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[@SulekBartek] Added Pandaset autonomous driving dataset class and co…
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…nfiguration file for RandLANet. (#611)

Fixup to earlier merge commit.
Add dataset info (license, citation, website, download, etc.)
Style fix

Author: SulekBartek
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@ssheorey
ssheorey committed Dec 22, 2023
1 parent 16f89cc commit a3ced7a
Showing 1 changed file with 106 additions and 82 deletions.
188 changes: 106 additions & 82 deletions ml3d/datasets/pandaset.py
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Original file line number Diff line number Diff line change
@@ -1,56 +1,81 @@
import os
from os.path import join
import numpy as np
import pandas as pd
from pathlib import Path
import logging
import numpy as np
import pandas as pd

from .base_dataset import BaseDataset, BaseDatasetSplit
from ..utils import make_dir, DATASET

log = logging.getLogger(__name__)


class Pandaset(BaseDataset):
""" This class is used to create a dataset based on the Pandaset autonomous
"""This class is used to create a dataset based on the Pandaset autonomous
driving dataset.
https://pandaset.org/
The dataset includes 42 semantic classes and covers more than 100 scenes,
each of which is 8 seconds long.
PandaSet aims to promote and advance research and development in autonomous
driving and machine learning. The first open-source AV dataset available for
both academic and commercial use, PandaSet combines Hesai’s best-in-class
LiDAR sensors with Scale AI’s high-quality data annotation.
PandaSet features data collected using a forward-facing LiDAR with
image-like resolution (PandarGT) as well as a mechanical spinning LiDAR
(Pandar64). The collected data was annotated with a combination of cuboid
and segmentation annotation (Scale 3D Sensor Fusion Segmentation).
It features::
- 48,000+ camera images
- 16,000+ LiDAR sweeps
- 100+ scenes of 8s each
- 28 annotation classes
- 37 semantic segmentation labels
- Full sensor suite: 1x mechanical spinning LiDAR, 1x forward-facing LiDAR, 6x cameras, On-board GPS/IMU
Website: https://pandaset.org/
Code: https://github.com/scaleapi/pandaset-devkit
Download: https://www.kaggle.com/datasets/usharengaraju/pandaset-dataset/data
Data License: CC0: Public Domain (https://scale.com/legal/pandaset-terms-of-use)
Citation: https://arxiv.org/abs/2112.12610
"""

def __init__(self,
dataset_path,
name="Pandaset",
cache_dir="./logs/cache",
use_cache=False,
ignored_label_inds=[],
test_result_folder='./logs/test_log',
test_split=['115', '116', '117', '119', '120', '124', '139', '149', '158'],
test_split=[
'115', '116', '117', '119', '120', '124', '139', '149',
'158'
],
training_split=[
'001', '002', '003', '005', '011', '013', '015', '016',
'017', '019', '021', '023', '024', '027', '028', '029',
'030', '032', '033', '034', '035', '037', '038', '039',
'040', '041', '042', '043', '044', '046', '052', '053',
'054', '056', '057', '058', '064', '065', '066', '067',
'070', '071', '072', '073', '077', '078', '080', '084',
'088', '089', '090', '094', '095', '097', '098', '101',
'102', '103', '105', '106', '109', '110', '112', '113'
'001', '002', '003', '005', '011', '013', '015', '016',
'017', '019', '021', '023', '024', '027', '028', '029',
'030', '032', '033', '034', '035', '037', '038', '039',
'040', '041', '042', '043', '044', '046', '052', '053',
'054', '056', '057', '058', '064', '065', '066', '067',
'070', '071', '072', '073', '077', '078', '080', '084',
'088', '089', '090', '094', '095', '097', '098', '101',
'102', '103', '105', '106', '109', '110', '112', '113'
],
validation_split=['122', '123'],
all_split=[
'001', '002', '003', '005', '011', '013', '015', '016',
'017', '019', '021', '023', '024', '027', '028', '029',
'030', '032', '033', '034', '035', '037', '038', '039',
'040', '041', '042', '043', '044', '046', '052', '053',
'054', '056', '057', '058', '064', '065', '066', '067',
'069', '070', '071', '072', '073', '077', '078', '080',
'084', '088', '089', '090', '094', '095', '097', '098',
'101', '102', '103', '105', '106', '109', '110', '112',
'113', '115', '116', '117', '119', '120', '122', '123',
'124', '139', '149', '158'
],
validation_split=['122', '123'],
all_split=['001', '002', '003', '005', '011', '013', '015', '016',
'017', '019', '021', '023', '024', '027', '028', '029',
'030', '032', '033', '034', '035', '037', '038', '039',
'040', '041', '042', '043', '044', '046', '052', '053',
'054', '056', '057', '058', '064', '065', '066', '067',
'069', '070', '071', '072', '073', '077', '078', '080',
'084', '088', '089', '090', '094', '095', '097', '098',
'101', '102', '103', '105', '106', '109', '110', '112',
'113', '115', '116', '117', '119', '120', '122', '123',
'124', '139', '149', '158'],
**kwargs):

"""Initialize the function by passing the dataset and other details.
Args:
Expand Down Expand Up @@ -79,7 +104,7 @@ def __init__(self,
self.label_to_names = self.get_label_to_names()
self.num_classes = len(self.label_to_names)
self.label_values = np.sort([k for k, v in self.label_to_names.items()])

@staticmethod
def get_label_to_names():
"""Returns a label to names dictionary object.
Expand All @@ -89,48 +114,48 @@ def get_label_to_names():
values are the corresponding names.
"""
label_to_names = {
1: "Reflection",
2: "Vegetation",
3: "Ground",
4: "Road",
5: "Lane Line Marking",
6: "Stop Line Marking",
7: "Other Road Marking",
8: "Sidewalk",
9: "Driveway",
10: "Car",
11: "Pickup Truck",
12: "Medium-sized Truck",
13: "Semi-truck",
14: "Towed Object",
15: "Motorcycle",
16: "Other Vehicle - Construction Vehicle",
17: "Other Vehicle - Uncommon",
18: "Other Vehicle - Pedicab",
19: "Emergency Vehicle",
20: "Bus",
21: "Personal Mobility Device",
22: "Motorized Scooter",
23: "Bicycle",
24: "Train",
25: "Trolley",
26: "Tram / Subway",
27: "Pedestrian",
28: "Pedestrian with Object",
29: "Animals - Bird",
30: "Animals - Other",
31: "Pylons",
32: "Road Barriers",
33: "Signs",
34: "Cones",
35: "Construction Signs",
36: "Temporary Construction Barriers",
37: "Rolling Containers",
38: "Building",
1: "Reflection",
2: "Vegetation",
3: "Ground",
4: "Road",
5: "Lane Line Marking",
6: "Stop Line Marking",
7: "Other Road Marking",
8: "Sidewalk",
9: "Driveway",
10: "Car",
11: "Pickup Truck",
12: "Medium-sized Truck",
13: "Semi-truck",
14: "Towed Object",
15: "Motorcycle",
16: "Other Vehicle - Construction Vehicle",
17: "Other Vehicle - Uncommon",
18: "Other Vehicle - Pedicab",
19: "Emergency Vehicle",
20: "Bus",
21: "Personal Mobility Device",
22: "Motorized Scooter",
23: "Bicycle",
24: "Train",
25: "Trolley",
26: "Tram / Subway",
27: "Pedestrian",
28: "Pedestrian with Object",
29: "Animals - Bird",
30: "Animals - Other",
31: "Pylons",
32: "Road Barriers",
33: "Signs",
34: "Cones",
35: "Construction Signs",
36: "Temporary Construction Barriers",
37: "Rolling Containers",
38: "Building",
39: "Other Static Object"
}
return label_to_names

def get_split(self, split):
"""Returns a dataset split.
Expand All @@ -142,7 +167,7 @@ def get_split(self, split):
A dataset split object providing the requested subset of the data.
"""
return PandasetSplit(self, split=split)

def get_split_list(self, split):
"""Returns the list of data splits available.
Expand All @@ -154,8 +179,8 @@ def get_split_list(self, split):
A dataset split object providing the requested subset of the data.
Raises:
ValueError: Indicates that the split name passed is incorrect. The split name should be one of
'training', 'test', 'validation', or 'all'.
ValueError: Indicates that the split name passed is incorrect. The
split name should be one of 'training', 'test', 'validation', or 'all'.
"""
cfg = self.cfg
dataset_path = cfg.dataset_path
Expand All @@ -179,7 +204,7 @@ def get_split_list(self, split):
file_list.append(join(pc_path, f))

return file_list

def is_tested(self, attr):
"""Checks if a datum in the dataset has been tested.
Expand Down Expand Up @@ -224,7 +249,7 @@ def save_test_result(self, results, attr):
class PandasetSplit(BaseDatasetSplit):
"""This class is used to create a split for Pandaset dataset.
Args:
Args:
dataset: The dataset to split.
split: A string identifying the dataset split that is usually one of
'training', 'test', 'validation', or 'all'.
Expand All @@ -233,6 +258,7 @@ class PandasetSplit(BaseDatasetSplit):
Returns:
A dataset split object providing the requested subset of the data.
"""

def __init__(self, dataset, split='train'):
super().__init__(dataset, split=split)
log.info("Found {} pointclouds for {}".format(len(self.path_list),
Expand All @@ -244,19 +270,16 @@ def __len__(self):
def get_data(self, idx):
pc_path = self.path_list[idx]
label_path = pc_path.replace('lidar', 'annotations/semseg')

points = pd.read_pickle(pc_path)
labels = pd.read_pickle(label_path)

intensity = points['i'].to_numpy().astype(np.float32)
points = points.drop(columns=['i', 't', 'd']).to_numpy().astype(np.float32)
points = points.drop(columns=['i', 't', 'd']).to_numpy().astype(
np.float32)
labels = labels.to_numpy().astype(np.int32)

data = {
'point': points,
'intensity': intensity,
'label': labels
}
data = {'point': points, 'intensity': intensity, 'label': labels}

return data

Expand All @@ -269,4 +292,5 @@ def get_attr(self, idx):
attr = {'name': name, 'path': pc_path, 'split': self.split}
return attr


DATASET._register_module(Pandaset)

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