def __init__(
self,
vocab_path=None,
model_paths=None,
weigths=None,
max_len=50,
min_len=3,
lowercase_tokens=False,
log=False,
iterations=3,
model_name='roberta',
special_tokens_fix=1,
is_ensemble=True,
min_error_probability=0.0,
confidence=0,
del_confidence=0,
resolve_cycles=False,
):
self.model_weights = list(map(
float, weigths)) if weigths else [1] * len(model_paths)
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
self.max_len = max_len
self.min_len = min_len
self.lowercase_tokens = lowercase_tokens
self.min_error_probability = min_error_probability
self.vocab = Vocabulary.from_files(vocab_path)
self.log = log
self.iterations = iterations
self.confidence = confidence
self.del_conf = del_confidence
self.resolve_cycles = resolve_cycles
# set training parameters and operations
self.indexers = []
self.models = []
for model_path in model_paths:
if is_ensemble:
model_name, special_tokens_fix = self._get_model_data(
model_path)
weights_name = get_weights_name(model_name, lowercase_tokens)
self.indexers.append(
self._get_indexer(weights_name, special_tokens_fix))
model = Seq2Labels(
vocab=self.vocab,
text_field_embedder=self._get_embbeder(weights_name,
special_tokens_fix),
confidence=self.confidence,
del_confidence=self.del_conf,
).to(self.device)
if torch.cuda.is_available():
model.load_state_dict(torch.load(model_path), strict=False)
else:
model.load_state_dict(torch.load(
model_path, map_location=torch.device('cpu')),
strict=False)
model.eval()
self.models.append(model)
def main(args):
fix_seed()
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
weights_name = get_weights_name(args.transformer_model,
args.lowercase_tokens)
# read datasets
reader = get_data_reader(weights_name,
args.max_len,
skip_correct=bool(args.skip_correct),
skip_complex=args.skip_complex,
test_mode=False,
tag_strategy=args.tag_strategy,
lowercase_tokens=args.lowercase_tokens,
max_pieces_per_token=args.pieces_per_token,
tn_prob=args.tn_prob,
tp_prob=args.tp_prob,
special_tokens_fix=args.special_tokens_fix)
train_data = reader.read(args.train_set)
dev_data = reader.read(args.dev_set)
default_tokens = [DEFAULT_OOV_TOKEN, DEFAULT_PADDING_TOKEN]
namespaces = ['labels', 'd_tags']
tokens_to_add = {x: default_tokens for x in namespaces}
# build vocab
if args.vocab_path:
vocab = Vocabulary.from_files(args.vocab_path)
else:
vocab = Vocabulary.from_instances(train_data,
max_vocab_size={
'tokens': 30000,
'labels': args.target_vocab_size,
'd_tags': 2
},
tokens_to_add=tokens_to_add)
vocab.save_to_files(os.path.join(args.model_dir, 'vocabulary'))
print("Data is loaded")
model = get_model(weights_name,
vocab,
tune_bert=args.tune_bert,
predictor_dropout=args.predictor_dropout,
label_smoothing=args.label_smoothing,
special_tokens_fix=args.special_tokens_fix)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
cuda_device = list(range(torch.cuda.device_count()))
else:
cuda_device = 0
else:
cuda_device = -1
if args.pretrain:
model.load_state_dict(
torch.load(os.path.join(args.pretrain_folder,
args.pretrain + '.th'),
map_location=torch.device('cpu')))
model = model.to(device)
print("Model is set")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.1,
patience=10)
instances_per_epoch = None if not args.updates_per_epoch else \
int(args.updates_per_epoch * args.batch_size * args.accumulation_size)
iterator = BucketIterator(
batch_size=args.batch_size,
sorting_keys=[("tokens", "num_tokens")],
biggest_batch_first=True,
max_instances_in_memory=args.batch_size * 20000,
instances_per_epoch=instances_per_epoch,
)
iterator.index_with(vocab)
trainer = Trainer(model=model,
optimizer=optimizer,
scheduler=scheduler,
iterator=iterator,
train_dataset=train_data,
validation_dataset=dev_data,
serialization_dir=args.model_dir,
patience=args.patience,
num_epochs=args.n_epoch,
cuda_device=cuda_device,
shuffle=False,
accumulated_batch_count=args.accumulation_size,
cold_step_count=args.cold_steps_count,
cold_lr=args.cold_lr,
cuda_verbose_step=int(args.cuda_verbose_steps)
if args.cuda_verbose_steps else None)
print("Start training")
trainer.train()
# Here's how to save the model.
out_model = os.path.join(args.model_dir, 'model.th')
with open(out_model, 'wb') as f:
torch.save(model.state_dict(), f)
print("Model is dumped")
def main(args):
# fix_seed() 不要fix效果才好.!!!!!!!!!!!!!!否则shuffle没用了
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
weights_name = get_weights_name(args.transformer_model,
args.lowercase_tokens)
# read datasets
reader = get_data_reader(weights_name,
args.max_len,
skip_correct=bool(args.skip_correct),
skip_complex=args.skip_complex,
test_mode=False,
tag_strategy=args.tag_strategy,
lowercase_tokens=args.lowercase_tokens,
max_pieces_per_token=args.pieces_per_token,
tn_prob=args.tn_prob,
tp_prob=args.tp_prob,
special_tokens_fix=args.special_tokens_fix)
train_data = reader.read(args.train_set)
dev_data = reader.read(args.dev_set)
# list(train_data)
default_tokens = [DEFAULT_OOV_TOKEN, DEFAULT_PADDING_TOKEN]
namespaces = ['labels', 'd_tags']
tokens_to_add = {x: default_tokens for x in namespaces}
# build vocab # 这里面是生成字典的算法
if args.vocab_path:
old_vocab = Vocabulary.from_files(args.vocab_path)
# 代码修改成,不管传入不传入都根据数据集重新简历字典,然后进行2个字典的合并.
if 1: # 生成字典. 利用数据集生成字典!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# 下面看看这个生成vocab 如何做的. 生成之后的东西就是对应到目录下的output_vocabulary里面的内容.
# 直接调用的是allennlp库包,还是需要看懂里面实现的算法.
#-------------需要对这个from_instances进行修改,看里面如何生成我的更大字典.这个是已经封装好的了.所以不用看了.改上面数据才行.
new_vocab = Vocabulary.from_instances(train_data,
max_vocab_size={
'tokens': 30000,
'labels':
args.target_vocab_size,
'd_tags': 2
},
tokens_to_add=tokens_to_add)
from allennlp.common.params import Params
params = Params({"non_padded_namespaces": set(namespaces)})
vocab = old_vocab
old_vocab.extend_from_instances(params, train_data)
old_vocab.save_to_files(os.path.join(args.model_dir, 'vocabulary'))
from pathlib import Path
vocabdir = Path(__file__).resolve().parent.parent / os.path.join(
args.model_dir, 'vocabulary', 'labels.txt')
print("Data is loaded")
model = get_model(weights_name,
vocab,
tune_bert=args.tune_bert,
predictor_dropout=args.predictor_dropout,
label_smoothing=args.label_smoothing,
special_tokens_fix=args.special_tokens_fix)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
cuda_device = list(range(torch.cuda.device_count()))
else:
cuda_device = 0
else:
cuda_device = -1
if args.pretrain: # 我们不用这个地方来加载
model.load_state_dict(
torch.load(
os.path.join(args.pretrain_folder, args.pretrain + '.th')))
model = model.to(device)
print("Model is set", '模型加载完毕')
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.1,
patience=10)
instances_per_epoch = None if not args.updates_per_epoch else \
int(args.updates_per_epoch * args.batch_size * args.accumulation_size)
iterator = BucketIterator(
batch_size=args.batch_size,
sorting_keys=[("tokens", "num_tokens")],
biggest_batch_first=True,
max_instances_in_memory=args.batch_size * 20000,
instances_per_epoch=instances_per_epoch,
)
iterator.index_with(vocab)
trainer = Trainer(
model=model,
optimizer=optimizer,
scheduler=scheduler,
iterator=iterator,
train_dataset=train_data,
validation_dataset=dev_data,
serialization_dir=args.model_dir,
patience=args.patience,
num_epochs=args.n_epoch,
cuda_device=cuda_device,
shuffle=True, # 吧这个地方改了.true
accumulated_batch_count=args.accumulation_size,
cold_step_count=args.cold_steps_count,
cold_lr=args.cold_lr,
cuda_verbose_step=int(args.cuda_verbose_steps)
if args.cuda_verbose_steps else None)
print("Start training")
trainer.train(args.oldmodel)
# Here's how to save the model. # 最优模型再存一遍.所以最后这个目录里面只存model.th即可.而不用管那些带系数的.
out_model = os.path.join(args.model_dir, 'model.th')
with open(out_model, 'wb') as f:
torch.save(model.state_dict(), f)
print("Model is dumped", "训练全部结束,model存在了", args.model_dir + ' / model.th')
def main(args):
fix_seed()
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
weights_name = get_weights_name(args.transformer_model,
args.lowercase_tokens)
# read datasets
reader = get_data_reader(weights_name,
args.max_len,
skip_correct=bool(args.skip_correct),
skip_complex=args.skip_complex,
test_mode=False,
tag_strategy=args.tag_strategy,
lowercase_tokens=args.lowercase_tokens,
max_pieces_per_token=args.pieces_per_token,
tn_prob=args.tn_prob,
tp_prob=args.tp_prob,
special_tokens_fix=args.special_tokens_fix)
train_data = reader.read(args.train_set)
dev_data = reader.read(args.dev_set)
default_tokens = [DEFAULT_OOV_TOKEN, DEFAULT_PADDING_TOKEN]
namespaces = ['labels', 'd_tags']
tokens_to_add = {x: default_tokens for x in namespaces}
# build vocab
if args.vocab_path:
vocab = Vocabulary.from_files(args.vocab_path)
else:
vocab = Vocabulary.from_instances(train_data,
max_vocab_size={
'tokens': 30000,
'labels': args.target_vocab_size,
'd_tags': 2
},
tokens_to_add=tokens_to_add)
vocab.save_to_files(os.path.join(args.model_dir, 'vocabulary'))
print("Data is loaded")
model = get_model(weights_name,
vocab,
tune_bert=args.tune_bert,
predictor_dropout=args.predictor_dropout,
label_smoothing=args.label_smoothing,
special_tokens_fix=args.special_tokens_fix)
# model = GecBERTModel(vocab_path=args.vocab_path,
# model_paths=args.model_path,
# max_len=args.max_len, min_len=args.min_len,
# iterations=args.iteration_count,
# min_error_probability=args.min_error_probability,
# min_probability=args.min_error_probability,
# lowercase_tokens=args.lowercase_tokens,
# model_name=args.transformer_model,
# special_tokens_fix=args.special_tokens_fix,
# log=False,
# confidence=args.additional_confidence,
# is_ensemble=args.is_ensemble,
# weigths=args.weights)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
cuda_device = list(range(torch.cuda.device_count()))
else:
cuda_device = 0
else:
cuda_device = -1
if args.pretrain:
model.load_state_dict(
torch.load(
os.path.join(args.pretrain_folder, args.pretrain + '.th')))
print('cuda_device:', cuda_device)
#exit(0)
model = model.to(device)
print("Model is set")
# print('model:', model)
def print_size_of_model(model):
torch.save(model.state_dict(), "temp.p")
print('Size (MB):', os.path.getsize("temp.p") / 1e6)
os.remove('temp.p')
if args.keep != 12:
prev_model = GecBERTModel(
vocab_path=args.vocab_path,
model_paths=args.model_path,
max_len=args.max_len,
min_len=args.min_len,
iterations=args.iteration_count,
min_error_probability=args.min_error_probability,
min_probability=args.min_error_probability,
lowercase_tokens=args.lowercase_tokens,
model_name=args.transformer_model,
special_tokens_fix=args.special_tokens_fix,
log=False,
confidence=args.additional_confidence,
is_ensemble=args.is_ensemble,
weigths=args.weights,
num_layers_to_keep=args.keep)
# print('prev_model:', prev_model.models)
# print(model)
print_size_of_model(model)
print_size_of_model(prev_model.models[0])
model.text_field_embedder.token_embedder_bert.bert_model.encoder.layer = \
prev_model.models[0].text_field_embedder.token_embedder_bert.bert_model.encoder.layer
print_size_of_model(model)
# exit(0)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
factor=0.1,
patience=10)
instances_per_epoch = None if not args.updates_per_epoch else \
int(args.updates_per_epoch * args.batch_size * args.accumulation_size)
iterator = BucketIterator(
batch_size=args.batch_size,
sorting_keys=[("tokens", "num_tokens")],
biggest_batch_first=True,
max_instances_in_memory=args.batch_size * 20000,
instances_per_epoch=instances_per_epoch,
)
iterator.index_with(vocab)
trainer = Trainer(model=model,
optimizer=optimizer,
scheduler=scheduler,
iterator=iterator,
train_dataset=train_data,
validation_dataset=dev_data,
serialization_dir=args.model_dir,
patience=args.patience,
num_epochs=args.n_epoch,
cuda_device=cuda_device,
shuffle=False,
accumulated_batch_count=args.accumulation_size,
cold_step_count=args.cold_steps_count,
cold_lr=args.cold_lr,
cuda_verbose_step=int(args.cuda_verbose_steps)
if args.cuda_verbose_steps else None)
GPUtil.showUtilization()
print("Start training")
trainer.train()
# Here's how to save the model.
out_model = os.path.join(args.model_dir, 'model.th')
with open(out_model, 'wb') as f:
torch.save(model.state_dict(), f)
print("Model is dumped")