update fuse_conv_and_bn()

utils/torch_utils.py

@@ -90,7 +90,7 @@ def prune(model, amount=0.3):
     import torch.nn.utils.prune as prune
     print('Pruning model... ', end='')
     for name, m in model.named_modules():
-        if isinstance(m, torch.nn.Conv2d):
+        if isinstance(m, nn.Conv2d):
             prune.l1_unstructured(m, name='weight', amount=amount)  # prune
             prune.remove(m, 'weight')  # make permanent
     print(' %.3g global sparsity' % sparsity(model))
@@ -100,12 +100,12 @@ def fuse_conv_and_bn(conv, bn):
     # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
     with torch.no_grad():
         # init
-        fusedconv = torch.nn.Conv2d(conv.in_channels,
+        fusedconv = nn.Conv2d(conv.in_channels,
                               conv.out_channels,
                               kernel_size=conv.kernel_size,
                               stride=conv.stride,
                               padding=conv.padding,
-                                    bias=True)
+                              bias=True).to(conv.weight.device)
 
         # prepare filters
         w_conv = conv.weight.clone().view(conv.out_channels, -1)
@@ -113,10 +113,7 @@ def fuse_conv_and_bn(conv, bn):
         fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
 
         # prepare spatial bias
-        if conv.bias is not None:
-            b_conv = conv.bias
-        else:
-            b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device)
+        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)
 
@@ -159,8 +156,8 @@ def load_classifier(name='resnet101', n=2):
 
     # Reshape output to n classes
     filters = model.fc.weight.shape[1]
-    model.fc.bias = torch.nn.Parameter(torch.zeros(n), requires_grad=True)
-    model.fc.weight = torch.nn.Parameter(torch.zeros(n, filters), requires_grad=True)
+    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
+    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
     model.fc.out_features = n
     return model