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()