Update resnet.py

archs/resnet.py

@@ -104,7 +104,7 @@ def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
         # Both self.conv2 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv1x1(inplanes, width)
         self.bn1 = norm_layer(width)
-        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.conv2 = conv3x3(width, width, stride)
         self.bn2 = norm_layer(width)
         self.conv3 = conv1x1(width, planes * self.expansion)
         self.bn3 = norm_layer(planes * self.expansion)
@@ -132,7 +132,7 @@ def forward(self, x):
         out += identity
         out = self.relu(out)
 
-        return
+        return  out
 
 
 class BasicBlock(nn.Module):
@@ -315,4 +315,4 @@ def resnet152_dynamic(pretrained=False, **kwargs):
     m = resnet50_dynamic(pretrained=True)
     print(m)
     x = torch.randn(size=(4, 3, 224, 224))
-    m(x)
+    m(x)