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<>简介
torchvision是pytorch的一个图形库，它服务于PyTorch深度学习框架的，主要用来构建计算机视觉模型。
torchvision.transforms主要是用于常见的一些图形变换。以下是torchvision的构成：
1.torchvision.datasets: 一些加载数据的函数及常用的数据集接口；
2.torchvision.models: 包含常用的模型结构（含预训练模型），例如AlexNet、VGG、ResNet等；
3.torchvision.transforms: 常用的图片变换，例如裁剪、旋转等；
4.torchvision.utils: 其他的一些有用的方法。
本文的主题是其中的torchvision.transforms.Compose()类。这个类的主要作用是串联多个图片变换的操作。
from torchvision.transforms import transforms train_transforms = transforms.
Compose([ transforms.Resize([224, 224]), # 将输入图片resize成统一尺寸 transforms.
RandomRotation(degrees=(-10, 10)), # 随机旋转，-10到10度之间随机选 transforms.
RandomHorizontalFlip(p=0.5), # 随机水平翻转 选择一个概率概率 transforms.RandomVerticalFlip(p=
0.5), # 随机垂直翻转 transforms.RandomPerspective(distortion_scale=0.6, p=1.0), # 随机视角
transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)), # 随机选择的高斯模糊模糊图像
transforms.ToTensor(), # 将PIL Image或numpy.ndarray转换为tensor，并归一化到[0,1]之间
transforms.Normalize( # 标准化处理-->转换为标准正太分布（高斯分布），使模型更容易收敛 mean=[0.485, 0.456,
0.406], std = [0.229, 0.224, 0.225]) # 其中
mean=[0.485,0.456,0.406]与std=[0.229,0.224,0.225] 从数据集中随机抽样计算得到的。 ])
