转载自：https://blog.csdn.net/weixin_41287060/article/details/105080705
"""Convert BERT checkpoint."""
import argparse
import logging
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
logging.basicConfig(level=logging.INFO)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
'''
tf_checkpoint_path: ckpt文件
bert_config_file: json文件
pytorch_dump_path: pytorch模型保存位置
'''
# 初始化pytorch模型
config = BertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForPreTraining(config)
# 从checkpoint中加载权重
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
# 保存pytorch模型
print("Save Pytorch model to {}".format(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 tf checkpoint")
parser.add_argument("--bert_config_file", default=None, type=str, required=True, help="The config file")
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.bert_config_file, args.pytorch_dump_path)
