feat(net): 为 DetailNode模块添加可选卷积块

- 在 DetailNode 类中引入 useBlock 参数，用于选择不同的卷积块
- 新增 DepthwiseSeparableConvBlock 类，实现深度可分离卷积
- 根据 useBlock 的值，选择使用 DepthwiseSeparableConvBlock 或 InvertedResidualBlock
- 优化了网络结构，提供了更多的灵活性和选择性

net.py

@@ -222,14 +222,36 @@ class InvertedResidualBlock(nn.Module):
     def forward(self, x):
         return self.bottleneckBlock(x)
 
+class DepthwiseSeparableConvBlock(nn.Module):
+    def __init__(self, inp, oup, kernel_size=3, stride=1, padding=1):
+        super(DepthwiseSeparableConvBlock, self).__init__()
+        self.depthwise = nn.Conv2d(inp, inp, kernel_size, stride, padding, groups=inp, bias=False)
+        self.pointwise = nn.Conv2d(inp, oup, 1, bias=False)
+        self.bn = nn.BatchNorm2d(oup)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.depthwise(x)
+        x = self.pointwise(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
 
 class DetailNode(nn.Module):
-    def __init__(self):
+    def __init__(self,useBlock=0):
         super(DetailNode, self).__init__()
-
-        self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
-        self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
-        self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+        if useBlock == 0:
+            self.theta_phi = DepthwiseSeparableConvBlock(inp=32, oup=32)
+            self.theta_rho = DepthwiseSeparableConvBlock(inp=32, oup=32)
+            self.theta_eta = DepthwiseSeparableConvBlock(inp=32, oup=32)
+        elif useBlock == 1:
+            self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+            self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+            self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+        else:
+            self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+            self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+            self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
         self.shffleconv = nn.Conv2d(64, 64, kernel_size=1,
                                     stride=1, padding=0, bias=True)