when load pretrained model of VoVNet99, missing key in state_dict

[tri-ml]

when load pretrained model of VoVNet99, missing key in state_dict

[LeopoldACC]

Maybe I solved it by edit the key name in pth file.

    model = build_vovnet_fpn_backbone(config,input_shape = ShapeSpec(channels=3, height=448, width=800, stride=None))
    pretrained_model_whole = torch.load('/data/CenterFusion0/models/vovnet99_dd3d15m.pth',map_location=torch.device('cpu'))
    pretrained_model =pretrained_model_whole['model']
    pretrained_model_with_new_key = OrderedDict([])
    
    keys2del = ["fpn_lateral2.norm.num_batches_tracked", "fpn_output2.norm.num_batches_tracked", "fpn_lateral3.norm.num_batches_tracked", "fpn_output3.norm.num_batches_tracked", "fpn_lateral4.norm.num_batches_tracked", "fpn_output4.norm.num_batches_tracked", "fpn_lateral5.norm.num_batches_tracked", "fpn_output5.norm.num_batches_tracked", "top_block.p6.weight", "top_block.p6.bias"]
    for key in pretrained_model:
        if 'backbone.' in key:
            new_key = key.replace('backbone.','')
            pretrained_model_with_new_key[new_key] = pretrained_model[key]
    for key in keys2del:
        del pretrained_model_with_new_key[key]
    # missing_keys, unexpected_keys  = model.load_state_dict(pretrained_model)
    missing_keys, unexpected_keys  = model.load_state_dict(pretrained_model_with_new_key)
    
    print("[missing_keys]:", *missing_keys, sep="\n")
    print("[unexpected_keys]:", *unexpected_keys, sep="\n")