IncompatibleKeys(missing_keys=[], unexpected_keys=[])
When I tried to use IBN-net as an feature extractor, I met an error.
class LargeModel(nn.Module):
def __init__(self, pretrain=True):
super(LargeModel, self).__init__()
# define ibn_model and initilize it with pretrained model.
IBN_model_name = 'resnet50_ibn_a.pth.tar'
IBN_model = resnet50_ibn_a(pretrained=False)
pretrained_model_weight = torch.load(IBN_model_name)['state_dict']
new_state_dict = OrderedDict()
for k, v in pretrained_model_weight.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
# for k, v in new_state_dict.items():
# print(k)
# print('----------')
# for k in IBN_model.state_dict().keys():
# print(k)
self.ibn_res50 = IBN_model.load_state_dict(new_state_dict)
print(self.ibn_res50)
# Define Extra layers and initilize them with uniform distribution
When I try to print self.ibn_res50
, it gives such error.
In fact, when I try to print new_state_dict
and IBN_model.state_dict().keys()
, both of them return the same keys.