{"id":188,"date":"2025-07-01T08:58:25","date_gmt":"2025-07-01T00:58:25","guid":{"rendered":"https:\/\/mianka.xyz\/?p=188"},"modified":"2025-07-01T09:08:51","modified_gmt":"2025-07-01T01:08:51","slug":"pytorch","status":"publish","type":"post","link":"https:\/\/www.mianka.xyz\/?p=188","title":{"rendered":"PyTorch\u81ea\u5b9a\u4e49\u6a21\u578b\u8bbe\u8ba1\u4e0e\u4f18\u5316\u5668\u9009\u62e9\u5168\u653b\u7565\uff1a\u4ece\u67b6\u6784\u8bbe\u8ba1\u5230\u8bad\u7ec3\u7b56\u7565"},"content":{"rendered":"

\u4e00\u3001\u81ea\u5b9a\u4e49\u6a21\u578b\u8bbe\u8ba1\uff1a\u4ece\u9700\u6c42\u5230\u67b6\u6784<\/span><\/h2>\n

1.1 \u6a21\u578b\u8bbe\u8ba1\u6838\u5fc3\u539f\u5219<\/span><\/h3>\n
    \n
  1. \n

    \u4efb\u52a1\u9002\u914d\u6027<\/span><\/p>\n<\/li>\n

      \n
    • \n

      CV\u4efb\u52a1<\/span>\uff1a\u4f18\u5148\u9009\u62e9\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u6216\u89c6\u89c9Transformer\uff08ViT\uff09\uff0c\u5229\u7528\u5c40\u90e8\u611f\u53d7\u91ce\u4e0e\u5e73\u79fb\u4e0d\u53d8\u6027\u3002<\/p>\n<\/li>\n

    • \n

      NLP\u4efb\u52a1<\/span>\uff1a\u91c7\u7528Transformer\u6216\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09\uff0c\u6355\u6349\u957f\u8ddd\u79bb\u4f9d\u8d56\u5173\u7cfb\u3002<\/p>\n<\/li>\n

    • \n

      \u751f\u6210\u6a21\u578b<\/span>\uff1aGAN\u6216\u6269\u6563\u6a21\u578b\u9700\u8bbe\u8ba1\u751f\u6210\u5668-\u5224\u522b\u5668\u5bf9\u79f0\u7ed3\u6784\uff0c\u6216U-Net\u7b49\u7f16\u7801\u5668-\u89e3\u7801\u5668\u67b6\u6784\u3002<\/p>\n<\/li>\n<\/ul>\n

    • \n

      \u6a21\u5757\u5316\u4e0e\u53ef\u6269\u5c55\u6027<\/span><\/p>\n<\/li>\n

        \n
      • \n

        \u4f7f\u7528nn.Module<\/code>\u5c01\u88c5\u53ef\u590d\u7528\u7ec4\u4ef6\uff08\u5982\u6b8b\u5dee\u5757\u3001\u6ce8\u610f\u529b\u5c42\uff09\uff0c\u901a\u8fc7\u7ee7\u627f\u4e0e\u7ec4\u5408\u5feb\u901f\u6784\u5efa\u590d\u6742\u6a21\u578b\u3002<\/p>\n<\/li>\n

      • \n

        \u793a\u4f8b\uff1a\u81ea\u5b9a\u4e49\u6b8b\u5dee\u5757<\/span><\/p>\n<\/li>\n

      • \n
        class ResidualBlock(nn.Module):\n    def __init__(self, in_channels):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n        self.bn1 = nn.BatchNorm2d(in_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1)\n        self.bn2 = nn.BatchNorm2d(in_channels)\n    \n    def forward(self, x):\n        identity = x\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out += identity  # \u6b8b\u5dee\u8fde\u63a5\n        return out<\/pre>\n<\/li>\n<\/ul>\n
      • \n
        \u8ba1\u7b97\u6548\u7387\u4e0e\u5185\u5b58\u4f18\u5316<\/span><\/p>\n<\/li>\n
          \n
        • \n
          \u4f7f\u7528nn.Sequential<\/code>\u7b80\u5316\u524d\u5411\u4f20\u64ad\uff0c\u907f\u514d\u5197\u4f59\u8ba1\u7b97\u3002<\/p>\n<\/li>\n
        • \n
          \u5bf9\u9ad8\u5206\u8fa8\u7387\u8f93\u5165\uff08\u5982\u533b\u5b66\u56fe\u50cf\uff09\uff0c\u91c7\u7528\u5206\u7ec4\u5377\u79ef\uff08nn.GroupConv<\/code>\uff09\u6216\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\uff08nn.SeparableConv2d<\/code>\uff09\u964d\u4f4e\u53c2\u6570\u91cf\u3002<\/p>\n<\/li>\n<\/ul>\n<\/ol>\n
          <\/p>\n
          1.2 \u5178\u578b\u573a\u666f\u6a21\u578b\u8bbe\u8ba1\u6848\u4f8b<\/span><\/h3>\n
          \u6848\u4f8b1\uff1a\u8f7b\u91cf\u5316\u56fe\u50cf\u5206\u7c7b\u6a21\u578b<\/span><\/h4>\n
          \u9700\u6c42<\/span>\uff1a\u5728\u5d4c\u5165\u5f0f\u8bbe\u5907\u4e0a\u90e8\u7f72MNIST\u5206\u7c7b\u6a21\u578b\uff0c\u8981\u6c42\u53c2\u6570\u91cf<100K\u3002
          \u8bbe\u8ba1<\/span>\uff1a<\/p>\n
            \n
          • \n
            \u4f7f\u7528\u6df1\u5ea6\u53ef\u5206\u79bb\u5377\u79ef\u66ff\u4ee3\u6807\u51c6\u5377\u79ef\uff0c\u51cf\u5c11\u53c2\u6570\u91cf\u3002<\/p>\n<\/li>\n
          • \n
            \u63d2\u5165\u901a\u9053\u6ce8\u610f\u529b\u6a21\u5757\uff08SE Block\uff09\u63d0\u5347\u7279\u5f81\u8868\u8fbe\u80fd\u529b\u3002<\/p>\n
            <\/p>\n<\/li>\n
          • \n
            class LightCNN(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)\n        self.dw_conv = nn.Sequential(\n            nn.Conv2d(32, 32, kernel_size=3, padding=1, groups=32),  # \u6df1\u5ea6\u5377\u79ef\n            nn.Conv2d(32, 64, kernel_size=1),  # \u70b9\u5377\u79ef\n            nn.BatchNorm2d(64),\n            nn.ReLU()\n        )\n        self.se_block = SEBlock(64)  # \u81ea\u5b9a\u4e49SE\u6ce8\u610f\u529b\u6a21\u5757\n        self.fc = nn.Linear(64*7*7, 10)\n    \n    def forward(self, x):\n        x = torch.relu(self.conv1(x))\n        x = self.dw_conv(x)\n        x = self.se_block(x)\n        x = torch.flatten(x, 1)\n        return self.fc(x)<\/pre>\n<\/li>\n<\/ul>\n
            \u6848\u4f8b2\uff1a\u57fa\u4e8eTransformer\u7684\u6587\u672c\u751f\u6210\u6a21\u578b<\/span><\/h4>\n
            \u9700\u6c42<\/span>\uff1a\u6784\u5efa\u4e00\u4e2a\u53ef\u751f\u6210\u77ed\u6587\u672c\u7684Transformer\u89e3\u7801\u5668\u6a21\u578b\u3002
            \u8bbe\u8ba1<\/span>\uff1a<\/p>\n
              \n
            • \n
              \u91c7\u7528\u81ea\u56de\u5f52\u7ed3\u6784\uff0c\u63a9\u7801\u591a\u5934\u6ce8\u610f\u529b\u9632\u6b62\u4fe1\u606f\u6cc4\u9732\u3002<\/p>\n<\/li>\n
            • \n
              \u4f7f\u7528\u76f8\u5bf9\u4f4d\u7f6e\u7f16\u7801\u66ff\u4ee3\u7edd\u5bf9\u4f4d\u7f6e\u7f16\u7801\uff0c\u63d0\u5347\u957f\u5e8f\u5217\u5efa\u6a21\u80fd\u529b\u3002<\/p>\n<\/li>\n
            • \n
              class TextGenerator(nn.Module):\n    def __init__(self, vocab_size, d_model=512, nhead=8, num_layers=6):\n        super().__init__()\n        encoder_layer = nn.TransformerEncoderLayer(\n            d_model=d_model, nhead=nhead, dim_feedforward=2048\n        )\n        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)\n        self.embedding = nn.Embedding(vocab_size, d_model)\n        self.pos_encoder = PositionalEncoding(d_model)  # \u81ea\u5b9a\u4e49\u4f4d\u7f6e\u7f16\u7801\n        self.decoder = nn.Linear(d_model, vocab_size)\n    \n    def forward(self, src):\n        src = self.embedding(src) * math.sqrt(self.d_model)\n        src = self.pos_encoder(src)\n        output = self.transformer(src)\n        return self.decoder(output)<\/pre>\n<\/li>\n<\/ul>\n
              \n
              \u4e8c\u3001\u4f18\u5316\u5668\u9009\u62e9\uff1a\u7406\u8bba\u3001\u5b9e\u8df5\u4e0e\u8c03\u53c2<\/span><\/h2>\n
              2.1 \u4f18\u5316\u5668\u6838\u5fc3\u7279\u6027\u5bf9\u6bd4<\/span><\/h3>\n
              <\/p>\n\n\n\n\n\n\n\n\n\n\n
              \u4f18\u5316\u5668<\/th>\n\u9002\u7528\u573a\u666f<\/th>\n\u4f18\u52bf<\/th>\n\u52a3\u52bf<\/th>\n<\/tr>\n<\/thead>\n
              SGD<\/span><\/td>\n\u5927\u89c4\u6a21\u6570\u636e\u3001\u7b80\u5355\u6a21\u578b\uff08\u5982ResNet\uff09<\/td>\n\u8bad\u7ec3\u7a33\u5b9a\uff0c\u6cdb\u5316\u80fd\u529b\u5f3a<\/td>\n\u9700\u7cbe\u7ec6\u8c03\u53c2\uff0c\u6536\u655b\u6162<\/td>\n<\/tr>\n
              Adam<\/span><\/td>\n\u590d\u6742\u6a21\u578b\uff08\u5982Transformer\u3001GAN\uff09<\/td>\n\u81ea\u9002\u5e94\u5b66\u4e60\u7387\uff0c\u52a0\u901f\u6536\u655b<\/td>\n\u53ef\u80fd\u8fc7\u62df\u5408\uff0c\u6cdb\u5316\u6027\u7a0d\u5f31<\/td>\n<\/tr>\n
              AdamW<\/span><\/td>\n\u9884\u8bad\u7ec3\u6a21\u578b\uff08\u5982BERT\u3001GPT\uff09<\/td>\n\u89e3\u8026\u6743\u91cd\u8870\u51cf\uff0c\u7a33\u5b9a\u8bad\u7ec3<\/td>\n\u5bf9\u5b66\u4e60\u7387\u654f\u611f<\/td>\n<\/tr>\n
              RAdam<\/span><\/td>\n\u8bad\u7ec3\u521d\u671f\u68af\u5ea6\u4e0d\u7a33\u5b9a\uff08\u5982GAN\u751f\u6210\u5668\uff09<\/td>\n\u52a8\u6001\u8c03\u6574\u52a8\u91cf\u8303\u56f4\uff0c\u89e3\u51b3\u51b7\u542f\u52a8<\/td>\n\u8ba1\u7b97\u5f00\u9500\u7565\u9ad8<\/td>\n<\/tr>\n
              LAMB<\/span><\/td>\n\u8d85\u5927\u89c4\u6a21\u6a21\u578b\uff08\u5982GPT-3\u3001ViT\uff09<\/td>\n\u5206\u5c42\u81ea\u9002\u5e94\u5b66\u4e60\u7387\uff0c\u652f\u6301\u767e\u4ebf\u53c2\u6570<\/td>\n\u9700\u8c03\u6574\u03b2\u53c2\u6570<\/td>\n<\/tr>\n
              Adafactor<\/span><\/td>\n\u5185\u5b58\u53d7\u9650\u573a\u666f\uff08\u5982\u957f\u5e8f\u5217RNN\uff09<\/td>\n\u56e0\u5f0f\u5206\u89e3\u68af\u5ea6\u77e9\u9635\uff0c\u51cf\u5c11\u5b58\u50a8<\/td>\n\u6536\u655b\u901f\u5ea6\u8f83\u6162<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
              <\/p>\n
              2.2 \u573a\u666f\u5316\u4f18\u5316\u5668\u9009\u62e9\u7b56\u7565<\/span><\/h3>\n
              \u573a\u666f1\uff1a\u8ba1\u7b97\u673a\u89c6\u89c9\uff08ResNet-50\u8bad\u7ec3\uff09<\/span><\/h4>\n
                \n
              • \n
                \u63a8\u8350\u4f18\u5316\u5668<\/span>\uff1aSGD + Momentum\uff08\u5b66\u4e60\u73870.1\uff0c\u52a8\u91cf0.9\uff09 + \u4f59\u5f26\u9000\u706b\u8c03\u5ea6\u5668<\/p>\n<\/li>\n
              • \n
                \u539f\u56e0<\/span>\uff1a<\/p>\n<\/li>\n
                  \n
                • \n
                  SGD\u7684\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7279\u6027\u53ef\u907f\u514d\u9677\u5165\u5c40\u90e8\u6700\u4f18\uff0c\u63d0\u5347\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/li>\n
                • \n
                  \u4f59\u5f26\u9000\u706b\u52a8\u6001\u8c03\u6574\u5b66\u4e60\u7387\uff0c\u5e73\u8861\u8bad\u7ec3\u521d\u671f\u4e0e\u672b\u671f\u7684\u6536\u655b\u901f\u5ea6\u3002<\/p>\n<\/li>\n<\/ul>\n
                • \n
                  \u4ee3\u7801\u793a\u4f8b<\/span>\uff1a<\/p>\n
                  optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=90, eta_min=0)<\/pre>\n<\/li>\n<\/ul>\n
                  \u573a\u666f2\uff1a\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08BERT\u5fae\u8c03\uff09<\/span><\/h4>\n
                    \n
                  • \n
                    \u63a8\u8350\u4f18\u5316\u5668<\/span>\uff1aAdamW\uff08\u5b66\u4e60\u73875e-5\uff0c\u03b21=0.9\uff0c\u03b22=0.999\uff09 + \u7ebf\u6027\u9884\u70ed\u8c03\u5ea6\u5668<\/p>\n<\/li>\n
                  • \n
                    \u539f\u56e0<\/span>\uff1a<\/p>\n<\/li>\n
                      \n
                    • \n
                      AdamW\u7684\u81ea\u9002\u5e94\u5b66\u4e60\u7387\u53ef\u7f13\u89e3\u5fae\u8c03\u9636\u6bb5\u68af\u5ea6\u6d88\u5931\u95ee\u9898\u3002<\/p>\n<\/li>\n
                    • \n
                      \u7ebf\u6027\u9884\u70ed\u9010\u6b65\u63d0\u5347\u5b66\u4e60\u7387\uff0c\u907f\u514d\u521d\u671f\u8bad\u7ec3\u4e0d\u7a33\u5b9a\u3002<\/p>\n<\/li>\n<\/ul>\n
                    • \n
                      \u4ee3\u7801\u793a\u4f8b<\/span>\uff1a<\/p>\n
                      optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.01)\nscheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, end_factor=1.0, total_iters=1000)<\/pre>\n<\/li>\n<\/ul>\n
                      \u573a\u666f3\uff1a\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08DCGAN\u8bad\u7ec3\uff09<\/span><\/h4>\n
                        \n
                      • \n
                        \u63a8\u8350\u4f18\u5316\u5668<\/span>\uff1a<\/p>\n<\/li>\n
                          \n
                        • \n
                          \u751f\u6210\u5668\uff1aAdam\uff08\u03b21=0.0\uff0c\u03b22=0.999\uff09<\/p>\n<\/li>\n
                        • \n
                          \u5224\u522b\u5668\uff1aRMSprop\uff08\u5b66\u4e60\u73870.0002\uff09<\/p>\n<\/li>\n<\/ul>\n
                        • \n
                          \u539f\u56e0<\/span>\uff1a<\/p>\n<\/li>\n
                            \n
                          • \n
                            \u751f\u6210\u5668\u9700\u4f4e\u03b21\u6291\u5236\u521d\u59cb\u68af\u5ea6\u9707\u8361\uff0c\u9ad8\u03b22\u7a33\u5b9a\u540e\u671f\u8bad\u7ec3\u3002<\/p>\n<\/li>\n
                          • \n
                            \u5224\u522b\u5668\u4f7f\u7528RMSprop\u907f\u514d\u8fc7\u65e9\u6536\u655b\u5bfc\u81f4\u6a21\u5f0f\u5d29\u6e83\u3002<\/p>\n<\/li>\n<\/ul>\n
                          • \n
                            \u4ee3\u7801\u793a\u4f8b<\/span>\uff1a<\/p>\n
                            optimizer_G = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.0, 0.999))\noptimizer_D = torch.optim.RMSprop(discriminator.parameters(), lr=0.0002, alpha=0.9)<\/pre>\n<\/li>\n<\/ul>\n
                            2.3 \u9ad8\u7ea7\u8c03\u53c2\u6280\u5de7<\/span><\/h3>\n
                              \n
                            1. \n
                              \u68af\u5ea6\u88c1\u526a<\/span>\uff1a\u9632\u6b62RNN\u6216GAN\u4e2d\u68af\u5ea6\u7206\u70b8<\/p>\n
                              torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)<\/pre>\n<\/li>\n
                            2. \n
                              \u5b66\u4e60\u7387\u9884\u70ed<\/span>\uff1a\u7ed3\u5408\u7ebf\u6027\u9884\u70ed\u4e0e\u4f59\u5f26\u9000\u706b<\/p>\n
                              scheduler = torch.optim.lr_scheduler.SequentialLR(\n    optimizer,\n    schedulers=[\n        torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, end_factor=1.0, total_iters=1000),\n        torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=89000, eta_min=0)\n    ],\n    milestones=[1000]\n)<\/pre>\n<\/li>\n
                            3. \n
                              \u81ea\u9002\u5e94\u6279\u91cf\u5f52\u4e00\u5316<\/span>\uff1a\u5bf9\u5c0f\u6279\u91cf\u6570\u636e\uff08\u5982\u533b\u5b66\u56fe\u50cf\uff09\u4f7f\u7528SyncBatchNorm<\/code>\u591aGPU\u540c\u6b65\u7edf\u8ba1\u91cf<\/p>\n
                              model = nn.SyncBatchNorm.convert_sync_batchnorm(model)<\/pre>\n<\/li>\n<\/ol>\n
                              \n
                              \u4e09\u3001\u5b8c\u6574\u8bad\u7ec3\u6d41\u7a0b\u793a\u4f8b<\/span><\/h2>\n
                              \u4ee5CIFAR-10\u5206\u7c7b\u4efb\u52a1\u4e3a\u4f8b\uff0c\u6574\u5408\u81ea\u5b9a\u4e49\u6a21\u578b\u4e0e\u4f18\u5316\u5668\uff1a<\/p>\n
                              import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\n\n# 1. \u5b9a\u4e49\u81ea\u5b9a\u4e49\u6a21\u578b\nclass CustomCNN(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n        self.pool = nn.MaxPool2d(2, 2)\n        self.fc1 = nn.Linear(64 * 8 * 8, 512)\n        self.fc2 = nn.Linear(512, 10)\n    \n    def forward(self, x):\n        x = self.pool(torch.relu(self.conv1(x)))\n        x = self.pool(torch.relu(self.conv2(x)))\n        x = torch.flatten(x, 1)\n        x = torch.relu(self.fc1(x))\n        return self.fc2(x)\n\n# 2. \u6570\u636e\u52a0\u8f7d\u4e0e\u9884\u5904\u7406\ntransform = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n])\ntrain_set = datasets.CIFAR10(root='.\/data', train=True, download=True, transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n\n# 3. \u521d\u59cb\u5316\u6a21\u578b\u3001\u635f\u5931\u51fd\u6570\u4e0e\u4f18\u5316\u5668\nmodel = CustomCNN()\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\nscheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)\n\n# 4. \u8bad\u7ec3\u5faa\u73af\nfor epoch in range(100):\n    for inputs, labels in train_loader:\n        optimizer.zero_grad()\n        outputs = model(inputs)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n    scheduler.step()\n    print(f'Epoch {epoch}, Loss: {loss.item():.4f}')<\/pre>\n
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