parameters: (input_sensor_dict)
return_statement: return dataset_processor.prepare_data(data_dict={'points': points_all})
docstring: Converts raw input evaluation data into a dictionary that can be fed into the OpenPCDet model.
  
    Args:
      input_sensor_dict: dictionary mapping string input data field name to a
      numpy array of the data.
  
    Returns:
      Dict with pre-processed input that can be passed to the model.
docstring_summary: Converts raw input evaluation data into a dictionary that can be fed into the OpenPCDet model.
docstring_tokens: Converts raw input evaluation data into a dictionary that can be fed into the OpenPCDet model .
function: def _process_inputs(input_sensor_dict):
    """Converts raw input evaluation data into a dictionary that can be fed into the OpenPCDet model.
  
    Args:
      input_sensor_dict: dictionary mapping string input data field name to a
      numpy array of the data.
  
    Returns:
      Dict with pre-processed input that can be passed to the model.
    """
    points = []
    for field in DATA_FIELDS:
      # H x W x 6
      lidar_range_image = input_sensor_dict[field]
      # Flatten and select all points with positive range.
      lidar_range_image = lidar_range_image[lidar_range_image[..., 0] > 0, :]
      # Also transform last dimension from
      #   (range, intensity, elongation, x, y, z) ->
      #   (x, y, z, intensity, elongation)
      points.append(lidar_range_image[:, [3, 4, 5, 1, 2]])
  
    # Concatenate points from all lidars.
    points_all = np.concatenate(points, axis=0).astype(np.float32)
    points_all[:, 3] = np.tanh(points_all[:, 3])
  
    return dataset_processor.prepare_data(data_dict={'points': points_all})
function_tokens: def _process_inputs ( input_sensor_dict ) : points = [ ] for field in DATA_FIELDS : # H x W x 6 lidar_range_image = input_sensor_dict [ field ] # Flatten and select all points with positive range. lidar_range_image = lidar_range_image [ lidar_range_image [ ... , 0 ] > 0 , : ] # Also transform last dimension from #   (range, intensity, elongation, x, y, z) -> #   (x, y, z, intensity, elongation) points . append ( lidar_range_image [ : , [ 3 , 4 , 5 , 1 , 2 ] ] ) # Concatenate points from all lidars. points_all = np . concatenate ( points , axis = 0 ) . astype ( np . float32 ) points_all [ : , 3 ] = np . tanh ( points_all [ : , 3 ] ) return dataset_processor . prepare_data ( data_dict = { 'points' : points_all } )