Method aggregate

pointersect/inference/structures.py:1387–1448

Aggregate a list of PointersectRecord of the same shape. Note that it will set many attributes to None.

(records: T.List['PointersectRecord'])

    @staticmethod
    def aggregate(records: T.List['PointersectRecord']) -> 'PointersectRecord':
        """
        Aggregate a list of PointersectRecord of the same shape.
        Note that it will set many attributes to None.
        """
        out = dict()
        # simple average
        for name in [
            'intersection_xyz_w', 'intersection_rgb',
            'ray_t', 'ray_hit', 'ray_hit_logit', 'model_attn_weights',
            'refined_ray_hit',
        ]:
            arr = [getattr(r, name, None) for r in records]
            arr = [a for a in arr if a is not None]
            if len(arr) == 0:
                out[name] = None
            else:
                out[name] = sum(arr) / len(arr)

        # set to be the first
        for name in [
            'blending_weights', 'neighbor_point_idxs', 'neighbor_point_valid_len',
            'model_attn_weights',
            'geometry_weights',
            'valid_neighbor_idx_mask',
        ]:
            arr = [getattr(r, name, None) for r in records]
            if len(arr) == 0:
                out[name] = None
            else:
                out[name] = arr[0]

        # sum -> normalize to unit norm
        for name in [
            'intersection_surface_normal_w',
            'intersection_plane_normals_w',
        ]:
            arr = [getattr(r, name, None) for r in records]
            arr = [a for a in arr if a is not None]
            if len(arr) == 0:
                out[name] = None
            else:
                out[name] = sum(arr)
                out[name] = torch.nn.functional.normalize(out[name], p=2, dim=-1)

        # set to be and
        for name in [
            'valid_plane_normal_mask'
        ]:
            arr = [getattr(r, name, None) for r in records]
            arr = [a for a in arr if a is not None]
            if len(arr) == 0:
                out[name] = None
            else:
                out[name] = arr[0]
                for i in range(1, len(arr)):
                    out[name] = torch.logical_and(out[name], arr[i])