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 train(epoch_num, model, train_dataloader, dev_dataloader, optimizer, criterion, label_list, out_model_file, log_dir,
print_step, clip):
model.train()
logging_dir = log_dir + time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime(time.time()))
writer = SummaryWriter(
log_dir=logging_dir)
global_step = 0
best_dev_loss = float('inf')
best_acc = 0.0
for epoch in range(int(epoch_num)):
print('---------------- 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")):
# zero the parameter gradients
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()
