.fuse() gradient introduction bug fix

utils/torch_utils.py

@@ -104,28 +104,28 @@ def prune(model, amount=0.3):
 
 
 def fuse_conv_and_bn(conv, bn):
-    # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    with torch.no_grad():
-        # init
-        fusedconv = nn.Conv2d(conv.in_channels,
-                              conv.out_channels,
-                              kernel_size=conv.kernel_size,
-                              stride=conv.stride,
-                              padding=conv.padding,
-                              groups=conv.groups,
-                              bias=True).to(conv.weight.device)
-
-        # prepare filters
-        w_conv = conv.weight.clone().view(conv.out_channels, -1)
-        w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-        fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
-
-        # prepare spatial bias
-        b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
-        b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
-        fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-        return fusedconv
+    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+
+    # init
+    fusedconv = nn.Conv2d(conv.in_channels,
+                          conv.out_channels,
+                          kernel_size=conv.kernel_size,
+                          stride=conv.stride,
+                          padding=conv.padding,
+                          groups=conv.groups,
+                          bias=True).requires_grad_(False).to(conv.weight.device)
+
+    # prepare filters
+    w_conv = conv.weight.clone().view(conv.out_channels, -1)
+    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
+
+    # prepare spatial bias
+    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+    return fusedconv
 
 
 def model_info(model, verbose=False):