UpDate README.md

autonomous_driving/Online-HD-Map-Construction-CVPR2023/README.md → autonomous_driving/Online-HD-Map-Construction/README.md

-<div id="top" align="center">
-
-# Online HD Map Construction Challenge For Autonomous Driving
- </div>
-
-
-
-If you need detaild information about the challenge, please refer to https://github.com/Tsinghua-MARS-Lab/Online-HD-Map-Construction-CVPR2023/tree/master
-#### 1. Requirements
-```bash
-python>=3.8
-torch==1.11 # recommend
-mmcv-full>=1.5.2
-mmdet==2.28.1
-mmsegmentation==0.29.1
-timm
-numpy==1.23.5
-mmdet3d==1.0.0rc6 # recommend
-```
-
-
-### 2. Install DCNv3 for InternImage
-```bash
-cd projects/ops_dcnv3
-bash make.sh # requires torch>=1.10
-```
-
-### 3. Train with InternImage-Small
-
-```bash
-bash tools/dist_train.sh src/configs/vectormapnet_intern.py ${NUM_GPUS}
-```
-
-Notes: InatenImage provides abundant pre-trained model weights that can be used!!!
-
-
-### 4. Performance compared to baseline
-
-model name|weight|$\mathrm{mAP}$ | $\mathrm{AP}_{pc}$ | $\mathrm{AP}_{div}$ | $\mathrm{AP}_{bound}$ | 
-----|:----------:| :--: | :--: | :--: | :--: | 
-vectormapnet_intern|[Checkpoint](https://github.com/OpenGVLab/InternImage/releases/download/track_model/vectormapnet_internimage.pth) | 49.35 | 45.05 | 56.78 | 46.22 | 
-vectormapnet_base|[Google Drive](https://drive.google.com/file/d/16D1CMinwA8PG1sd9PV9_WtHzcBohvO-D/view) | 42.79 | 37.22 | 50.47	 | 40.68 | 
-
-
-
-
-
-## Citation
-
-The evaluation metrics of this challenge follows [HDMapNet](https://arxiv.org/abs/2107.06307). We provide [VectorMapNet](https://arxiv.org/abs/2206.08920) as the baseline. Please cite:
-
-```
-@article{li2021hdmapnet,
-    title={HDMapNet: An Online HD Map Construction and Evaluation Framework},
-    author={Qi Li and Yue Wang and Yilun Wang and Hang Zhao},
-    journal={arXiv preprint arXiv:2107.06307},
-    year={2021}
-}
-```
-
-Our dataset is built on top of the [Argoverse 2](https://www.argoverse.org/av2.html) dataset. Please also cite:
-
-```
-@INPROCEEDINGS {Argoverse2,
-  author = {Benjamin Wilson and William Qi and Tanmay Agarwal and John Lambert and Jagjeet Singh and Siddhesh Khandelwal and Bowen Pan and Ratnesh Kumar and Andrew Hartnett and Jhony Kaesemodel Pontes and Deva Ramanan and Peter Carr and James Hays},
-  title = {Argoverse 2: Next Generation Datasets for Self-driving Perception and Forecasting},
-  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
-  year = {2021}
-}
-```
-
-
-## License
-
-Before participating in our challenge, you should register on the website and agree to the terms of use of the [Argoverse 2](https://www.argoverse.org/av2.html) dataset.
-All code in this project is released under [GNU General Public License v3.0](./LICENSE).
+<div id="top" align="center">
+
+# InternImage-based Baseline for Online HD Map Construction Challenge For Autonomous Driving
+ </div>
+
+
+
+If you need detaild information about the challenge, please refer to https://github.com/Tsinghua-MARS-Lab/Online-HD-Map-Construction-CVPR2023/tree/master
+#### 1. Requirements
+```bash
+python>=3.8
+torch==1.11 # recommend
+mmcv-full>=1.5.2
+mmdet==2.28.1
+mmsegmentation==0.29.1
+timm
+numpy==1.23.5
+mmdet3d==1.0.0rc6 # recommend
+```
+
+
+### 2. Install DCNv3 for InternImage
+```bash
+cd projects/ops_dcnv3
+bash make.sh # requires torch>=1.10
+```
+
+### 3. Train with InternImage-Small
+
+```bash
+bash tools/dist_train.sh src/configs/vectormapnet_intern.py ${NUM_GPUS}
+```
+
+Notes: InatenImage provides abundant pre-trained model weights that can be used!!!
+
+
+### 4. Performance compared to baseline
+
+model name|weight|$\mathrm{mAP}$ | $\mathrm{AP}_{pc}$ | $\mathrm{AP}_{div}$ | $\mathrm{AP}_{bound}$ | 
+----|:----------:| :--: | :--: | :--: | :--: | 
+vectormapnet_intern|[Checkpoint](https://github.com/OpenGVLab/InternImage/releases/download/track_model/vectormapnet_internimage.pth) | 49.35 | 45.05 | 56.78 | 46.22 | 
+vectormapnet_base|[Google Drive](https://drive.google.com/file/d/16D1CMinwA8PG1sd9PV9_WtHzcBohvO-D/view) | 42.79 | 37.22 | 50.47	 | 40.68 | 
+
+
+
+
+
+## Citation
+
+The evaluation metrics of this challenge follows [HDMapNet](https://arxiv.org/abs/2107.06307). We provide [VectorMapNet](https://arxiv.org/abs/2206.08920) as the baseline. Please cite:
+
+```
+@article{li2021hdmapnet,
+    title={HDMapNet: An Online HD Map Construction and Evaluation Framework},
+    author={Qi Li and Yue Wang and Yilun Wang and Hang Zhao},
+    journal={arXiv preprint arXiv:2107.06307},
+    year={2021}
+}
+```
+
+Our dataset is built on top of the [Argoverse 2](https://www.argoverse.org/av2.html) dataset. Please also cite:
+
+```
+@INPROCEEDINGS {Argoverse2,
+  author = {Benjamin Wilson and William Qi and Tanmay Agarwal and John Lambert and Jagjeet Singh and Siddhesh Khandelwal and Bowen Pan and Ratnesh Kumar and Andrew Hartnett and Jhony Kaesemodel Pontes and Deva Ramanan and Peter Carr and James Hays},
+  title = {Argoverse 2: Next Generation Datasets for Self-driving Perception and Forecasting},
+  booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks 2021)},
+  year = {2021}
+}
+```
+
+
+## License
+
+Before participating in our challenge, you should register on the website and agree to the terms of use of the [Argoverse 2](https://www.argoverse.org/av2.html) dataset.
+All code in this project is released under [GNU General Public License v3.0](./LICENSE).

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/__init__.py → autonomous_driving/Online-HD-Map-Construction/src/__init__.py

-from .models import *
+from .models import *
 from .datasets import *
\ No newline at end of file

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/coco_instance.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/coco_instance.py

-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_train2017.json',
-        img_prefix=data_root + 'train2017/',
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/instances_val2017.json',
-        img_prefix=data_root + 'val2017/',
-        pipeline=test_pipeline))
-evaluation = dict(metric=['bbox', 'segm'])
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/kitti-3d-3class.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/kitti-3d-3class.py

-# dataset settings
-dataset_type = 'KittiDataset'
-data_root = 'data/kitti/'
-class_names = ['Pedestrian', 'Cyclist', 'Car']
-point_cloud_range = [0, -40, -3, 70.4, 40, 1]
-input_modality = dict(use_lidar=True, use_camera=False)
-db_sampler = dict(
-    data_root=data_root,
-    info_path=data_root + 'kitti_dbinfos_train.pkl',
-    rate=1.0,
-    prepare=dict(
-        filter_by_difficulty=[-1],
-        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
-    classes=class_names,
-    sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6))
-
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
-
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=4,
-        use_dim=4,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=True,
-        with_label_3d=True,
-        file_client_args=file_client_args),
-    dict(type='ObjectSample', db_sampler=db_sampler),
-    dict(
-        type='ObjectNoise',
-        num_try=100,
-        translation_std=[1.0, 1.0, 0.5],
-        global_rot_range=[0.0, 0.0],
-        rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.78539816, 0.78539816],
-        scale_ratio_range=[0.95, 1.05]),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=4,
-        use_dim=4,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=4,
-        use_dim=4,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=2,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            ann_file=data_root + 'kitti_infos_train.pkl',
-            split='training',
-            pts_prefix='velodyne_reduced',
-            pipeline=train_pipeline,
-            modality=input_modality,
-            classes=class_names,
-            test_mode=False,
-            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-            box_type_3d='LiDAR')),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'kitti_infos_val.pkl',
-        split='training',
-        pts_prefix='velodyne_reduced',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'kitti_infos_val.pkl',
-        split='training',
-        pts_prefix='velodyne_reduced',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'))
-
-evaluation = dict(interval=1, pipeline=eval_pipeline)
+# dataset settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(
+        filter_by_difficulty=[-1],
+        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+    classes=class_names,
+    sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6))
+
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        file_client_args=file_client_args),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        translation_std=[1.0, 1.0, 0.5],
+        global_rot_range=[0.0, 0.0],
+        rot_range=[-0.78539816, 0.78539816]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'kitti_infos_train.pkl',
+            split='training',
+            pts_prefix='velodyne_reduced',
+            pipeline=train_pipeline,
+            modality=input_modality,
+            classes=class_names,
+            test_mode=False,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR')),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'))
+
+evaluation = dict(interval=1, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/kitti-3d-car.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/kitti-3d-car.py

-# dataset settings
-dataset_type = 'KittiDataset'
-data_root = 'data/kitti/'
-class_names = ['Car']
-point_cloud_range = [0, -40, -3, 70.4, 40, 1]
-input_modality = dict(use_lidar=True, use_camera=False)
-db_sampler = dict(
-    data_root=data_root,
-    info_path=data_root + 'kitti_dbinfos_train.pkl',
-    rate=1.0,
-    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
-    classes=class_names,
-    sample_groups=dict(Car=15))
-
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
-
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=4,
-        use_dim=4,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=True,
-        with_label_3d=True,
-        file_client_args=file_client_args),
-    dict(type='ObjectSample', db_sampler=db_sampler),
-    dict(
-        type='ObjectNoise',
-        num_try=100,
-        translation_std=[1.0, 1.0, 0.5],
-        global_rot_range=[0.0, 0.0],
-        rot_range=[-0.78539816, 0.78539816]),
-    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.78539816, 0.78539816],
-        scale_ratio_range=[0.95, 1.05]),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=4,
-        use_dim=4,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=4,
-        use_dim=4,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=6,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=2,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            ann_file=data_root + 'kitti_infos_train.pkl',
-            split='training',
-            pts_prefix='velodyne_reduced',
-            pipeline=train_pipeline,
-            modality=input_modality,
-            classes=class_names,
-            test_mode=False,
-            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-            box_type_3d='LiDAR')),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'kitti_infos_val.pkl',
-        split='training',
-        pts_prefix='velodyne_reduced',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'kitti_infos_val.pkl',
-        split='training',
-        pts_prefix='velodyne_reduced',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'))
-
-evaluation = dict(interval=1, pipeline=eval_pipeline)
+# dataset settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Car']
+point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
+    classes=class_names,
+    sample_groups=dict(Car=15))
+
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        file_client_args=file_client_args),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        translation_std=[1.0, 1.0, 0.5],
+        global_rot_range=[0.0, 0.0],
+        rot_range=[-0.78539816, 0.78539816]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'kitti_infos_train.pkl',
+            split='training',
+            pts_prefix='velodyne_reduced',
+            pipeline=train_pipeline,
+            modality=input_modality,
+            classes=class_names,
+            test_mode=False,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR')),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'kitti_infos_val.pkl',
+        split='training',
+        pts_prefix='velodyne_reduced',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'))
+
+evaluation = dict(interval=1, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/lyft-3d.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/lyft-3d.py

-# If point cloud range is changed, the models should also change their point
-# cloud range accordingly
-point_cloud_range = [-80, -80, -5, 80, 80, 3]
-# For Lyft we usually do 9-class detection
-class_names = [
-    'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
-    'bicycle', 'pedestrian', 'animal'
-]
-dataset_type = 'LyftDataset'
-data_root = 'data/lyft/'
-# Input modality for Lyft dataset, this is consistent with the submission
-# format which requires the information in input_modality.
-input_modality = dict(
-    use_lidar=True,
-    use_camera=False,
-    use_radar=False,
-    use_map=False,
-    use_external=False)
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel',
-#     path_mapping=dict({
-#         './data/lyft/': 's3://lyft/lyft/',
-#         'data/lyft/': 's3://lyft/lyft/'
-#    }))
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.3925, 0.3925],
-        scale_ratio_range=[0.95, 1.05],
-        translation_std=[0, 0, 0]),
-    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'lyft_infos_train.pkl',
-        pipeline=train_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=False),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'lyft_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=True),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'lyft_infos_test.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=True))
-# For Lyft dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-80, -80, -5, 80, 80, 3]
+# For Lyft we usually do 9-class detection
+class_names = [
+    'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
+    'bicycle', 'pedestrian', 'animal'
+]
+dataset_type = 'LyftDataset'
+data_root = 'data/lyft/'
+# Input modality for Lyft dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(
+    use_lidar=True,
+    use_camera=False,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/lyft/': 's3://lyft/lyft/',
+#         'data/lyft/': 's3://lyft/lyft/'
+#    }))
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'lyft_infos_train.pkl',
+        pipeline=train_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=False),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'lyft_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'lyft_infos_test.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=True))
+# For Lyft dataset, we usually evaluate the model at the end of training.
+# Since the models are trained by 24 epochs by default, we set evaluation
+# interval to be 24. Please change the interval accordingly if you do not
+# use a default schedule.
+evaluation = dict(interval=24, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/nuim_instance.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/nuim_instance.py

-dataset_type = 'CocoDataset'
-data_root = 'data/nuimages/'
-class_names = [
-    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
-    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
-]
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(
-        type='Resize',
-        img_scale=[(1280, 720), (1920, 1080)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1600, 900),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/nuimages_v1.0-train.json',
-        img_prefix=data_root,
-        classes=class_names,
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
-        img_prefix=data_root,
-        classes=class_names,
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
-        img_prefix=data_root,
-        classes=class_names,
-        pipeline=test_pipeline))
-evaluation = dict(metric=['bbox', 'segm'])
+dataset_type = 'CocoDataset'
+data_root = 'data/nuimages/'
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1280, 720), (1920, 1080)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1600, 900),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/nuimages_v1.0-train.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/nus-3d.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/nus-3d.py

-# If point cloud range is changed, the models should also change their point
-# cloud range accordingly
-point_cloud_range = [-50, -50, -5, 50, 50, 3]
-# For nuScenes we usually do 10-class detection
-class_names = [
-    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
-    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
-]
-dataset_type = 'NuScenesDataset'
-data_root = 'data/nuscenes/'
-# Input modality for nuScenes dataset, this is consistent with the submission
-# format which requires the information in input_modality.
-input_modality = dict(
-    use_lidar=True,
-    use_camera=False,
-    use_radar=False,
-    use_map=False,
-    use_external=False)
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel',
-#     path_mapping=dict({
-#         './data/nuscenes/': 's3://nuscenes/nuscenes/',
-#         'data/nuscenes/': 's3://nuscenes/nuscenes/'
-#     }))
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.3925, 0.3925],
-        scale_ratio_range=[0.95, 1.05],
-        translation_std=[0, 0, 0]),
-    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectNameFilter', classes=class_names),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_train.pkl',
-        pipeline=train_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=False,
-        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-        box_type_3d='LiDAR'),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=True,
-        box_type_3d='LiDAR'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=True,
-        box_type_3d='LiDAR'))
-# For nuScenes dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-50, -50, -5, 50, 50, 3]
+# For nuScenes we usually do 10-class detection
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+# Input modality for nuScenes dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(
+    use_lidar=True,
+    use_camera=False,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/nuscenes/': 's3://nuscenes/nuscenes/',
+#         'data/nuscenes/': 's3://nuscenes/nuscenes/'
+#     }))
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectNameFilter', classes=class_names),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_train.pkl',
+        pipeline=train_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=False,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='LiDAR'),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='LiDAR'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='LiDAR'))
+# For nuScenes dataset, we usually evaluate the model at the end of training.
+# Since the models are trained by 24 epochs by default, we set evaluation
+# interval to be 24. Please change the interval accordingly if you do not
+# use a default schedule.
+evaluation = dict(interval=24, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/nus-mono3d.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/nus-mono3d.py

-dataset_type = 'NuScenesMonoDataset'
-data_root = 'data/nuscenes/'
-class_names = [
-    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
-    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
-]
-# Input modality for nuScenes dataset, this is consistent with the submission
-# format which requires the information in input_modality.
-input_modality = dict(
-    use_lidar=False,
-    use_camera=True,
-    use_radar=False,
-    use_map=False,
-    use_external=False)
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFileMono3D'),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox=True,
-        with_label=True,
-        with_attr_label=True,
-        with_bbox_3d=True,
-        with_label_3d=True,
-        with_bbox_depth=True),
-    dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
-    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(
-        type='Collect3D',
-        keys=[
-            'img', 'gt_bboxes', 'gt_labels', 'attr_labels', 'gt_bboxes_3d',
-            'gt_labels_3d', 'centers2d', 'depths'
-        ]),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFileMono3D'),
-    dict(
-        type='MultiScaleFlipAug',
-        scale_factor=1.0,
-        flip=False,
-        transforms=[
-            dict(type='RandomFlip3D'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['img']),
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(type='LoadImageFromFileMono3D'),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['img'])
-]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_train_mono3d.coco.json',
-        img_prefix=data_root,
-        classes=class_names,
-        pipeline=train_pipeline,
-        modality=input_modality,
-        test_mode=False,
-        box_type_3d='Camera'),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
-        img_prefix=data_root,
-        classes=class_names,
-        pipeline=test_pipeline,
-        modality=input_modality,
-        test_mode=True,
-        box_type_3d='Camera'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
-        img_prefix=data_root,
-        classes=class_names,
-        pipeline=test_pipeline,
-        modality=input_modality,
-        test_mode=True,
-        box_type_3d='Camera'))
-evaluation = dict(interval=2)
+dataset_type = 'NuScenesMonoDataset'
+data_root = 'data/nuscenes/'
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+# Input modality for nuScenes dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(
+    use_lidar=False,
+    use_camera=True,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=True,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(
+        type='Collect3D',
+        keys=[
+            'img', 'gt_bboxes', 'gt_labels', 'attr_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers2d', 'depths'
+        ]),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=False,
+        transforms=[
+            dict(type='RandomFlip3D'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['img']),
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(type='LoadImageFromFileMono3D'),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['img'])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_train_mono3d.coco.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        box_type_3d='Camera'),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='Camera'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'nuscenes_infos_val_mono3d.coco.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        box_type_3d='Camera'))
+evaluation = dict(interval=2)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/range100_lyft-3d.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/range100_lyft-3d.py

-# If point cloud range is changed, the models should also change their point
-# cloud range accordingly
-point_cloud_range = [-100, -100, -5, 100, 100, 3]
-# For Lyft we usually do 9-class detection
-class_names = [
-    'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
-    'bicycle', 'pedestrian', 'animal'
-]
-dataset_type = 'LyftDataset'
-data_root = 'data/lyft/'
-# Input modality for Lyft dataset, this is consistent with the submission
-# format which requires the information in input_modality.
-input_modality = dict(
-    use_lidar=True,
-    use_camera=False,
-    use_radar=False,
-    use_map=False,
-    use_external=False)
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel',
-#     path_mapping=dict({
-#         './data/lyft/': 's3://lyft/lyft/',
-#         'data/lyft/': 's3://lyft/lyft/'
-#    }))
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.3925, 0.3925],
-        scale_ratio_range=[0.95, 1.05],
-        translation_std=[0, 0, 0]),
-    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadPointsFromMultiSweeps',
-        sweeps_num=10,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'lyft_infos_train.pkl',
-        pipeline=train_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=False),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'lyft_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=True),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'lyft_infos_test.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        modality=input_modality,
-        test_mode=True))
-# For Lyft dataset, we usually evaluate the model at the end of training.
-# Since the models are trained by 24 epochs by default, we set evaluation
-# interval to be 24. Please change the interval accordingly if you do not
-# use a default schedule.
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-100, -100, -5, 100, 100, 3]
+# For Lyft we usually do 9-class detection
+class_names = [
+    'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
+    'bicycle', 'pedestrian', 'animal'
+]
+dataset_type = 'LyftDataset'
+data_root = 'data/lyft/'
+# Input modality for Lyft dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(
+    use_lidar=True,
+    use_camera=False,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/lyft/': 's3://lyft/lyft/',
+#         'data/lyft/': 's3://lyft/lyft/'
+#    }))
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'lyft_infos_train.pkl',
+        pipeline=train_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=False),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'lyft_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'lyft_infos_test.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        modality=input_modality,
+        test_mode=True))
+# For Lyft dataset, we usually evaluate the model at the end of training.
+# Since the models are trained by 24 epochs by default, we set evaluation
+# interval to be 24. Please change the interval accordingly if you do not
+# use a default schedule.
+evaluation = dict(interval=24, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/s3dis_seg-3d-13class.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/s3dis_seg-3d-13class.py

-# dataset settings
-dataset_type = 'S3DISSegDataset'
-data_root = './data/s3dis/'
-class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
-               'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
-num_points = 4096
-train_area = [1, 2, 3, 4, 6]
-test_area = 5
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        use_color=True,
-        load_dim=6,
-        use_dim=[0, 1, 2, 3, 4, 5]),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=False,
-        with_label_3d=False,
-        with_mask_3d=False,
-        with_seg_3d=True),
-    dict(
-        type='PointSegClassMapping',
-        valid_cat_ids=tuple(range(len(class_names))),
-        max_cat_id=13),
-    dict(
-        type='IndoorPatchPointSample',
-        num_points=num_points,
-        block_size=1.0,
-        ignore_index=len(class_names),
-        use_normalized_coord=True,
-        enlarge_size=0.2,
-        min_unique_num=None),
-    dict(type='NormalizePointsColor', color_mean=None),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        use_color=True,
-        load_dim=6,
-        use_dim=[0, 1, 2, 3, 4, 5]),
-    dict(type='NormalizePointsColor', color_mean=None),
-    dict(
-        # a wrapper in order to successfully call test function
-        # actually we don't perform test-time-aug
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(
-                type='RandomFlip3D',
-                sync_2d=False,
-                flip_ratio_bev_horizontal=0.0,
-                flip_ratio_bev_vertical=0.0),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-# we need to load gt seg_mask!
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        use_color=True,
-        load_dim=6,
-        use_dim=[0, 1, 2, 3, 4, 5]),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=False,
-        with_label_3d=False,
-        with_mask_3d=False,
-        with_seg_3d=True),
-    dict(
-        type='PointSegClassMapping',
-        valid_cat_ids=tuple(range(len(class_names))),
-        max_cat_id=13),
-    dict(
-        type='DefaultFormatBundle3D',
-        with_label=False,
-        class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
-]
-
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=4,
-    # train on area 1, 2, 3, 4, 6
-    # test on area 5
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_files=[
-            data_root + f's3dis_infos_Area_{i}.pkl' for i in train_area
-        ],
-        pipeline=train_pipeline,
-        classes=class_names,
-        test_mode=False,
-        ignore_index=len(class_names),
-        scene_idxs=[
-            data_root + f'seg_info/Area_{i}_resampled_scene_idxs.npy'
-            for i in train_area
-        ]),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        ignore_index=len(class_names),
-        scene_idxs=data_root +
-        f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        ignore_index=len(class_names)))
-
-evaluation = dict(pipeline=eval_pipeline)
+# dataset settings
+dataset_type = 'S3DISSegDataset'
+data_root = './data/s3dis/'
+class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
+               'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
+num_points = 4096
+train_area = [1, 2, 3, 4, 6]
+test_area = 5
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True),
+    dict(
+        type='PointSegClassMapping',
+        valid_cat_ids=tuple(range(len(class_names))),
+        max_cat_id=13),
+    dict(
+        type='IndoorPatchPointSample',
+        num_points=num_points,
+        block_size=1.0,
+        ignore_index=len(class_names),
+        use_normalized_coord=True,
+        enlarge_size=0.2,
+        min_unique_num=None),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(
+        # a wrapper in order to successfully call test function
+        # actually we don't perform test-time-aug
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.0,
+                flip_ratio_bev_vertical=0.0),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+# we need to load gt seg_mask!
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True),
+    dict(
+        type='PointSegClassMapping',
+        valid_cat_ids=tuple(range(len(class_names))),
+        max_cat_id=13),
+    dict(
+        type='DefaultFormatBundle3D',
+        with_label=False,
+        class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    # train on area 1, 2, 3, 4, 6
+    # test on area 5
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_files=[
+            data_root + f's3dis_infos_Area_{i}.pkl' for i in train_area
+        ],
+        pipeline=train_pipeline,
+        classes=class_names,
+        test_mode=False,
+        ignore_index=len(class_names),
+        scene_idxs=[
+            data_root + f'seg_info/Area_{i}_resampled_scene_idxs.npy'
+            for i in train_area
+        ]),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        ignore_index=len(class_names),
+        scene_idxs=data_root +
+        f'seg_info/Area_{test_area}_resampled_scene_idxs.npy'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_files=data_root + f's3dis_infos_Area_{test_area}.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        ignore_index=len(class_names)))
+
+evaluation = dict(pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/scannet-3d-18class.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/scannet-3d-18class.py

-# dataset settings
-dataset_type = 'ScanNetDataset'
-data_root = './data/scannet/'
-class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
-               'bookshelf', 'picture', 'counter', 'desk', 'curtain',
-               'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
-               'garbagebin')
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=True,
-        load_dim=6,
-        use_dim=[0, 1, 2]),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=True,
-        with_label_3d=True,
-        with_mask_3d=True,
-        with_seg_3d=True),
-    dict(type='GlobalAlignment', rotation_axis=2),
-    dict(
-        type='PointSegClassMapping',
-        valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
-                       36, 39),
-        max_cat_id=40),
-    dict(type='IndoorPointSample', num_points=40000),
-    dict(
-        type='RandomFlip3D',
-        sync_2d=False,
-        flip_ratio_bev_horizontal=0.5,
-        flip_ratio_bev_vertical=0.5),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.087266, 0.087266],
-        scale_ratio_range=[1.0, 1.0],
-        shift_height=True),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(
-        type='Collect3D',
-        keys=[
-            'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
-            'pts_instance_mask'
-        ])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=True,
-        load_dim=6,
-        use_dim=[0, 1, 2]),
-    dict(type='GlobalAlignment', rotation_axis=2),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(
-                type='RandomFlip3D',
-                sync_2d=False,
-                flip_ratio_bev_horizontal=0.5,
-                flip_ratio_bev_vertical=0.5),
-            dict(type='IndoorPointSample', num_points=40000),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        load_dim=6,
-        use_dim=[0, 1, 2]),
-    dict(type='GlobalAlignment', rotation_axis=2),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=5,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            ann_file=data_root + 'scannet_infos_train.pkl',
-            pipeline=train_pipeline,
-            filter_empty_gt=False,
-            classes=class_names,
-            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-            box_type_3d='Depth')),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'scannet_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='Depth'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'scannet_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='Depth'))
-
-evaluation = dict(pipeline=eval_pipeline)
+# dataset settings
+dataset_type = 'ScanNetDataset'
+data_root = './data/scannet/'
+class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
+               'bookshelf', 'picture', 'counter', 'desk', 'curtain',
+               'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
+               'garbagebin')
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2]),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_mask_3d=True,
+        with_seg_3d=True),
+    dict(type='GlobalAlignment', rotation_axis=2),
+    dict(
+        type='PointSegClassMapping',
+        valid_cat_ids=(3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34,
+                       36, 39),
+        max_cat_id=40),
+    dict(type='IndoorPointSample', num_points=40000),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.087266, 0.087266],
+        scale_ratio_range=[1.0, 1.0],
+        shift_height=True),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(
+        type='Collect3D',
+        keys=[
+            'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
+            'pts_instance_mask'
+        ])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2]),
+    dict(type='GlobalAlignment', rotation_axis=2),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.5,
+                flip_ratio_bev_vertical=0.5),
+            dict(type='IndoorPointSample', num_points=40000),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        load_dim=6,
+        use_dim=[0, 1, 2]),
+    dict(type='GlobalAlignment', rotation_axis=2),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'scannet_infos_train.pkl',
+            pipeline=train_pipeline,
+            filter_empty_gt=False,
+            classes=class_names,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='Depth')),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'scannet_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='Depth'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'scannet_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='Depth'))
+
+evaluation = dict(pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/scannet_seg-3d-20class.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/scannet_seg-3d-20class.py

-# dataset settings
-dataset_type = 'ScanNetSegDataset'
-data_root = './data/scannet/'
-class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
-               'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
-               'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
-               'bathtub', 'otherfurniture')
-num_points = 8192
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        use_color=True,
-        load_dim=6,
-        use_dim=[0, 1, 2, 3, 4, 5]),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=False,
-        with_label_3d=False,
-        with_mask_3d=False,
-        with_seg_3d=True),
-    dict(
-        type='PointSegClassMapping',
-        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
-                       33, 34, 36, 39),
-        max_cat_id=40),
-    dict(
-        type='IndoorPatchPointSample',
-        num_points=num_points,
-        block_size=1.5,
-        ignore_index=len(class_names),
-        use_normalized_coord=False,
-        enlarge_size=0.2,
-        min_unique_num=None),
-    dict(type='NormalizePointsColor', color_mean=None),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        use_color=True,
-        load_dim=6,
-        use_dim=[0, 1, 2, 3, 4, 5]),
-    dict(type='NormalizePointsColor', color_mean=None),
-    dict(
-        # a wrapper in order to successfully call test function
-        # actually we don't perform test-time-aug
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(
-                type='RandomFlip3D',
-                sync_2d=False,
-                flip_ratio_bev_horizontal=0.0,
-                flip_ratio_bev_vertical=0.0),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-# we need to load gt seg_mask!
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        use_color=True,
-        load_dim=6,
-        use_dim=[0, 1, 2, 3, 4, 5]),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=False,
-        with_label_3d=False,
-        with_mask_3d=False,
-        with_seg_3d=True),
-    dict(
-        type='PointSegClassMapping',
-        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
-                       33, 34, 36, 39),
-        max_cat_id=40),
-    dict(
-        type='DefaultFormatBundle3D',
-        with_label=False,
-        class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
-]
-
-data = dict(
-    samples_per_gpu=8,
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'scannet_infos_train.pkl',
-        pipeline=train_pipeline,
-        classes=class_names,
-        test_mode=False,
-        ignore_index=len(class_names),
-        scene_idxs=data_root + 'seg_info/train_resampled_scene_idxs.npy'),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'scannet_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        ignore_index=len(class_names)),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'scannet_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        ignore_index=len(class_names)))
-
-evaluation = dict(pipeline=eval_pipeline)
+# dataset settings
+dataset_type = 'ScanNetSegDataset'
+data_root = './data/scannet/'
+class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
+               'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
+               'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
+               'bathtub', 'otherfurniture')
+num_points = 8192
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True),
+    dict(
+        type='PointSegClassMapping',
+        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
+                       33, 34, 36, 39),
+        max_cat_id=40),
+    dict(
+        type='IndoorPatchPointSample',
+        num_points=num_points,
+        block_size=1.5,
+        ignore_index=len(class_names),
+        use_normalized_coord=False,
+        enlarge_size=0.2,
+        min_unique_num=None),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(
+        # a wrapper in order to successfully call test function
+        # actually we don't perform test-time-aug
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.0,
+                flip_ratio_bev_vertical=0.0),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+# we need to load gt seg_mask!
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True),
+    dict(
+        type='PointSegClassMapping',
+        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
+                       33, 34, 36, 39),
+        max_cat_id=40),
+    dict(
+        type='DefaultFormatBundle3D',
+        with_label=False,
+        class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'scannet_infos_train.pkl',
+        pipeline=train_pipeline,
+        classes=class_names,
+        test_mode=False,
+        ignore_index=len(class_names),
+        scene_idxs=data_root + 'seg_info/train_resampled_scene_idxs.npy'),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'scannet_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        ignore_index=len(class_names)),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'scannet_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        ignore_index=len(class_names)))
+
+evaluation = dict(pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/sunrgbd-3d-10class.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/sunrgbd-3d-10class.py

-dataset_type = 'SUNRGBDDataset'
-data_root = 'data/sunrgbd/'
-class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
-               'night_stand', 'bookshelf', 'bathtub')
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=True,
-        load_dim=6,
-        use_dim=[0, 1, 2]),
-    dict(type='LoadAnnotations3D'),
-    dict(
-        type='RandomFlip3D',
-        sync_2d=False,
-        flip_ratio_bev_horizontal=0.5,
-    ),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.523599, 0.523599],
-        scale_ratio_range=[0.85, 1.15],
-        shift_height=True),
-    dict(type='IndoorPointSample', num_points=20000),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=True,
-        load_dim=6,
-        use_dim=[0, 1, 2]),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(
-                type='RandomFlip3D',
-                sync_2d=False,
-                flip_ratio_bev_horizontal=0.5,
-            ),
-            dict(type='IndoorPointSample', num_points=20000),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='DEPTH',
-        shift_height=False,
-        load_dim=6,
-        use_dim=[0, 1, 2]),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=16,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=5,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            ann_file=data_root + 'sunrgbd_infos_train.pkl',
-            pipeline=train_pipeline,
-            classes=class_names,
-            filter_empty_gt=False,
-            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-            box_type_3d='Depth')),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'sunrgbd_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='Depth'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'sunrgbd_infos_val.pkl',
-        pipeline=test_pipeline,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='Depth'))
-
-evaluation = dict(pipeline=eval_pipeline)
+dataset_type = 'SUNRGBDDataset'
+data_root = 'data/sunrgbd/'
+class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
+               'night_stand', 'bookshelf', 'bathtub')
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2]),
+    dict(type='LoadAnnotations3D'),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+    ),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.523599, 0.523599],
+        scale_ratio_range=[0.85, 1.15],
+        shift_height=True),
+    dict(type='IndoorPointSample', num_points=20000),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2]),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.5,
+            ),
+            dict(type='IndoorPointSample', num_points=20000),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        load_dim=6,
+        use_dim=[0, 1, 2]),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'sunrgbd_infos_train.pkl',
+            pipeline=train_pipeline,
+            classes=class_names,
+            filter_empty_gt=False,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='Depth')),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'sunrgbd_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='Depth'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'sunrgbd_infos_val.pkl',
+        pipeline=test_pipeline,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='Depth'))
+
+evaluation = dict(pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/waymoD5-3d-3class.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/waymoD5-3d-3class.py

-# dataset settings
-# D5 in the config name means the whole dataset is divided into 5 folds
-# We only use one fold for efficient experiments
-dataset_type = 'WaymoDataset'
-data_root = 'data/waymo/kitti_format/'
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel', path_mapping=dict(data='s3://waymo_data/'))
-
-class_names = ['Car', 'Pedestrian', 'Cyclist']
-point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
-input_modality = dict(use_lidar=True, use_camera=False)
-db_sampler = dict(
-    data_root=data_root,
-    info_path=data_root + 'waymo_dbinfos_train.pkl',
-    rate=1.0,
-    prepare=dict(
-        filter_by_difficulty=[-1],
-        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
-    classes=class_names,
-    sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
-    points_loader=dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=[0, 1, 2, 3, 4],
-        file_client_args=file_client_args))
-
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=6,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=True,
-        with_label_3d=True,
-        file_client_args=file_client_args),
-    dict(type='ObjectSample', db_sampler=db_sampler),
-    dict(
-        type='RandomFlip3D',
-        sync_2d=False,
-        flip_ratio_bev_horizontal=0.5,
-        flip_ratio_bev_vertical=0.5),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.78539816, 0.78539816],
-        scale_ratio_range=[0.95, 1.05]),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=6,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=6,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=2,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            ann_file=data_root + 'waymo_infos_train.pkl',
-            split='training',
-            pipeline=train_pipeline,
-            modality=input_modality,
-            classes=class_names,
-            test_mode=False,
-            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-            box_type_3d='LiDAR',
-            # load one frame every five frames
-            load_interval=5)),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'waymo_infos_val.pkl',
-        split='training',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'waymo_infos_val.pkl',
-        split='training',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'))
-
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+# dataset settings
+# D5 in the config name means the whole dataset is divided into 5 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel', path_mapping=dict(data='s3://waymo_data/'))
+
+class_names = ['Car', 'Pedestrian', 'Cyclist']
+point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'waymo_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(
+        filter_by_difficulty=[-1],
+        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+    classes=class_names,
+    sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=[0, 1, 2, 3, 4],
+        file_client_args=file_client_args))
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        file_client_args=file_client_args),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'waymo_infos_train.pkl',
+            split='training',
+            pipeline=train_pipeline,
+            modality=input_modality,
+            classes=class_names,
+            test_mode=False,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR',
+            # load one frame every five frames
+            load_interval=5)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'waymo_infos_val.pkl',
+        split='training',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'waymo_infos_val.pkl',
+        split='training',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'))
+
+evaluation = dict(interval=24, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/datasets/waymoD5-3d-car.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/datasets/waymoD5-3d-car.py

-# dataset settings
-# D5 in the config name means the whole dataset is divided into 5 folds
-# We only use one fold for efficient experiments
-dataset_type = 'WaymoDataset'
-data_root = 'data/waymo/kitti_format/'
-file_client_args = dict(backend='disk')
-# Uncomment the following if use ceph or other file clients.
-# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
-# for more details.
-# file_client_args = dict(
-#     backend='petrel', path_mapping=dict(data='s3://waymo_data/'))
-
-class_names = ['Car']
-point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
-input_modality = dict(use_lidar=True, use_camera=False)
-db_sampler = dict(
-    data_root=data_root,
-    info_path=data_root + 'waymo_dbinfos_train.pkl',
-    rate=1.0,
-    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
-    classes=class_names,
-    sample_groups=dict(Car=15),
-    points_loader=dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=5,
-        use_dim=[0, 1, 2, 3, 4],
-        file_client_args=file_client_args))
-
-train_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=6,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='LoadAnnotations3D',
-        with_bbox_3d=True,
-        with_label_3d=True,
-        file_client_args=file_client_args),
-    dict(type='ObjectSample', db_sampler=db_sampler),
-    dict(
-        type='RandomFlip3D',
-        sync_2d=False,
-        flip_ratio_bev_horizontal=0.5,
-        flip_ratio_bev_vertical=0.5),
-    dict(
-        type='GlobalRotScaleTrans',
-        rot_range=[-0.78539816, 0.78539816],
-        scale_ratio_range=[0.95, 1.05]),
-    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
-    dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
-]
-test_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=6,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='MultiScaleFlipAug3D',
-        img_scale=(1333, 800),
-        pts_scale_ratio=1,
-        flip=False,
-        transforms=[
-            dict(
-                type='GlobalRotScaleTrans',
-                rot_range=[0, 0],
-                scale_ratio_range=[1., 1.],
-                translation_std=[0, 0, 0]),
-            dict(type='RandomFlip3D'),
-            dict(
-                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
-]
-# construct a pipeline for data and gt loading in show function
-# please keep its loading function consistent with test_pipeline (e.g. client)
-eval_pipeline = [
-    dict(
-        type='LoadPointsFromFile',
-        coord_type='LIDAR',
-        load_dim=6,
-        use_dim=5,
-        file_client_args=file_client_args),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
-]
-
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=2,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            ann_file=data_root + 'waymo_infos_train.pkl',
-            split='training',
-            pipeline=train_pipeline,
-            modality=input_modality,
-            classes=class_names,
-            test_mode=False,
-            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
-            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
-            box_type_3d='LiDAR',
-            # load one frame every five frames
-            load_interval=5)),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'waymo_infos_val.pkl',
-        split='training',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        ann_file=data_root + 'waymo_infos_val.pkl',
-        split='training',
-        pipeline=test_pipeline,
-        modality=input_modality,
-        classes=class_names,
-        test_mode=True,
-        box_type_3d='LiDAR'))
-
-evaluation = dict(interval=24, pipeline=eval_pipeline)
+# dataset settings
+# D5 in the config name means the whole dataset is divided into 5 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+file_client_args = dict(backend='disk')
+# Uncomment the following if use ceph or other file clients.
+# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
+# for more details.
+# file_client_args = dict(
+#     backend='petrel', path_mapping=dict(data='s3://waymo_data/'))
+
+class_names = ['Car']
+point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'waymo_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
+    classes=class_names,
+    sample_groups=dict(Car=15),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=[0, 1, 2, 3, 4],
+        file_client_args=file_client_args))
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        file_client_args=file_client_args),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(type='RandomFlip3D'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(
+                type='DefaultFormatBundle3D',
+                class_names=class_names,
+                with_label=False),
+            dict(type='Collect3D', keys=['points'])
+        ])
+]
+# construct a pipeline for data and gt loading in show function
+# please keep its loading function consistent with test_pipeline (e.g. client)
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        file_client_args=file_client_args),
+    dict(
+        type='DefaultFormatBundle3D',
+        class_names=class_names,
+        with_label=False),
+    dict(type='Collect3D', keys=['points'])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file=data_root + 'waymo_infos_train.pkl',
+            split='training',
+            pipeline=train_pipeline,
+            modality=input_modality,
+            classes=class_names,
+            test_mode=False,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR',
+            # load one frame every five frames
+            load_interval=5)),
+    val=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'waymo_infos_val.pkl',
+        split='training',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'),
+    test=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=data_root + 'waymo_infos_val.pkl',
+        split='training',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        classes=class_names,
+        test_mode=True,
+        box_type_3d='LiDAR'))
+
+evaluation = dict(interval=24, pipeline=eval_pipeline)

autonomous_driving/Online-HD-Map-Construction-CVPR2023/src/configs/_base_/default_runtime.py → autonomous_driving/Online-HD-Map-Construction/src/configs/_base_/default_runtime.py

-checkpoint_config = dict(interval=1)
-# yapf:disable push
-# By default we use textlogger hook and tensorboard
-# For more loggers see
-# https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook
-log_config = dict(
-    interval=50,
-    hooks=[
-        dict(type='TextLoggerHook'),
-        dict(type='TensorboardLoggerHook')
-    ])
-# yapf:enable
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-work_dir = None
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
+checkpoint_config = dict(interval=1)
+# yapf:disable push
+# By default we use textlogger hook and tensorboard
+# For more loggers see
+# https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = None
+load_from = None
+resume_from = None
+workflow = [('train', 1)]