• 对比 [1]，主要改变是改写 MultiCompose 类，并将 to_multi 吸收入内。
• MultiCompose 的用法还是和 torchvision.transforms.Compose 几乎一致，不过支持独用 augmentation：只要为各图指定各自的 augmentation 类/函数即可。见下一节例程。

def to_multi():"""不用单独的 to_multi 打包了，已并入 MultiCompose"""raise NotImplementedErrorclass MultiCompose:"""扩展 torchvision.transforms.Compose：支持输入多图，且保证各 augmentation 中所有输入都用同一随机状态（如旋转同一随机角度），分割任务有用。"""# numpy.random.seed range error:#   ValueError: Seed must be between 0 and 2**32 - 1MIN_SEED = 0 # - 0x8000_0000_0000_0000MAX_SEED = min(2**32 - 1, 0xffff_ffff_ffff_ffff)def __init__(self, transforms):"""输入：一个 list/tuple，其中每个元素可以是一个 augmentation 对象（transform）/函数，各输入同用；或一个嵌套的 list/tuple，为每个输入指定独用的 augmentation。"""# self.transforms = [to_multi(t) for t in transforms]no_op = lambda x: x # i.e. identity functionself.transforms = []for t in transforms:if isinstance(t, (tuple, list)):# convert `None` to `no_op` for convenienceself.transforms.append([no_op if _t is None else _t for _t in t])else:self.transforms.append(t)def __call__(self, *images):for t in self.transforms:if isinstance(t, (tuple, list)): # 独用assert len(images) <= len(t) # allow redundant transformelse: # 同用t = [t] * len(images)_aug_images = []_seed = random.randint(self.MIN_SEED, self.MAX_SEED)for _im, _t in zip(images, t):seed_everything(_seed)_aug_images.append(_t(_im))images = _aug_imagesif len(images) == 1:images = images[0]return images