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Unverified Commit e943e84d authored by Sun Jiahao's avatar Sun Jiahao Committed by GitHub
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[Enhance] Format semantickitti label mapping (#2323) 

* fix polarmix UT

* format

* rename SemanticKittiDataset

* add platte & fix data info bug

* fix ut

* fix semantic_kitti ut

* fix ut label_map

* fix ut label_map

* add indication

* add indication

* fix typo
parent 0b42e351
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Showing with 144 additions and 212 deletions
configs/_base_/datasets/semantickitti.py
View file @ e943e84d
# dataset settings
dataset_type = 'SemanticKITTIDataset'
# For SemanticKitti we usually do 19-class segmentation.
# For labels_map we follow the uniform format of MMDetection & MMSegmentation
# i.e. we consider the unlabeled class as the last one, which is different
# from the original implementation of some methods e.g. Cylinder3D.
dataset_type = 'SemanticKittiDataset'
data_root = 'data/semantickitti/'
class_names = [
'unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person',
'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground',
'building', 'fence', 'vegetation', 'trunck', 'terrian', 'pole',
'traffic-sign'
'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist',
'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building',
'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'
]
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
labels_map = {
0: 0, # "unlabeled"
1: 0, # "outlier" mapped to "unlabeled" --------------mapped
10: 1, # "car"
11: 2, # "bicycle"
13: 5, # "bus" mapped to "other-vehicle" --------------mapped
15: 3, # "motorcycle"
16: 5, # "on-rails" mapped to "other-vehicle" ---------mapped
18: 4, # "truck"
20: 5, # "other-vehicle"
30: 6, # "person"
31: 7, # "bicyclist"
32: 8, # "motorcyclist"
40: 9, # "road"
44: 10, # "parking"
48: 11, # "sidewalk"
49: 12, # "other-ground"
50: 13, # "building"
51: 14, # "fence"
52: 0, # "other-structure" mapped to "unlabeled" ------mapped
60: 9, # "lane-marking" to "road" ---------------------mapped
70: 15, # "vegetation"
71: 16, # "trunk"
72: 17, # "terrain"
80: 18, # "pole"
81: 19, # "traffic-sign"
99: 0, # "other-object" to "unlabeled" ----------------mapped
252: 1, # "moving-car" to "car" ------------------------mapped
253: 7, # "moving-bicyclist" to "bicyclist" ------------mapped
254: 6, # "moving-person" to "person" ------------------mapped
255: 8, # "moving-motorcyclist" to "motorcyclist" ------mapped
256: 5, # "moving-on-rails" mapped to "other-vehic------mapped
257: 5, # "moving-bus" mapped to "other-vehicle" -------mapped
258: 4, # "moving-truck" to "truck" --------------------mapped
259: 5 # "moving-other"-vehicle to "other-vehicle"-----mapped
0: 19, # "unlabeled"
1: 19, # "outlier" mapped to "unlabeled" --------------mapped
10: 0, # "car"
11: 1, # "bicycle"
13: 4, # "bus" mapped to "other-vehicle" --------------mapped
15: 2, # "motorcycle"
16: 4, # "on-rails" mapped to "other-vehicle" ---------mapped
18: 3, # "truck"
20: 4, # "other-vehicle"
30: 5, # "person"
31: 6, # "bicyclist"
32: 7, # "motorcyclist"
40: 8, # "road"
44: 9, # "parking"
48: 10, # "sidewalk"
49: 11, # "other-ground"
50: 12, # "building"
51: 13, # "fence"
52: 19, # "other-structure" mapped to "unlabeled" ------mapped
60: 8, # "lane-marking" to "road" ---------------------mapped
70: 14, # "vegetation"
71: 15, # "trunk"
72: 16, # "terrain"
80: 17, # "pole"
81: 18, # "traffic-sign"
99: 19, # "other-object" to "unlabeled" ----------------mapped
252: 0, # "moving-car" to "car" ------------------------mapped
253: 6, # "moving-bicyclist" to "bicyclist" ------------mapped
254: 5, # "moving-person" to "person" ------------------mapped
255: 7, # "moving-motorcyclist" to "motorcyclist" ------mapped
256: 4, # "moving-on-rails" mapped to "other-vehic------mapped
257: 4, # "moving-bus" mapped to "other-vehicle" -------mapped
258: 3, # "moving-truck" to "truck" --------------------mapped
259: 4 # "moving-other"-vehicle to "other-vehicle"-----mapped
}
metainfo = dict(
classes=class_names,
palette=palette,
seg_label_mapping=labels_map,
max_label=259)
classes=class_names, seg_label_mapping=labels_map, max_label=259)
input_modality = dict(use_lidar=True, use_camera=False)
......@@ -99,7 +75,10 @@ train_pipeline = [
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_seg_3d=True,
seg_3d_dtype='np.int32',
seg_offset=2**16,
dataset_type='semantickitti',
backend_args=backend_args),
......@@ -126,7 +105,10 @@ test_pipeline = [
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_seg_3d=True,
seg_3d_dtype='np.int32',
seg_offset=2**16,
dataset_type='semantickitti',
backend_args=backend_args),
......@@ -144,7 +126,10 @@ eval_pipeline = [
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=False,
with_label_3d=False,
with_seg_3d=True,
seg_3d_dtype='np.int32',
seg_offset=2**16,
dataset_type='semantickitti',
backend_args=backend_args),
......@@ -153,7 +138,7 @@ eval_pipeline = [
]
train_dataloader = dict(
batch_size=4,
batch_size=2,
num_workers=4,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
......@@ -162,10 +147,11 @@ train_dataloader = dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='train_infos.pkl',
ann_file='semantickitti_infos_train.pkl',
pipeline=train_pipeline,
metainfo=metainfo,
modality=input_modality,
ignore_index=19,
backend_args=backend_args)),
)
......@@ -179,10 +165,11 @@ test_dataloader = dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='valid_infos.pkl',
ann_file='semantickitti_infos_val.pkl',
pipeline=test_pipeline,
metainfo=metainfo,
modality=input_modality,
ignore_index=19,
test_mode=True,
backend_args=backend_args)),
)
......@@ -191,3 +178,7 @@ val_dataloader = test_dataloader
val_evaluator = dict(type='SegMetric')
test_evaluator = val_evaluator
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
mmdet3d/datasets/__init__.py
View file @ e943e84d
......@@ -9,7 +9,7 @@ from .s3dis_dataset import S3DISDataset, S3DISSegDataset
from .scannet_dataset import (ScanNetDataset, ScanNetInstanceSegDataset,
ScanNetSegDataset)
from .seg3d_dataset import Seg3DDataset
from .semantickitti_dataset import SemanticKITTIDataset
from .semantickitti_dataset import SemanticKittiDataset
from .sunrgbd_dataset import SUNRGBDDataset
# yapf: disable
from .transforms import (AffineResize, BackgroundPointsFilter, GlobalAlignment,
......@@ -33,7 +33,7 @@ __all__ = [
'NormalizePointsColor', 'IndoorPatchPointSample', 'IndoorPointSample',
'PointSample', 'LoadAnnotations3D', 'GlobalAlignment', 'SUNRGBDDataset',
'ScanNetDataset', 'ScanNetSegDataset', 'ScanNetInstanceSegDataset',
'SemanticKITTIDataset', 'Det3DDataset', 'Seg3DDataset',
'SemanticKittiDataset', 'Det3DDataset', 'Seg3DDataset',
'LoadPointsFromMultiSweeps', 'WaymoDataset', 'BackgroundPointsFilter',
'VoxelBasedPointSampler', 'get_loading_pipeline', 'RandomDropPointsColor',
'RandomJitterPoints', 'ObjectNameFilter', 'AffineResize',
......
mmdet3d/datasets/seg3d_dataset.py
View file @ e943e84d
......@@ -256,6 +256,9 @@ class Seg3DDataset(BaseDataset):
self.data_prefix.get('pts', ''),
info['lidar_points']['lidar_path'])
info['num_pts_feats'] = info['lidar_points']['num_pts_feats']
info['lidar_path'] = info['lidar_points']['lidar_path']
if self.modality['use_camera']:
for cam_id, img_info in info['images'].items():
if 'img_path' in img_info:
......
mmdet3d/datasets/semantickitti_dataset.py
View file @ e943e84d
......@@ -8,8 +8,8 @@ from .seg3d_dataset import Seg3DDataset
@DATASETS.register_module()
class SemanticKITTIDataset(Seg3DDataset):
r"""SemanticKITTI Dataset.
class SemanticKittiDataset(Seg3DDataset):
r"""SemanticKitti Dataset.
This class serves as the API for experiments on the SemanticKITTI Dataset
Please refer to <http://www.semantic-kitti.org/dataset.html>`_
......@@ -44,14 +44,20 @@ class SemanticKITTIDataset(Seg3DDataset):
Defaults to False.
"""
METAINFO = {
'classes': ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck',
'bus', 'person', 'bicyclist', 'motorcyclist', 'road',
'parking', 'sidewalk', 'other-ground', 'building', 'fence',
'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'),
'classes': ('car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person',
'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk',
'other-ground', 'building', 'fence', 'vegetation',
'trunck', 'terrian', 'pole', 'traffic-sign'),
'palette': [[100, 150, 245], [100, 230, 245], [30, 60, 150],
[80, 30, 180], [100, 80, 250], [155, 30, 30],
[255, 40, 200], [150, 30, 90], [255, 0, 255],
[255, 150, 255], [75, 0, 75], [175, 0, 75], [255, 200, 0],
[255, 120, 50], [0, 175, 0], [135, 60, 0], [150, 240, 80],
[255, 240, 150], [255, 0, 0]],
'seg_valid_class_ids':
tuple(range(20)),
tuple(range(19)),
'seg_all_class_ids':
tuple(range(20)),
tuple(range(19)),
}
def __init__(self,
......@@ -59,7 +65,7 @@ class SemanticKITTIDataset(Seg3DDataset):
ann_file: str = '',
metainfo: Optional[dict] = None,
data_prefix: dict = dict(
pts='points',
pts='',
img='',
pts_instance_mask='',
pts_semantic_mask=''),
......@@ -83,7 +89,7 @@ class SemanticKITTIDataset(Seg3DDataset):
**kwargs)
def get_seg_label_mapping(self, metainfo):
seg_label_mapping = np.zeros(metainfo['max_label'] + 1)
seg_label_mapping = np.zeros(metainfo['max_label'] + 1, dtype=np.int64)
for idx in metainfo['seg_label_mapping']:
seg_label_mapping[idx] = metainfo['seg_label_mapping'][idx]
return seg_label_mapping
tests/test_datasets/test_semantickitti_dataset.py
View file @ e943e84d
......@@ -3,75 +3,53 @@ import unittest
import numpy as np
from mmdet3d.datasets import SemanticKITTIDataset
from mmdet3d.datasets import SemanticKittiDataset
from mmdet3d.utils import register_all_modules
def _generate_semantickitti_dataset_config():
data_root = './tests/data/semantickitti/'
ann_file = 'semantickitti_infos.pkl'
classes = ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus',
'person', 'bicyclist', 'motorcyclist', 'road', 'parking',
'sidewalk', 'other-ground', 'building', 'fence', 'vegetation',
'trunck', 'terrian', 'pole', 'traffic-sign')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
classes = ('car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person',
'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk',
'other-ground', 'building', 'fence', 'vegetation', 'trunck',
'terrian', 'pole', 'traffic-sign')
seg_label_mapping = {
0: 0, # "unlabeled"
1: 0, # "outlier" mapped to "unlabeled" --------------mapped
10: 1, # "car"
11: 2, # "bicycle"
13: 5, # "bus" mapped to "other-vehicle" --------------mapped
15: 3, # "motorcycle"
16: 5, # "on-rails" mapped to "other-vehicle" ---------mapped
18: 4, # "truck"
20: 5, # "other-vehicle"
30: 6, # "person"
31: 7, # "bicyclist"
32: 8, # "motorcyclist"
40: 9, # "road"
44: 10, # "parking"
48: 11, # "sidewalk"
49: 12, # "other-ground"
50: 13, # "building"
51: 14, # "fence"
52: 0, # "other-structure" mapped to "unlabeled" ------mapped
60: 9, # "lane-marking" to "road" ---------------------mapped
70: 15, # "vegetation"
71: 16, # "trunk"
72: 17, # "terrain"
80: 18, # "pole"
81: 19, # "traffic-sign"
99: 0, # "other-object" to "unlabeled" ----------------mapped
252: 1, # "moving-car" to "car" ------------------------mapped
253: 7, # "moving-bicyclist" to "bicyclist" ------------mapped
254: 6, # "moving-person" to "person" ------------------mapped
255: 8, # "moving-motorcyclist" to "motorcyclist" ------mapped
256: 5, # "moving-on-rails" mapped to "other-vehic------mapped
257: 5, # "moving-bus" mapped to "other-vehicle" -------mapped
258: 4, # "moving-truck" to "truck" --------------------mapped
259: 5 # "moving-other"-vehicle to "other-vehicle"-----mapped
0: 19, # "unlabeled"
1: 19, # "outlier" mapped to "unlabeled" --------------mapped
10: 0, # "car"
11: 1, # "bicycle"
13: 4, # "bus" mapped to "other-vehicle" --------------mapped
15: 2, # "motorcycle"
16: 4, # "on-rails" mapped to "other-vehicle" ---------mapped
18: 3, # "truck"
20: 4, # "other-vehicle"
30: 5, # "person"
31: 6, # "bicyclist"
32: 7, # "motorcyclist"
40: 8, # "road"
44: 9, # "parking"
48: 10, # "sidewalk"
49: 11, # "other-ground"
50: 12, # "building"
51: 13, # "fence"
52: 19, # "other-structure" mapped to "unlabeled" ------mapped
60: 8, # "lane-marking" to "road" ---------------------mapped
70: 14, # "vegetation"
71: 15, # "trunk"
72: 16, # "terrain"
80: 17, # "pole"
81: 18, # "traffic-sign"
99: 19, # "other-object" to "unlabeled" ----------------mapped
252: 0, # "moving-car" to "car" ------------------------mapped
253: 6, # "moving-bicyclist" to "bicyclist" ------------mapped
254: 5, # "moving-person" to "person" ------------------mapped
255: 7, # "moving-motorcyclist" to "motorcyclist" ------mapped
256: 4, # "moving-on-rails" mapped to "other-vehic------mapped
257: 4, # "moving-bus" mapped to "other-vehicle" -------mapped
258: 3, # "moving-truck" to "truck" --------------------mapped
259: 4 # "moving-other"-vehicle to "other-vehicle"-----mapped
}
max_label = 259
modality = dict(use_lidar=True, use_camera=False)
......@@ -96,25 +74,24 @@ def _generate_semantickitti_dataset_config():
data_prefix = dict(
pts='sequences/00/velodyne', pts_semantic_mask='sequences/00/labels')
return (data_root, ann_file, classes, palette, data_prefix, pipeline,
modality, seg_label_mapping, max_label)
return (data_root, ann_file, classes, data_prefix, pipeline, modality,
seg_label_mapping, max_label)
class TestSemanticKITTIDataset(unittest.TestCase):
class TestSemanticKittiDataset(unittest.TestCase):
def test_semantickitti(self):
(data_root, ann_file, classes, palette, data_prefix, pipeline,
modality, seg_label_mapping,
(data_root, ann_file, classes, data_prefix, pipeline, modality,
seg_label_mapping,
max_label) = _generate_semantickitti_dataset_config()
register_all_modules()
np.random.seed(0)
semantickitti_dataset = SemanticKITTIDataset(
semantickitti_dataset = SemanticKittiDataset(
data_root,
ann_file,
metainfo=dict(
classes=classes,
palette=palette,
seg_label_mapping=seg_label_mapping,
max_label=max_label),
data_prefix=data_prefix,
......@@ -129,10 +106,9 @@ class TestSemanticKITTIDataset(unittest.TestCase):
self.assertEqual(points.shape[0], pts_semantic_mask.shape[0])
expected_pts_semantic_mask = np.array([
13., 13., 13., 15., 15., 13., 0., 13., 15., 13., 13., 15., 16., 0.,
15., 13., 13., 13., 13., 0., 13., 13., 13., 13., 13., 15., 13.,
16., 13., 15., 15., 18., 13., 15., 15., 15., 16., 15., 13., 13.,
15., 13., 18., 15., 13., 15., 13., 15., 15., 13.
12, 12, 12, 14, 14, 12, 19, 12, 14, 12, 12, 14, 15, 19, 14, 12, 12,
12, 12, 19, 12, 12, 12, 12, 12, 14, 12, 15, 12, 14, 14, 17, 12, 14,
14, 14, 15, 14, 12, 12, 14, 12, 17, 14, 12, 14, 12, 14, 14, 12
])
self.assertTrue(
......
tests/test_datasets/test_transforms/test_transforms_3d.py
View file @ e943e84d
......@@ -7,7 +7,7 @@ import torch
from mmengine.testing import assert_allclose
from mmdet3d.datasets import (GlobalAlignment, RandomFlip3D,
SemanticKITTIDataset)
SemanticKittiDataset)
from mmdet3d.datasets.transforms import GlobalRotScaleTrans, LaserMix, PolarMix
from mmdet3d.structures import LiDARPoints
from mmdet3d.testing import create_data_info_after_loading
......@@ -124,32 +124,10 @@ class TestPolarMix(unittest.TestCase):
seg_3d_dtype='np.int32'),
dict(type='PointSegClassMapping'),
]
classes = ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus',
'person', 'bicyclist', 'motorcyclist', 'road', 'parking',
'sidewalk', 'other-ground', 'building', 'fence',
'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
classes = ('car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person',
'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk',
'other-ground', 'building', 'fence', 'vegetation', 'trunck',
'terrian', 'pole', 'traffic-sign')
seg_label_mapping = {
0: 0, # "unlabeled"
1: 0, # "outlier" mapped to "unlabeled" --------------mapped
......@@ -187,12 +165,11 @@ class TestPolarMix(unittest.TestCase):
259: 5 # "moving-other"-vehicle to "other-vehicle"-----mapped
}
max_label = 259
self.dataset = SemanticKITTIDataset(
self.dataset = SemanticKittiDataset(
'./tests/data/semantickitti/',
'semantickitti_infos.pkl',
metainfo=dict(
classes=classes,
palette=palette,
seg_label_mapping=seg_label_mapping,
max_label=max_label),
data_prefix=dict(
......@@ -242,32 +219,10 @@ class TestLaserMix(unittest.TestCase):
seg_3d_dtype='np.int32'),
dict(type='PointSegClassMapping'),
]
classes = ('unlabeled', 'car', 'bicycle', 'motorcycle', 'truck', 'bus',
'person', 'bicyclist', 'motorcyclist', 'road', 'parking',
'sidewalk', 'other-ground', 'building', 'fence',
'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign')
palette = [
[174, 199, 232],
[152, 223, 138],
[31, 119, 180],
[255, 187, 120],
[188, 189, 34],
[140, 86, 75],
[255, 152, 150],
[214, 39, 40],
[197, 176, 213],
[148, 103, 189],
[196, 156, 148],
[23, 190, 207],
[247, 182, 210],
[219, 219, 141],
[255, 127, 14],
[158, 218, 229],
[44, 160, 44],
[112, 128, 144],
[227, 119, 194],
[82, 84, 163],
]
classes = ('car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person',
'bicyclist', 'motorcyclist', 'road', 'parking', 'sidewalk',
'other-ground', 'building', 'fence', 'vegetation', 'trunck',
'terrian', 'pole', 'traffic-sign')
seg_label_mapping = {
0: 0, # "unlabeled"
1: 0, # "outlier" mapped to "unlabeled" --------------mapped
......@@ -305,12 +260,11 @@ class TestLaserMix(unittest.TestCase):
259: 5 # "moving-other"-vehicle to "other-vehicle"-----mapped
}
max_label = 259
self.dataset = SemanticKITTIDataset(
self.dataset = SemanticKittiDataset(
'./tests/data/semantickitti/',
'semantickitti_infos.pkl',
metainfo=dict(
classes=classes,
palette=palette,
seg_label_mapping=seg_label_mapping,
max_label=max_label),
data_prefix=dict(
......
tools/dataset_converters/semantickitti_converter.py
View file @ e943e84d
......@@ -62,7 +62,9 @@ def get_semantickitti_info(split):
'lidar_path':
osp.join('sequences',
str(i_folder).zfill(2), 'velodyne',
str(j).zfill(6) + '.bin')
str(j).zfill(6) + '.bin'),
'num_pts_feats':
4
},
'pts_semantic_mask_path':
osp.join('sequences',
......
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