pytorch，tensor2im，im2tensor

def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
    image_numpy = image_tensor.cpu().float().detach().numpy()
    if normalize:
        image_numpy = (image_numpy+1)*255.0*0.5
    else:
        image_numpy = (image_numpy+1)*255.0
    image_numpy = np.clip(image_numpy, 0, 255)    
    blank_image = np.zeros((image_tensor.shape[1],image_tensor.shape[2],image_tensor.shape[0]), np.uint8)
    if image_tensor.shape[0] == 3:  
        blank_image[:,:,0]=image_numpy[2,:,:]
        blank_image[:,:,1]=image_numpy[1,:,:]
        blank_image[:,:,2]=image_numpy[0,:,:]
    else:
        blank_image[:,:,:]=image_numpy[:,:,:]
    return blank_image
def im2tensor(image_numpy, normalize=True):
    if normalize:
        image_numpy = (image_numpy/255.0)*2.0-1.0
    else:
        image_numpy = image_numpy/255.0
    image_numpy = np.clip(image_numpy, -1, 1)    
    blank_image = np.zeros((image_numpy.shape[2],image_numpy.shape[0],image_numpy.shape[1]))
    if image_numpy.shape[2] == 3:  
        blank_image[2,:,:]=image_numpy[:,:,0]
        blank_image[1,:,:]=image_numpy[:,:,1]
        blank_image[0,:,:]=image_numpy[:,:,2]
    else:
        blank_image[:,:,:]=image_numpy[:,:,:]
    image_tensor = torch.Tensor(blank_image)
    return image_tensor

调用：

            f_fake_image = tensor2im(fake_image[ii])    
            new_fake_image[ii]=im2tensor(f_fake_image_new, normalize=True)