Pytorch实战之数据加载和处理详解

# 在进行下面代码学习前需要安装torchvision==0.8.2
!pip install torchvision==0.8.2 --user

from PIL import Image
from torchvision import transforms as T
import torch as t
to_tensor = T.ToTensor()
to_pil = T.ToPILImage()
cat = Image.open('./cat.jpeg')  

# torchvision.transforms.CenterCrop
transforms = T.Compose([T.Resize(224),T.CenterCrop(224),T.ToTensor()])  # Resize：缩放
cat_t = transforms(cat)  # 传入transforms中的数据是PIL数据，lena_t为tensor
cat_t.shape  # 3*224*224 ; 当T.CenterCrop()的参数大于T.Resize()的参数时，周围用0填充
to_pil(cat_t)

# torchvision.transforms.RandomCrop
transforms = T.Compose([T.Resize(224),T.RandomCrop(224, padding=(16, 64)),T.ToTensor()])  # Resize：缩放
cat_t = transforms(cat)  # 传入transforms中的数据是PIL数据，lena_t为tensor
cat_t.shape  # 3*224*224 ; 当T.CenterCrop()的参数大于T.Resize()的参数时，周围用0填充
to_pil(cat_t)

# torchvision.transforms.RandomHorizontalFlip
transforms = T.Compose([T.Resize(224),T.RandomHorizontalFlip(p=0.5),T.ToTensor()])  # Resize：缩放
cat_t = transforms(cat)  # 传入transforms中的数据是PIL数据，lena_t为tensor
cat_t.shape  # 3*224*224 ; 当T.CenterCrop()的参数大于T.Resize()的参数时，周围用0填充
to_pil(cat_t)

# torchvision.transforms.RandomRotation
transforms = T.Compose([T.Resize(224),T.RandomRotation(30, center=(0, 0), expand=True),T.ToTensor()])  # Resize：缩放
cat_t = transforms(cat)  # 传入transforms中的数据是PIL数据，lena_t为tensor
cat_t.shape  # 3*224*224 ; 当T.CenterCrop()的参数大于T.Resize()的参数时，周围用0填充
to_pil(cat_t)

class Compose(object):
    def __call__(self, img):
        for t in transforms:
            img = t(img)
        return img

class YourTransforms(object):
    def __init__(self, transforms):
        self.transforms = transforms
    def __call__(self, img):
        for t in self.transforms:
            img = t(img)
        return img

class AddPepperNoise(object):
    def __init__(self, snr, p):
        self.snr = snr
        self.p = p
    def __call__(self, img):
        # 添加椒盐噪声具体实现过程
        img = None
        return img