本文整理汇总了Python中modeling.BertConfig.from_json_file方法的典型用法代码示例。如果您正苦于以下问题：Python BertConfig.from_json_file方法的具体用法？Python BertConfig.from_json_file怎么用？Python BertConfig.from_json_file使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类modeling.BertConfig的用法示例。
在下文中一共展示了BertConfig.from_json_file方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: convert_tf_checkpoint_to_pytorch
# 需要导入模块: from modeling import BertConfig [as 别名]
# 或者: from modeling.BertConfig import from_json_file [as 别名]
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
config_path = os.path.abspath(bert_config_file)
tf_path = os.path.abspath(tf_checkpoint_path)
print("Converting TensorFlow checkpoint from {} with config at {}".format(tf_path, config_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
print("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
# Initialise PyTorch model
config = BertConfig.from_json_file(bert_config_file)
print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForPreTraining(config)
for name, array in zip(names, arrays):
name = name.split('/')
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(n in ["adam_v", "adam_m"] for n in name):
print("Skipping {}".format("/".join(name)))
continue
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
l = re.split(r'_(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'kernel' or l[0] == 'gamma':
pointer = getattr(pointer, 'weight')
elif l[0] == 'output_bias' or l[0] == 'beta':
pointer = getattr(pointer, 'bias')
elif l[0] == 'output_weights':
pointer = getattr(pointer, 'weight')
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
if m_name[-11:] == '_embeddings':
pointer = getattr(pointer, 'weight')
elif m_name == 'kernel':
array = np.transpose(array)
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
print("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path) 示例2: convert
# 需要导入模块: from modeling import BertConfig [as 别名]
# 或者: from modeling.BertConfig import from_json_file [as 别名]
def convert():
# Initialise PyTorch model
config = BertConfig.from_json_file(args.bert_config_file)
model = BertModel(config)
# Load weights from TF model
path = args.tf_checkpoint_path
print("Converting TensorFlow checkpoint from {}".format(path))
init_vars = tf.train.list_variables(path)
names = []
arrays = []
for name, shape in init_vars:
print("Loading {} with shape {}".format(name, shape))
array = tf.train.load_variable(path, name)
print("Numpy array shape {}".format(array.shape))
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name[5:] # skip "bert/"
print("Loading {}".format(name))
name = name.split('/')
if name[0] in ['redictions', 'eq_relationship']:
print("Skipping")
continue
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
l = re.split(r'_(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'kernel':
pointer = getattr(pointer, 'weight')
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
if m_name[-11:] == '_embeddings':
pointer = getattr(pointer, 'weight')
elif m_name == 'kernel':
array = np.transpose(array)
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
pointer.data = torch.from_numpy(array)
# Save pytorch-model
torch.save(model.state_dict(), args.pytorch_dump_path) 开发者ID:eva-n27，项目名称:BERT-for-Chinese-Question-Answering，代码行数:54，代码来源:convert_tf_checkpoint_to_pytorch.py
示例3: convert
# 需要导入模块: from modeling import BertConfig [as 别名]
# 或者: from modeling.BertConfig import from_json_file [as 别名]
def convert():
# Initialise PyTorch model
config = BertConfig.from_json_file(args.bert_config_file)
model = BertModel(config)
# Load weights from TF model
path = args.tf_checkpoint_path
print("Converting TensorFlow checkpoint from {}".format(path))
init_vars = tf.train.list_variables(path)
names = []
arrays = []
for name, shape in init_vars:
print("Loading {} with shape {}".format(name, shape))
array = tf.train.load_variable(path, name)
print("Numpy array shape {}".format(array.shape))
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name[5:] # skip "bert/"
print("Loading {}".format(name))
name = name.split('/')
if any(n in ["adam_v", "adam_m","l_step"] for n in name):
print("Skipping {}".format("/".join(name)))
continue
if name[0] in ['redictions', 'eq_relationship']:
print("Skipping")
continue
pointer = model
for m_name in name:
if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
l = re.split(r'_(\d+)', m_name)
else:
l = [m_name]
if l[0] == 'kernel':
pointer = getattr(pointer, 'weight')
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
if m_name[-11:] == '_embeddings':
pointer = getattr(pointer, 'weight')
elif m_name == 'kernel':
array = np.transpose(array)
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
pointer.data = torch.from_numpy(array)
# Save pytorch-model
torch.save(model.state_dict(), args.pytorch_dump_path)