diff --git a/hubconf.py b/hubconf.py
index 0eaf70787e64..1e6b9c78ac6a 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -38,9 +38,10 @@ def create(name, pretrained, channels, classes, autoshape):
             fname = f'{name}.pt'  # checkpoint filename
             attempt_download(fname)  # download if not found locally
             ckpt = torch.load(fname, map_location=torch.device('cpu'))  # load
-            state_dict = ckpt['model'].float().state_dict()  # to FP32
-            state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape}  # filter
-            model.load_state_dict(state_dict, strict=False)  # load
+            msd = model.state_dict()   # model state_dict
+            csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
+            csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape}  # filter
+            model.load_state_dict(csd, strict=False)  # load
             if len(ckpt['model'].names) == classes:
                 model.names = ckpt['model'].names  # set class names attribute
             if autoshape:
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index d6da0cae8945..9991e5ec87d8 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -191,7 +191,7 @@ def fuse_conv_and_bn(conv, bn):
     # 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()))
+    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
 
     # prepare spatial bias
     b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias