Python skills---Usage of tqdm library

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1. Basic knowledge of tqdm

"tqdm" is a Python library for creating progress bars in the command line interface.
The basic usage is as follows:

from tqdm import tqdm
import time

items = range(10)
for item in tqdm(items, desc="Test", total=len(items)):
	time.sleep(1)

The documentation is as follows:

Only the three parameters passed in are introduced: iterable, desc, total
iterable: is an iterable object
desc: descriptive information before the progress bar
total: the length of the iterable object.
The results are as follows:

you can see that there is descriptive Information, progress bar, how much time has been running, how much time is left, and speed. You can also add a suffix description later, see below.

2. Use tqdm in pytorch

Generally, tqdm is used in the train function. Dataloader is passed into tqdm as an iterable object.

loop = tqdm((dataloader_train), desc=f"Epoch: [{
      
      epoch}/20]", total=len(dataloader_train))
    for img, label in loop:
        img = img.to(device)
        label = label.to(device)
        output = model(img)
        optimizer.zero_grad()
        loss = criterion(output,label)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        correct += (torch.argmax(output,dim=1) == label).sum().item()
        loop.set_postfix(loss=loss.item() / label.shape[0])
    print("epoch: {i}    Train Loss: {loss}".format(i=epoch, loss=train_loss))
    print("epoch: {i}    Train Accuracy: {acc}".format(i=epoch, acc=correct / len(dataset_train)))