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python 身份验证识别 基于Python的身份证验证识别和数据处理详解

郎志刚 人气:1
想了解基于Python的SFZ验证识别和数据处理详解的相关内容吗,郎志刚在本文为您仔细讲解python 身份验证识别的相关知识和一些Code实例,欢迎阅读和指正,我们先划重点:python,身份验证识别,python数据处理,下面大家一起来学习吧。

根据GB11643-1999公民SFZ号码是特征组合码,由十七位数字本体码和一位数字校验码组成,排列顺序从左至右依次为:

六位数字地址码八位数字出生日期码三位数字顺序码一位数字校验码(数字10用罗马X表示)

校验系统:

校验码采用ISO7064:1983,MOD11-2校验码系统(图为校验规则样例)

用SFZ号的前17位的每一位号码字符值分别乘上对应的加权因子值,得到的结果求和后对11进行取余,最后的结果放到表2检验码字符值..换算关系表中得出最后的一位SFZ号码

代码:

# 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
# limitations under the License.
"""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)

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