d3d.dataset.waymo

class d3d.dataset.waymo.loader.WaymoLoader(base_path, phase='training', inzip=False, trainval_split=None, trainval_random=False, nframes=0)

Bases: d3d.dataset.base.TrackingDatasetBase

Load Waymo dataset into a usable format. Please use the d3d_waymo_convert command to convert the dataset first into following formats

• Directory Structure:

  - <base_path directory>
      - training
          - xxxxxxxxxxxxxxxxxxxx_xxx_xxx_xxx_xxx(.zip)
          - ...
      - validation
          - xxxxxxxxxxxxxxxxxxxx_xxx_xxx_xxx_xxx(.zip)
          - ...
  

For description of constructor parameters, please refer to d3d.dataset.base.TrackingDatasetBase

Parameters

• base_path – directory containing the zip files, or the required data

• inzip – whether the dataset is store in original zip archives or unzipped

• phase – training, validation or testing

• trainval_split – the ratio to split training dataset. See documentation of split_trainval_seq() for detail.

• trainval_random – whether select the train/val split randomly. See documentation of split_trainval_seq() for detail.

• nframes –

  number of consecutive frames returned from the accessors

  • If it’s a positive number, then it returns adjacent frames with total number reduced

  • If it’s a negative number, absolute value of it is consumed

  • If it’s zero, then it act like object detection dataset, which means the methods will return unpacked data

• trainval_byseq – Whether split trainval partitions by sequences instead of frames

VALID_OBJ_CLASSES

alias of d3d.dataset.waymo.loader.WaymoObjectClass

annotation_3dobject(idx, raw=False)

Return list of converted ground truth targets in lidar frame.

Parameters

• idx – index of requested frame

• raw – if false, targets will be converted to d3d d3d.abstraction.Target3DArray format, otherwise raw data will be returned in original format.

calibration_data(idx)

Return the calibration data. Notices that we assume the calibration is fixed among one squence, so it always return a single object.

Parameters

• idx – index of requested lidar frames

• raw – If false, converted d3d.abstraction.TransformSet will be returned, otherwise raw data will be returned in original format

camera_data(idx, names=None)

Parameters

names – frame names of camera to be loaded

dump_detection_output(idx, detections, fout)

Parameters

• detections (d3d.abstraction.Target3DArray) – detection result

• ids – auxiliary information for output, each item contains context name and timestamp

• fout (io.RawIOBase) – output file-like object

• idx (Union[int, tuple]) –

Return type

None

identity(idx)

Return something that can track the data back to original dataset

Parameters

idx – index of requested frame to be parsed

Returns

if nframes > 0, then the function return a list of ids which are consistent with other functions.

lidar_data(idx, names=None, formatted=False)

If multiple frames are requested, the results will be a list of list. Outer list corresponds to frame names and inner list corresponds to time sequence. So len(names) × len(frames) data objects will be returned

Parameters

• names – name of requested lidar sensors. The default frame is the first element in VALID_LIDAR_NAMES.

• idx – index of requested lidar frames

• formatted –

  if true, the point cloud wrapped in a numpy record array will be returned

  • If single index is given, then the frame indexing is done on the whole dataset with trainval split

  • If a tuple is given, it’s considered to be a unique id of the frame (from identity() method), trainval split is ignored in this way and nframes offset is not added

pose(idx, raw=False)

Return (relative) pose of the vehicle for the frame. The base frame should be ground attached which means the base frame will follow a East-North-Up axis order.

Parameters

• idx – index of requested frame

• names – specify the sensor whose pose is requested. This option only make sense when the dataset contains separate timestamps for data from each sensor. In this case, the pose either comes from dataset, or from interpolation.

• raw – if false, targets will be converted to d3d d3d.abstraction.EgoPose format, otherwise raw data will be returned in original format.

property pose_name

Return the sensor frame name whose coordinate the pose is reported in. This frame can be different from the default frame in the calibration TransformSet.

property sequence_ids

Return the list of sequence ids

property sequence_sizes

Return the mapping from sequence id to sequence sizes

timestamp(idx)

Return the timestamp of frame specified by the index, represented by Unix timestamp in macroseconds (usually 16 digits integer)

Parameters

• idx – index of requested frame

• names – specify the sensor whose pose is requested. This option only make sense when the dataset contains separate timestamps for data from each sensor.

d3d.dataset.waymo.loader.create_submission(result_path, output_file, exec_path, meta_path, model_name=None)

Execute create_submission from waymo_open_dataset :param exec_path: path to create_submission executable from waymo devkit :param result_path: path (or list of path) to detection result in binary protobuf from dump_detection_output :param meta_path: path to the metadata file (example: waymo_open_dataset/metrics/tools/submission.txtpb) :param output_path: output path for the created submission archive

d3d.dataset.waymo.loader.execute_official_evaluator(exec_path, label_path, result_path, output_path, model_name=None, show_output=True)

Execute compute_detection_metrics_main from waymo_open_dataset :param exec_path: path to compute_detection_metrics_main

class d3d.dataset.waymo.loader.WaymoObjectClass(value)

Bases: enum.Enum

Category of objects in KITTI dataset

Cyclist = 4

Pedestrian = 2

Sign = 3

Unknown = 0

Vehicle = 1