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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/VGG.py

+import torch
+import torch
+import torch.nn as nn
+
+
+class VGG(nn.Module):
+    def __init__(self, num_classes=1000):
+        super(VGG, self).__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 64, kernel_size=3, padding=1),  # output[64, 224, 224]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(64, 64, kernel_size=3, padding=1),  # output[64, 224, 224]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # output[64, 112, 112]
+
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),  # output[128, 112, 112]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 128, kernel_size=3, padding=1),  # output[128, 112, 112]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # output[128, 56, 56]
+
+            nn.Conv2d(128, 256, kernel_size=3, padding=1),  # output[256, 56, 56]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),  # output[256, 56, 56]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1),  # output[256, 56, 56]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # output[256, 28, 28]
+
+            nn.Conv2d(256, 512, kernel_size=3, padding=1),  # output[512, 28, 28]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),  # output[512, 28, 28]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),  # output[512, 28, 28]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # output[512, 14, 14]
+
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),  # output[512, 14, 14]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),  # output[512, 14, 14]
+            nn.ReLU(inplace=True),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1),  # output[512, 14, 14]
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=2, stride=2),  # output[512, 7, 7]
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(512 * 7 * 7, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.5),
+            nn.Linear(4096, 4096),
+            nn.ReLU(inplace=True),
+            nn.Dropout(p=0.5),
+            nn.Linear(4096, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = torch.flatten(x, start_dim=1)
+        x = self.classifier(x)
+        return x
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