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人工智能系统实战第三期/实战代码/计算机视觉/CNN/network/ResNet.py

+import torch
+import torch
+import torch.nn as nn
+
+
+# 定义ResNet的基本块
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_channels, out_channels, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_channels != self.expansion * out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(self.expansion * out_channels)
+            )
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        out += self.shortcut(identity)
+        out = self.relu(out)
+
+        return out
+
+
+# 定义ResNet的主体结构
+class ResNet(nn.Module):
+    def __init__(self, block, layers, num_classes=1000):
+        super(ResNet, self).__init__()
+        self.in_channels = 64
+
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.layer1 = self._make_layer(block, 64, layers[0], stride=1)  # 输入图像大小: (224, 224) -> 输出图像大小: (56, 56)
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)  # 输入图像大小: (56, 56) -> 输出图像大小: (28, 28)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)  # 输入图像大小: (28, 28) -> 输出图像大小: (14, 14)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)  # 输入图像大小: (14, 14) -> 输出图像大小: (7, 7)
+
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+    def _make_layer(self, block, out_channels, blocks, stride=1):
+        layers = []
+        layers.append(block(self.in_channels, out_channels, stride))
+        self.in_channels = out_channels * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.in_channels, out_channels))
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = self.conv1(x)  # 输入图像大小: (224, 224) -> 输出图像大小: (112, 112)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.maxpool(out)  # 输入图像大小: (112, 112) -> 输出图像大小: (56, 56)
+
+        out = self.layer1(out)  # 输入图像大小: (56, 56) -> 输出图像大小: (56, 56)
+        out = self.layer2(out)  # 输入图像大小: (56, 56) -> 输出图像大小: (28, 28)
+        out = self.layer3(out)  # 输入图像大小: (28, 28) -> 输出图像大小: (14, 14)
+        out = self.layer4(out)  # 输入图像大小: (14, 14) -> 输出图像大小: (7, 7)
+
+        out = self.avgpool(out)  # [1]
+        out = torch.flatten(out, 1)
+        out = self.fc(out)
+
+        return out
+
+
+# 创建ResNet-50模型
+def resnet50(num_classes=1000):
+    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes)
+
+def resnet101(num_classes=1000):
+    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes)
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