pytorch中数据增强-1

train_transforms = transforms.Compose([
    transforms.Resize((224, 224)),
    # 1.CenterCrop
    # transforms.CenterCrop(512),  # 196、512

    # 2.RandomCrop
    # transforms.RandomCrop(224,padding=16),
    # transforms.RandomCrop(224,padding=(16, 64)),
    # transforms.RandomCrop(224, padding=16, fill=(255,0,0)),
    # transforms.RandomCrop(512,pad_if_needed=True),
    # transforms.RandomCrop(224,padding=64,padding_mode="edge"),
    # transforms.RandomCrop(224,padding=64,padding_mode="reflect"),
    # transforms.RandomCrop(224,padding=64,padding_mode="symmetric"),
    # transforms.RandomCrop(1024,padding=1024,padding_mode="symmetric"),

    # 3.RandomResizeCrop
    # transforms.RandomResizedCrop(size=224, scale=(0.08, 1)),
    # transforms.RandomResizedCrop(size=224, scale=(0.5,0.5)),

    # 翻转
    # 4.HorizontalFlip
    # transforms.RandomHorizontalFlip(p=1),
    # 5.VerticalFlip
    # transforms.RandomVerticalFlip(p=0.5),

    # 旋转
    # 6.RandomRotation
    # transforms.RandomRotation(90),
    # transforms.RandomRotation((90), expand=True),  # 保图填充
    # transforms.RandomRotation(30, center=(0, 0)),   # (0, 0)左上角为旋转中心
    # transforms.RandomRotation(30, center=(0, 0), expand=True),

    # 7.pad
    # transforms.Pad(padding=32,fill=(255,0,0),padding_mode="constant"),
    # transforms.Pad(padding=(8,64), fill=(255,0,0), padding_mode="constant"),
    # transforms.Pad(padding=(8,16,32,64),fill=(255,0,0),padding_mode="constant"),
    # transforms.Pad(padding=(8,16,32,64),padding_mode="symmetric"),

    # 8.色彩变换（ColoJitter）
    # transforms.ColorJitter(brightness=0.1),  # 亮度：越小越亮
    # transforms.ColorJitter(contrast=1),  # 对比度：升高图像会发灰
    # transforms.ColorJitter(saturation=0.1),  # 饱和度：升高图像会变暗淡
    # transforms.ColorJitter(hue=0.3),  # 色相

    # 9.灰度变化（Grayscale）
    # transforms.Grayscale(num_output_channels=1),
    # transforms.RandomGrayscale(p=0.9),

    # 10.仿射变换（包括：旋转、平移、缩放、错切、翻转）
    # transforms.RandomAffine(degrees=30),  # （-30，+30）之间进行中心旋转
    # 平移，范围左右（-0.2w，+0.2w），上下（-0.2w，+0.2w），四个边先填充，然后裁剪
    # transforms.RandomAffine(degrees=0, translate=(0.2, 0.2), fillcolor=(255,0,0)),
    # transforms.RandomAffine(degrees=0, scale=(0.7,0.7)),  # 缩放
    # transforms.RandomAffine(degrees=0, shear=(0,0,0,45)),  # 设置y轴进行错切
    # transforms.RandomAffine(degrees=0, shear=(0,90,0,0)),  # 设置x轴进行错切
    # transforms.RandomAffine(degrees=0, shear=90),  # 设置x轴进行错切

    # 11.随机遮挡 ==> 这里直接对tensor进行操作，之前的方法都是在PIL.image上进行计算的
    # transforms.ToTensor(),  # 注意要注释掉后面Totensor和Normalize.
    # transforms.RandomErasing(p=1, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=(254/255, 0, 0)),   # 纯色填充
    # transforms.RandomErasing(p=1, scale=(0.02, 0.33), ratio=(0.3, 3.3), value="1234"),  # 实现任意像素块遮挡

    # 组合
    # 1.随机选择一种
    # transforms.RandomChoice([transforms.RandomVerticalFlip(p=1),
    #                          transforms.RandomHorizontalFlip(p=1)]),
    # 2.依据概率p执行一组transforms操作
    # transforms.RandomApply([transforms.RandomAffine(degrees=0, shear=45, fillcolor=(255, 0, 0)),
    #                         transforms.Grayscale(num_output_channels=3)],
    #                        p=0.5),
    # 3.对一组transforms操作打乱顺序
    transforms.RandomOrder([transforms.RandomRotation(15),
                            transforms.RandomAffine(degrees=0, translate=(0.01, 0.1), scale=(0.9, 1.1)),
                            transforms.Pad(padding=32)]),

    transforms.ToTensor(),  # 进行归一化
    transforms.Normalize(norm_mean, norm_std)  # 标准化
])