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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": [ "9c92fac257224340a2ece0bf2e6a1480", "96075dc5f90146ed9fc46f67747e5928", "5ded111571fa459981ec2a20880bb9ba", "ed204e2100bd40bfb443d9178b5aa8e2", "5446ad576b83488b9d827e02b2d8f524", "65e4a6fa0a884ba48cb353a0f08f236a", "fab2e8f255444972bf71390828606fc4", "0c320b4028324e8d8f38a8b3807578f7" ] }, "outputId": "d31be60d-b3ff-4d4b-e8ff-9965bf61e3fb" }, "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": "9c92fac257224340a2ece0bf2e6a1480", "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": [ "class LabelSmoothingCrossEntropy(nn.Module):\n", " def __init__(self):\n", " super(LabelSmoothingCrossEntropy, self).__init__()\n", " def forward(self, y, targets, smoothing=0.1):\n", " confidence = 1. - smoothing\n", " log_probs = F.log_softmax(y, dim=-1) # 예측 확률 계산\n", " true_probs = torch.zeros_like(log_probs)\n", " true_probs.fill_(smoothing / (y.shape[1] - 1))\n", " true_probs.scatter_(1, targets.data.unsqueeze(1), confidence) # 정답 인덱스의 정답 확률을 confidence로 변경\n", " return torch.mean(torch.sum(true_probs * -log_probs, dim=-1)) # negative log likelihood" ], "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 = LabelSmoothingCrossEntropy()\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", " optimizer.zero_grad()\n", "\n", " outputs = net(inputs)\n", " loss = criterion(outputs, targets)\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 = predicted.eq(targets).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": "f989f0b3-6901-4655-eaf4-2955ac706d66" }, "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: 0.03125\n", "Current batch average train loss: 0.01880522444844246\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.265625\n", "Current batch average train loss: 0.016130464151501656\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.2734375\n", "Current batch average train loss: 0.01486880425363779\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.3671875\n", "Current batch average train loss: 0.013500366359949112\n", "\n", "Total average train accuarcy: 0.3018\n", "Total average train loss: 0.01592603366136551\n", "\n", "[ Test epoch: 0 ]\n", "\n", "Total average test accuarcy: 0.377\n", "Total average test loss: 0.01908127553462982\n", "Model Saved!\n", "\n", "Time elapsed: 30.87809419631958\n", "\n", "[ Train epoch: 1 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.375\n", "Current batch average train loss: 0.014214173890650272\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.4921875\n", "Current batch average train loss: 0.01341882161796093\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.5\n", "Current batch average train loss: 0.012172220274806023\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.53125\n", "Current batch average train loss: 0.012011099606752396\n", "\n", "Total average train accuarcy: 0.48536\n", "Total average train loss: 0.012660184478759766\n", "\n", "[ Test epoch: 1 ]\n", "\n", "Total average test accuarcy: 0.3633\n", "Total average test loss: 0.020856681036949157\n", "Model Saved!\n", "\n", "Time elapsed: 61.54679274559021\n", "\n", "[ Train epoch: 2 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.5390625\n", "Current batch average train loss: 0.01180250197649002\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.609375\n", "Current batch average train loss: 0.011584784835577011\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.6953125\n", "Current batch average train loss: 0.009848041459918022\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.6015625\n", "Current batch average train loss: 0.01071525365114212\n", "\n", "Total average train accuarcy: 0.5941\n", "Total average train loss: 0.011073713901042939\n", "\n", "[ Test epoch: 2 ]\n", "\n", "Total average test accuarcy: 0.5377\n", "Total average test loss: 0.01581684398651123\n", "Model Saved!\n", "\n", "Time elapsed: 92.15917730331421\n", "\n", "[ Train epoch: 3 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.59375\n", "Current batch average train loss: 0.010738015174865723\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.6328125\n", "Current batch average train loss: 0.010080509819090366\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.6015625\n", "Current batch average train loss: 0.010458030737936497\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.6796875\n", "Current batch average train loss: 0.009776456281542778\n", "\n", "Total average train accuarcy: 0.67052\n", "Total average train loss: 0.009885173313617707\n", "\n", "[ Test epoch: 3 ]\n", "\n", "Total average test accuarcy: 0.6083\n", "Total average test loss: 0.013877481889724732\n", "Model Saved!\n", "\n", "Time elapsed: 122.73742628097534\n", "\n", "[ Train epoch: 4 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.6953125\n", "Current batch average train loss: 0.009270559065043926\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.7734375\n", "Current batch average train loss: 0.00845109298825264\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.6640625\n", "Current batch average train loss: 0.009542472660541534\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.6953125\n", "Current batch average train loss: 0.008902426809072495\n", "\n", "Total average train accuarcy: 0.72096\n", "Total average train loss: 0.009049770716428757\n", "\n", "[ Test epoch: 4 ]\n", "\n", "Total average test accuarcy: 0.716\n", "Total average test loss: 0.011751194977760315\n", "Model Saved!\n", "\n", "Time elapsed: 153.3404083251953\n", "\n", "[ Train epoch: 5 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.7421875\n", "Current batch average train loss: 0.008570997044444084\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.7265625\n", "Current batch average train loss: 0.008392542600631714\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.734375\n", "Current batch average train loss: 0.009212355129420757\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.796875\n", "Current batch average train loss: 0.008193189278244972\n", "\n", "Total average train accuarcy: 0.76912\n", "Total average train loss: 0.008334182484149933\n", "\n", "[ Test epoch: 5 ]\n", "\n", "Total average test accuarcy: 0.7684\n", "Total average test loss: 0.010591164660453796\n", "Model Saved!\n", "\n", "Time elapsed: 183.97001314163208\n", "\n", "[ Train epoch: 6 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.7578125\n", "Current batch average train loss: 0.008239216171205044\n", "\n", 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"Current batch average train loss: 0.0058960262686014175\n", "\n", "Total average train accuarcy: 0.93224\n", "Total average train loss: 0.005488414610624314\n", "\n", "[ Test epoch: 37 ]\n", "\n", "Total average test accuarcy: 0.8694\n", "Total average test loss: 0.008543139100074768\n", "Model Saved!\n", "\n", "Time elapsed: 1161.2228908538818\n", "\n", "[ Train epoch: 38 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.9140625\n", "Current batch average train loss: 0.005830290727317333\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.9453125\n", "Current batch average train loss: 0.00526583194732666\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.90625\n", "Current batch average train loss: 0.00564062874764204\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.9453125\n", "Current batch average train loss: 0.005252964794635773\n", "\n", "Total average train accuarcy: 0.93508\n", 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"\n", "Total average test accuarcy: 0.8877\n", "Total average test loss: 0.008029633057117461\n", "Model Saved!\n", "\n", "Time elapsed: 1496.8555335998535\n", "\n", "[ Train epoch: 49 ]\n", "\n", "Current batch: 0\n", "Current batch average train accuracy: 0.953125\n", "Current batch average train loss: 0.005324383266270161\n", "\n", "Current batch: 100\n", "Current batch average train accuracy: 0.9609375\n", "Current batch average train loss: 0.005233401898294687\n", "\n", "Current batch: 200\n", "Current batch average train accuracy: 0.9296875\n", "Current batch average train loss: 0.005477895960211754\n", "\n", "Current batch: 300\n", "Current batch average train accuracy: 0.9375\n", "Current batch average train loss: 0.005554812494665384\n", "\n", "Total average train accuarcy: 0.93806\n", "Total average train loss: 0.005385970480442047\n", "\n", "[ Test epoch: 49 ]\n", "\n", "Total average test accuarcy: 0.8724\n", "Total average test loss: 0.008432556354999543\n", "Model 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