tqdm 详解-ag真人游戏

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。

• 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)  

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()