style_imgs = next(iter(style_data_loader))['images'].to(device)\n",
"\n",
" output_imgs, transformed_features = style_transfer_network(content_imgs, style_imgs, train = True)\n",
"\n",
" output_features = encoder_net(output_imgs)\n",
" style_features = encoder_net(style_imgs)\n",
"\n",
" cnt_loss = content_loss(transformed_features, output_features[-1])\n",
" st_loss = style_loss(output_features, style_features)\n",
" tv_loss = total_variational_loss(output_imgs)\n",
" cnt_w, style_w, tv_w = loss_weight\n",
" total_loss = cnt_w * tv_loss + style_w * st_loss + tv_w * tv_loss\n",
"\n",
" optimizer.zero_grad()\n",
" total_loss.backward()\n",
" optimizer.step()\n",
"\n",
" if i % check_iter == 0:\n",
" print('-' * 80)\n",
" print(\"Iteration {} loss: {}\".format(i, total_loss))\n",
"\n",
" if i % test_iter == 0:\n",
" #save_img(torch.cat([content_imgs[0], style_imgs[0], output_imgs[0]], dim = 0), \"training_image.png\")\n",
" torch.save({'iteration':iteration+1,\n",
" 'state_dict':style_transfer_network.state_dict()},\n",
" 'check_point1.pth')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------------------------------------------------------\n",
"Iteration 0 loss: 0.8845198750495911\n",
"--------------------------------------------------------------------------------\n",
"Iteration 1 loss: 1.8098524808883667\n",
"--------------------------------------------------------------------------------\n",
"Iteration 2 loss: 1.868203043937683\n",
"--------------------------------------------------------------------------------\n",
"Iteration 3 loss: 1.1070071458816528\n",
"--------------------------------------------------------------------------------\n",
"Iteration 4 loss: 2.0751609802246094\n",
"--------------------------------------------------------------------------------\n",
"Iteration 5 loss: 2.7107627391815186\n",
"--------------------------------------------------------------------------------\n",
"Iteration 6 loss: 1.4618340730667114\n",
"--------------------------------------------------------------------------------\n",
"Iteration 7 loss: 1.2351319789886475\n",
"--------------------------------------------------------------------------------\n",
"Iteration 8 loss: 1.3090686798095703\n",
"--------------------------------------------------------------------------------\n",
"Iteration 9 loss: 1.7165802717208862\n",
"--------------------------------------------------------------------------------\n",
"Iteration 10 loss: 1.9655226469039917\n",
"--------------------------------------------------------------------------------\n",
"Iteration 11 loss: 1.8032971620559692\n",
"--------------------------------------------------------------------------------\n",
"Iteration 12 loss: 1.757157802581787\n",
"--------------------------------------------------------------------------------\n",
"Iteration 13 loss: 1.2641586065292358\n",
"--------------------------------------------------------------------------------\n",
"Iteration 14 loss: 1.230526328086853\n",
"--------------------------------------------------------------------------------\n",
"Iteration 15 loss: 1.8332327604293823\n",
"--------------------------------------------------------------------------------\n",
"Iteration 16 loss: 2.347355365753174\n",
"--------------------------------------------------------------------------------\n",
"Iteration 17 loss: 0.8620480298995972\n",
"--------------------------------------------------------------------------------\n",
"Iteration 18 loss: 1.572771668434143\n",
"--------------------------------------------------------------------------------\n",
"Iteration 19 loss: 2.281660795211792\n",
"--------------------------------------------------------------------------------\n",
"Iteration 20 loss: 1.417534589767456\n",
"--------------------------------------------------------------------------------\n",
"Iteration 21 loss: 1.848774790763855\n",
"--------------------------------------------------------------------------------\n",
"Iteration 22 loss: 1.1456807851791382\n",
"--------------------------------------------------------------------------------\n",
"Iteration 23 loss: 1.2357560396194458\n",
"--------------------------------------------------------------------------------\n",
"Iteration 24 loss: 0.6565238833427429\n",
"--------------------------------------------------------------------------------\n",
"Iteration 25 loss: 1.2375402450561523\n",
"--------------------------------------------------------------------------------\n",
"Iteration 26 loss: 2.1140313148498535\n",
"--------------------------------------------------------------------------------\n",
"Iteration 27 loss: 1.0238616466522217\n",
"--------------------------------------------------------------------------------\n",
"Iteration 28 loss: 2.618056058883667\n",
"--------------------------------------------------------------------------------\n",
"Iteration 29 loss: 1.1616159677505493\n",
"--------------------------------------------------------------------------------\n",
"Iteration 30 loss: 1.919601559638977\n",
"--------------------------------------------------------------------------------\n",
"Iteration 31 loss: 1.0250651836395264\n",
"--------------------------------------------------------------------------------\n",
"Iteration 32 loss: 1.1823596954345703\n",
"--------------------------------------------------------------------------------\n",
"Iteration 33 loss: 0.8185012936592102\n",
"--------------------------------------------------------------------------------\n",
"Iteration 34 loss: 1.1374247074127197\n",
"--------------------------------------------------------------------------------\n",
"Iteration 35 loss: 1.9250235557556152\n",
"--------------------------------------------------------------------------------\n",
"Iteration 36 loss: 1.466286540031433\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.9/dist-packages/PIL/Image.py:3035: DecompressionBombWarning: Image size (99962094 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"--------------------------------------------------------------------------------\n",
"Iteration 37 loss: 0.7055997848510742\n",
"--------------------------------------------------------------------------------\n",
"Iteration 38 loss: 1.3557121753692627\n",
"--------------------------------------------------------------------------------\n",
"Iteration 39 loss: 1.0668007135391235\n",
"--------------------------------------------------------------------------------\n",
"Iteration 40 loss: 1.1934823989868164\n",
"--------------------------------------------------------------------------------\n",
"Iteration 41 loss: 0.7692145109176636\n",
"--------------------------------------------------------------------------------\n",
"Iteration 42 loss: 1.141457438468933\n",
"--------------------------------------------------------------------------------\n",
"Iteration 43 loss: 1.5705242156982422\n",
"--------------------------------------------------------------------------------\n",
"Iteration 44 loss: 1.7851486206054688\n",
"--------------------------------------------------------------------------------\n",
"Iteration 45 loss: 0.7252503633499146\n",
"--------------------------------------------------------------------------------\n",
"Iteration 46 loss: 1.1291860342025757\n",
"--------------------------------------------------------------------------------\n",
"Iteration 47 loss: 1.3588659763336182\n",
"--------------------------------------------------------------------------------\n",
"Iteration 48 loss: 0.9960977435112\n",
"--------------------------------------------------------------------------------\n",
"Iteration 49 loss: 0.9272828102111816\n",
"--------------------------------------------------------------------------------\n",
"Iteration 50 loss: 2.4692296981811523\n"
]
}
],
"source": [
"train_network(iteration = 300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"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",
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}
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