transform.invert 预处理逆操作

from PIL import Image
from torchvision import transforms
import torch
import numpy as np

def transform_invert(img_, transform_train):
    """
    将data 进行反transfrom操作
    :param img_: tensor
    :param transform_train: torchvision.transforms
    :return: PIL image
    """
    if 'Normalize' in str(transform_train):
        # 分析transforms里的Normalize
        norm_transform = list(filter(lambda x: isinstance(x, transforms.Normalize), transform_train.transforms))
        mean = torch.tensor(norm_transform[0].mean, dtype=img_.dtype, device=img_.device)
        std = torch.tensor(norm_transform[0].std, dtype=img_.dtype, device=img_.device)
        img_.mul_(std[:, None, None]).add_(mean[:, None, None])  # 广播三个维度 乘标准差 加均值

    img_ = img_.transpose(0, 2).transpose(0, 1)  # C*H*W --> H*W*C

    # 如果有ToTensor，那么之前数值就会被压缩至0-1之间。现在需要反变换回来，也就是乘255
    if 'ToTensor' in str(transform_train):
        img_ = np.array(img_) * 255

    # 先将np的元素转换为uint8数据类型，然后转换为PIL.Image类型
    if img_.shape[2] == 3:  # 若通道数为3 需要转为RGB类型
        img_ = Image.fromarray(img_.astype('uint8')).convert('RGB')
    elif img_.shape[2] == 1:  # 若通道数为1 需要压缩张量的维度至2D
        img_ = Image.fromarray(img_.astype('uint8').squeeze())
    else:
        raise Exception("Invalid img shape, expected 1 or 3 in axis 2, but got {}!".format(img_.shape[2]))

    return img_

if __name__ == '__main__':
    
    img = Image.open(r"./test.jpg").convert('RGB')
    img_transform = transforms.Compose([transforms.ToTensor()])
    img_tensor = img_transform(img)
    # img_tensor.unsqueeze_(dim=0)    # C*H*W to B*C*H*W
    print(img_tensor)
    print(img_tensor.shape)
    
    img = transform_invert(img_tensor, img_transform)  # input: shape=[c h w]
    img.show()