def voxelize_temporal()

in datasets/transforms/voxelizer.py [0:0]

• 38 lines of code
• 13 McCabe index (conditional complexity)

  def voxelize_temporal(self,
                        coords_t,
                        feats_t,
                        labels_t,
                        centers=None,
                        return_transformation=False):
    # Legacy code, remove
    if centers is None:
      centers = [
          None,
      ] * len(coords_t)
    coords_tc, feats_tc, labels_tc, transformation_tc = [], [], [], []

    # ######################### Data Augmentation #############################
    # Get rotation and scale
    M_v, M_r = self.get_transformation_matrix()
    # Apply transformations
    rigid_transformation = M_v
    if self.use_augmentation:
      rigid_transformation = M_r @ rigid_transformation
    # ######################### Voxelization #############################
    # Voxelize coords
    for coords, feats, labels, center in zip(coords_t, feats_t, labels_t, centers):

      ###################################
      # Clip the data if bound exists
      if self.clip_bound is not None:
        trans_aug_ratio = np.zeros(3)
        if self.use_augmentation and self.translation_augmentation_ratio_bound is not None:
          for axis_ind, trans_ratio_bound in enumerate(self.translation_augmentation_ratio_bound):
            trans_aug_ratio[axis_ind] = np.random.uniform(*trans_ratio_bound)

        clip_inds = self.clip(coords, center, trans_aug_ratio)
        if clip_inds is not None:
          coords, feats = coords[clip_inds], feats[clip_inds]
          if labels is not None:
            labels = labels[clip_inds]
      ###################################

      homo_coords = np.hstack((coords, np.ones((coords.shape[0], 1), dtype=coords.dtype)))
      coords_aug = np.floor(homo_coords @ rigid_transformation.T)[:, :3]

      coords_aug, feats, labels = ME.utils.sparse_quantize(
          coords_aug, feats, labels=labels, ignore_label=self.ignore_label)

      coords_tc.append(coords_aug)
      feats_tc.append(feats)
      labels_tc.append(labels)
      transformation_tc.append(rigid_transformation.flatten())

    return_args = [coords_tc, feats_tc, labels_tc]
    if return_transformation:
      return_args.append(transformation_tc)

    return tuple(return_args)