def convert_model(base_model, path, new_path):
model = T5ForConditionalGeneration(T5Config.from_pretrained(base_model))
print("loading weights...")
load_tf_weights_in_t5(model, None, path)
model.eval()
print("saving HF weights...")
model.save_pretrained(new_path)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config = T5Config.from_json_file(config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = T5Model(config)
# Load weights from tf checkpoint
load_tf_weights_in_t5(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)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file,
pytorch_dump_path):
# Initialise PyTorch model
config = T5Config.from_json_file(config_file)
print(f"Building PyTorch model from configuration: {config}")
model = T5ForConditionalGeneration(config)
# Load weights from tf checkpoint
load_tf_weights_in_t5(model, config, tf_checkpoint_path)
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}")
model.save_pretrained(pytorch_dump_path)
from transformers import MT5Config, MT5ForConditionalGeneration, load_tf_weights_in_t5
import torch
config = MT5Config.from_pretrained('config.json')
model = MT5ForConditionalGeneration(config)
ckpt = 'D:\\BaiduNetdiskDownload\\chinese_t5_pegasus_base\\chinese_t5_pegasus_base\\model.ckpt'
model = load_tf_weights_in_t5(model, config, ckpt)
torch.save(model.state_dict(), 'pytorch_model.bin')
