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Added Pandaset autonomous driving dataset class and configuration file for RandLANet. (#611)
Fixup to earlier merge commit. Add dataset info (license, citation, website, download, etc.) Style fix
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‎ml3d/datasets/pandaset.py‎

Lines changed: 106 additions & 82 deletions
Original file line numberDiff line numberDiff line change
@@ -1,56 +1,81 @@
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import os
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from os.path import join
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import numpy as np
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import pandas as pd
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from pathlib import Path
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import logging
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import numpy as np
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import pandas as pd
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from .base_dataset import BaseDataset, BaseDatasetSplit
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from ..utils import make_dir, DATASET
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log = logging.getLogger(__name__)
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class Pandaset(BaseDataset):
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""" This class is used to create a dataset based on the Pandaset autonomous
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"""This class is used to create a dataset based on the Pandaset autonomous
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driving dataset.
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https://pandaset.org/
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The dataset includes 42 semantic classes and covers more than 100 scenes,
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each of which is 8 seconds long.
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PandaSet aims to promote and advance research and development in autonomous
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driving and machine learning. The first open-source AV dataset available for
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both academic and commercial use, PandaSet combines Hesai’s best-in-class
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LiDAR sensors with Scale AI’s high-quality data annotation.
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PandaSet features data collected using a forward-facing LiDAR with
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image-like resolution (PandarGT) as well as a mechanical spinning LiDAR
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(Pandar64). The collected data was annotated with a combination of cuboid
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and segmentation annotation (Scale 3D Sensor Fusion Segmentation).
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It features::
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- 48,000+ camera images
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- 16,000+ LiDAR sweeps
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- 100+ scenes of 8s each
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- 28 annotation classes
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- 37 semantic segmentation labels
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- Full sensor suite: 1x mechanical spinning LiDAR, 1x forward-facing LiDAR, 6x cameras, On-board GPS/IMU
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Website: https://pandaset.org/
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Code: https://github.com/scaleapi/pandaset-devkit
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Download: https://www.kaggle.com/datasets/usharengaraju/pandaset-dataset/data
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Data License: CC0: Public Domain (https://scale.com/legal/pandaset-terms-of-use)
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Citation: https://arxiv.org/abs/2112.12610
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"""
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def __init__(self,
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dataset_path,
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name="Pandaset",
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cache_dir="./logs/cache",
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use_cache=False,
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ignored_label_inds=[],
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test_result_folder='./logs/test_log',
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test_split=['115', '116', '117', '119', '120', '124', '139', '149', '158'],
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test_split=[
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'115', '116', '117', '119', '120', '124', '139', '149',
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'158'
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],
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training_split=[
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'001', '002', '003', '005', '011', '013', '015', '016',
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'017', '019', '021', '023', '024', '027', '028', '029',
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'030', '032', '033', '034', '035', '037', '038', '039',
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'040', '041', '042', '043', '044', '046', '052', '053',
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'054', '056', '057', '058', '064', '065', '066', '067',
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'070', '071', '072', '073', '077', '078', '080', '084',
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'088', '089', '090', '094', '095', '097', '098', '101',
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'102', '103', '105', '106', '109', '110', '112', '113'
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'001', '002', '003', '005', '011', '013', '015', '016',
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'017', '019', '021', '023', '024', '027', '028', '029',
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'030', '032', '033', '034', '035', '037', '038', '039',
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'040', '041', '042', '043', '044', '046', '052', '053',
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'054', '056', '057', '058', '064', '065', '066', '067',
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'070', '071', '072', '073', '077', '078', '080', '084',
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'088', '089', '090', '094', '095', '097', '098', '101',
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'102', '103', '105', '106', '109', '110', '112', '113'
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],
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validation_split=['122', '123'],
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all_split=[
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'001', '002', '003', '005', '011', '013', '015', '016',
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'017', '019', '021', '023', '024', '027', '028', '029',
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'030', '032', '033', '034', '035', '037', '038', '039',
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'040', '041', '042', '043', '044', '046', '052', '053',
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'054', '056', '057', '058', '064', '065', '066', '067',
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'069', '070', '071', '072', '073', '077', '078', '080',
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'084', '088', '089', '090', '094', '095', '097', '098',
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'101', '102', '103', '105', '106', '109', '110', '112',
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'113', '115', '116', '117', '119', '120', '122', '123',
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'124', '139', '149', '158'
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],
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validation_split=['122', '123'],
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all_split=['001', '002', '003', '005', '011', '013', '015', '016',
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'017', '019', '021', '023', '024', '027', '028', '029',
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'030', '032', '033', '034', '035', '037', '038', '039',
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'040', '041', '042', '043', '044', '046', '052', '053',
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'054', '056', '057', '058', '064', '065', '066', '067',
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'069', '070', '071', '072', '073', '077', '078', '080',
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'084', '088', '089', '090', '094', '095', '097', '098',
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'101', '102', '103', '105', '106', '109', '110', '112',
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'113', '115', '116', '117', '119', '120', '122', '123',
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'124', '139', '149', '158'],
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**kwargs):
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"""Initialize the function by passing the dataset and other details.
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Args:
@@ -79,7 +104,7 @@ def __init__(self,
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self.label_to_names = self.get_label_to_names()
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self.num_classes = len(self.label_to_names)
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self.label_values = np.sort([k for k, v in self.label_to_names.items()])
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@staticmethod
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def get_label_to_names():
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"""Returns a label to names dictionary object.
@@ -89,48 +114,48 @@ def get_label_to_names():
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values are the corresponding names.
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"""
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label_to_names = {
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1: "Reflection",
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2: "Vegetation",
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3: "Ground",
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4: "Road",
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5: "Lane Line Marking",
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6: "Stop Line Marking",
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7: "Other Road Marking",
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8: "Sidewalk",
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9: "Driveway",
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10: "Car",
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11: "Pickup Truck",
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12: "Medium-sized Truck",
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13: "Semi-truck",
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14: "Towed Object",
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15: "Motorcycle",
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16: "Other Vehicle - Construction Vehicle",
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17: "Other Vehicle - Uncommon",
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18: "Other Vehicle - Pedicab",
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19: "Emergency Vehicle",
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20: "Bus",
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21: "Personal Mobility Device",
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22: "Motorized Scooter",
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23: "Bicycle",
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24: "Train",
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25: "Trolley",
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26: "Tram / Subway",
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27: "Pedestrian",
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28: "Pedestrian with Object",
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29: "Animals - Bird",
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30: "Animals - Other",
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31: "Pylons",
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32: "Road Barriers",
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33: "Signs",
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34: "Cones",
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35: "Construction Signs",
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36: "Temporary Construction Barriers",
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37: "Rolling Containers",
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38: "Building",
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1: "Reflection",
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2: "Vegetation",
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3: "Ground",
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4: "Road",
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5: "Lane Line Marking",
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6: "Stop Line Marking",
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7: "Other Road Marking",
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8: "Sidewalk",
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9: "Driveway",
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10: "Car",
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11: "Pickup Truck",
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12: "Medium-sized Truck",
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13: "Semi-truck",
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14: "Towed Object",
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15: "Motorcycle",
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16: "Other Vehicle - Construction Vehicle",
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17: "Other Vehicle - Uncommon",
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18: "Other Vehicle - Pedicab",
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19: "Emergency Vehicle",
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20: "Bus",
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21: "Personal Mobility Device",
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22: "Motorized Scooter",
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23: "Bicycle",
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24: "Train",
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25: "Trolley",
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26: "Tram / Subway",
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27: "Pedestrian",
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28: "Pedestrian with Object",
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29: "Animals - Bird",
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30: "Animals - Other",
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31: "Pylons",
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32: "Road Barriers",
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33: "Signs",
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34: "Cones",
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35: "Construction Signs",
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36: "Temporary Construction Barriers",
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37: "Rolling Containers",
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38: "Building",
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39: "Other Static Object"
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}
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return label_to_names
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def get_split(self, split):
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"""Returns a dataset split.
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@@ -142,7 +167,7 @@ def get_split(self, split):
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A dataset split object providing the requested subset of the data.
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"""
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return PandasetSplit(self, split=split)
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def get_split_list(self, split):
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"""Returns the list of data splits available.
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A dataset split object providing the requested subset of the data.
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Raises:
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ValueError: Indicates that the split name passed is incorrect. The split name should be one of
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'training', 'test', 'validation', or 'all'.
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ValueError: Indicates that the split name passed is incorrect. The
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split name should be one of 'training', 'test', 'validation', or 'all'.
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"""
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cfg = self.cfg
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dataset_path = cfg.dataset_path
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file_list.append(join(pc_path, f))
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return file_list
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def is_tested(self, attr):
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"""Checks if a datum in the dataset has been tested.
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class PandasetSplit(BaseDatasetSplit):
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"""This class is used to create a split for Pandaset dataset.
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Args:
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Args:
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dataset: The dataset to split.
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split: A string identifying the dataset split that is usually one of
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'training', 'test', 'validation', or 'all'.
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Returns:
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A dataset split object providing the requested subset of the data.
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"""
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def __init__(self, dataset, split='train'):
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super().__init__(dataset, split=split)
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log.info("Found {} pointclouds for {}".format(len(self.path_list),
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def get_data(self, idx):
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pc_path = self.path_list[idx]
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label_path = pc_path.replace('lidar', 'annotations/semseg')
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points = pd.read_pickle(pc_path)
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labels = pd.read_pickle(label_path)
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intensity = points['i'].to_numpy().astype(np.float32)
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points = points.drop(columns=['i', 't', 'd']).to_numpy().astype(np.float32)
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points = points.drop(columns=['i', 't', 'd']).to_numpy().astype(
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np.float32)
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labels = labels.to_numpy().astype(np.int32)
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data = {
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'point': points,
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'intensity': intensity,
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'label': labels
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}
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data = {'point': points, 'intensity': intensity, 'label': labels}
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return data
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attr = {'name': name, 'path': pc_path, 'split': self.split}
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return attr
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DATASET._register_module(Pandaset)

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