F(G(X)) \u2248 X<\/em> \uc5ec\uc57c \ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n3. CycleGAN \uad6c\ud604\ud558\uae30<\/h2>\n
\n \uc774\uc81c CycleGAN\uc744 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ub370\uc774\ud130 \uc900\ube44, \ubaa8\ub378 \uc815\uc758, \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc124\uc815, \ud559\uc2b5 \ub8e8\ud504, \uadf8\ub9ac\uace0 \uacb0\uacfc \uc2dc\uac01\ud654 \ub4f1\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.\n <\/p>\n
3.1. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\n CycleGAN\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \ub450 \uac1c\uc758 \uc774\ubbf8\uc9c0 \ub3c4\uba54\uc778\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc608\uc2dc\ub85c ‘\uc5ec\ub984’\uacfc ‘\uaca8\uc6b8’ \uc774\ubbf8\uc9c0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \ud574\ub2f9 \ub370\uc774\ud130\uc14b\uc740 Apple2Orange<\/strong>, Horse2Zebra<\/strong>\uc640 \uac19\uc740 \uc720\uba85\ud55c \uacf5\uac1c \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n <\/p>\n\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import ImageFolder\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130 \ubcc0\ud658 \uc815\uc758\ntransform = transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(256),\n transforms.ToTensor(),\n])\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\nsummer_dataset = ImageFolder(root='data\/summer', transform=transform)\nwinter_dataset = ImageFolder(root='data\/winter', transform=transform)\n\nsummer_loader = DataLoader(summer_dataset, batch_size=1, shuffle=True)\nwinter_loader = DataLoader(winter_dataset, batch_size=1, shuffle=True)\n <\/code><\/pre>\n3.2. \ubaa8\ub378 \uc815\uc758<\/h3>\n
\n CycleGAN\uc5d0\uc11c\ub294 \uace0\ucc28\uc6d0 \uae30\ub2a5\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d U-Net\uacfc \uac19\uc740 \uad6c\uc870\ub97c \ub530\ub974\ub294 \uc0dd\uc131\uae30\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\uc5d0\uc11c\ub294 \ub2e8\uc21c\ud55c \uc0dd\uc131\uae30 \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n <\/p>\n
\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.hidden_layers = nn.Sequential(\n nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3),\n nn.ReLU(),\n nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),\n nn.ReLU(),\n # \uc911\uac04 \ub808\uc774\uc5b4 \ucd94\uac00\n nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),\n nn.ReLU(),\n # \ub514\ucf54\ub354\n nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1),\n nn.ReLU(),\n nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1),\n nn.ReLU(),\n nn.Conv2d(64, 3, kernel_size=7, stride=1, padding=3),\n )\n\n def forward(self, x):\n return self.hidden_layers(x)\n <\/code><\/pre>\n3.3. \uc190\uc2e4 \ud568\uc218 \ubc0f \ucd5c\uc801\ud654 \uc124\uc815<\/h3>\n
\n \uc774\uc81c \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uc815\ud569\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc9c4\uc9dc-\uac00\uc9dc \ud310\ubcc4\uc744 \uc704\ud55c \uc774\uc9c4 \uad50\ucc28 \uc5d4\ud2b8\ub85c\ud53c \uc190\uc2e4 \ud568\uc218\uc640 Cycle Consistency Loss\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
\ncriterion_gan = nn.BCELoss()\ncriterion_cycle = nn.L1Loss()\n\n# Adam \uc635\ud2f0\ub9c8\uc774\uc800\noptimizer_G = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n <\/code><\/pre>\n3.4. \ud559\uc2b5 \ub8e8\ud504<\/h3>\n
\n \ud559\uc2b5 \ub8e8\ud504\uc5d0\uc11c\ub294 \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0 \uc190\uc2e4 \uac12\uc744 \uae30\ub85d\ud569\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc778 \ud559\uc2b5 \ub8e8\ud504\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc870\ub85c \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\nnum_epochs = 200\nfor epoch in range(num_epochs):\n for (summer_images, winter_images) in zip(summer_loader, winter_loader):\n real_A = summer_images[0].to(device)\n real_B = winter_images[0].to(device)\n\n # \uc0dd\uc131\uc801 \uc190\uc2e4 \uacc4\uc0b0\n fake_B = generator_G(real_A)\n cycled_A = generator_F(fake_B)\n\n loss_cycle = criterion_cycle(cycled_A, real_A) \n\n # Adversarial Loss \uacc4\uc0b0\n loss_G = criterion_gan(discriminator_D_Y(fake_B), real_labels) + loss_cycle\n\n # \uc5ed\uc804\ud30c \ubc0f \ucd5c\uc801\ud654\n optimizer_G.zero_grad()\n loss_G.backward()\n optimizer_G.step()\n\n # \uacb0\uacfc \uae30\ub85d\n print(f'Epoch [{epoch}\/{num_epochs}], Loss: {loss_G.item()}')\n <\/code><\/pre>\n3.5. \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\n \ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, CycleGAN\uc758 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574 \uba87 \uac00\uc9c0 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0 \uc774\ub97c \uc0ac\uc6a9\uc790\uc5d0\uac8c \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 \uacb0\uacfc \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uace0 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n <\/p>\n
\nimport matplotlib.pyplot as plt\n\n# \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uace0 \uc800\uc7a5\ud558\ub294 \ud568\uc218\ndef save_image(tensor, filename):\n image = tensor.detach().cpu().numpy()\n image = image.transpose((1, 2, 0))\n plt.imsave(filename, (image * 255).astype('uint8'))\n\n# \ud6c8\ub828\ub41c \uc0dd\uc131\uae30\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \uc0dd\uc131\nwith torch.no_grad():\n for i, summer_images in enumerate(summer_loader):\n fake_images = generator_G(summer_images[0].to(device))\n save_image(fake_images, f'output\/image_{i}.png')\n break\n <\/code><\/pre>\n4. CycleGAN\uc758 \ud65c\uc6a9<\/h2>\n
\n CycleGAN\uc740 \uc774\ubbf8\uc9c0 \ubcc0\ud658 \ubc0f \uc2a4\ud0c0\uc77c \ubcc0\ud658 \uc678\uc5d0\ub3c4 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc758\ub8cc \uc774\ubbf8\uc9d5, \ube44\ub514\uc624 \ubcc0\ud658, \uadf8\ub9ac\uace0 \ud328\uc158 \ub514\uc790\uc778 \ub4f1\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.1. \uc758\ub8cc \uc774\ubbf8\uc9c0 \ucc98\ub9ac<\/h3>\n
\n CycleGAN\uc740 \uc758\ud559\uc801 \uc774\ubbf8\uc9c0\uc5d0\uc11c \ubcd1\ub9ac\uc801 \ubcc0\ud654\ub97c \uc2dd\ubcc4\ud558\ub294 \ub370 \ud070 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4. \ud658\uc790\uc758 CT \uc2a4\uce94\uc744 MRI \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud658\ud568\uc73c\ub85c\uc368 \uc758\uc0ac\ub4e4\uc774 \ube44\uad50\ud558\uace0 \ubd84\uc11d\ud558\uae30 \uc27d\uac8c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.2. \ube44\ub514\uc624 \ubcc0\ud658<\/h3>\n
\n CycleGAN\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\ub514\uc624\uc758 \ud55c \uc2a4\ud0c0\uc77c\uc744 \ub2e4\ub978 \uc2a4\ud0c0\uc77c\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2e4\uc2dc\uac04 \ube44\ub514\uc624 \uc2a4\ud2b8\ub9bc\uc5d0\uc11c \uc5ec\ub984\uc758 \ud48d\uacbd\uc744 \uaca8\uc6b8\ub85c \ubcc0\ud658\ud558\ub294\ub370 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.3. \ud328\uc158 \ub514\uc790\uc778<\/h3>\n
\n CycleGAN\uc740 \ud328\uc158 \ub514\uc790\uc778 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ud601\uc2e0\uc744 \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub514\uc790\uc774\ub108\uac00 \ub2e4\uc591\ud55c \uc2a4\ud0c0\uc77c\uc758 \uc758\uc0c1\uc744 \uc2dc\ubbac\ub808\uc774\uc158\ud558\uace0 \ub514\uc790\uc778\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
5. \uacb0\ub860<\/h2>\n
\n CycleGAN\uc740 \uc774\ubbf8\uc9c0 \ubcc0\ud658 \ubd84\uc57c\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \ube44\ub514\uc624, \ud328\uc158 \ub4f1 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0 \uc801\ud569\ud558\uba70, \ube44\uc804 \ubd84\uc57c\uc5d0\uc11c\uc758 \ud55c\uacc4\ub97c \uadf9\ubcf5\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.
\n \uc774 \uae00\uc5d0\uc11c\ub294 CycleGAN\uc758 \uae30\ubcf8 \uc6d0\ub9ac\ubd80\ud130 \uad6c\ud604, \uacb0\uacfc \uc2dc\uac01\ud654\uae4c\uc9c0\uc758 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \uc5f0\uad6c\uc640 \ubc1c\uc804\uc774 \uae30\ub300\ub418\uba70, CycleGAN\uc5d0 \ub300\ud55c \uc774\ud574\uac00 \ud5a5\ud6c4 \uac1c\ubc1c\uc5d0 \ud070 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c\uc758 \uc774\ubbf8\uc9c0 \ubcc0\ud658 \ubc0f \uc0dd\uc131 \ubaa8\ub378\uc758 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5c8\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GANs)\uc740 \uc774\ub7ec\ud55c \ubc1c\uc804\uc758 \uc911\ud575\uc744 \uc774\ub8e8\uba70, \uadf8 \uc911\uc5d0\uc11c\ub3c4 CycleGAN\uc740 \ud2b9\ud788 \uc2a4\ud0c0\uc77c \ubcc0\ud658\uc5d0 \uc720\uc6a9\ud55c \ubaa8\ub378\ub85c \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 CycleGAN\uc758 \uc6d0\ub9ac, \ud65c\uc6a9\ubc95 \ubc0f \ud30c\uc774\uc36c\uc758 PyTorch \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud55c \uad6c\ud604 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. CycleGAN\uc758 \uac1c\uc694 CycleGAN\uc740 \ub450 \uac1c\uc758 \uc774\ubbf8\uc9c0 \ub3c4\uba54\uc778 \uac04\uc758 \uc774\ubbf8\uc9c0 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, CycleGAN”<\/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":[33],"tags":[],"class_list":["post-29955","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, CycleGAN - \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\/29955\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, CycleGAN - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c\uc758 \uc774\ubbf8\uc9c0 \ubcc0\ud658 \ubc0f \uc0dd\uc131 \ubaa8\ub378\uc758 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5c8\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(Generative Adversarial Networks, GANs)\uc740 \uc774\ub7ec\ud55c \ubc1c\uc804\uc758 \uc911\ud575\uc744 \uc774\ub8e8\uba70, \uadf8 \uc911\uc5d0\uc11c\ub3c4 CycleGAN\uc740 \ud2b9\ud788 \uc2a4\ud0c0\uc77c \ubcc0\ud658\uc5d0 \uc720\uc6a9\ud55c \ubaa8\ub378\ub85c \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 CycleGAN\uc758 \uc6d0\ub9ac, \ud65c\uc6a9\ubc95 \ubc0f \ud30c\uc774\uc36c\uc758 PyTorch \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud55c \uad6c\ud604 \uacfc\uc815\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. 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