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)