codingutf8 2018 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license convert lxmert checkpoint import argparse import torch from transformers import lxmertconfig lxmertforpretraining loadtfweightsinlxmert from transformers utils import logging logging setverbosityinfo def converttfcheckpointtopytorchtfcheckpointpath configfile pytorchdumppath initialise pytorch model config lxmertconfig fromjsonfileconfigfile printfbuilding pytorch model from configuration config model lxmertforpretrainingconfig load weights from tf checkpoint loadtfweightsinlxmertmodel config tfcheckpointpath save pytorchmodel printfsave pytorch model to pytorchdumppath torch savemodel statedict pytorchdumppath if name main parser argparse argumentparser required parameters parser addargument tfcheckpointpath defaultnone typestr requiredtrue helppath to the tensorflow checkpoint path parser addargument configfile defaultnone typestr requiredtrue helpthe config json file corresponding to the pretrained model nthis specifies the model architecture parser addargument pytorchdumppath defaultnone typestr requiredtrue helppath to the output pytorch model args parser parseargs converttfcheckpointtopytorchargs tfcheckpointpath args configfile args pytorchdumppath coding utf 8 2018 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license convert lxmert checkpoint initialise pytorch model load weights from tf checkpoint save pytorch model required parameters
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
    
    config = LxmertConfig.from_json_file(config_file)
    print(f"Building PyTorch model from configuration: {config}")
    model = LxmertForPreTraining(config)

    
    load_tf_weights_in_lxmert(model, config, tf_checkpoint_path)

    
    print(f"Save PyTorch model to {pytorch_dump_path}")
    torch.save(model.state_dict(), pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)