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, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
"""
This file contains implementation of transformation tensorflow Bert model to pytorch representation.
"""
import torch
from transformers.modeling_bert import BertConfig, BertForPreTraining, load_tf_weights_in_bert
# This script is used to convert tensorflow bert model to pytorch representation publicly known
# path to dictionary
bert_dir='/mnt/data/xkloco00_pc5/external/multi_cased_L-12_H-768_A-12'
# important files
tf_checkpoint_path=bert_dir+'/'+"bert_model.ckpt"
bert_config_file = bert_dir+'/'+"bert_config.json"
pytorch_dump_path=bert_dir+'/'+"pytorch_model.bin"
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)