tqdm高级使用方法(类keras进度条)

简介

在很多场景，我们希望对一个进度条标识其运行的内容(

set_description

)，同时也希望在进度条中增加一些信息，如模型训练的精度等。本文就将基于tqdm，在实际应用中充实进度条。

一、简单示例

from tqdm import tqdm
tq_bar = tqdm(range(10))for idx, i inenumerate(tq_bar):
    acc_ = i*10
    loss =1/((i+1)*10)
    tq_bar.set_description(f'SimpleLoop [{idx+1}]')
    tq_bar.set_postfix(dict(acc=f'{acc_}%', loss=f'{loss:.3f}'))

• 结果

SimpleLoop [10]: 100%|██████████████████████████████████| 10/10 [00:01<00:00,  8.30it/s, acc=90%, loss=0.010]

二、在深度学习训练中使用(pytorch 类似 keras)

import torch
from torch import nn
from torch.nn import functional as F
from torch.optim import AdamW, Adam
from torch.utils.data import Dataset, TensorDataset, DataLoader
import torchvision as tv
from torchvision import transforms
classsimpleCNN(nn.Module):def__init__(self, input_dim=3, n_class=10):super(simpleCNN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(input_dim,32, kernel_size=7, padding=2,dilation=2, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3,2,1))
        self.clf = nn.Sequential(
            nn.Linear(4608,128),
            nn.ReLU(inplace=True),
            nn.Dropout(0.2),
            nn.Linear(128,64),
            nn.ReLU(inplace=True),
            nn.Dropout(0.2),
            nn.Linear(64, n_class))defforward(self, x):
        out = self.features(x)
        out = out.view(out.size(0),-1)return self.clf(out)
transform = transforms.Compose([transforms.ToTensor(),#转为tensor
                                transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),#归一化])
dt = tv.datasets.CIFAR10(train=True, download=True, root=r'D:\work\my_project\play_data', transform=transform)# dt = tv.datasets.CIFAR10(train=True, download=False, root=r'D:\work\my_project\play_data', transform=transform)
dt_loader = DataLoader(dt, batch_size=256)
model = simpleCNN(3,10)
loss_func = nn.CrossEntropyLoss()
optm = AdamW(model.parameters(), lr=1e-3)for ep inrange(5):
    one_batch_bar = tqdm(dt_loader)
    one_batch_bar.set_description(f'[ epoch: {ep+1} ]')
    step_counts =0
    step_loss_sum =0
    step_right =0
    step_samples =0for tmp_x, tmp_y in one_batch_bar:# forward
        optm.zero_grad()
        step_pred = model(tmp_x)
        step_loss = loss_func(step_pred, tmp_y)
        loss_print = step_loss.detach().numpy()
        step_right_i =(torch.argmax(step_pred, dim=1)== tmp_y).detach().numpy().sum()# backword
        step_loss.backward()
        optm.step()# info
        step_counts +=1
        step_loss_sum += loss_print
        step_right += step_right_i
        step_samples +=len(tmp_y)
        one_batch_bar.set_postfix(dict(
            loss=f'{step_loss_sum/step_counts:.5f}',
            acc=f'{step_right/step_samples*100:.2f}%'))

• 结果

[ epoch: 1 ]: 100%|██████████████████████████████████████████████████████| 196/196 [00:39<00:00,  4.95it/s, loss=1.73634, acc=36.93%] 
[ epoch: 2 ]: 100%|██████████████████████████████████████████████████████| 196/196 [00:38<00:00,  5.13it/s, loss=1.43507, acc=48.46%] 
[ epoch: 3 ]: 100%|██████████████████████████████████████████████████████| 196/196 [00:45<00:00,  4.34it/s, loss=1.30025, acc=53.85%] 
[ epoch: 4 ]: 100%|██████████████████████████████████████████████████████| 196/196 [00:37<00:00,  5.28it/s, loss=1.22050, acc=57.06%] 
[ epoch: 5 ]: 100%|██████████████████████████████████████████████████████| 196/196 [00:42<00:00,  4.65it/s, loss=1.16387, acc=58.78%]