加入CNN+Attention的图像分类模块

nn/model.py

@@ -0,0 +1,48 @@
+import torch.nn as nn
+
+
+# 更简化的注意力模块
+class UltraSimplifiedAttention(nn.Module):
+    def __init__(self, in_channels):
+        super(UltraSimplifiedAttention, self).__init__()
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(in_channels, in_channels // 64, bias=False),
+            nn.ReLU(inplace=True),
+            nn.Linear(in_channels // 64, in_channels, bias=False),
+            nn.Sigmoid()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y.expand_as(x)
+
+
+# CNN结合更简化的注意力机制模型
+class CNNWithUltraSimplifiedAttention(nn.Module):
+    def __init__(self, num_classes):
+        super(CNNWithUltraSimplifiedAttention, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
+        self.attention1 = UltraSimplifiedAttention(16)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.attention2 = UltraSimplifiedAttention(32)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.fc_input_dim = 32 * 256 * 200
+        self.fc1 = nn.Linear(self.fc_input_dim, 128)
+        self.relu3 = nn.ReLU(inplace=True)
+        self.fc2 = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.attention1(self.conv1(x))))
+        x = self.pool2(self.relu2(self.attention2(self.conv2(x))))
+        x = x.view(-1, self.fc_input_dim)
+        x = self.relu3(self.fc1(x))
+        x = self.fc2(x)
+        return x