文章目录

介绍

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。

安装

pip install tqdm

使用方法

1.传入可迭代对象
import time
from tqdm import *
for i in tqdm(range(1000)):
    time.sleep(.01)   #进度条每0.01s前进一次，总时间为1000*0.01=10s 

# 运行结果如下
100%|██████████| 1000/1000 [00:10<00:00, 93.21it/s]
使用trange

trange(i) 是 tqdm(range(i)) 的简单写法

from tqdm import trange

for i in trange(1000):
    time.sleep(.01)

# 运行结果如下
100%|██████████| 1000/1000 [00:10<00:00, 93.21it/s]
2.为进度条设置描述

在for循环外部初始化tqdm，可以打印其他信息：

import time
from tqdm import tqdm

pbar = tqdm(["a","b","c","d"])

for char in pbar:
    pbar.set_description("Processing %s" % char) # 设置描述
    time.sleep(1)  # 每个任务分配1s
    
# 结果如下
  0%|          | 0/4 [00:00<?, ?it/s]

Processing a:   0%|          | 0/4 [00:00<?, ?it/s]

Processing a:  25%|██▌       | 1/4 [00:01<00:03,  1.01s/it]

Processing b:  25%|██▌       | 1/4 [00:01<00:03,  1.01s/it]

Processing b:  50%|█████     | 2/4 [00:02<00:02,  1.01s/it]

Processing c:  50%|█████     | 2/4 [00:02<00:02,  1.01s/it]

Processing c:  75%|███████▌  | 3/4 [00:03<00:01,  1.01s/it]

Processing d:  75%|███████▌  | 3/4 [00:03<00:01,  1.01s/it]

Processing d: 100%|██████████| 4/4 [00:04<00:00,  1.01s/it]
3.手动控制进度
import time
from tqdm import tqdm

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

# 结果如下，一共更新了20次
0%|          | 0/200 [00:00<?, ?it/s]

 10%|█         | 20/200 [00:00<00:00, 199.48it/s]

 15%|█▌        | 30/200 [00:00<00:01, 150.95it/s]

 20%|██        | 40/200 [00:00<00:01, 128.76it/s]

 25%|██▌       | 50/200 [00:00<00:01, 115.72it/s]

 30%|███       | 60/200 [00:00<00:01, 108.84it/s]

 35%|███▌      | 70/200 [00:00<00:01, 104.22it/s]

 40%|████      | 80/200 [00:00<00:01, 101.42it/s]

 45%|████▌     | 90/200 [00:00<00:01, 98.83it/s] 

 50%|█████     | 100/200 [00:00<00:01, 97.75it/s]

 55%|█████▌    | 110/200 [00:01<00:00, 97.00it/s]

 60%|██████    | 120/200 [00:01<00:00, 96.48it/s]

 65%|██████▌   | 130/200 [00:01<00:00, 96.05it/s]

 70%|███████   | 140/200 [00:01<00:00, 95.25it/s]

 75%|███████▌  | 150/200 [00:01<00:00, 94.94it/s]

 80%|████████  | 160/200 [00:01<00:00, 95.08it/s]

 85%|████████▌ | 170/200 [00:01<00:00, 93.52it/s]

 90%|█████████ | 180/200 [00:01<00:00, 94.28it/s]

 95%|█████████▌| 190/200 [00:01<00:00, 94.43it/s]

100%|██████████| 200/200 [00:02<00:00, 94.75it/s]
4.tqdm的write方法
bar = trange(10)
for i in bar:
    time.sleep(0.1)
    if not (i % 3):
        tqdm.write("Done task %i" % i)
# 结果如下

Done task 0
0%|                                                                                           | 0/10 [00:10<?, ?it/s]
  0%|                                                                                           | 0/10 [00:00<?, ?it/s]
 10%|████████▎                                                                          | 1/10 [00:00<00:01,  8.77it/s]
 20%|████████████████▌                                                                  | 2/10 [00:00<00:00,  9.22it/s]
                                                                                                                       
                                                                                                                       

Done task 3


  0%|                                                                                           | 0/10 [00:10<?, ?it/s]
 30%|████████████████████████▉                                                          | 3/10 [00:00<00:01,  6.91it/s]
 40%|█████████████████████████████████▏                                                 | 4/10 [00:00<00:00,  9.17it/s]
 50%|█████████████████████████████████████████▌                                         | 5/10 [00:00<00:00,  9.28it/s]
                                                                                                                       
                                                                                                                       

Done task 6


  0%|                                                                                           | 0/10 [00:10<?, ?it/s]
 60%|█████████████████████████████████████████████████▊                                 | 6/10 [00:00<00:00,  7.97it/s]
 70%|██████████████████████████████████████████████████████████                         | 7/10 [00:00<00:00,  9.25it/s]
 80%|██████████████████████████████████████████████████████████████████▍                | 8/10 [00:00<00:00,  9.31it/s]
                                                                                                                       
                                                                                                                       

Done task 9


  0%|                                                                                           | 0/10 [00:11<?, ?it/s]
 90%|██████████████████████████████████████████████████████████████████████████▋        | 9/10 [00:01<00:00,  8.37it/s]
100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00,  9.28it/s]
5.手动设置处理的进度

通过update方法可以控制每次进度条更新的进度：

from tqdm import tqdm 
import time
#total参数设置进度条的总长度
with tqdm(total=100) as pbar:
    for i in range(100):
        time.sleep(0.1)
        pbar.update(1)  #每次更新进度条的长度
#结果
  0%|          | 0/100 [00:00<?, ?it/s]
  1%|          | 1/100 [00:00<00:09,  9.98it/s]
  2%|▏         | 2/100 [00:00<00:09,  9.83it/s]
  3%|▎         | 3/100 [00:00<00:10,  9.65it/s]
  4%|▍         | 4/100 [00:00<00:10,  9.53it/s]
  5%|▌         | 5/100 [00:00<00:09,  9.55it/s]
  ...
  100%|██████████| 100/100 [00:10<00:00,  9.45it/s]

除了使用with之外，还可以使用另外一种方法实现上面的效果：

from tqdm import tqdm
import time
 
#total参数设置进度条的总长度
pbar = tqdm(total=100)
for i in range(100):
  time.sleep(0.05)
  #每次更新进度条的长度
  pbar.update(1)
#别忘了关闭占用的资源
pbar.close()
6.自定义进度条显示信息

通过set_description和set_postfix方法设置进度条显示信息：

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

在深度学习中如何使用

下面是一段手写数字识别代码

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

# Set device
device = torch.device("cuda"if torch.cuda.is_available() else "cpu")
print(device)
# Hyperparameters
in_channels = 1
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 5

# Load Data
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)

# Initialize network
model = CNN().to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(),lr=learning_rate)

# Train Network

for epoch in range(num_epochs):
    # for data,targets in tqdm(train_loadr,leave=False) # 进度显示在一行
    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()

如果不给train_loader加上tqdm会什么也不现实，加入后显示如下：

100%|██████████| 938/938 [00:06<00:00, 152.23it/s]
100%|██████████| 938/938 [00:06<00:00, 153.74it/s]
100%|██████████| 938/938 [00:06<00:00, 155.11it/s]
100%|██████████| 938/938 [00:06<00:00, 153.08it/s]
100%|██████████| 938/938 [00:06<00:00, 153.57it/s]

对应5个eopch的5个进度显示
如果我们想要它显示在一行，在tqdm中添加leave=False参数即可

for data,targets in tqdm(train_loadr,leave=False) # 进度显示在一行

注意
我们将tqdm加到train_loader无法得到索引，要如何得到索引呢？可以使用下面的代码

for index,（data,targets） in tqdm(enumerate(train_loader),total=len(train_loader),leave = True):

我们觉得还有点不太满足现在的进度条，我们得给他加上我们需要的信息，比如准确率，loss值，如何加呢？

for epoch in range(num_epochs):
    losses = []
    accuracy = []
    # for data,targets in tqdm(train_loadr,leave=False) # 进度显示在一行
    loop = tqdm((train_loader), total = len(train_loader))
    for data,targets in loop:
        # Get data to cuda if possible
        data = data.to(device=device)
        targets = targets.to(device=device)

        # forward
        scores = model(data)
        loss = criterion(scores,targets)
        losses.append(loss)
        # backward
        optimizer.zero_grad()
        loss.backward()
        _,predictions = scores.max(1)
        num_correct = (predictions == targets).sum()
        running_train_acc = float(num_correct) / float(data.shape[0])
        accuracy.append(running_train_acc)
        # gardient descent or adam step
        optimizer.step()
        loop.set_description(f'Epoch [{epoch}/{num_epochs}]')
        loop.set_postfix(loss = loss.item(),acc = running_train_acc)

在这里插入图片描述
可以看到我们的acc和epoch 还有loss都打在了控制台中。以上就是相关tqdm的使用

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