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)
def convert_tf_checkpoint_to_pytorch(config):
tf_checkpoint_path = config["tf_checkpoint_path"]
bert_config_file = config["bert_config_file"]
pytorch_dump_path = Path(config["pytorch_dump_path"])
# 初始化pytorch模型
config = BertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForPreTraining(config)
# 加载tf权重
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
# 保持pytorch模型
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, bert_config_file,
pytorch_dump_path):
# Initialise PyTorch model
config = BertConfig.from_json_file(bert_config_file)
# print(f"Building PyTorch model from configuration: {config}")
# model = BertForPreTraining(config)
config.num_labels = 2
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForSequenceClassification(config)
# Load weights from tf checkpoint
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}")
torch.save(model.state_dict(), pytorch_dump_path)
def download(self):
# Iterate over urls: download, unzip, verify sha256sum
found_mismatch_sha = False
for model in self.model_urls:
url = self.model_urls[model][0]
file = self.save_path + '/' + self.model_urls[model][1]
print('Downloading', url)
response = urllib.request.urlopen(url)
with open(file, 'wb') as handle:
handle.write(response.read())
print('Unzipping', file)
zip = zipfile.ZipFile(file, 'r')
extract_to_path = pathlib.Path(self.save_path) / (pathlib.Path(file).stem if model == "bert_tiny_uncased" else "")
zip.extractall(path=extract_to_path)
zip.close()
sha_dict = self.model_sha[model]
for extracted_file in sha_dict:
sha = sha_dict[extracted_file]
if sha != self.sha256sum(file[:-4] + '/' + extracted_file):
found_mismatch_sha = True
print('SHA256sum does not match on file:', extracted_file, 'from download url:', url)
else:
print(file[:-4] + '/' + extracted_file, '\t', 'verified')
config = BertConfig.from_json_file(extract_to_path / "bert_config.json")
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_bert(model, config, extract_to_path / "bert_model.ckpt")
# Save pytorch-model
print("Save PyTorch model to {}".format(extract_to_path))
torch.save({'model': model.state_dict(),
'optimizer': None,
'master params': None,
'files': None,
'epoch': None,
'data_loader': None}, extract_to_path / "ckpt_pretrained.pt")
if not found_mismatch_sha:
print("All downloads pass sha256sum verification.")
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file,
pytorch_dump_path):
"""
:param tf_checkpoint_path: Path to the TensorFlow checkpoint path.
:param bert_config_file: The config json file corresponding to the pre-trained BERT model.
:param pytorch_dump_path: Path to the output PyTorch model.
:return:
"""
# 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)
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file,
pytorch_dump_path):
'''
tf_checkpoint_path: ckpt文件
bert_config_file: json文件
pytorch_dump_path: pytorch模型保存位置
'''
# 初始化pytorch模型
config = BertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForPreTraining(config)
# 从checkpoint中加载权重
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
# 保存pytorch模型
print("Save Pytorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
def __init__(self, bert_config, checkpoint_path=None):
super(BertForQuestionAnswering, self).__init__(bert_config)
if config.bert == 'bert':
self.bert = BertModel(bert_config)
elif config.bert == 'albert':
self.bert = AlbertModel(bert_config)
if checkpoint_path:
"""加载tf模型"""
self.bert = load_tf_weights_in_bert(
self.bert, config=None, tf_checkpoint_path=checkpoint_path)
if config.use_origin_bert == 'dym':
self.hidden_size = 512
else:
self.hidden_size = 768
self.qa_outputs = nn.Linear(self.hidden_size, 2) # start/end
self.dropout = nn.Dropout(bert_config.hidden_dropout_prob)
# self.cls = nn.Linear(self.hidden_size, 2) #for cls
self.classifier = nn.Linear(768, 1) #for dym's dense
self.dense_final = nn.Sequential(nn.Linear(768, self.hidden_size),
nn.ReLU(True)) #动态最后的维度
# self.classifier = nn.Linear(1024, 1) #for dym's dense
# self.dense_final = nn.Sequential(nn.Linear(1024, self.hidden_size), nn.ReLU(True)) #动态最后的维度#large
if config.lstm:
num_layers = config.num_layer
lstm_num = int(self.hidden_size / 2)
self.lstm = nn.LSTM(
self.hidden_size,
lstm_num,
num_layers,
batch_first=True, #第一维度是否为batch_size
bidirectional=True) #双向
elif config.gru:
num_layers = config.num_layer
lstm_num = int(self.hidden_size / 2)
self.lstm = nn.GRU(
self.hidden_size,
lstm_num,
num_layers,
batch_first=True, #第一维度是否为batch_size
bidirectional=True) #双向
self.init_weights()
