def __init__()

in threestudio/models/isosurface.py [0:0]

• 56 lines of code
• 1 McCabe index (conditional complexity)

    def __init__(self, resolution: int, tets_path: str):
        super().__init__()
        self.resolution = resolution
        self.tets_path = tets_path

        self.triangle_table: Float[Tensor, "..."]
        self.register_buffer(
            "triangle_table",
            torch.as_tensor(
                [
                    [-1, -1, -1, -1, -1, -1],
                    [1, 0, 2, -1, -1, -1],
                    [4, 0, 3, -1, -1, -1],
                    [1, 4, 2, 1, 3, 4],
                    [3, 1, 5, -1, -1, -1],
                    [2, 3, 0, 2, 5, 3],
                    [1, 4, 0, 1, 5, 4],
                    [4, 2, 5, -1, -1, -1],
                    [4, 5, 2, -1, -1, -1],
                    [4, 1, 0, 4, 5, 1],
                    [3, 2, 0, 3, 5, 2],
                    [1, 3, 5, -1, -1, -1],
                    [4, 1, 2, 4, 3, 1],
                    [3, 0, 4, -1, -1, -1],
                    [2, 0, 1, -1, -1, -1],
                    [-1, -1, -1, -1, -1, -1],
                ],
                dtype=torch.long,
            ),
            persistent=False,
        )
        self.num_triangles_table: Integer[Tensor, "..."]
        self.register_buffer(
            "num_triangles_table",
            torch.as_tensor(
                [0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long
            ),
            persistent=False,
        )
        self.base_tet_edges: Integer[Tensor, "..."]
        self.register_buffer(
            "base_tet_edges",
            torch.as_tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long),
            persistent=False,
        )

        tets = np.load(self.tets_path)
        self._grid_vertices: Float[Tensor, "..."]
        self.register_buffer(
            "_grid_vertices",
            torch.from_numpy(tets["vertices"]).float(),
            persistent=False,
        )
        self.indices: Integer[Tensor, "..."]
        self.register_buffer(
            "indices", torch.from_numpy(tets["indices"]).long(), persistent=False
        )

        self._all_edges: Optional[Integer[Tensor, "Ne 2"]] = None