def __getitem__()

in downstream/semseg/lib/dataset.py [0:0]

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

  def __getitem__(self, index):
    coords, feats, labels, center = self.load_ply(index)
    # Downsample the pointcloud with finer voxel size before transformation for memory and speed
    if self.PREVOXELIZATION_VOXEL_SIZE is not None:
      inds = ME.utils.sparse_quantize(
          coords / self.PREVOXELIZATION_VOXEL_SIZE, return_index=True)
      coords = coords[inds]
      feats = feats[inds]
      labels = labels[inds]

    # Prevoxel transformations
    if self.prevoxel_transform is not None:
      coords, feats, labels = self.prevoxel_transform(coords, feats, labels)

    coords, feats, labels, transformation = self.voxelizer.voxelize(
        coords, feats, labels, center=center)

    # map labels not used for evaluation to ignore_label
    if self.input_transform is not None:
      coords, feats, labels = self.input_transform(coords, feats, labels)
    if self.target_transform is not None:
      coords, feats, labels = self.target_transform(coords, feats, labels)
    if self.IGNORE_LABELS is not None:
      labels = np.array([self.label_map[x] for x in labels], dtype=np.int)

    # Use coordinate features if config is set
    if self.AUGMENT_COORDS_TO_FEATS:
      coords, feats, labels = self._augment_coords_to_feats(coords, feats, labels)

    return_args = [coords, feats, labels]
    if self.return_transformation:
      return_args.append(transformation.astype(np.float32))

    return tuple(return_args)