Use reshape instead of view in std_conv, causing issues in recent PyTorch in channels_last

timm/models/layers/std_conv.py

@@ -41,7 +41,7 @@ class StdConv2d(nn.Conv2d):
 
     def forward(self, x):
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
         return x
@@ -67,7 +67,7 @@ class StdConv2dSame(nn.Conv2d):
         if self.same_pad:
             x = pad_same(x, self.kernel_size, self.stride, self.dilation)
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
         return x
@@ -96,7 +96,7 @@ class ScaledStdConv2d(nn.Conv2d):
 
     def forward(self, x):
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             weight=(self.gain * self.scale).view(-1),
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
@@ -127,7 +127,7 @@ class ScaledStdConv2dSame(nn.Conv2d):
         if self.same_pad:
             x = pad_same(x, self.kernel_size, self.stride, self.dilation)
         weight = F.batch_norm(
-            self.weight.view(1, self.out_channels, -1), None, None,
+            self.weight.reshape(1, self.out_channels, -1), None, None,
             weight=(self.gain * self.scale).view(-1),
             training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
         return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)