{"id":29933,"date":"2024-10-28T03:18:45","date_gmt":"2024-10-28T03:18:45","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29933"},"modified":"2024-11-26T06:50:46","modified_gmt":"2024-11-26T06:50:46","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-pspnet","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29933\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, PSPNet"},"content":{"rendered":"<p><body><\/p>\n<p>\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560\uc758 \ucd5c\uc2e0 \uae30\ubc95 \uc911 \ud558\ub098\uc778 PSPNet(Pyramid Scene Parsing Network)\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PSPNet\uc740 \uc774\ubbf8\uc9c0\uc758 \uc758\ubbf8\ub860\uc801 \ubd84\ud560\uc744 \uc218\ud589\ud558\ub294 \ub370 \uc788\uc5b4 \ud2b9\ud788 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubb38\uc81c\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. PSPNet \uac1c\uc694<\/h2>\n<p>PSPNet\uc740 2017\ub144 Zhang \ub4f1\uc5d0\uc11c \ubc1c\ud45c\ud55c \ub124\ud2b8\uc6cc\ud06c\ub85c, \uc774\ubbf8\uc9c0\uc758 \uc804\uc5ed \ucee8\ud14d\uc2a4\ud2b8\ub97c \ud3ec\ucc29\ud558\uc5ec \uac01 \ud53d\uc140\uc5d0 \ub300\ud55c \ud074\ub798\uc2a4 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1 \ubaa8\ub4c8(PPM, Pyramid Pooling Module)\uc744 \ud1b5\ud574 \ub2e4\uc591\ud55c \uc2a4\ucf00\uc77c\uc5d0\uc11c \uc815\ubcf4\ub97c \ud1b5\ud569\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uad6c\uc870\ub294 \uac1d\uccb4 \uc778\uc2dd\uc5d0 \uc720\ub9ac\ud558\uc5ec \ub2e4\uc591\ud55c \uac1d\uccb4 \ud06c\uae30\uc5d0 \ub300\ud55c \uc778\uc2dd\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/p>\n<h3>1.1. \uc8fc\uc694 \ud2b9\uc9d5<\/h3>\n<ul>\n<li><strong>\ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1 \ubaa8\ub4c8:<\/strong> \uc774\ubbf8\uc9c0\uc758 \uc5ec\ub7ec \ud06c\uae30\uc5d0\uc11c \ud53c\ucc98\ub97c \ucd94\ucd9c\ud558\uace0 \ud1b5\ud569\ud558\uc5ec \ubcf4\ub2e4 \ud3ec\uad04\uc801\uc778 \ub9e5\ub77d \uc815\ubcf4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc804\uc5ed \uc815\ubcf4 \ud1b5\ud569:<\/strong> \ub124\ud2b8\uc6cc\ud06c\uc758 \ub9c8\uc9c0\ub9c9 \ub2e8\uacc4\uc5d0\uc11c \uc804\uc5ed \uc815\ubcf4\ub97c \ud1b5\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \uac15\ud654\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc6b0\uc218\ud55c \uc131\ub2a5:<\/strong> \uc5ec\ub7ec \ubca4\uce58\ub9c8\ud06c \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ub098\ud0c0\ub0b4\uba70, \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. PSPNet \uad6c\uc870<\/h2>\n<p>PSPNet\uc758 \uae30\ubcf8 \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ol>\n<li>\ubc31\ubcf8 \ub124\ud2b8\uc6cc\ud06c: ResNet \ub4f1\uc758 CNN \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/li>\n<li>\ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1 \ubaa8\ub4c8: \ub2e4\uc911 \uc2a4\ucf00\uc77c\uc758 \ud53c\ucc98 \ub9f5\uc744 \ud1b5\ud569\ud558\uc5ec \uc804\ubc18\uc801\uc778 \ub9e5\ub77d\uc744 \ud3ec\ucc29\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc5c5\uc0d8\ud50c\ub9c1: \ucd5c\uc885 \uc608\uce21\uc744 \uc704\ud574 \uc801\uc808\ud55c \ud574\uc0c1\ub3c4\ub85c \uc870\uc815\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<h3>2.1. \ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1 \ubaa8\ub4c8<\/h3>\n<p>PPM\uc740 \uc785\ub825 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud574 \uc5ec\ub7ec \ud574\uc0c1\ub3c4\uc5d0\uc11c \ud53c\ucc98 \ub9f5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub4c8\uc740 \uac01\uae30 \ub2e4\ub978 \ud06c\uae30\uc758 \ud480\ub9c1 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\uc5ec \uacf5\uac04 \uc815\ubcf4\ub97c \uc218\uc9d1\ud558\uace0, \uc774\ub97c \ub2e4\uc2dc \uc6d0\ubcf8 \ud574\uc0c1\ub3c4\ub85c \ud1b5\ud569\ud569\ub2c8\ub2e4. PPM\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\uc785\ub825 \ud53c\ucc98 \ub9f5\uc5d0 \ub300\ud574 \ub2e4\uc591\ud55c \uc0ac\uc774\uc988\uc758 \ud480\ub9c1 \uc5f0\uc0b0\uc744 \uc218\ud589\ud569\ub2c8\ub2e4 (\uc608: 1&#215;1, 2&#215;2, 3&#215;3, 6&#215;6).<\/li>\n<li>\uac01 \ud480\ub9c1 \ub2e8\uacc4\uc5d0\uc11c \ucd9c\ub825\ub41c \ud53c\ucc98 \ub9f5\uc744 \ub2e4\uc2dc \uc6d0\ubcf8 \ud574\uc0c1\ub3c4\ub85c \uc5c5\uc0d8\ud50c\ub9c1\ud569\ub2c8\ub2e4.<\/li>\n<li>\ucd5c\uc885\uc801\uc73c\ub85c \ubaa8\ub4e0 \uc5c5\uc0d8\ud50c\ub9c1 \ub41c \ud53c\ucc98 \ub9f5\uc744 concatenate\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ud53c\ucc98 \ub9f5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>3. PyTorch\ub85c PSPNet \uad6c\ud604\ud558\uae30<\/h2>\n<p>\uc774\uc81c PSPNet\uc744 PyTorch\ub85c \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub294 PSPNet\uc758 \uad6c\uc870\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n<h3>3.1. \ud658\uacbd \uc124\uc815<\/h3>\n<pre><code>import torch\nimport torch.nn as nn\nimport torchvision.models as models\n    <\/code><\/pre>\n<h3>3.2. PSPNet \ud074\ub798\uc2a4 \uc815\uc758<\/h3>\n<p>PSPNet \ud074\ub798\uc2a4\ub294 \ubc31\ubcf8 \ub124\ud2b8\uc6cc\ud06c\uc640 \ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1 \ubaa8\ub4c8\uc744 \ud1b5\ud569\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \uc815\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<pre><code>class PSPModule(nn.Module):\n    def __init__(self, in_channels, out_channels):\n        super(PSPModule, self).__init__()\n        self.pool1 = nn.AvgPool2d(1, stride=1)\n        self.pool2 = nn.AvgPool2d(2, stride=2)\n        self.pool3 = nn.AvgPool2d(3, stride=3)\n        self.pool4 = nn.AvgPool2d(6, stride=6)\n        self.conv1x1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)\n        self.bn = nn.BatchNorm2d(out_channels)\n\n    def forward(self, x):\n        size = x.size()[2:]\n        p1 = self.conv1x1(self.pool1(x))\n        p2 = self.conv1x1(self.pool2(x))\n        p3 = self.conv1x1(self.pool3(x))\n        p4 = self.conv1x1(self.pool4(x))\n\n        p1 = nn.functional.interpolate(p1, size, mode='bilinear', align_corners=True)\n        p2 = nn.functional.interpolate(p2, size, mode='bilinear', align_corners=True)\n        p3 = nn.functional.interpolate(p3, size, mode='bilinear', align_corners=True)\n        p4 = nn.functional.interpolate(p4, size, mode='bilinear', align_corners=True)\n\n        return torch.cat((x, p1, p2, p3, p4), dim=1)\n\nclass PSPNet(nn.Module):\n    def __init__(self, num_classes):\n        super(PSPNet, self).__init__()\n        self.backbone = models.resnet101(pretrained=True)\n        self.ppm = PSPModule(2048, 512)\n        self.final_convolution = nn.Conv2d(2048 + 512 * 4, num_classes, kernel_size=1)\n\n    def forward(self, x):\n        x = self.backbone(x)\n        x = self.ppm(x)\n        x = self.final_convolution(x)\n        return x<\/code><\/pre>\n<h3>3.3. \ubaa8\ub378 \ud559\uc2b5\ud558\uae30<\/h3>\n<p>\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130\uc14b \uc900\ube44, \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815, \uadf8\ub9ac\uace0 \ud559\uc2b5 \ub8e8\ud504\ub97c \uc791\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. torchvision\uc758 <code>Cityscapes<\/code> \ub370\uc774\ud130\uc14b\uc744 \uc608\ub85c \ub4e4\uc5b4\ubd05\uc2dc\ub2e4.<\/p>\n<pre><code>from torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n# \ub370\uc774\ud130\uc14b \uc900\ube44\ntransform = transforms.Compose([\n    transforms.Resize((256, 256)),\n    transforms.ToTensor(),\n])\n\ntrain_dataset = datasets.Cityscapes(root='path\/to\/cityscapes\/', split='train', mode='fine', target_type='semantic', transform=transform)\ntrain_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)\n\n# \ubaa8\ub378\uacfc \uc635\ud2f0\ub9c8\uc774\uc800 \uc124\uc815\nmodel = PSPNet(num_classes=19).to(device)\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\ncriterion = nn.CrossEntropyLoss()\n\n# \ud559\uc2b5 \ub8e8\ud504\nfor epoch in range(num_epochs):\n    model.train()\n    for images, masks in train_loader:\n        images, masks = images.to(device), masks.to(device)\n\n        optimizer.zero_grad()\n        outputs = model(images)\n        loss = criterion(outputs, masks)\n        loss.backward()\n        optimizer.step()\n\n    print(f'Epoch [{epoch + 1}\/{num_epochs}], Loss: {loss.item():.4f}')\n<\/code><\/pre>\n<h2>4. \uc2e4\ud5d8 \ubc0f \ud3c9\uac00<\/h2>\n<p>\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ud6c4, \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 validation \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \uc131\ub2a5\uc744 \uce21\uc815\ud569\ub2c8\ub2e4. \ud3c9\uac00 \uc9c0\ud45c\ub85c\ub294 \uc8fc\ub85c <strong>IoU(Intersection over Union)<\/strong> \ub610\ub294 <strong>Pixel Accuracy<\/strong>\uac00 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub2e4\uc74c\uc758 \ucf54\ub4dc\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n<pre><code>def evaluate(model, val_loader):\n    model.eval()\n    total_loss = 0\n    total_correct = 0\n    total_pixels = 0\n\n    with torch.no_grad():\n        for images, masks in val_loader:\n            images, masks = images.to(device), masks.to(device)\n            outputs = model(images)\n            loss = criterion(outputs, masks)\n\n            total_loss += loss.item()\n            preds = outputs.argmax(dim=1)\n            total_correct += (preds == masks).sum().item()\n            total_pixels += masks.numel()\n\n    print(f'Validation Loss: {total_loss \/ len(val_loader):.4f}, Pixel Accuracy: {total_correct \/ total_pixels:.4f}')\n\n# \ud3c9\uac00 \uc218\ud589\ud558\uae30\nevaluate(model, val_loader)\n<\/code><\/pre>\n<h2>5. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 PSPNet\uc758 \uad6c\uc870\uc640 \ub3d9\uc791 \uc6d0\ub9ac\ub97c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. PyTorch\ub85c \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc744 \ud1b5\ud574 \uc758\ubbf8\ub860\uc801 \ubd84\ud560 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc744 \uc774\ud574\ud558\uc168\uae38 \ubc14\ub78d\ub2c8\ub2e4. PSPNet\uc740 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ub124\ud2b8\uc6cc\ud06c\ub85c, \uc2e4\uc81c \uc774\ubbf8\uc9c0 \ucc98\ub9ac \ubb38\uc81c\uc640 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<div class=\"note\">\n<strong>\ucc38\uace0 \uc790\ub8cc:<\/strong><\/p>\n<ul>\n<li>Zhang, Y., et al. (2017). <em>Pyramid Scene Parsing Network<\/em>. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). <\/li>\n<li>PyTorch. (n.d.). <em>PyTorch Documentation<\/em>. Retrieved from <a href=\"https:\/\/pytorch.org\/docs\/stable\/index.html\">https:\/\/pytorch.org\/docs\/stable\/index.html<\/a><\/li>\n<\/ul>\n<\/div>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560\uc758 \ucd5c\uc2e0 \uae30\ubc95 \uc911 \ud558\ub098\uc778 PSPNet(Pyramid Scene Parsing Network)\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PSPNet\uc740 \uc774\ubbf8\uc9c0\uc758 \uc758\ubbf8\ub860\uc801 \ubd84\ud560\uc744 \uc218\ud589\ud558\ub294 \ub370 \uc788\uc5b4 \ud2b9\ud788 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubb38\uc81c\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. PSPNet \uac1c\uc694 PSPNet\uc740 2017\ub144 Zhang \ub4f1\uc5d0\uc11c \ubc1c\ud45c\ud55c \ub124\ud2b8\uc6cc\ud06c\ub85c, \uc774\ubbf8\uc9c0\uc758 \uc804\uc5ed \ucee8\ud14d\uc2a4\ud2b8\ub97c \ud3ec\ucc29\ud558\uc5ec \uac01 \ud53d\uc140\uc5d0 \ub300\ud55c \ud074\ub798\uc2a4 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29933\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, PSPNet&#8221;<\/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-29933","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, PSPNet - \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\/29933\/\" \/>\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, PSPNet - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc774\ubbf8\uc9c0 \ubd84\ud560\uc758 \ucd5c\uc2e0 \uae30\ubc95 \uc911 \ud558\ub098\uc778 PSPNet(Pyramid Scene Parsing Network)\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PSPNet\uc740 \uc774\ubbf8\uc9c0\uc758 \uc758\ubbf8\ub860\uc801 \ubd84\ud560\uc744 \uc218\ud589\ud558\ub294 \ub370 \uc788\uc5b4 \ud2b9\ud788 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc8fc\uba70, \ub2e4\uc591\ud55c \uc774\ubbf8\uc9c0 \uc778\uc2dd \ubb38\uc81c\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. 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