python中tqdm使用,对于for和while下的两种不同情况问题

import time
from tqdm import range
# 自动更新
for i in tqdm(range(10)): # 共可以更新10次进度条
    time. Sleep(0.5) # 每次更新间隔0.5s

import time
from tqdm import trange
for i in trange(10):
    time.sleep(0.5)

from tqdm import tqdm
# 用with语句，进度条完成后会自动关闭
with tqdm(total=10) as pbar:
    for i in range(10):
        time. Sleep(0.5)
        pbar.update(1)
# 变量在外部创建，进度结束后手动关闭
phar = tqdm(total=100)
for i in range(100):
    time.sleep(0.1)
    phar.update(1)
phar.close()

# 开始训练
for epoch in range(num_epochs):
    train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time. Time()
    with tqdm(total=468, desc="Epoch: %d" % epoch) as pbar:
        for idx, (X, y) in enumerate(train_iter, 0):
            X = X.to(device)
            y = y.to(device)
            y_hat = net(X)
            l = loss(y_hat, y).sum()
            optimizer.zero_grad()
            l.backward()
            optimizer. Step()
            train_l_sum += l.item()
            train_acc_sum += (y_hat.argmax(dim=1) == y).float().sum().item()
            n += y.shape[0]
            pbar.update(1) # 更新一次进度1
        test_acc = evaluate_accuracy(test_iter, net)
        # 在进度条上显示有关信息
        pbar.set_postfix({
            'loss': '%.4f' % (train_l_sum / n),
            'train acc': '%.3f' % (train_acc_sum / n),
            'test acc': '%.3f' % test_acc ,
            'time': '%.1f time. Time.time() - start)
        })