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* Update waymo.rst * Update waymo.rst * allow generate all data * update readme * update * better logging info * more info * up * fix * add note on GPU * better log * format
97 lines
3.5 KiB
ReStructuredText
97 lines
3.5 KiB
ReStructuredText
#############################
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Waymo
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#############################
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| Website: https://waymo.com/open/
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| Download: https://waymo.com/open/download/
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| Paper: https://arxiv.org/abs/2104.10133
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The dataset includes:
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- 103,354, 20s 10Hz segments (over 20 million frames), mined for interesting interactions
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- 574 hours of data
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- Sensor data
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- 4 short-range lidars
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- 1 mid-range lidar
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- Object data
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- 10.8M objects with tracking IDs
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- Labels for 3 object classes - Vehicles, Pedestrians, Cyclists
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- 3D bounding boxes for each object
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- Mined for interesting behaviors and scenarios for behavior prediction research, such as unprotected turns, merges, lane changes, and intersections
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- 3D bounding boxes are generated by a model trained on the Perception Dataset and detailed in our paper: Offboard 3D Object Detection from Point Cloud Sequences
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Map data
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- 3D map data for each segment
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- Locations include: San Francisco, Phoenix, Mountain View, Los Angeles, Detroit, and Seattle
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- Added entrances to driveways (the map already Includes lane centers, lane boundaries, road boundaries, crosswalks, speed bumps and stop signs)
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- Adjusted some road edge boundary height estimates
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1. Install Requirements
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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First of all, we have to install tensorflow and Protobuf::
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pip install tensorflow==2.11.0
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conda install protobuf==3.20
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.. note::
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You may fail to install ``protobuf`` if using ``pip install protobuf==3.20``. If so, install via ``conda install protobuf=3.20``.
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2. Download TFRecord
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Waymo motion dataset is at `Google Cloud <https://console.cloud.google.com/storage/browser/waymo_open_dataset_motion_v_1_2_0>`_.
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For downloading all datasets, ``gsutil`` is required.
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The installation tutorial is at https://cloud.google.com/storage/docs/gsutil_install.
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Login you google account via::
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gcloud init
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After this, you can access all data and download them to current directory ``./`` by (don't forget the dot!)::
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gsutil -m cp -r "gs://waymo_open_dataset_motion_v_1_2_0/uncompressed/scenario" .
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Or one just can download a part of the dataset using command like::
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gsutil -m cp -r "gs://waymo_open_dataset_motion_v_1_2_0/uncompressed/scenario/training_20s" .
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The downloaded data should be stored in a directory like this::
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waymo
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├── training_20s/
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| ├── training_20s.tfrecord-00000-of-01000
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| ├── training_20s.tfrecord-00001-of-01000
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| └── ...
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├── validation/
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| ├── validation.tfrecord-00000-of-00150
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| ├── validation.tfrecord-00001-of-00150
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| └── ...
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└── testing/
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├── testing.tfrecord-00000-of-00150
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├── testing.tfrecord-00001-of-00150
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└── ...
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3. Build Waymo Database
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Run the following command to extract scenarios in any directory containing ``tfrecord``.
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Here we take converting raw data in ``training_20s`` as an example::
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python -m scenarionet.convert_waymo -d /path/to/your/database --raw_data_path ./waymo/training_20s --num_workers 64
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Now all converted scenarios will be placed at ``/path/to/your/database`` and are ready to be used in your work.
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.. note::
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When running the conversion, please double check whether GPU is being used. This converter should NOT use GPU.
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We have disable GPU usage by ``os.environ["CUDA_VISIBLE_DEVICES"] = ""``.
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Known Issues: Waymo
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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N/A
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