1. 简介

tqdm是 Python 进度条库，可以在 Python长循环中添加一个进度提示信息。用户只需要封装任意的迭代器，是一个快速、扩展性强的进度条工具库。

2. 使用方法

传入可迭代对象

import time
from tqdm import *for i in tqdm(range(100)):time.sleep(0.01)

trange(i)：tqdm(range(i))的简单写法

for t in trange(100):time.sleep(0.01)

update()方法手动控制进度条更新的进度

with tqdm(total=200) as pbar:for i in range(20):  # 总共更新 20 次pbar.update(10)  # 每次更新步长为 10time.sleep(1)

或者

pbar = tqdm(total=200)for i in range (20):pbar.update(10)time.sleep(1)pbar.close()

write()方法

pbar = trange(10)for i in pbar:time.sleep(1)if not (i % 3):tqdm.write('Done task %i' %i)

通过set_description()和set_postfix()设置进度条显示信息

from random import random,randintwith trange(10) as t:for i in t:                t.set_description("GEN %i"%i)  # 进度条左边显示信息        t.set_postfix(loss=random(), gen=randint(1,999), str="h", lst=[1,2])  # 进度条右边显示信息time.sleep(0.1)

3. 实例 - 手写数字识别

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from tqdm import tqdmclass CNN(nn.Module):def __init__(self,in_channels=1,num_classes=10):super().__init__()self.conv1 = nn.Conv2d(in_channels=1,out_channels=8,kernel_size=(3,3),stride=(1,1),padding=(1,1))self.pool = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2))self.conv2 = nn.Conv2d(in_channels=8,out_channels=16,kernel_size=(3,3),stride=(1,1),padding=(1,1))self.fc1 = nn.Linear(16*7*7,num_classes)def forward(self,x):x = F.relu(self.conv1(x))x = self.pool(x)x = F.relu(self.conv2(x))x = self.pool(x)x = x.reshape(x.shape[0],-1)x = self.fc1(x)return xdevice = torch.device("cuda"if torch.cuda.is_available() else "cpu")in_channels = 1
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 5train_dataset = datasets.MNIST(root="dataset/",train=True,transform=transforms.ToTensor(),download=True)
train_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)test_dataset = datasets.MNIST(root="dataset/",train=False,transform=transforms.ToTensor(),download=True)
test_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)model = CNN().to(device)criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(),lr=learning_rate)for index,（data,targets） in tqdm(enumerate(train_loader),total=len(train_loader),leave = True):for data,targets in tqdm(train_loader):# Get data to cuda if possibledata = data.to(device=device)targets = targets.to(device=device)# forwardscores = model(data)loss = criterion(scores,targets)# backwardoptimizer.zero_grad()loss.backward()# gardient descent or adam stepoptimizer.step()