Tqdm库简单使用方法

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tqdm库在深度学习中可以显示训练过程的进度条。

    for epoch in range(epochs):
        # train
        ...
        train_bar = tqdm(train_loader, file=sys.stdout)
        for step, data in enumerate(train_bar):
            ...
            train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,
                                                                     epochs,
                                                                     loss)

        # validate
        net.eval()
        acc = 0.0  # accumulate accurate number / epoch
        with torch.no_grad():
            val_bar = tqdm(validate_loader, file=sys.stdout)
            for val_data in val_bar:
                ...
        val_accurate = acc / val_num
        print('[epoch %d] train_loss: %.3f  val_accuracy: %.3f' %
              (epoch + 1, running_loss / train_steps, val_accurate))

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基本用法

from tqdm import tqdm
import time

# 示例：显示一个简单的进度条
for i in tqdm(range(100), desc="Processing"):
    time.sleep(0.1)  # 模拟耗时操作

• desc="Processing"：设置进度条的描述。

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自定义进度条

for i in tqdm(range(100), desc="Custom Progress", unit="item", ncols=80, colour="green"):
    time.sleep(0.05)

• unit="item"：自定义单位。
• ncols=80：设置进度条的宽度。
• colour="green"：改变进度条的颜色。