基于Python的身份证验证识别和数据处理详解

# coding=utf-8
# Copyright 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
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\"\"\"Convert BERT checkpoint.\"\"\"
 
 
import argparse
 
import torch
 
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
 
 
logging.set_verbosity_info()
 
 
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
 # Initialise PyTorch model
 config = BertConfig.from_json_file(bert_config_file)
 print(\"Building PyTorch model from configuration: {}\".format(str(config)))
 model = BertForPreTraining(config)
 
 # Load weights from tf checkpoint
 load_tf_weights_in_bert(model, config, tf_checkpoint_path)
 
 # Save pytorch-model
 print(\"Save PyTorch model to {}\".format(pytorch_dump_path))
 torch.save(model.state_dict(), pytorch_dump_path)
 
 
if __name__ == \"__main__\":
 parser = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
  \"--tf_checkpoint_path\", default=None, type=str, required=True, help=\"Path to the TensorFlow checkpoint path.\"
 )
 parser.add_argument(
  \"--bert_config_file\",
  default=None,
  type=str,
  required=True,
  help=\"The config json file corresponding to the pre-trained BERT model. \\n\"
  \"This 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.bert_config_file, args.pytorch_dump_path)