Add Conv2() module (#2820)

ultralytics/nn/modules/conv.py

@@ -43,6 +43,27 @@ class Conv(nn.Module):
         return self.act(self.conv(x))
 
 
+class Conv2(Conv):
+    """Simplified RepConv module with Conv fusing."""
+
+    def __init__(self, c1, c2, k=3, s=1, p=None, g=1, d=1, act=True):
+        """Initialize Conv layer with given arguments including activation."""
+        super().__init__(c1, c2, k, s, p, g=g, d=d, act=act)
+        self.cv2 = nn.Conv2d(c1, c2, 1, s, autopad(1, p, d), groups=g, dilation=d, bias=False)  # add 1x1 conv
+
+    def forward(self, x):
+        """Apply convolution, batch normalization and activation to input tensor."""
+        return self.act(self.bn(self.conv(x) + self.cv2(x)))
+
+    def fuse_convs(self):
+        """Fuse parallel convolutions."""
+        w = torch.zeros_like(self.conv.weight.data)
+        i = [x // 2 for x in w.shape[2:]]
+        w[:, :, i[0] - 1:i[0], i[1] - 1:i[1]] = self.cv2.weight.data.clone()
+        self.conv.weight.data += w
+        self.__delattr__('cv2')
+
+
 class LightConv(nn.Module):
     """Light convolution with args(ch_in, ch_out, kernel).
     https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/modeling/backbones/hgnet_v2.py