Add DWConvClass() (#4274)

* Add `DWConvClass()`

* Cleanup

* Cleanup2

models/common.py

@@ -30,7 +30,7 @@ def autopad(k, p=None):  # kernel, padding
 
 
 def DWConv(c1, c2, k=1, s=1, act=True):
-    # Depth-wise convolution
+    # Depth-wise convolution function
     return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
 
 
@@ -45,10 +45,17 @@ class Conv(nn.Module):
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
 
-    def fuseforward(self, x):
+    def forward_fuse(self, x):
         return self.act(self.conv(x))
 
 
+class DWConvClass(Conv):
+    # Depth-wise convolution class
+    def __init__(self, c1, c2, k=1, s=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__(c1, c2, k, s, act)
+        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k), groups=math.gcd(c1, c2), bias=False)
+
+
 class TransformerLayer(nn.Module):
     # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
     def __init__(self, c, num_heads):

models/experimental.py

@@ -72,7 +72,7 @@ class GhostBottleneck(nn.Module):
 
 
 class MixConv2d(nn.Module):
-    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
+    # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
     def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
         super().__init__()
         groups = len(k)

models/yolo.py

@@ -202,10 +202,10 @@ class Model(nn.Module):
     def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
         LOGGER.info('Fusing layers... ')
         for m in self.model.modules():
-            if type(m) is Conv and hasattr(m, 'bn'):
+            if isinstance(m, (Conv, DWConvClass)) and hasattr(m, 'bn'):
                 m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                 delattr(m, 'bn')  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
+                m.forward = m.forward_fuse  # update forward
         self.info()
         return self