def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
"""
Args:
img: numpy arrays with required dimension `dim` removed
"""
if self.dim is None:
return img.squeeze()
# for pytorch/numpy unification
if img.shape[self.dim] != 1:
raise ValueError("Can only squeeze singleton dimension")
return img.squeeze(self.dim)
def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
# if img has channel dim, squeeze it
if img.ndim == 4 and img.shape[0] == 1:
img = img.squeeze(0)
result = [(img == 1) | (img == 4),
(img == 1) | (img == 4) | (img == 2), img == 4]
# merge labels 1 (tumor non-enh) and 4 (tumor enh) and 2 (large edema) to WT
# label 4 is ET
return torch.stack(result, dim=0) if isinstance(
img, torch.Tensor) else np.stack(result, axis=0)