import os\nimport glob\nimport random\nfrom PIL import Image\nimport torchvision.transforms as transforms\n\ndef load_data(image_path, image_size=(256, 256)):\n images = glob.glob(os.path.join(image_path, '*.jpg'))\n dataset = []\n for img in images:\n image = Image.open(img).convert('RGB')\n transform = transforms.Compose([\n transforms.Resize(image_size),\n transforms.ToTensor(),\n ])\n image = transform(image)\n dataset.append(image)\n return dataset\n\n# \uc774\ubbf8\uc9c0 \uacbd\ub85c \uc124\uc815\nmonet_path = '.\/data\/monet\/'\nphoto_path = '.\/data\/photos\/'\n\nmonet_images = load_data(monet_path)\nphoto_images = load_data(photo_path)\n<\/code><\/pre>\n4. CycleGAN \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
CycleGAN \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 \uae30\ubcf8\uc801\uc778 \uc0dd\uc131\uae30\uc640 \ud310\ubcc4\uae30\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \uc0dd\uc131\uae30 \uc815\uc758<\/h3>\n
\uc5ec\uae30\uc11c\ub294 U-Net \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uc0dd\uc131\uae30\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\n\nclass UNetGenerator(nn.Module):\n def __init__(self):\n super(UNetGenerator, self).__init__()\n self.encoder1 = self.contracting_block(3, 64)\n self.encoder2 = self.contracting_block(64, 128)\n self.encoder3 = self.contracting_block(128, 256)\n self.encoder4 = self.contracting_block(256, 512)\n self.decoder1 = self.expansive_block(512, 256)\n self.decoder2 = self.expansive_block(256, 128)\n self.decoder3 = self.expansive_block(128, 64)\n self.decoder4 = nn.ConvTranspose2d(64, 3, kernel_size=3, stride=1, padding=1)\n\n def contracting_block(self, in_channels, out_channels):\n return nn.Sequential(\n nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True)\n )\n \n def expansive_block(self, in_channels, out_channels):\n return nn.Sequential(\n nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(inplace=True)\n )\n \n def forward(self, x):\n e1 = self.encoder1(x)\n e2 = self.encoder2(e1)\n e3 = self.encoder3(e2)\n e4 = self.encoder4(e3)\n d1 = self.decoder1(e4)\n d2 = self.decoder2(d1 + e3) # Skip connection\n d3 = self.decoder3(d2 + e2) # Skip connection\n output = self.decoder4(d3 + e1) # Skip connection\n return output\n<\/code><\/pre>\n4.2 \ud310\ubcc4\uae30 \uc815\uc758<\/h3>\n
\ud328\uce58 \uae30\ubc18 \uad6c\uc870\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud310\ubcc4\uae30\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
class PatchDiscriminator(nn.Module):\n def __init__(self):\n super(PatchDiscriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),\n nn.BatchNorm2d(256),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),\n nn.BatchNorm2d(512),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Conv2d(512, 1, kernel_size=4, stride=1, padding=1)\n )\n\n def forward(self, x):\n return self.model(x)\n<\/code><\/pre>\n5. \uc190\uc2e4 \ud568\uc218 \uad6c\ud604<\/h2>\n
CycleGAN\uc758 \uc190\uc2e4 \ud568\uc218\ub97c \uad6c\ud604\ud569\ub2c8\ub2e4. \uc0dd\uc131\uae30\uc758 \uc190\uc2e4\uacfc \ud310\ubcc4\uae30\uc758 \uc190\uc2e4\uc744 \ubaa8\ub450 \uace0\ub824\ud569\ub2c8\ub2e4.<\/p>\n
def compute_gan_loss(predictions, targets):\n return nn.BCEWithLogitsLoss()(predictions, targets)\n\ndef compute_cycle_loss(real_image, cycled_image, lambda_cycle):\n return lambda_cycle * nn.L1Loss()(real_image, cycled_image)\n\ndef compute_total_loss(real_images_X, real_images_Y, \n fake_images_Y, fake_images_X, \n cycled_images_X, cycled_images_Y, \n D_X, D_Y, lambda_cycle):\n loss_GAN_X = compute_gan_loss(D_Y(fake_images_Y), torch.ones_like(fake_images_Y))\n loss_GAN_Y = compute_gan_loss(D_X(fake_images_X), torch.ones_like(fake_images_X))\n loss_cycle = compute_cycle_loss(real_images_X, cycled_images_X, lambda_cycle) + \\\n compute_cycle_loss(real_images_Y, cycled_images_Y, lambda_cycle)\n return loss_GAN_X + loss_GAN_Y + loss_cycle\n<\/code><\/pre>\n6. \ud559\uc2b5 \uacfc\uc815<\/h2>\n
\uc774\uc81c \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \ucc28\ub840\uc785\ub2c8\ub2e4. \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc124\uc815\ud558\uace0, \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud55c \ud6c4, \uc190\uc2e4\uc744 \uc800\uc7a5\ud558\uace0 \uc5c5\ub370\uc774\ud2b8\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
from torch.utils.data import DataLoader\n\ndef train_cyclegan(monet_loader, photo_loader, epochs=200, lambda_cycle=10):\n G = UNetGenerator()\n F = UNetGenerator()\n D_X = PatchDiscriminator()\n D_Y = PatchDiscriminator()\n\n # Optimizers \uc124\uc815\n optimizer_G = torch.optim.Adam(G.parameters(), lr=0.0002, betas=(0.5, 0.999))\n optimizer_F = torch.optim.Adam(F.parameters(), lr=0.0002, betas=(0.5, 0.999))\n optimizer_D_X = torch.optim.Adam(D_X.parameters(), lr=0.0002, betas=(0.5, 0.999))\n optimizer_D_Y = torch.optim.Adam(D_Y.parameters(), lr=0.0002, betas=(0.5, 0.999))\n\n for epoch in range(epochs):\n for real_images_X, real_images_Y in zip(monet_loader, photo_loader):\n # \uc0dd\uc131\uae30 \ud559\uc2b5\n fake_images_Y = G(real_images_X)\n cycled_images_X = F(fake_images_Y)\n\n optimizer_G.zero_grad()\n optimizer_F.zero_grad()\n total_loss = compute_total_loss(real_images_X, real_images_Y, \n fake_images_Y, fake_images_X, \n cycled_images_X, cycled_images_Y, \n D_X, D_Y, lambda_cycle)\n total_loss.backward()\n optimizer_G.step()\n optimizer_F.step()\n\n # \ud310\ubcc4\uae30 \ud559\uc2b5\n optimizer_D_X.zero_grad()\n optimizer_D_Y.zero_grad()\n loss_D_X = compute_gan_loss(D_X(real_images_X), torch.ones_like(real_images_X)) + \\\n compute_gan_loss(D_X(fake_images_X.detach()), torch.zeros_like(fake_images_X))\n loss_D_Y = compute_gan_loss(D_Y(real_images_Y), torch.ones_like(real_images_Y)) + \\\n compute_gan_loss(D_Y(fake_images_Y.detach()), torch.zeros_like(fake_images_Y))\n loss_D_X.backward()\n loss_D_Y.backward()\n optimizer_D_X.step()\n optimizer_D_Y.step()\n\n print(f'Epoch [{epoch+1}\/{epochs}], Loss: {total_loss.item()}')\n<\/code><\/pre>\n7. \uacb0\uacfc \uc0dd\uc131<\/h2>\n
\ubaa8\ub378\uc774 \ud559\uc2b5\uc744 \ub9c8\uce58\uba74, \uc0c8\ub85c\uc6b4 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud14c\uc2a4\ud2b8 \uc774\ubbf8\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131\ub41c \ubaa8\ub124 \uc2a4\ud0c0\uc77c\uc758 \uadf8\ub9bc\uc744 \ud655\uc778\ud574\ubd05\uc2dc\ub2e4.<\/p>\n
def generate_images(test_loader, model_G):\n model_G.eval()\n for real_images in test_loader:\n with torch.no_grad():\n fake_images = model_G(real_images)\n # \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uac70\ub098 \uc2dc\uac01\ud654\ud558\ub294 \ucf54\ub4dc \ucd94\uac00\n<\/code><\/pre>\n\uc774\ubbf8\uc9c0\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud55c \ub0b4\uc7a5 \ud568\uc218\ub97c \ucd94\uac00\ud569\ub2c8\ub2e4:<\/p>\n
import matplotlib.pyplot as plt\n\ndef visualize_results(real_images, fake_images):\n plt.figure(figsize=(10, 5))\n plt.subplot(1, 2, 1)\n plt.title('Real Images')\n plt.imshow(real_images.permute(1, 2, 0).numpy())\n \n plt.subplot(1, 2, 2)\n plt.title('Fake Images (Monet Style)')\n plt.imshow(fake_images.permute(1, 2, 0).numpy())\n plt.show()\n<\/code><\/pre>\n8. \uacb0\ub860<\/h2>\n
\uc774 \uae00\uc5d0\uc11c\ub294 CycleGAN\uc744 \ud65c\uc6a9\ud558\uc5ec \ubaa8\ub124 \uc2a4\ud0c0\uc77c\uc758 \uadf8\ub9bc\uc744 \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\ubc95\ub860\uc740 \ub9ce\uc740 \uc751\uc6a9\uc774 \uac00\ub2a5\ud558\uba70, \ud5a5\ud6c4 \ub354 \ub9ce\uc740 \ub3c4\uba54\uc778 \uac04\uc758 \ubcc0\ud658 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. CycleGAN\uc758 \ud2b9\uc9d5\uc778 \uc21c\ud658 \uc77c\uad00\uc131 \ub610\ud55c \ub2e4\uc591\ud55c GAN \ubcc0\ud615\uc5d0 \uc801\uc6a9\ub420 \uc218 \uc788\uc5b4 \uc55e\uc73c\ub85c\uc758 \uc5f0\uad6c \ubc29\ud5a5\uc774 \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
\uc774 \uc608\uc81c\ub97c \ud1b5\ud574 \ud30c\uc774\ud1a0\uce58\uc5d0\uc11c CycleGAN\uc744 \uad6c\ud604\ud558\ub294 \uae30\ucd08\ub97c \uc2b5\ub4dd\ud558\uc168\uae38 \ubc14\ub78d\ub2c8\ub2e4. GAN\uc740 \ub192\uc740 \ud004\ub9ac\ud2f0\uc758 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \uc788\uc5b4 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \uc9c0\ub2c8\uace0 \uc788\uc73c\uba70, \uc774 \uae30\uc220\uc758 \ubc1c\uc804\uc774 \ub354 \ub9ce\uc740 \ubd84\uc57c\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd \ubd84\uc57c\ub294 \ub370\uc774\ud130\uc640 \uc5f0\uc0b0 \ub2a5\ub825\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \uc2e4\uc9c8\uc801\uc778 \uc131\uacfc\ub97c \ub9ce\uc774 \ub0b8 \ubd84\uc57c\uc785\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 GAN(Generative Adversarial Network)\uc740 \uac00\uc7a5 \ud601\uc2e0\uc801\uc778 \uacb0\uacfc\ub97c \ubcf4\uc5ec\uc900 \ubaa8\ub378 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c \uc911 \ud558\ub098\uc778 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec CycleGAN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c \ubaa8\ub124(Monet) \uc2a4\ud0c0\uc77c\uc758 \uadf8\ub9bc\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud560 \uac83\uc785\ub2c8\ub2e4. 1. CycleGAN \uac1c\uc694 CycleGAN\uc740 \ub450 \uac1c\uc758 \ub3c4\uba54\uc778 \uac04 \ubcc0\ud658\uc744 \uc704\ud55c GAN\uc758 \uc77c\uc885\uc785\ub2c8\ub2e4. \uc608\ub97c … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, CycleGAN\uc73c\ub85c \ubaa8\ub124 \uadf8\ub9bc \uadf8\ub9ac\uae30”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[32],"tags":[],"class_list":["post-29790","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"\n\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, CycleGAN\uc73c\ub85c \ubaa8\ub124 \uadf8\ub9bc \uadf8\ub9ac\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29790\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, CycleGAN\uc73c\ub85c \ubaa8\ub124 \uadf8\ub9bc \uadf8\ub9ac\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd \ubd84\uc57c\ub294 \ub370\uc774\ud130\uc640 \uc5f0\uc0b0 \ub2a5\ub825\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \uc2e4\uc9c8\uc801\uc778 \uc131\uacfc\ub97c \ub9ce\uc774 \ub0b8 \ubd84\uc57c\uc785\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 GAN(Generative Adversarial Network)\uc740 \uac00\uc7a5 \ud601\uc2e0\uc801\uc778 \uacb0\uacfc\ub97c \ubcf4\uc5ec\uc900 \ubaa8\ub378 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c \uc911 \ud558\ub098\uc778 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec CycleGAN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c \ubaa8\ub124(Monet) \uc2a4\ud0c0\uc77c\uc758 \uadf8\ub9bc\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud560 \uac83\uc785\ub2c8\ub2e4. 1. 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