transform = Compose([
   transforms.RandomResizedCrop(int(image_size*1.2)),
   # transforms.ToPILImage(),
   transforms.RandomAffine(15),
   # transforms.RandomHorizontalFlip(),
   transforms.RandomVerticalFlip(),
   transforms.RandomRotation(10),
   transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),
   transforms.RandomGrayscale(),
   transforms.TenCrop(image_size),
   transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
   transforms.Lambda(lambda crops: torch.stack([transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop) for crop in crops])),
])
 

• 注意:此时image部分得到的是一个5维的tensor(batch_size,10,channels,H,W)，而我们一般训练的时候需要的是4维tensor（batch_size,channels,H,W），所以具体使用的时候还需要进行一波转换（融合batch中的原始图片和每个原始图片的crop出来的ten个图片变成一个新的大的batch）

transform = Compose([
    TenCrop(size), # this is a list of PIL Images
    Lambda(lambda crops: torch.stack([ToTensor()(crop) for crop in crops])) # returns a 4D tensor
])

#In your test loop you can do the following:
input, target = batch # input is a 5d tensor, target is 2d
bs, ncrops, c, h, w = input.size()
result = model(input.view(-1, c, h, w)) # fuse batch size and ncrops
result_avg = result.view(bs, ncrops, -1).mean(1) # avg over crops