Bump version to v1.1.0rc2

Bump to v1.1.0rc2

mmdet3d/structures/bbox_3d/base_box3d.py

@@ -360,7 +360,7 @@ class BaseInstance3DBoxes(object):
             with_yaw=boxes_list[0].with_yaw)
         return cat_boxes
 
-    def to(self, device):
+    def to(self, device, *args, **kwargs):
         """Convert current boxes to a specific device.
 
         Args:
@@ -372,7 +372,7 @@ class BaseInstance3DBoxes(object):
         """
         original_type = type(self)
         return original_type(
-            self.tensor.to(device),
+            self.tensor.to(device, *args, **kwargs),
             box_dim=self.box_dim,
             with_yaw=self.with_yaw)
 

mmdet3d/structures/det3d_data_sample.py

@@ -2,9 +2,9 @@
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
+from mmdet.structures import DetDataSample
 from mmengine.structures import InstanceData
 
-from mmdet.structures import DetDataSample
 from .point_data import PointData
 
 

mmdet3d/structures/ops/iou3d_calculator.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import torch
+from mmdet.structures.bbox import bbox_overlaps
 
 from mmdet3d.registry import TASK_UTILS
 from mmdet3d.structures.bbox_3d import get_box_type
-from mmdet.structures.bbox import bbox_overlaps
 
 
 @TASK_UTILS.register_module()

mmdet3d/testing/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.
+from .data_utils import (create_data_info_after_loading,
+                         create_dummy_data_info,
+                         create_mono3d_data_info_after_loading)
+from .model_utils import (create_detector_inputs, get_detector_cfg,
+                          get_model_cfg, setup_seed)
+
+__all__ = [
+    'create_dummy_data_info', 'create_data_info_after_loading',
+    'create_mono3d_data_info_after_loading', 'create_detector_inputs',
+    'get_detector_cfg', 'get_model_cfg', 'setup_seed'
+]

tests/utils/data_utils.py → mmdet3d/testing/data_utils.py

@@ -3,6 +3,7 @@ import numpy as np
 
 # create a dummy `results` to test the pipeline
 from mmdet3d.datasets import LoadAnnotations3D, LoadPointsFromFile
+from mmdet3d.datasets.transforms.loading import LoadImageFromFileMono3D
 from mmdet3d.structures import LiDARInstance3DBoxes
 
 
@@ -20,6 +21,10 @@ def create_dummy_data_info(with_ann=True):
                   -1.5808]])),
         'gt_labels_3d':
         np.array([1]),
+        'centers_2d':
+        np.array([[765.04, 214.56]]),
+        'depths':
+        np.array([8.410]),
         'num_lidar_pts':
         np.array([377]),
         'difficulty':
@@ -134,6 +139,9 @@ def create_dummy_data_info(with_ann=True):
             ],
             'bbox_label_3d':
             -1,
+            'center_2d': [765.04, 214.56],
+            'depth':
+            8.410,
             'num_lidar_pts':
             377,
             'difficulty':
@@ -168,3 +176,17 @@ def create_data_info_after_loading():
     data_info = load_points_transform(data_info)
     data_info_after_loading = load_anns_transform(data_info)
     return data_info_after_loading
+
+
+def create_mono3d_data_info_after_loading():
+    load_anns_transform = LoadAnnotations3D(
+        with_bbox=True,
+        with_label=True,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True)
+    load_img_transform = LoadImageFromFileMono3D()
+    data_info = create_dummy_data_info()
+    data_info = load_img_transform(data_info)
+    data_info_after_loading = load_anns_transform(data_info)
+    return data_info_after_loading

tests/utils/model_utils.py → mmdet3d/testing/model_utils.py

@@ -12,7 +12,7 @@ from mmdet3d.structures import (CameraInstance3DBoxes, DepthInstance3DBoxes,
                                 PointData)
 
 
-def _setup_seed(seed):
+def setup_seed(seed):
     torch.manual_seed(seed)
     torch.cuda.manual_seed_all(seed)
     np.random.seed(seed)
@@ -44,7 +44,7 @@ def _get_config_module(fname):
     return config_mod
 
 
-def _get_model_cfg(fname):
+def get_model_cfg(fname):
     """Grab configs necessary to create a model.
 
     These are deep copied to allow for safe modification of parameters without
@@ -56,7 +56,7 @@ def _get_model_cfg(fname):
     return model
 
 
-def _get_detector_cfg(fname):
+def get_detector_cfg(fname):
     """Grab configs necessary to create a detector.
 
     These are deep copied to allow for safe modification of parameters without
@@ -73,19 +73,19 @@ def _get_detector_cfg(fname):
     return model
 
 
-def _create_detector_inputs(seed=0,
-                            with_points=True,
-                            with_img=False,
-                            img_size=10,
-                            num_gt_instance=20,
-                            num_points=10,
-                            points_feat_dim=4,
-                            num_classes=3,
-                            gt_bboxes_dim=7,
-                            with_pts_semantic_mask=False,
-                            with_pts_instance_mask=False,
-                            bboxes_3d_type='lidar'):
-    _setup_seed(seed)
+def create_detector_inputs(seed=0,
+                           with_points=True,
+                           with_img=False,
+                           img_size=10,
+                           num_gt_instance=20,
+                           num_points=10,
+                           points_feat_dim=4,
+                           num_classes=3,
+                           gt_bboxes_dim=7,
+                           with_pts_semantic_mask=False,
+                           with_pts_instance_mask=False,
+                           bboxes_3d_type='lidar'):
+    setup_seed(seed)
     assert bboxes_3d_type in ('lidar', 'depth', 'cam')
     bbox_3d_class = {
         'lidar': LiDARInstance3DBoxes,

mmdet3d/utils/collect_env.py

 # Copyright (c) OpenMMLab. All rights reserved.
+import mmdet
 from mmengine.utils import get_git_hash
 from mmengine.utils.dl_utils import collect_env as collect_base_env
 
-import mmdet
 import mmdet3d
 
 

mmdet3d/utils/typing.py

@@ -2,11 +2,11 @@
 """Collecting some commonly used type hint in MMDetection3D."""
 from typing import List, Optional, Union
 
+from mmdet.models.task_modules.samplers import SamplingResult
 from mmengine.config import ConfigDict
 from mmengine.structures import InstanceData
 
 from mmdet3d.structures.det3d_data_sample import Det3DDataSample
-from mmdet.models.task_modules.samplers import SamplingResult
 
 # Type hint of config data
 ConfigType = Union[ConfigDict, dict]

mmdet3d/version.py

 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '1.1.0rc1'
+__version__ = '1.1.0rc2'
 short_version = __version__
 
 
 def parse_version_info(version_str):
+    """Parse a version string into a tuple.
+
+    Args:
+        version_str (str): The version string.
+
+    Returns:
+        tuple[int | str]: The version info, e.g., "1.3.0" is parsed into
+            (1, 3, 0), and "2.0.0rc1" is parsed into (2, 0, 0, 'rc1').
+    """
     version_info = []
     for x in version_str.split('.'):
         if x.isdigit():

mmdet3d/visualization/local_visualizer.py

@@ -8,30 +8,28 @@ import numpy as np
 from matplotlib.collections import PatchCollection
 from matplotlib.patches import PathPatch
 from matplotlib.path import Path
-from mmengine.dist import master_only
-from torch import Tensor
-
-from mmdet3d.structures.bbox_3d.box_3d_mode import Box3DMode
 from mmdet.visualization import DetLocalVisualizer
-
-try:
-    import open3d as o3d
-    from open3d import geometry
-except ImportError:
-    raise ImportError(
-        'Please run "pip install open3d" to install open3d first.')
-
+from mmengine.dist import master_only
 from mmengine.structures import InstanceData
 from mmengine.visualization.utils import check_type, tensor2ndarray
+from torch import Tensor
 
 from mmdet3d.registry import VISUALIZERS
 from mmdet3d.structures import (BaseInstance3DBoxes, CameraInstance3DBoxes,
                                 Coord3DMode, DepthInstance3DBoxes,
                                 Det3DDataSample, LiDARInstance3DBoxes,
                                 PointData, points_cam2img)
+from mmdet3d.structures.bbox_3d.box_3d_mode import Box3DMode
 from .vis_utils import (proj_camera_bbox3d_to_img, proj_depth_bbox3d_to_img,
                         proj_lidar_bbox3d_to_img, to_depth_mode)
 
+try:
+    import open3d as o3d
+    from open3d import geometry
+    from open3d.visualization import Visualizer
+except ImportError:
+    o3d = geometry = Visualizer = None
+
 
 @VISUALIZERS.register_module()
 class Det3DLocalVisualizer(DetLocalVisualizer):
@@ -120,7 +118,15 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             self.set_points(points, pcd_mode=pcd_mode, frame_cfg=frame_cfg)
         self.pts_seg_num = 0
 
-    def _initialize_o3d_vis(self, frame_cfg) -> tuple:
+    def _clear_o3d_vis(self) -> None:
+        """Clear open3d vis."""
+
+        if hasattr(self, 'o3d_vis'):
+            del self.o3d_vis
+            del self.pcd
+            del self.points_colors
+
+    def _initialize_o3d_vis(self, frame_cfg) -> Visualizer:
         """Initialize open3d vis according to frame_cfg.
 
         Args:
@@ -130,6 +136,9 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         Returns:
             :obj:`o3d.visualization.Visualizer`: Created open3d vis.
         """
+        if o3d is None or geometry is None:
+            raise ImportError(
+                'Please run "pip install open3d" to install open3d first.')
         o3d_vis = o3d.visualization.Visualizer()
         o3d_vis.create_window()
         # create coordinate frame
@@ -141,8 +150,8 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
     def set_points(self,
                    points: np.ndarray,
                    pcd_mode: int = 0,
+                   vis_mode: str = 'replace',
                    frame_cfg: dict = dict(size=1, origin=[0, 0, 0]),
-                   vis_task: str = 'lidar_det',
                    points_color: Tuple = (0.5, 0.5, 0.5),
                    points_size: int = 2,
                    mode: str = 'xyz') -> None:
@@ -154,11 +163,15 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             pcd_mode (int): The point cloud mode (coordinates):
                 0 represents LiDAR, 1 represents CAMERA, 2
                 represents Depth. Defaults to 0.
+            vis_mode (str): The visualization mode in Open3D:
+                'replace': Replace the existing point cloud with
+                    input point cloud.
+                'add': Add input point cloud into existing point
+                    cloud.
+                Defaults to 'replace'.
             frame_cfg (dict): The coordinate frame config while Open3D
                 visualization initialization.
                 Defaults to dict(size=1, origin=[0, 0, 0]).
-            vis_task (str): Visualiztion task, it includes:
-                'lidar_det', 'multi-modality_det', 'mono_det', 'lidar_seg'.
             point_color (tuple[float], optional): the color of points.
                 Default: (0.5, 0.5, 0.5).
             points_size (int, optional): the size of points to show
@@ -167,6 +180,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                 available mode ['xyz', 'xyzrgb']. Default: 'xyz'.
         """
         assert points is not None
+        assert vis_mode in ('replace', 'add')
         check_type('points', points, np.ndarray)
 
         if not hasattr(self, 'o3d_vis'):
@@ -176,7 +190,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         if pcd_mode != Coord3DMode.DEPTH:
             points = Coord3DMode.convert(points, pcd_mode, Coord3DMode.DEPTH)
 
-        if hasattr(self, 'pcd') and vis_task != 'lidar_seg':
+        if hasattr(self, 'pcd') and vis_mode != 'add':
             self.o3d_vis.remove_geometry(self.pcd)
 
         # set points size in Open3D
@@ -524,8 +538,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         self.o3d_vis.add_geometry(mesh_frame)
         seg_points = copy.deepcopy(seg_mask_colors)
         seg_points[:, 0] += offset
-        self.set_points(
-            seg_points, vis_task='lidar_seg', pcd_mode=2, mode='xyzrgb')
+        self.set_points(seg_points, pcd_mode=2, vis_mode='add', mode='xyzrgb')
 
     def _draw_instances_3d(self, data_input: dict, instances: InstanceData,
                            input_meta: dict, vis_task: str,
@@ -559,7 +572,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             else:
                 bboxes_3d_depth = bboxes_3d.clone()
 
-            self.set_points(points, pcd_mode=2, vis_task=vis_task)
+            self.set_points(points, pcd_mode=2)
             self.draw_bboxes_3d(bboxes_3d_depth)
 
             data_3d['bboxes_3d'] = tensor2ndarray(bboxes_3d_depth.tensor)
@@ -614,7 +627,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         pts_color = palette[pts_sem_seg]
         seg_color = np.concatenate([points[:, :3], pts_color], axis=1)
 
-        self.set_points(points, pcd_mode=2, vis_task='lidar_seg')
+        self.set_points(points, pcd_mode=2, vis_mode='add')
         self.draw_seg_mask(seg_color)
 
     @master_only
@@ -644,6 +657,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
             if save_path is not None:
                 self.o3d_vis.capture_screen_image(save_path)
             self.o3d_vis.destroy_window()
+            self._clear_o3d_vis()
 
         if hasattr(self, '_image'):
             if drawn_img_3d is None:
@@ -662,7 +676,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                        show: bool = False,
                        wait_time: float = 0,
                        out_file: Optional[str] = None,
-                       save_path: Optional[str] = None,
+                       o3d_save_path: Optional[str] = None,
                        vis_task: str = 'mono_det',
                        pred_score_thr: float = 0.3,
                        step: int = 0) -> None:
@@ -673,9 +687,8 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         ground truth and the right image is the prediction.
         - If ``show`` is True, all storage backends are ignored, and
         the images will be displayed in a local window.
-        - If ``out_file`` is specified, the drawn point cloud or
-        image will be saved to ``out_file``. t is usually used when
-        the display is not available.
+        - If ``out_file`` is specified, the drawn image will be saved to
+        ``out_file``. It is usually used when the display is not available.
 
         Args:
             name (str): The image identifier.
@@ -691,8 +704,8 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
                 image. Default to False.
             wait_time (float): The interval of show (s). Defaults to 0.
             out_file (str): Path to output file. Defaults to None.
-            save_path (str, optional): Path to save open3d visualized results.
-                Default: None.
+            o3d_save_path (str, optional): Path to save open3d visualized
+                results Default: None.
             vis-task (str): Visualization task. Defaults to 'mono_det'.
             pred_score_thr (float): The threshold to visualize the bboxes
                 and masks. Defaults to 0.3.
@@ -786,8 +799,7 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
 
         if show:
             self.show(
-                vis_task,
-                save_path,
+                o3d_save_path,
                 drawn_img_3d,
                 drawn_img,
                 win_name=name,

projects/example_project/README.md

+# Dummy ResNet Wrapper
+
+This is an example README for community `projects/`. We have provided detailed explanations for each field in the form of html comments, which are visible when you read the source of this README file. If you wish to submit your project to our main repository, then all the fields in this README are mandatory for others to understand what you have achieved in this implementation.
+
+## Description
+
+<!-- Share any information you would like others to know. For example:
+Author: @xxx.
+This is an implementation of \[XXX\]. -->
+
+This project implements a dummy ResNet wrapper, which literally does nothing new but prints "hello world" during initialization.
+
+## Usage
+
+<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
+
+### Training commands
+
+In MMDet3D's root directory, run the following command to train the model:
+
+```bash
+python tools/train.py projects/example_project/configs/fcos3d_dummy-resnet-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py
+```
+
+### Testing commands
+
+In MMDet3D's root directory, run the following command to test the model:
+
+```bash
+python tools/test.py projects/example_project/configs/fcos3d_dummy-resnet-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py ${CHECKPOINT_PATH}
+```
+
+## Results
+
+<!-- List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmdetection3d/edit/dev-1.x/configs/fcos3d/README.md)
+You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
+
+|                                                     Backbone                                                     | Lr schd | Mem (GB) | Inf time (fps) | mAP  | NDS  |         Download         |
+| :--------------------------------------------------------------------------------------------------------------: | :-----: | :------: | :------------: | :--: | :--: | :----------------------: |
+| [FCOS3D_dummy](projects/example_project/configs/fcos3d_dummy-resnet-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py) |   1x    |   8.69   |                | 29.8 | 37.7 | [model](<>) \| [log](<>) |
+
+## Citation
+
+<!-- You may remove this section if not applicable. -->
+
+```latex
+@inproceedings{wang2021fcos3d,
+	title={{FCOS3D: Fully} Convolutional One-Stage Monocular 3D Object Detection},
+	author={Wang, Tai and Zhu, Xinge and Pang, Jiangmiao and Lin, Dahua},
+	booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
+	year={2021}
+}
+# For the original 2D version
+@inproceedings{tian2019fcos,
+  title     =  {{FCOS: Fully} Convolutional One-Stage Object Detection},
+  author    =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
+  booktitle =  {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  year      =  {2019}
+}
+```
+
+## Checklist
+
+<!-- Here is a checklist illustrating a usual development workflow of a successful project, and also serves as an overview of this project's progress. The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.
+OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.
+Note that keeping this section up-to-date is crucial not only for this project's developers but the entire community, since there might be some other contributors joining this project and deciding their starting point from this list. It also helps maintainers accurately estimate time and effort on further code polishing, if needed.
+A project does not necessarily have to be finished in a single PR, but it's essential for the project to at least reach the first milestone in its very first PR. -->
+
+- [ ] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
+
+  - [ ] Finish the code
+
+    <!-- The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmdet3d.registry.MODELS` and configurable via a config file. -->
+
+  - [ ] Basic docstrings & proper citation
+
+    <!-- Each major object should contain a docstring, describing its functionality and arguments. If you have adapted the code from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. [A Short Guide to Open Source Licenses](https://medium.com/nationwide-technology/a-short-guide-to-open-source-licenses-cf5b1c329edd) -->
+
+  - [ ] Test-time correctness
+
+    <!-- If you are reproducing the result from a paper, make sure your model's inference-time performance matches that in the original paper. The weights usually could be obtained by simply renaming the keys in the official pre-trained weights. This test could be skipped though, if you are able to prove the training-time correctness and check the second milestone. -->
+
+  - [ ] A full README
+
+    <!-- As this template does. -->
+
+- [ ] Milestone 2: Indicates a successful model implementation.
+
+  - [ ] Training-time correctness
+
+    <!-- If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range. -->
+
+- [ ] Milestone 3: Good to be a part of our core package!
+
+  - [ ] Type hints and docstrings
+
+    <!-- Ideally *all* the methods should have [type hints](https://www.pythontutorial.net/python-basics/python-type-hints/) and [docstrings](https://google.github.io/styleguide/pyguide.html#381-docstrings). [Example](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/models/detectors/fcos_mono3d.py) -->
+
+  - [ ] Unit tests
+
+    <!-- Unit tests for each module are required. [Example](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tests/test_models/test_dense_heads/test_fcos_mono3d_head.py) -->
+
+  - [ ] Code polishing
+
+    <!-- Refactor your code according to reviewer's comment. -->
+
+  - [ ] Metafile.yml
+
+    <!-- It will be parsed by MIM and Inferencer. [Example](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/fcos3d/metafile.yml) -->
+
+- [ ] Move your modules into the core package following the codebase's file hierarchy structure.
+
+  <!-- In particular, you may have to refactor this README into a standard one. [Example](/configs/textdet/dbnet/README.md) -->
+
+- [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.

projects/example_project/configs/fcos3d_dummy-resnet-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py

+_base_ = [
+    '../../../configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py'  # noqa
+]
+
+custom_imports = dict(imports=['projects.example_project.dummy'])
+
+_base_.model.backbone.type = 'DummyResNet'

projects/example_project/dummy/__init__.py

+from .dummy_resnet import DummyResNet
+
+__all__ = ['DummyResNet']

projects/example_project/dummy/dummy_resnet.py

+from mmdet.models.backbones import ResNet
+
+from mmdet3d.registry import MODELS
+
+
+@MODELS.register_module()
+class DummyResNet(ResNet):
+    """Implements a dummy ResNet wrapper for demonstration purpose.
+    Args:
+        **kwargs: All the arguments are passed to the parent class.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        print('Hello world!')
+        super().__init__(**kwargs)

requirements/mminstall.txt

-mmcv-full>=2.0.0rc0,<2.1.0
+mmcv>=2.0.0rc0,<2.1.0
 mmdet>=3.0.0rc0,<3.1.0
+mmengine>=0.1.0,<1.0.0

requirements/readthedocs.txt

-mmcv>=1.4.8
-mmdet>=2.24.0
-mmsegmentation>=0.20.1
+mmcv>=2.0.0rc0
+mmdet>=3.0.0rc0
+mmengine>=0.1.0
 torch
 torchvision

setup.cfg

@@ -7,8 +7,8 @@ SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
 line_length = 79
 multi_line_output = 0
 extra_standard_library = setuptools
-known_first_party = mmdet,mmseg,mmdet3d
-known_third_party = cv2,imageio,indoor3d_util,load_scannet_data,lyft_dataset_sdk,m2r,matplotlib,mmcv,nuimages,numba,numpy,nuscenes,pandas,plyfile,pycocotools,pyquaternion,pytest,pytorch_sphinx_theme,recommonmark,requests,scannet_utils,scipy,seaborn,shapely,skimage,sphinx,tensorflow,terminaltables,torch,trimesh,ts,waymo_open_dataset
+known_first_party = mmdet3d
+known_third_party = cv2,imageio,indoor3d_util,load_scannet_data,lyft_dataset_sdk,m2r,matplotlib,mmcv,mmdet,mmengine,nuimages,numba,numpy,nuscenes,pandas,plyfile,pycocotools,pyquaternion,pytest,pytorch_sphinx_theme,recommonmark,requests,scannet_utils,scipy,seaborn,shapely,skimage,sphinx,tensorflow,terminaltables,torch,trimesh,ts,waymo_open_dataset
 no_lines_before = STDLIB,LOCALFOLDER
 default_section = THIRDPARTY
 

tests/test_datasets/test_kitti_dataset.py

@@ -54,7 +54,7 @@ def test_getitem():
             img='training/image_2',
         ),
         pipeline=pipeline,
-        metainfo=dict(CLASSES=classes),
+        metainfo=dict(classes=classes),
         modality=modality)
 
     kitti_dataset.prepare_data(0)
@@ -94,7 +94,7 @@ def test_getitem():
             img='training/image_2',
         ),
         pipeline=pipeline,
-        metainfo=dict(CLASSES=['Car']),
+        metainfo=dict(classes=['Car']),
         modality=modality)
 
     input_dict = car_kitti_dataset.get_data_info(0)
@@ -105,4 +105,4 @@ def test_getitem():
     assert ann_info['gt_labels_3d'].dtype == np.int64
     # all instance have been filtered by classes
     assert len(ann_info['gt_labels_3d']) == 0
-    assert len(car_kitti_dataset.metainfo['CLASSES']) == 1
+    assert len(car_kitti_dataset.metainfo['classes']) == 1