pytorch lightning 手写数字分类实例

Posted 2021-07-21 Tina姐

今天通过手写数字来学习如何利用pytorch-lightning进行分类
代码同第二部分的差不多，新增了断点训练和测试部分。
项目使用jupyter notebook演示
此部分代码很简单，小白也能上手，赶快来试一试吧～～～

该系列还有
pytorch-lightning入门（一）—— 初了解
如何从Pytorch 到 Pytorch Lightning （二） | 简要介绍

文章目录

• SET SEED
• dataloader
• training
• testing
• • 测试结果显示

import torch
from torch import nn
import pytorch_lightning as pl
from torch.utils.data import DataLoader, random_split
from torch.nn import functional as F
from torchvision.datasets import MNIST
from torchvision import datasets, transforms
import os
from pytorch_lightning import seed_everything
import numpy as np
import matplotlib.pyplot as plt

SET SEED

# 首先设置随机数种子
seed_everything(seed=42)

# 定义模型
class LightningMNISTClassifier(pl.LightningModule):

    def __init__(self):
        super().__init__()

        # mnist images are (1, 28, 28) (channels, width, height)
        self.layer_1 = torch.nn.Linear(28 * 28, 128)
        self.layer_2 = torch.nn.Linear(128, 256)
        self.layer_3 = torch.nn.Linear(256, 10)

    def forward(self, x):
        batch_size, channels, width, height = x.size()

        # (b, 1, 28, 28) -> (b, 1*28*28)
        x = x.view(batch_size, -1)

        # layer 1 (b, 1*28*28) -> (b, 128)
        x = self.layer_1(x)
        x = torch.relu(x)

        # layer 2 (b, 128) -> (b, 256)
        x = self.layer_2(x)
        x = torch.relu(x)

        # layer 3 (b, 256) -> (b, 10)
        x = self.layer_3(x)

        # probability distribution over labels
        x = torch.log_softmax(x, dim=1)

        return x

    def cross_entropy_loss(self, logits, labels):
        return F.nll_loss(logits, labels)

    def training_step(self, train_batch, batch_idx):
        x, y = train_batch
        logits = self.forward(x)
        loss = self.cross_entropy_loss(logits, y)
        self.log('train_loss', loss)
        return loss

    def validation_step(self, val_batch, batch_idx):
        x, y = val_batch
        logits = self.forward(x)
        loss = self.cross_entropy_loss(logits, y)
        self.log('val_loss', loss)

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
        return optimizer

dataloader

# data
# transforms for images
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.1307,), (0.3081,))])

# prepare transforms standard to MNIST
mnist_train = MNIST(os.getcwd(), train=True, download=True, transform=transform)
mnist_test = MNIST(os.getcwd(), train=False, download=True, transform=transform)

train_dataloader = DataLoader(mnist_train, batch_size=64)
val_loader = DataLoader(mnist_test, batch_size=64)

没有下载的会自动进行下载，如果速度慢，就手动下载保存。有VPN 可以直接运行代码

training

接下来开始训练，提供两种训练方法

• 从头训练
• 从断点开始训练

# train
model = LightningMNISTClassifier()

# resume training
RESUME = False
if RESUME:
    resume_checkpoint_dir = './lightning_logs/version_1/checkpoints/'
    checkpoint_path = os.listdir(resume_checkpoint_dir)[0]
    resume_checkpoint_path = resume_checkpoint_dir + checkpoint_path

    trainer = pl.Trainer(gpus='1',
                         max_epochs=10,
                         resume_from_checkpoint=resume_checkpoint_path)

    trainer.fit(model, train_dataloader, val_loader)
else:
    trainer = pl.Trainer(gpus='1', max_epochs=20)

    trainer.fit(model, train_dataloader, val_loader)

输出包括：

训练结果默认保存在文件夹： ./lightning_logs.
会根据你运行的次数自动命名这是版本x。

运行期间可以打开tensorboard 查看运行情况

testing

# test
checkpoint_dir = 'lightning_logs/version_2/checkpoints/'
checkpoint_path = checkpoint_dir + os.listdir(checkpoint_dir)[0]
model = LightningMNISTClassifier()
model.load_state_dict(torch.load(checkpoint_path)['state_dict'])

inputs, labels = next(iter(val_loader))
# inference
outputs = model(inputs)

这里，我只测试一个batch的数据。

测试结果显示

def imshow(inp):
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    inp = np.clip(inp, 0, 1)
    plt.imshow(inp)
    plt.show()

import torchvision

# 求outputs最大索引
_, preds = torch.max(outputs, dim=1)

# print images and ground truth
imshow(torchvision.utils.make_grid(inputs))
print('GroundTruth:', labels)
print ('Prediction:', preds)

可见，这部分的准确率为100%

tips：使用lightning模式，用jupyter notebook显示进度条不是很良好，应该要进行相应的设置，我这里不太想去研究了。在终端运行，显示效果更好。

此部分代码很简单，赶快来试一试吧

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