Please point the differences between your boundary loss and the reference paper 20 in your paper about boundary-loss HOT 1 CLOSED

class BoundaryLoss():
    def __init__(self, **kwargs):
        # Self.idc is used to filter out some classes of the target mask. Use fancy indexing
        self.idc: List[int] = kwargs["idc"]
        self.nd: str = kwargs["nd"]
        print(f"Initialized {self.__class__.__name__} with {kwargs}")

    def __call__(self, probs: Tensor, dist_maps: Tensor, _: Tensor, __, ___) -> Tensor:
        assert simplex(probs)
        assert not one_hot(dist_maps)

        pc = probs[:, self.idc, ...].type(torch.float32)
        dc = dist_maps[:, self.idc, ...].type(torch.float32)

        multipled = einsum(f"bk{self.nd},bk{self.nd}->bk{self.nd}", pc, dc)

        loss = multipled.mean()

        return loss

class HausdorffLoss():
    """
    Implementation heavily inspired from https://github.com/JunMa11/SegWithDistMap
    """
    def __init__(self, **kwargs):
        # Self.idc is used to filter out some classes of the target mask. Use fancy indexing
        self.idc: List[int] = kwargs["idc"]
        self.nd: str = kwargs["nd"]
        print(f"Initialized {self.__class__.__name__} with {kwargs}")

    def __call__(self, probs: Tensor, target: Tensor, _: Tensor, __, ___) -> Tensor:
        assert simplex(probs)
        assert simplex(target)
        assert probs.shape == target.shape

        B, K, *xyz = probs.shape  # type: ignore

        pc = cast(Tensor, probs[:, self.idc, ...].type(torch.float32))
        tc = cast(Tensor, target[:, self.idc, ...].type(torch.float32))
        assert pc.shape == tc.shape == (B, len(self.idc), *xyz)

        target_dm_npy: np.ndarray = np.stack([one_hot2hd_dist(tc[b].cpu().detach().numpy())
                                              for b in range(B)], axis=0)
        assert target_dm_npy.shape == tc.shape == pc.shape
        tdm: Tensor = torch.tensor(target_dm_npy, device=probs.device, dtype=torch.float32)

        pred_segmentation: Tensor = probs2one_hot(probs).cpu().detach()
        pred_dm_npy: np.nparray = np.stack([one_hot2hd_dist(pred_segmentation[b, self.idc, ...].numpy())
                                            for b in range(B)], axis=0)
        assert pred_dm_npy.shape == tc.shape == pc.shape
        pdm: Tensor = torch.tensor(pred_dm_npy, device=probs.device, dtype=torch.float32)

        delta = (pc - tc)**2
        dtm = tdm**2 + pdm**2

        multipled = einsum(f"bk{self.nd},bk{self.nd}->bk{self.nd}", delta, dtm)

        loss = multipled.mean()

        return loss