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"padding": null, "grid_auto_rows": null, "grid_gap": null, "max_width": null, "order": null, "_view_module_version": "1.2.0", "grid_template_areas": null, "object_position": null, "object_fit": null, "grid_auto_columns": null, "margin": null, "display": null, "left": null } } } } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "x2SSsQMSknm2" }, "source": [ "#### ResNet18 모델 아키텍처 정의" ] }, { "cell_type": "code", "metadata": { "id": "zpUcgk5xkgGZ" }, "source": [ "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import os\n", "\n", "\n", "# ResNet18을 위해 최대한 간단히 수정한 BasicBlock 클래스\n", "class BasicBlock(nn.Module):\n", " def __init__(self, in_planes, planes, stride=1):\n", " super(BasicBlock, self).__init__()\n", "\n", " # 3x3 필터를 사용 (너비와 높이를 줄일 때는 stride 값 조절)\n", " self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n", " self.bn1 = nn.BatchNorm2d(planes) # 배치 정규화(batch normalization)\n", "\n", " # 3x3 필터를 사용 (패딩을 1만큼 주기 때문에 너비와 높이가 동일)\n", " self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n", " self.bn2 = nn.BatchNorm2d(planes) # 배치 정규화(batch normalization)\n", "\n", " self.shortcut = nn.Sequential() # identity인 경우\n", " if stride != 1: # stride가 1이 아니라면, identity mapping이 아닌 경우\n", " self.shortcut = nn.Sequential(\n", " nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),\n", " nn.BatchNorm2d(planes)\n", " )\n", "\n", " def forward(self, x):\n", " out = F.relu(self.bn1(self.conv1(x)))\n", " out = self.bn2(self.conv2(out))\n", " out += self.shortcut(x) # (핵심) skip connection\n", " out = F.relu(out)\n", " return out\n", "\n", "\n", "# ResNet 클래스 정의\n", "class ResNet(nn.Module):\n", " def __init__(self, block, num_blocks, num_classes=10):\n", " super(ResNet, self).__init__()\n", " self.in_planes = 64\n", "\n", " # 64개의 3x3 필터(filter)를 사용\n", " self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)\n", " self.bn1 = nn.BatchNorm2d(64)\n", " self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)\n", " self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n", " self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n", " self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n", " self.linear = nn.Linear(512, num_classes)\n", "\n", " def _make_layer(self, block, planes, num_blocks, stride):\n", " strides = [stride] + [1] * (num_blocks - 1)\n", " layers = []\n", " for stride in strides:\n", " layers.append(block(self.in_planes, planes, stride))\n", " self.in_planes = planes # 다음 레이어를 위해 채널 수 변경\n", " return nn.Sequential(*layers)\n", "\n", " def forward(self, x):\n", " out = F.relu(self.bn1(self.conv1(x)))\n", " out = self.layer1(out)\n", " out = self.layer2(out)\n", " out = self.layer3(out)\n", " out = self.layer4(out) # 출력: [batch_size, 512, 4, 4]\n", " out = F.avg_pool2d(out, 4) # 출력: [batch_size, 512, 1, 1]\n", " out = out.view(out.size(0), -1) # 출력: [batch_size, 512]\n", " out = self.linear(out)\n", " return out\n", "\n", "\n", "# ResNet18 함수 정의\n", "def ResNet18():\n", " return ResNet(BasicBlock, [2, 2, 2, 2])" ], "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "nCNacrgtktlr" }, "source": [ "#### 데이터셋(Dataset) 다운로드 및 불러오기" ] }, { "cell_type": "code", "metadata": { "id": "EmmQZ8p5kq_C", "colab": { "base_uri": "https://localhost:8080/", "height": 118, "referenced_widgets": [ "46443cb3568c452a8fc7a751b359a277", "9d8f767760744cd59d641e4cf48124b6", "06b81518437f4b179d8cda791929e005", "ef1d7d3977c64f1190589ca8618cae67", "ce8b3cc50a824c6a9f63184093f6dd66", "88f1db43922a47bfb5d56d430b4728c0", "526514b46353489faed97cabbb9c94da", "57262bbc88c4433eaa7fb23bd5923212" ] }, "outputId": "af99feb0-c22c-40d3-88ef-6e48445accc9" }, "source": [ "import torchvision\n", "import torchvision.transforms as transforms\n", "\n", "transform_train = transforms.Compose([\n", " transforms.RandomCrop(32, padding=4),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", "])\n", "\n", "transform_test = transforms.Compose([\n", " transforms.ToTensor(),\n", "])\n", "\n", "train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)\n", "test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n", "\n", "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)\n", "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=2)" ], "execution_count": 2, "outputs": [ { "output_type": "stream", "text": [ "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "46443cb3568c452a8fc7a751b359a277", "version_minor": 0, "version_major": 2 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=170498071.0), HTML(value='')))" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "\n", "Extracting ./data/cifar-10-python.tar.gz to ./data\n", "Files already downloaded and verified\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "NUXHwcAS9UhB" }, "source": [ "#### 필요한 라이브러리 구현" ] }, { "cell_type": "code", "metadata": { "id": "fWXXtLSn9WUe" }, "source": [ "import numpy as np\n", "\n", "mixup_alpha = 1.0\n", "\n", "def mixup_data(x, y):\n", " lam = np.random.beta(mixup_alpha, mixup_alpha)\n", " batch_size = x.size()[0]\n", " index = torch.randperm(batch_size).cuda()\n", " mixed_x = lam * x + (1 - lam) * x[index]\n", " y_a, y_b = y, y[index]\n", " return mixed_x, y_a, y_b, lam\n", "\n", "\n", "def mixup_criterion(criterion, pred, y_a, y_b, lam):\n", " return lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)" ], "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Dl1-47E7pHD_" }, "source": [ "#### 환경 설정 및 학습(Training) 함수 정의" ] }, { "cell_type": "code", "metadata": { "id": "bhm_eVykk-Z8" }, "source": [ "device = 'cuda'\n", "\n", "net = ResNet18()\n", "net = net.to(device)\n", "\n", "learning_rate = 0.1\n", "file_name = 'resnet18_cifar10.pth'\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0002)\n", "\n", "\n", "def train(epoch):\n", " print('\\n[ Train epoch: %d ]' % epoch)\n", " net.train()\n", " train_loss = 0\n", " correct = 0\n", " total = 0\n", "\n", " for batch_idx, (inputs, targets) in enumerate(train_loader):\n", " inputs, targets = inputs.to(device), targets.to(device)\n", " inputs, targets_a, targets_b, lam = mixup_data(inputs, targets)\n", " optimizer.zero_grad()\n", "\n", " outputs = net(inputs)\n", " loss = mixup_criterion(criterion, outputs, targets_a, targets_b, lam)\n", " loss.backward()\n", "\n", " optimizer.step()\n", " train_loss += loss.item()\n", " _, predicted = outputs.max(1)\n", "\n", " total += targets.size(0)\n", " current_correct = (lam * predicted.eq(targets_a).sum().item() + (1 - lam) * predicted.eq(targets_b).sum().item())\n", " correct += current_correct\n", "\n", " if batch_idx % 100 == 0:\n", " print('\\nCurrent batch:', str(batch_idx))\n", " print('Current batch average train accuracy:', current_correct / targets.size(0))\n", " print('Current batch average train loss:', loss.item() / targets.size(0))\n", "\n", " print('\\nTotal average train accuarcy:', correct / total)\n", " print('Total average train loss:', train_loss / total)\n", "\n", "\n", "def test(epoch):\n", " print('\\n[ Test epoch: %d ]' % epoch)\n", " net.eval()\n", " loss = 0\n", " correct = 0\n", " total = 0\n", "\n", " for batch_idx, (inputs, targets) in enumerate(test_loader):\n", " inputs, targets = inputs.to(device), targets.to(device)\n", " total += targets.size(0)\n", "\n", " outputs = net(inputs)\n", " loss += criterion(outputs, targets).item()\n", "\n", " _, predicted = outputs.max(1)\n", " correct += predicted.eq(targets).sum().item()\n", "\n", " print('\\nTotal average test accuarcy:', correct / total)\n", " print('Total average test loss:', loss / total)\n", "\n", " state = {\n", " 'net': net.state_dict()\n", " }\n", " if not os.path.isdir('checkpoint'):\n", " os.mkdir('checkpoint')\n", " torch.save(state, './checkpoint/' + file_name)\n", " print('Model Saved!')" ], "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "0mv5z7CEMRrn" }, "source": [ "#### 학습(Training) 진행" ] }, { "cell_type": "code", "metadata": { "id": "4voLj7TKlaB1", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9202e802-dba2-473a-b182-135625d69d65" }, "source": [ "import time\n", "\n", "\n", "def adjust_learning_rate(optimizer, epoch):\n", " lr = learning_rate\n", " if epoch >= 50:\n", " lr /= 10\n", " if epoch >= 100:\n", " lr /= 10\n", " for param_group in optimizer.param_groups:\n", " param_group['lr'] = lr\n", "\n", "start_time = time.time()\n", "\n", "for epoch in range(0, 150):\n", " adjust_learning_rate(optimizer, epoch)\n", " train(epoch)\n", " test(epoch)\n", " print('\\nTime elapsed:', time.time() - start_time)" ], "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ "\n", "[ Train epoch: 0 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.09727836367854385\n", "Current batch average train loss: 0.018746694549918175\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.23465121261291963\n", "Current batch average train loss: 0.016454745084047318\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.333794545157437\n", "Current batch average train loss: 0.014404334127902985\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.26906486580878847\n", "Current batch average train loss: 0.015901871025562286\n", "\n", "Total average train accuarcy: 0.2438999810762169\n", "Total average train loss: 0.01657938927412033\n", "\n", "[ Test epoch: 0 ]\n", "\n", "Total average test accuarcy: 0.2885\n", "Total average test loss: 0.019975642216205597\n", "Model Saved!\n", "\n", "Time elapsed: 67.31932187080383\n", "\n", "[ Train epoch: 1 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.24919886781709963\n", "Current batch average train loss: 0.015324986539781094\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.4636160828714663\n", "Current batch average train loss: 0.012422642670571804\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.34419466939811794\n", "Current batch average train loss: 0.015307758003473282\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.31797102300464986\n", "Current batch average train loss: 0.01510600931942463\n", "\n", "Total average train accuarcy: 0.35010402917211925\n", "Total average train loss: 0.014451653120517731\n", "\n", "[ Test epoch: 1 ]\n", "\n", "Total average test accuarcy: 0.4511\n", "Total average test loss: 0.015768038177490236\n", "Model Saved!\n", "\n", "Time elapsed: 134.12930130958557\n", "\n", "[ Train epoch: 2 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.39064543301020394\n", "Current batch average train loss: 0.01368321105837822\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.4536923300926184\n", "Current batch average train loss: 0.013418509624898434\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.5843046093906161\n", "Current batch average train loss: 0.010685338638722897\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.4138342762235759\n", "Current batch average train loss: 0.013748647645115852\n", "\n", "Total average train accuarcy: 0.42018612307862413\n", "Total average train loss: 0.01337722238779068\n", "\n", "[ Test epoch: 2 ]\n", "\n", "Total average test accuarcy: 0.3748\n", "Total average test loss: 0.01846726050376892\n", "Model Saved!\n", "\n", "Time elapsed: 200.83397006988525\n", "\n", "[ Train epoch: 3 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.6173987355731223\n", "Current batch average train loss: 0.00914319884032011\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.5900493613504846\n", "Current batch average train loss: 0.009842365980148315\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.5708004272097587\n", "Current batch average train loss: 0.010575332678854465\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.5454333471487535\n", "Current batch average train loss: 0.011715037748217583\n", "\n", "Total average train accuarcy: 0.4552326009648567\n", "Total average train loss: 0.012816074323654175\n", "\n", "[ Test epoch: 3 ]\n", "\n", "Total average test accuarcy: 0.542\n", "Total average test loss: 0.013031093001365662\n", "Model Saved!\n", "\n", "Time elapsed: 267.6033661365509\n", "\n", "[ Train epoch: 4 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.5670800999019762\n", "Current batch average train loss: 0.011077708564698696\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.33942700380630286\n", "Current batch average train loss: 0.01571563258767128\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.5527230620729884\n", "Current batch average train loss: 0.011261177249252796\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.41191504465987094\n", "Current batch average train loss: 0.01367579773068428\n", "\n", "Total average train accuarcy: 0.4815957606387106\n", "Total average train loss: 0.012333299213647843\n", "\n", "[ Test epoch: 4 ]\n", "\n", "Total average test accuarcy: 0.533\n", "Total average test loss: 0.013741466772556304\n", "Model Saved!\n", "\n", "Time elapsed: 334.2372772693634\n", "\n", "[ Train epoch: 5 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.32909885804028555\n", "Current batch average train loss: 0.014123043045401573\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.35232902116638143\n", "Current batch average train loss: 0.01461983285844326\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.40546838587707035\n", "Current batch average train loss: 0.01375819556415081\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.37276580497994516\n", "Current batch average train loss: 0.014229300431907177\n", "\n", "Total average train accuarcy: 0.5138012749180852\n", "Total average train loss: 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"Total average test accuarcy: 0.6475\n", "Total average test loss: 0.010523168969154359\n", "Model Saved!\n", "\n", "Time elapsed: 467.57990646362305\n", "\n", "[ Train epoch: 7 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.6297623337920402\n", "Current batch average train loss: 0.010165085084736347\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.6370089383482624\n", "Current batch average train loss: 0.009543618187308311\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.4830356924365709\n", "Current batch average train loss: 0.01263895258307457\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.48627439472570266\n", "Current batch average train loss: 0.01262323372066021\n", "\n", "Total average train accuarcy: 0.5658904739480652\n", "Total average train loss: 0.01078166707277298\n", "\n", "[ Test epoch: 7 ]\n", "\n", "Total average test accuarcy: 0.7351\n", "Total average test 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"\n", "Total average train accuarcy: 0.6513210440523681\n", "Total average train loss: 0.00902064241528511\n", "\n", "[ Test epoch: 20 ]\n", "\n", "Total average test accuarcy: 0.8127\n", "Total average test loss: 0.006664279979467392\n", "Model Saved!\n", "\n", "Time elapsed: 1402.1581897735596\n", "\n", "[ Train epoch: 21 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.7956980515751807\n", "Current batch average train loss: 0.007270255126059055\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.629545271031531\n", "Current batch average train loss: 0.010497888550162315\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.8386429675644214\n", "Current batch average train loss: 0.005098198540508747\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.6457638938643855\n", "Current batch average train loss: 0.009745929390192032\n", "\n", "Total average train accuarcy: 0.6575348683048398\n", 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"Current batch average train loss: 0.007459779269993305\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.5366580251696094\n", "Current batch average train loss: 0.008897706866264343\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.5199660891453104\n", "Current batch average train loss: 0.009945034980773926\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.5325260146009075\n", "Current batch average train loss: 0.009534681215882301\n", "\n", "Total average train accuarcy: 0.7302046571534938\n", "Total average train loss: 0.006882723136991262\n", "\n", "[ Test epoch: 62 ]\n", "\n", "Total average test accuarcy: 0.9403\n", "Total average test loss: 0.0034108455032110215\n", "Model Saved!\n", "\n", "Time elapsed: 4202.915333509445\n", "\n", "[ Train epoch: 63 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.6777282361936686\n", "Current batch average train loss: 0.008359001018106937\n", 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