def run_train(args):
# --------- data
processor = BertProcessor(vocab_path=config['bert_vocab_path'], do_lower_case=args.do_lower_case)
label_list = processor.get_labels()
label2id = {label: i for i, label in enumerate(label_list)}
id2label = {i: label for i, label in enumerate(label_list)}
train_data = processor.get_train(config['data_dir'] / f"{args.data_name}.train.pkl")
train_examples = processor.create_examples(lines=train_data,
example_type='train',
cached_examples_file=config['data_dir'] / f"cached_train_examples_{args.arch}")
train_features = processor.create_features(examples=train_examples,
max_seq_len=args.train_max_seq_len,
cached_features_file=config[
'data_dir'] / "cached_train_features_{}_{}".format(
args.train_max_seq_len, args.arch
))
train_dataset = processor.create_dataset(train_features, is_sorted=args.sorted)
if args.sorted:
train_sampler = SequentialSampler(train_dataset)
else:
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
valid_data = processor.get_dev(config['data_dir'] / f"{args.data_name}.valid.pkl")
valid_examples = processor.create_examples(lines=valid_data,
example_type='valid',
cached_examples_file=config['data_dir'] / f"cached_valid_examples_{args.arch}")
valid_features = processor.create_features(examples=valid_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config['data_dir'] / "cached_valid_features_{}_{}".format(
args.eval_max_seq_len, args.arch))
valid_dataset = processor.create_dataset(valid_features)
valid_sampler = SequentialSampler(valid_dataset)
valid_dataloader = DataLoader(valid_dataset, sampler=valid_sampler, batch_size=args.eval_batch_size)
# ------- model
logger.info("initializing model")
if args.resume_path:
args.resume_path = Path(args.resume_path)
model = BertForMultiClass.from_pretrained(args.resume_path, num_labels=len(label_list))
else:
model = BertForMultiClass.from_pretrained(config['bert_model_dir'], num_labels=len(label_list))
t_total = int(len(train_dataloader) / args.gradient_accumulation_steps * args.epochs)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': args.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
warmup_steps = int(t_total * args.warmup_proportion)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
lr_scheduler = WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, t_total=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# ---- callbacks
logger.info("initializing callbacks")
train_monitor = TrainingMonitor(file_dir=config['figure_dir'], arch=args.arch)
model_checkpoint = ModelCheckpoint(checkpoint_dir=config['checkpoint_dir'], mode=args.mode,
monitor=args.monitor, arch=args.arch,
save_best_only=args.save_best)
# **************************** training model ***********************
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Num Epochs = %d", args.epochs)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps * (
torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
trainer = Trainer(n_gpu=args.n_gpu,
model=model,
epochs=args.epochs,
logger=logger,
criterion=CrossEntropy(),
optimizer=optimizer,
lr_scheduler=lr_scheduler,
early_stopping=None,
training_monitor=train_monitor,
fp16=args.fp16,
resume_path=args.resume_path,
grad_clip=args.grad_clip,
model_checkpoint=model_checkpoint,
gradient_accumulation_steps=args.gradient_accumulation_steps,
evaluate=F1Score(),
class_report=ClassReport(target_names=[id2label[x] for x in range(len(label2id))]))
trainer.train(train_data=train_dataloader, valid_data=valid_dataloader, seed=args.seed)
def run_train(args):
# --------- data
processor = BertProcessor(vocab_path=config['bert_vocab_path'],
do_lower_case=args.do_lower_case)
label_list = processor.get_labels(args.task_type)
label2id = {label: i for i, label in enumerate(label_list)}
id2label = {i: label for i, label in enumerate(label_list)}
train_data = processor.get_train(
config['data_dir'] / f"{args.data_name}.train.{args.task_type}.pkl")
train_examples = processor.create_examples(
lines=train_data,
example_type=f'train_{args.task_type}',
cached_examples_file=config['data_dir'] /
f"cached_train_{args.task_type}_examples_{args.arch}")
train_features = processor.create_features(
examples=train_examples,
max_seq_len=args.train_max_seq_len,
cached_features_file=config['data_dir'] /
"cached_train_{}_features_{}_{}".format(
args.task_type, args.train_max_seq_len, args.arch))
train_dataset = processor.create_dataset(train_features,
is_sorted=args.sorted)
if args.sorted:
train_sampler = SequentialSampler(train_dataset)
else:
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=collate_fn)
valid_data = processor.get_dev(
config['data_dir'] / f"{args.data_name}.valid.{args.task_type}.pkl")
valid_examples = processor.create_examples(
lines=valid_data,
example_type=f'valid_{args.task_type}',
cached_examples_file=config['data_dir'] /
f"cached_valid_{args.task_type}_examples_{args.arch}")
valid_features = processor.create_features(
examples=valid_examples,
max_seq_len=args.eval_max_seq_len,
cached_features_file=config['data_dir'] /
"cached_valid_{}_features_{}_{}".format(
args.task_type, args.eval_max_seq_len, args.arch))
valid_dataset = processor.create_dataset(valid_features)
valid_sampler = SequentialSampler(valid_dataset)
valid_dataloader = DataLoader(valid_dataset,
sampler=valid_sampler,
batch_size=args.eval_batch_size,
collate_fn=collate_fn)
# ------- model
logger.info("initializing model")
if args.resume_path:
args.resume_path = Path(args.resume_path)
model = BertForMultiLable.from_pretrained(args.resume_path,
num_labels=len(label_list))
else:
if args.task_type == 'trans':
model = BertForMultiLable_Fewshot.from_pretrained(
Path('pybert/output/checkpoints/bert/base'),
num_labels=len(label_list))
#model = BertForMultiLable.from_pretrained(config['bert_model_dir'], num_labels=len(label_list))
else:
model = BertForMultiLable.from_pretrained(
config['bert_model_dir'], num_labels=len(label_list))
t_total = int(
len(train_dataloader) / args.gradient_accumulation_steps * args.epochs)
# 下面是optimizer和scheduler的设计
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
args.weight_decay
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
warmup_steps = int(t_total * args.warmup_proportion)
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# ---- callbacks
logger.info("initializing callbacks")
train_monitor = TrainingMonitor(
file_dir=config['figure_dir'], arch=args.arch
) # TODO: 理解train_monitor的作用，感觉就是一个用来绘图的东西，用于记录每一个epoch中得到的结果
model_checkpoint = ModelCheckpoint(checkpoint_dir=config['checkpoint_dir'],
mode=args.mode,
monitor=args.monitor,
arch=args.arch,
save_best_only=args.save_best)
# **************************** training model ***********************
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Num Epochs = %d", args.epochs)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
trainer = Trainer(
args=args,
model=model,
logger=logger,
criterion=BCEWithLogLoss(),
optimizer=optimizer,
scheduler=scheduler,
early_stopping=None,
training_monitor=train_monitor,
model_checkpoint=model_checkpoint,
batch_metrics=[
AccuracyThresh(thresh=0.5)
], # 作用于batch之上的metrics，在每次loss.backward()之后都会执行计算，记得区分它与loss
epoch_metrics=[
AUC(average='micro', task_type='binary'), # 作用于epoch之上的metrics
MultiLabelReport(id2label=id2label),
F1Score(task_type='binary', average='micro', search_thresh=True)
]) # TODO: 考虑是否应该使用F1-score替代指标
trainer.train(train_data=train_dataloader, valid_data=valid_dataloader)
