main_cls.py [191:211]:
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        if os.path.isfile(args.pretrained):
            print("=> loading pretrained model from '{}'".format(args.pretrained))
            checkpoint = torch.load(args.pretrained, map_location="cpu")
            state_dict = checkpoint['state_dict']
            for k in list(state_dict.keys()):
                new_key = k.replace("module.", "")
                state_dict[new_key] = state_dict[k]
                del state_dict[k]
            model_num_cls = state_dict['fc.weight'].shape[0]
            if model_num_cls != args.cls:
                # if num_cls don't match, remove the last layer
                del state_dict['fc.weight']
                del state_dict['fc.bias']
                msg = model.load_state_dict(state_dict, strict=False)
                assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}, \
                    "missing keys:\n{}".format('\n'.join(msg.missing_keys))
            else:
                model.load_state_dict(state_dict)
            print("=> loaded pre-trained model '{}' (epoch {})".format(args.pretrained, checkpoint['epoch']))
        else:
            print("=> no pretrained model found at '{}'".format(args.pretrained))
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main_fixmatch.py [243:263]:
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        if os.path.isfile(args.pretrained):
            print("=> loading pretrained model from '{}'".format(args.pretrained))
            checkpoint = torch.load(args.pretrained, map_location="cpu")
            state_dict = checkpoint['state_dict']
            for k in list(state_dict.keys()):
                new_key = k.replace("module.", "")
                state_dict[new_key] = state_dict[k]
                del state_dict[k]
            model_num_cls = state_dict['fc.weight'].shape[0]
            if model_num_cls != args.cls:
                # if num_cls don't match, remove the last layer
                del state_dict['fc.weight']
                del state_dict['fc.bias']
                msg = model.load_state_dict(state_dict, strict=False)
                assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}, \
                    "missing keys:\n{}".format('\n'.join(msg.missing_keys))
            else:
                model.load_state_dict(state_dict)
            print("=> loaded pre-trained model '{}' (epoch {})".format(args.pretrained, checkpoint['epoch']))
        else:
            print("=> no pretrained model found at '{}'".format(args.pretrained))
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