tqdm 详解

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 10
        time.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,randint

with 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 tqdm

class 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 x

device = 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 = 5

train_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 possible
        data = data.to(device=device)
        targets = targets.to(device=device)

        # forward
        scores = model(data)
        loss = criterion(scores,targets)

        # backward
        optimizer.zero_grad()
        loss.backward()

        # gardient descent or adam step
        optimizer.step()

【转载自】

1. 【PyTorch总结】tqdm的使用；
2. Python 超方便的迭代进度条 (Tqdm)；