PyTorch - 图像增广

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• d2l : 13.1. 图像增广
  https://zh.d2l.ai/chapter_computer-vision/image-augmentation.html
• PyTorch 官方：Transforming and augmenting images
  https://pytorch.org/vision/stable/transforms.html

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一、常用的图像增广方法

%matplotlib inline
import os
import time
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader
import torchvision
from PIL import Image

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

import sys
sys.path.append("..") 

from d2l import torch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(torch.__version__)
print(device)

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d2l.set_figsize()

file_path = '/Users/xx/Downloads/cat.jpg'
img = Image.open(file_path)
d2l.plt.imshow(img)

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显示图片的方法

def show_images(imgs, num_rows, num_cols, scale=2):
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
    for i in range(num_rows):
        for j in range(num_cols):
            axes[i][j].imshow(imgs[i * num_cols + j])
            axes[i][j].axes.get_xaxis().set_visible(False)
            axes[i][j].axes.get_yaxis().set_visible(False)
    return axes

# aug : 图像增广方法; num_rows * num_cols: 重复作用多少次； scale ： 图片尺寸倍数
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
    Y = [aug(img) for _ in range(num_rows * num_cols)]
    show_images(Y, num_rows, num_cols, scale)

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1、翻转和裁剪

apply(img, torchvision.transforms.RandomHorizontalFlip())  # 左右翻转

​

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apply(img, torchvision.transforms.RandomVerticalFlip()) # 上下翻转

​

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# 随机剪裁，最后输出 200*200 ； ratio: 高宽比 
shape_aug = torchvision.transforms.RandomResizedCrop(200, scale=(0.1, 1), ratio=(0.5, 2))
apply(img, shape_aug)

​

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2、变化颜色

apply(img, torchvision.transforms.ColorJitter(brightness=0.5, contrast=0, saturation=0, hue=0))

​

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# 改变色调 hue  
apply(img, torchvision.transforms.ColorJitter(brightness=0, contrast=0, saturation=0, hue=0.5))

​

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apply(img, torchvision.transforms.ColorJitter(brightness=0, contrast=0.5, saturation=0, hue=0))

​

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color_aug = torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
apply(img, color_aug)

​

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3、叠加多个图像增广方法

augs = torchvision.transforms.Compose([
    torchvision.transforms.RandomHorizontalFlip(), color_aug, shape_aug])
apply(img, augs)

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二、使用图像增广训练模型

all_images = torchvision.datasets.CIFAR10(train=True, root=root_dir,
                                          download=True)
d2l.show_images([all_images[i][0] for i in range(32)], 4, 8, scale=0.8);

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train_augs = torchvision.transforms.Compose([
     torchvision.transforms.RandomHorizontalFlip(),
     torchvision.transforms.ToTensor()])

test_augs = torchvision.transforms.Compose([
     torchvision.transforms.ToTensor()])

def load_cifar10(is_train, augs, batch_size):
    dataset = torchvision.datasets.CIFAR10(root=root_dir, train=is_train,
                                           transform=augs, download=True)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                    shuffle=is_train, num_workers=d2l.get_dataloader_workers())
    return dataloader

#@save
def train_batch_ch13(net, X, y, loss, trainer, devices):
    """用多GPU进行小批量训练"""
    
    if isinstance(X, list):
        # 微调BERT中所需
        X = [x.to(devices[0]) for x in X]
    else:
        X = X.to(devices[0])
        
    y = y.to(devices[0])
    net.train()
    trainer.zero_grad()
    pred = net(X)
    l = loss(pred, y)
    l.sum().backward()
    trainer.step()
    train_loss_sum = l.sum()
    train_acc_sum = d2l.accuracy(pred, y)
    return train_loss_sum, train_acc_sum

#@save
def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,
               devices=d2l.try_all_gpus()):
    
    """用多GPU进行模型训练"""
    timer, num_batches = d2l.Timer(), len(train_iter)
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
                            legend=['train loss', 'train acc', 'test acc'])
                            
    net = nn.DataParallel(net, device_ids=devices).to(devices[0])
    
    for epoch in range(num_epochs):
        
        # 4个维度：储存训练损失，训练准确度，实例数，特点数
        metric = d2l.Accumulator(4)
        
        for i, (features, labels) in enumerate(train_iter):
            timer.start()
            l, acc = train_batch_ch13(
                net, features, labels, loss, trainer, devices)
            metric.add(l, acc, labels.shape[0], labels.numel())
            timer.stop()
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches,
                             (metric[0] / metric[2], metric[1] / metric[3],
                              None))
                
        test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
        animator.add(epoch + 1, (None, None, test_acc))
        
    print(f'loss {
      
      metric[0] / metric[2]:.3f}, train acc '
          f'{
      
      metric[1] / metric[3]:.3f}, test acc {
      
      test_acc:.3f}')
    print(f'{
      
      metric[2] * num_epochs / timer.sum():.1f} examples/sec on '
          f'{
      
      str(devices)}')

batch_size, devices, net = 256, d2l.try_all_gpus(), d2l.resnet18(10, 3)

def init_weights(m):
    if type(m) in [nn.Linear, nn.Conv2d]:
        nn.init.xavier_uniform_(m.weight)

net.apply(init_weights) 

def train_with_data_aug(train_augs, test_augs, net, lr=0.001):
    train_iter = load_cifar10(True, train_augs, batch_size)
    test_iter = load_cifar10(False, test_augs, batch_size)
    loss = nn.CrossEntropyLoss(reduction="none")
    trainer = torch.optim.Adam(net.parameters(), lr=lr)
    train_ch13(net, train_iter, test_iter, loss, trainer, 10, devices)

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train_with_data_aug(train_augs, test_augs, net)

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2023-03-29