def evaluate(model, iterator, criterion, label_list):
model.eval()
epoch_loss = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
with torch.no_grad():
for batch in iterator:
with torch.no_grad():
logits = model(batch)
loss = criterion(logits.view(-1, len(label_list)), batch.label)
labels = batch.label.detach().cpu().numpy()
preds = np.argmax(logits.detach().cpu().numpy(), axis=1)
all_preds = np.append(all_preds, preds)
all_labels = np.append(all_labels, labels)
epoch_loss += loss.item()
acc, report = classifiction_metric(
all_preds, all_labels, label_list)
return epoch_loss/len(iterator), acc, report
def evaluate(model, dataloader, criterion, device, label_list):
model.eval()
epoch_loss = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
for batch in tqdm(dataloader, desc="Eval"):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, labels=None)
loss = criterion(logits.view(-1, len(label_list)), label_ids.view(-1))
preds = logits.detach().cpu().numpy()
outputs = np.argmax(preds, axis=1)
all_preds = np.append(all_preds, outputs)
label_ids = label_ids.to('cpu').numpy()
all_labels = np.append(all_labels, label_ids)
epoch_loss += loss.mean().item()
acc, report = classifiction_metric(all_preds, all_labels, label_list)
return epoch_loss / len(dataloader), acc, report
def evaluate(model, iterator, criterion, num_labels):
epoch_loss = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
model.eval()
with torch.no_grad():
for batch in iterator:
logits = model(batch.text)
loss = criterion(logits.view(-1, num_labels), batch.label)
labels = batch.label.detach().cpu().numpy()
preds = np.argmax(logits.detach().cpu().numpy(), axis=1)
all_preds = np.append(all_preds, preds)
all_labels = np.append(all_labels, labels)
epoch_loss += loss.item()
report, acc = classifiction_metric(all_preds, all_labels,
["negative", "positive"])
return epoch_loss / len(iterator), acc, report
def train(model, iterator, optimizer, criterion, num_labels):
epoch_loss = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
model.train()
for batch in iterator:
optimizer.zero_grad()
logits = model(batch.text)
loss = criterion(logits.view(-1, num_labels), batch.label)
labels = batch.label.detach().cpu().numpy()
preds = np.argmax(logits.detach().cpu().numpy(), axis=1)
all_preds = np.append(all_preds, preds)
all_labels = np.append(all_labels, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
report, acc = classifiction_metric(all_preds, all_labels,
["negative", "positive"])
return epoch_loss / len(iterator), acc, report
def train(epoch_num, model, train_dataloader, dev_dataloader, optimizer, criterion, label_list, out_model_file, log_dir, print_step, clip):
model.train()
writer = SummaryWriter(
log_dir=log_dir + '/' + time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime(time.time())))
global_step = 0
best_dev_loss = float('inf')
best_acc = 0.0
for epoch in range(int(epoch_num)):
print(f'---------------- Epoch: {epoch+1:02} ----------')
epoch_loss = 0
train_steps = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
optimizer.zero_grad()
logits = model(batch)
loss = criterion(logits.view(-1, len(label_list)), batch.label)
labels = batch.label.detach().cpu().numpy()
preds = np.argmax(logits.detach().cpu().numpy(), axis=1)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
optimizer.step()
global_step += 1
epoch_loss += loss.item()
train_steps += 1
all_preds = np.append(all_preds, preds)
all_labels = np.append(all_labels, labels)
if global_step % print_step == 0:
train_loss = epoch_loss / train_steps
train_acc, train_report = classifiction_metric(
all_preds, all_labels, label_list)
dev_loss, dev_acc, dev_report = evaluate(
model, dev_dataloader, criterion, label_list)
c = global_step // print_step
writer.add_scalar("loss/train", train_loss, c)
writer.add_scalar("loss/dev", dev_loss, c)
writer.add_scalar("acc/train", train_acc, c)
writer.add_scalar("acc/dev", dev_acc, c)
for label in label_list:
writer.add_scalar(label + ":" + "f1/train",
train_report[label]['f1-score'], c)
writer.add_scalar(label + ":" + "f1/dev",
dev_report[label]['f1-score'], c)
print_list = ['macro avg', 'weighted avg']
for label in print_list:
writer.add_scalar(label + ":" + "f1/train",
train_report[label]['f1-score'], c)
writer.add_scalar(label + ":" + "f1/dev",
dev_report[label]['f1-score'], c)
# if dev_loss < best_dev_loss:
# best_dev_loss = dev_loss
if dev_acc > best_acc:
best_acc = dev_acc
torch.save(model.state_dict(), out_model_file)
model.train()
writer.close()
def train(epoch_num, n_gpu, train_dataloader, dev_dataloader, model, optimizer,
criterion, gradient_accumulation_steps, device, label_list,
output_model_file, output_config_file, log_dir, print_step):
model.train()
writer = SummaryWriter(
log_dir=log_dir + '/' +
time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime(time.time())))
best_dev_loss = float('inf')
global_step = 0
for epoch in range(int(epoch_num)):
print(f'---------------- Epoch: {epoch+1:02} ----------')
epoch_loss = 0
train_steps = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
logits = model(input_ids, segment_ids, input_mask)
loss = criterion(logits.view(-1, len(label_list)),
label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean()
if gradient_accumulation_steps > 1:
loss = loss / gradient_accumulation_steps
train_steps += 1
# 反向传播
loss.backward()
epoch_loss += loss.item()
preds = logits.detach().cpu().numpy()
outputs = np.argmax(preds, axis=1)
all_preds = np.append(all_preds, outputs)
label_ids = label_ids.to('cpu').numpy()
all_labels = np.append(all_labels, label_ids)
if (step + 1) % gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step != 0 and global_step % print_step == 0:
train_loss = epoch_loss / train_steps
train_acc, train_report = classifiction_metric(
all_preds, all_labels, label_list)
dev_loss, dev_acc, dev_report = evaluate(
model, dev_dataloader, criterion, device, label_list)
c = global_step // print_step
writer.add_scalar("loss/train", train_loss, c)
writer.add_scalar("loss/dev", dev_loss, c)
writer.add_scalar("acc/train", train_acc, c)
writer.add_scalar("acc/dev", dev_acc, c)
for label in label_list:
writer.add_scalar(label + ":" + "f1/train",
train_report[label]['f1-score'], c)
writer.add_scalar(label + ":" + "f1/dev",
dev_report[label]['f1-score'], c)
print_list = ['macro avg', 'weighted avg']
for label in print_list:
writer.add_scalar(label + ":" + "f1/train",
train_report[label]['f1-score'], c)
writer.add_scalar(label + ":" + "f1/dev",
dev_report[label]['f1-score'], c)
if dev_loss < best_dev_loss:
best_dev_loss = dev_loss
model_to_save = model.module if hasattr(
model, 'module') else model
torch.save(model_to_save.state_dict(), output_model_file)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
writer.close()
def train(epoch_num, n_gpu, model, train_dataloader, dev_dataloader, optimizer,
criterion, gradient_accumulation_steps, device, label_list,
output_model_file, output_config_file, log_dir, print_step,
early_stop):
""" 模型训练过程
Args:
epoch_num: epoch 数量
n_gpu: 使用的 gpu 数量
train_dataloader: 训练数据的Dataloader
dev_dataloader: 测试数据的 Dataloader
optimizer: 优化器
criterion： 损失函数定义
gradient_accumulation_steps: 梯度积累
device: 设备，cuda， cpu
label_list: 分类的标签数组
output_model_file: 用于保存 Bert 模型
output_config_file: 用于 Bert 配置文件
log_dir: tensorboard 读取的日志目录，用于后续分析
print_step: 多少步保存一次模型，日志等信息
early_stop: 提前终止
"""
early_stop_times = 0
writer = SummaryWriter(
log_dir=log_dir + '/' +
time.strftime("%Y-%m-%d-%H:%M:%S", time.localtime(time.time())))
best_dev_loss = float('inf')
best_auc = 0
best_acc = 0
global_step = 0
for epoch in range(int(epoch_num)):
if early_stop_times >= early_stop:
break
print(f'---------------- Epoch: {epoch+1:02} ----------')
epoch_loss = 0
train_steps = 0
all_preds = np.array([], dtype=int)
all_labels = np.array([], dtype=int)
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
model.train()
batch = tuple(t.to(device) for t in batch)
_, input_ids, input_mask, segment_ids, label_ids = batch
logits = model(input_ids, segment_ids, input_mask, labels=None)
loss = criterion(logits.view(-1, len(label_list)),
label_ids.view(-1))
""" 修正 loss """
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulation_steps > 1:
loss = loss / gradient_accumulation_steps
train_steps += 1
loss.backward()
# 用于画图和分析的数据
epoch_loss += loss.item()
preds = logits.detach().cpu().numpy()
outputs = np.argmax(preds, axis=1)
all_preds = np.append(all_preds, outputs)
label_ids = label_ids.to('cpu').numpy()
all_labels = np.append(all_labels, label_ids)
if (step + 1) % gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % print_step == 0 and global_step != 0:
""" 打印Train此时的信息 """
train_loss = epoch_loss / train_steps
train_acc, train_report, train_auc = classifiction_metric(
all_preds, all_labels, label_list)
dev_loss, dev_acc, dev_report, dev_auc = evaluate(
model, dev_dataloader, criterion, device, label_list)
c = global_step // print_step
writer.add_scalar("loss/train", train_loss, c)
writer.add_scalar("loss/dev", dev_loss, c)
writer.add_scalar("acc/train", train_acc, c)
writer.add_scalar("acc/dev", dev_acc, c)
writer.add_scalar("auc/train", train_auc, c)
writer.add_scalar("auc/dev", dev_auc, c)
for label in label_list:
writer.add_scalar(label + ":" + "f1/train",
train_report[label]['f1-score'], c)
writer.add_scalar(label + ":" + "f1/dev",
dev_report[label]['f1-score'], c)
print_list = ['macro avg', 'weighted avg']
for label in print_list:
writer.add_scalar(label + ":" + "f1/train",
train_report[label]['f1-score'], c)
writer.add_scalar(label + ":" + "f1/dev",
dev_report[label]['f1-score'], c)
# # 以损失取优
# if dev_loss < best_dev_loss:
# best_dev_loss = dev_loss
# 以 acc 取优
if dev_acc > best_acc:
best_acc = dev_acc
# 以 auc 取优
# if dev_auc > best_auc:
# best_auc = dev_auc
model_to_save = model.module if hasattr(
model, 'module') else model
torch.save(model_to_save.state_dict(),
output_model_file)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
early_stop_times = 0
else:
early_stop_times += 1
writer.close()
