l5kit.evaluation.csv_utils module

l5kit.evaluation.csv_utils.MAX_MODES = 3

These utilities write and read csv with ground-truth and prediction data. Both share the first two fields (timestamp and track_id) which are used to identify a single record.

GT: Single mode future prediction with availabilities (either 1->available or 0->unavailable). Header fields have these meanings: timestamp, track_id, avail_time_0, avail_time_1, …, coord_x_time_0, coord_y_time_0, …

PRED: Multi mode future prediction with confidence score (one value per mode up to MAX_MODES, sum to 1). Header fields have these meanings: timestamp, track_id, conf_mode_0, conf_mode_1, …, coord_x_time_0_mode_0, coord_y_time_0_mode_0, …, coord_x_time_0_mode_1, coord_y_time_0_mode_1, …

l5kit.evaluation.csv_utils.read_gt_csv(csv_path: str) → Iterator[dict]

Generator function that returns a line at a time from the csv file as a dict

Parameters

csv_path (str) – path of the csv to read

Returns

dict keys are the csv header fieldnames

Return type

Iterator[dict]

l5kit.evaluation.csv_utils.read_pred_csv(csv_path: str, max_modes: int = 3) → Iterator[dict]

Generator function that returns a line at the time from the csv file as a dict

Parameters

  • csv_path (str) – path of the csv to read

  • max_modes (int) – maximum number of predicted modes

Returns

dict keys are the csv header fieldnames

Return type

Iterator[dict]

l5kit.evaluation.csv_utils.write_gt_csv(csv_path: str, timestamps: numpy.ndarray, track_ids: numpy.ndarray, coords: numpy.ndarray, avails: numpy.ndarray) → None

Encode the ground truth into a csv file

Parameters

  • csv_path (str) – path to the csv to write

  • timestamps (np.ndarray) – (num_example,) frame timestamps

  • track_ids (np.ndarray) – (num_example,) agent ids

  • coords (np.ndarray) – (num_example x future_len x num_coords) meters displacements

  • avails (np.ndarray) – (num_example x future_len) array with value 0 (discard in eval) or 1 (keep in eval)

Returns:

l5kit.evaluation.csv_utils.write_pred_csv(csv_path: str, timestamps: numpy.ndarray, track_ids: numpy.ndarray, coords: numpy.ndarray, confs: Optional[numpy.ndarray] = None, max_modes: int = 3) → None

Encode the predictions into a csv file. Coords can have an additional axis for multi-mode. We handle up to max_modes modes. For the uni-modal case (i.e. all predictions have just a single mode), coords should not have the additional axis and confs should be set to None. In this case, a single mode with confidence 1 will be written.

Parameters

  • csv_path (str) – path to the csv to write

  • timestamps (np.ndarray) – (num_example,) frame timestamps

  • track_ids (np.ndarray) – (num_example,) agent ids

  • coords (np.ndarray) – (num_example x (modes) x future_len x num_coords) meters displacements

  • confs (Optional[np.ndarray]) – (num_example x modes) confidence of each modes in each example.

  • 1 (Rows should sum to) –

  • max_modes (int) – maximum number of predicted modes

Returns: