def train(args,model,processor):
train_dataset = load_and_cache_examples(args, processor, data_type='train')
train_loader = DatasetLoader(data=train_dataset, batch_size=args.batch_size,
shuffle=False, seed=args.seed, sort=True,
vocab = processor.vocab,label2id = args.label2id)
parameters = [p for p in model.parameters() if p.requires_grad]
optimizer = optim.Adam(parameters, lr=args.learning_rate)
scheduler = ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=3,
verbose=1, epsilon=1e-4, cooldown=0, min_lr=0, eps=1e-8)
best_f1 = 0
for epoch in range(1, 1 + args.epochs):
print(f"Epoch {epoch}/{args.epochs}")
pbar = ProgressBar(n_total=len(train_loader), desc='Training')
train_loss = AverageMeter()
model.train()
assert model.training
for step, batch in enumerate(train_loader):
input_ids, input_mask, input_tags, input_lens = batch
input_ids = input_ids.to(args.device)
input_mask = input_mask.to(args.device)
input_tags = input_tags.to(args.device)
features, loss = model.forward_loss(input_ids, input_mask, input_lens, input_tags)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_norm)
optimizer.step()
optimizer.zero_grad()
pbar(step=step, info={'loss': loss.item()})
train_loss.update(loss.item(), n=1)
print(" ")
train_log = {'loss': train_loss.avg}
if 'cuda' in str(args.device):
torch.cuda.empty_cache()
eval_log, class_info = evaluate(args,model,processor)
logs = dict(train_log, **eval_log)
show_info = f'\nEpoch: {epoch} - ' + "-".join([f' {key}: {value:.4f} ' for key, value in logs.items()])
logger.info(show_info)
scheduler.epoch_step(logs['eval_f1'], epoch)
if logs['eval_f1'] > best_f1:
logger.info(f"\nEpoch {epoch}: eval_f1 improved from {best_f1} to {logs['eval_f1']}")
logger.info("save model to disk.")
best_f1 = logs['eval_f1']
if isinstance(model, nn.DataParallel):
model_stat_dict = model.module.state_dict()
else:
model_stat_dict = model.state_dict()
state = {'epoch': epoch, 'arch': args.arch, 'state_dict': model_stat_dict}
model_path = args.output_dir / 'best-model.bin'
torch.save(state, str(model_path))
print("Eval Entity Score: ")
for key, value in class_info.items():
info = f"Subject: {key} - Acc: {value['acc']} - Recall: {value['recall']} - F1: {value['f1']}"
logger.info(info)
def train(args, model, processor):
tokenizer = BertTokenizer.from_pretrained(
'./BERT_model/bert_pretrain/vocab.txt')
train_dataset = load_and_cache_examples(args, processor, data_type='train')
train_loader = DatasetLoader(data=train_dataset,
batch_size=args.batch_size,
shuffle=False,
seed=args.seed,
sort=True,
vocab=processor.vocab,
label2id=args.label2id,
tokenizer=tokenizer)
# train_loader = DatasetLoader(data=train_dataset, batch_size=args.batch_size,
# shuffle=False, seed=args.seed, sort=True,
# vocab=processor.vocab, label2id=args.label2id)
parameters = [p for p in model.parameters() if p.requires_grad]
optimizer = optim.Adam(parameters, lr=args.learning_rate)
scheduler = ReduceLROnPlateau(optimizer,
mode='max',
factor=0.5,
patience=3,
verbose=1,
epsilon=1e-4,
cooldown=0,
min_lr=0,
eps=1e-8)
train_metric = SeqEntityScore(args.id2label, markup=args.markup)
best_f1 = 0
for epoch in range(1, 1 + args.epochs):
strat_epoch_time = time.time()
logger.info(f"Epoch {epoch}/{args.epochs}")
#pbar = ProgressBar(n_total=len(train_loader), desc='Training') #进度条样式
train_loss = AverageMeter()
model.train()
assert model.training
for step, batch in enumerate(train_loader):
strat_batch_time = time.time()
input_ids, input_mask, input_tags, input_lens = batch
input_ids = input_ids.to(args.device)
input_mask = input_mask.to(args.device)
input_tags = input_tags.to(args.device)
features, loss = model.forward_loss(input_ids, input_mask,
input_lens, input_tags)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_norm)
optimizer.step()
optimizer.zero_grad()
# pbar(step=step, info={'loss': loss.item()})
train_loss.update(loss.item(), n=1)
tags, _ = model.crf._obtain_labels(features, args.id2label,
input_lens)
input_tags = input_tags.cpu().numpy()
target = [
input_[:len_] for input_, len_ in zip(input_tags, input_lens)
]
pre_train = train_metric.compute_train_pre(label_paths=target,
pred_paths=tags)
logger.info(
f'time: {time.time() - strat_batch_time:.1f} train_loss: {loss.item():.4f} train_pre: {pre_train:.4f}'
)
print(" ")
logger.info(f'train_total_time: {time.time() - strat_epoch_time}')
if 'cuda' in str(args.device):
torch.cuda.empty_cache() # 释放显存
strat_eval_time = time.time()
eval_f1 = evaluate(args, model, processor)
show_info = f'eval_time: {time.time() - strat_eval_time:.1f} train_avg_loss: {train_loss.avg:.4f} eval_f1: {eval_f1:.4f} '
logger.info(show_info)
scheduler.epoch_step(eval_f1, epoch)
if eval_f1 > best_f1:
# Epoch 1: eval_f1 improved from 0 to 0.4023105674481821
logger.info(
f"\nEpoch {epoch}: eval_f1 improved from {best_f1} to {eval_f1}"
)
best_f1 = eval_f1
model_stat_dict = model.state_dict()
state = {
'epoch': epoch,
'arch': args.arch,
'state_dict': model_stat_dict
}
model_path = args.output_dir / 'best-model.bin'
torch.save(state, str(model_path))