generation/models/base_model.py [73:96]:
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            try:
                network.load_state_dict(torch.load(save_path))
            except:
                pretrained_dict = torch.load(save_path)
                model_dict = network.state_dict()
                try:
                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
                    network.load_state_dict(pretrained_dict)
                    if self.opt.verbose:
                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
                except:
                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
                    for k, v in pretrained_dict.items():
                        if v.size() == model_dict[k].size():
                            model_dict[k] = v

                    if sys.version_info >= (3,0):
                        not_initialized = set()
                    else:
                        from sets import Set
                        not_initialized = Set()

                    for k, v in model_dict.items():
                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
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separate_vae/models/separate_clothing_encoder_models.py [283:306]:
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            try:
                network.load_state_dict(torch.load(save_path))
            except:
                pretrained_dict = torch.load(save_path)
                model_dict = network.state_dict()
                try:
                    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
                    network.load_state_dict(pretrained_dict)
                    if self.opt.verbose:
                        print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
                except:
                    print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
                    for k, v in pretrained_dict.items():
                        if v.size() == model_dict[k].size():
                            model_dict[k] = v

                    if sys.version_info >= (3,0):
                        not_initialized = set()
                    else:
                        from sets import Set
                        not_initialized = Set()

                    for k, v in model_dict.items():
                        if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
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