:hammer: [Fix] the used functions in module.py

model/module.py

@@ -11,10 +11,10 @@ class Conv(nn.Module):
         out_channels,
         kernel_size,
         stride=1,
-        padding=0,
+        padding=None,
         dilation=1,
         groups=1,
-        act=nn.ReLU(),
+        act=nn.SiLU(),
         bias=False,
         auto_padding=True,
         padding_mode="zeros",
@@ -48,10 +48,10 @@ class Conv(nn.Module):
 # RepVGG
 class RepConv(nn.Module):
     # https://github.com/DingXiaoH/RepVGG
-    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, groups=1, act=nn.ReLU()):
+    def __init__(self, in_channels, out_channels, kernel_size=3, padding=None, stride=1, groups=1, act=nn.SiLU(), deploy=False):
 
         super().__init__()
-
+        self.deploy = deploy 
         self.conv1 = Conv(in_channels, out_channels, kernel_size, stride, groups=groups, act=False)
         self.conv2 = Conv(in_channels, out_channels, 1, stride, groups=groups, act=False)
         self.act = act if isinstance(act, nn.Module) else nn.Identity()
@@ -64,6 +64,28 @@ class RepConv(nn.Module):
 
     # to be implement
     # def fuse_convs(self):
+    def fuse_conv_bn(self, conv, bn):
+
+        std = (bn.running_var + bn.eps).sqrt()
+        bias = bn.bias - bn.running_mean * bn.weight / std
+
+        t = (bn.weight / std).reshape(-1, 1, 1, 1)
+        weights = conv.weight * t
+
+        bn = nn.Identity()
+        conv = nn.Conv2d(in_channels = conv.in_channels,
+                              out_channels = conv.out_channels,
+                              kernel_size = conv.kernel_size,
+                              stride=conv.stride,
+                              padding = conv.padding,
+                              dilation = conv.dilation,
+                              groups = conv.groups,
+                              bias = True,
+                              padding_mode = conv.padding_mode)
+
+        conv.weight = torch.nn.Parameter(weights)
+        conv.bias = torch.nn.Parameter(bias)
+        return conv
 
 
 # ResNet