{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":21755,"databundleVersionId":1475600,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import transforms\nfrom torch.utils.data import DataLoader, Dataset\nimport os\nimport zipfile\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Defining the Generator (U-Net Style)\nclass Generator(nn.Module):\n    def __init__(self):\n        super(Generator, self).__init__()\n        self.encoder = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),\n            nn.ReLU(True),\n            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),\n            nn.BatchNorm2d(128),\n            nn.ReLU(True),\n            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),\n            nn.BatchNorm2d(256),\n            nn.ReLU(True)\n        )\n        self.decoder = nn.Sequential(\n            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),\n            nn.BatchNorm2d(128),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),\n            nn.BatchNorm2d(64),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),\n            nn.Tanh()\n        )\n\n    def forward(self, x):\n        x = self.encoder(x)\n        x = self.decoder(x)\n        return x\n\n\n# Defining the Discriminator (PatchGAN)\nclass Discriminator(nn.Module):\n    def __init__(self):\n        super(Discriminator, self).__init__()\n        self.model = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),\n            nn.LeakyReLU(0.2, True),\n            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),\n            nn.BatchNorm2d(128),\n            nn.LeakyReLU(0.2, True),\n            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),\n            nn.BatchNorm2d(256),\n            nn.LeakyReLU(0.2, True),\n            nn.Conv2d(256, 1, kernel_size=4, stride=1, padding=1)\n        )\n\n    def forward(self, x):\n        return self.model(x)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Initialize Models\ngenerator = Generator()\ndiscriminator = Discriminator()\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ngenerator = generator.to(device)\ndiscriminator = discriminator.to(device)\n\n# Loss Functions and Optimizers\nadversarial_loss = nn.MSELoss()\ncycle_loss = nn.L1Loss()\nidentity_loss = nn.L1Loss()\n\noptimizer_G = optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))\noptimizer_D = optim.Adam(discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))\n\n# Dataset Preparation\ndata_transforms = transforms.Compose([\n    transforms.Resize((256, 256)),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n])\n\ndata_path = '/kaggle/input/gan-getting-started/'\n\nphotos_path = os.path.join(data_path, 'photo_jpg')\nmonet_path = os.path.join(data_path, 'monet_jpg')\n\nphoto_files = [os.path.join(photos_path, file) for file in os.listdir(photos_path)]\nmonet_files = [os.path.join(monet_path, file) for file in os.listdir(monet_path)]\n\n\nclass FlatImageDataset(Dataset):\n    def __init__(self, file_paths, transform=None):\n        self.file_paths = file_paths\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.file_paths)\n\n    def __getitem__(self, idx):\n        img_path = self.file_paths[idx]\n        img = Image.open(img_path).convert(\"RGB\")\n        if self.transform:\n            img = self.transform(img)\n        return img\n\n\nphoto_dataset = FlatImageDataset(photo_files, transform=data_transforms)\nmonet_dataset = FlatImageDataset(monet_files, transform=data_transforms)\n\nphoto_loader = DataLoader(photo_dataset, batch_size=16, shuffle=True)\nmonet_loader = DataLoader(monet_dataset, batch_size=4, shuffle=True)\n\n# Training Loop\nnum_epochs = 10  \nfor epoch in range(num_epochs):\n    for i, (photo_batch, monet_batch) in enumerate(zip(photo_loader, monet_loader)):\n        # Move data to device\n        real_photos = photo_batch.to(device)\n        real_monets = monet_batch.to(device)\n\n        # Train Generator\n        optimizer_G.zero_grad()\n        fake_monets = generator(real_photos)\n        validity = discriminator(fake_monets)\n        g_loss = adversarial_loss(validity, torch.ones_like(validity)) + \\\n                 cycle_loss(generator(fake_monets), real_photos) + \\\n                 identity_loss(generator(real_photos), real_photos)\n        g_loss.backward()\n        optimizer_G.step()\n\n        # Train Discriminator\n        optimizer_D.zero_grad()\n        real_validity = discriminator(real_monets)\n        fake_validity = discriminator(fake_monets.detach())\n        d_loss = (adversarial_loss(real_validity, torch.ones_like(real_validity)) + \\\n                 adversarial_loss(fake_validity, torch.zeros_like(fake_validity))) / 2\n        d_loss.backward()\n        optimizer_D.step()\n\n        # Print Progress\n        if i % 10 == 0:\n            print(f\"Epoch [{epoch+1}/{num_epochs}], Batch [{i}], G_Loss: {g_loss.item()}, D_Loss: {d_loss.item()}\")\n\n# Visualization Function\ndef visualize_predictions(loader, generator, num_images=5):\n    generator.eval()\n    with torch.no_grad():\n        for i, photo_batch in enumerate(loader):\n            real_photos = photo_batch.to(device)\n            fake_monets = generator(real_photos)\n            fake_monets = (fake_monets * 0.5 + 0.5).clamp(0, 1)  # Denormalize to [0, 1]\n\n            for j in range(min(num_images, len(fake_monets))):\n                real_img = transforms.ToPILImage()(real_photos[j].cpu())\n                fake_img = transforms.ToPILImage()(fake_monets[j].cpu())\n\n                plt.figure(figsize=(8, 4))\n                plt.subplot(1, 2, 1)\n                plt.title(\"Real Photo\")\n                plt.imshow(real_img)\n                plt.axis(\"off\")\n\n                plt.subplot(1, 2, 2)\n                plt.title(\"Generated Monet\")\n                plt.imshow(fake_img)\n                plt.axis(\"off\")\n                plt.show()\n\n                num_images -= 1\n                if num_images <= 0:\n                    return\n\nvisualize_predictions(photo_loader, generator)\n\n# Save Generated Images as Zip\noutput_dir = \"../tmp/generated_images\"\nos.makedirs(output_dir, exist_ok=True)\n\nwith zipfile.ZipFile(\"images.zip\", \"w\") as zipf:\n    image_counter = 1  # Initialize a global counter for unique file names\n    for i, photo_batch in enumerate(photo_loader):\n        real_photos = photo_batch.to(device)\n        fake_monets = generator(real_photos)\n        fake_monets = (fake_monets * 0.5 + 0.5).clamp(0, 1)  # Denormalize to [0, 1]\n\n        for j, img in enumerate(fake_monets):\n            img = transforms.ToPILImage()(img.cpu())\n            img_path = os.path.join(output_dir, f\"image_{image_counter}.jpg\")\n            img.save(img_path)\n            zipf.write(img_path, os.path.basename(img_path))\n            image_counter += 1  # Increment the counter for each image\n\n            if image_counter > 8000:  # Stop after generating 8000 images\n                break\n        if image_counter > 8000:\n            break","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}