| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4||79.07|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json)|
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|5.4||79.07|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json)|
| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|2.9||78.72|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)|
| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)| Car |cyclic 80e|2.9||78.72|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)|
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4||64.41|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238-9208083a.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20200620_230238.log.json)|
| [SECFPN](./hv_second_secfpn_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|5.4||65.74|[model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth)|[log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017log.json)|
| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|2.9||67.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json)|
| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py)| 3 Class |cyclic 80e|2.9||67.4|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth)|[log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json)|
**Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version < 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_votenet_checkpoints.py](../../tools/model_converters/convert_votenet_checkpoints.py):
**Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version < 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_votenet_checkpoints.py](../../tools/model_converters/convert_votenet_checkpoints.py):
- We migrate all the mmdet3d ops to mmcv and do not need to compile them when installing mmdet3d.
- To fix the imprecise timestamp and optimize its saving method, we reformat the point cloud data during Waymo data conversion. The data conversion time is also optimized significantly by supporting parallel processing. Please re-generate KITTI format Waymo data if necessary. See more details in the [compatibility documentation](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/compatibility.md).
- We update some of the model checkpoints after the refactor of coordinate systems. Please stay tuned for the release of the remaining model checkpoints.
| | Fully Updated | Partially Updated | In Progress | No Influcence |
- Add ScanNet instance segmentation dataset with metrics
- Better compatibility for windows with CI support, op migration and bug fixes
- Support loading annotations from Ceph
#### New Features
- Add ScanNet instance segmentation dataset with metrics (#1230)
- Support different random seeds for different ranks (#1321)
- Support loading annotations from Ceph (#1325)
- Support resuming from the latest checkpoint automatically (#1329)
- Add windows CI (#1345)
#### Improvements
- Update the table format and OpenMMLab project orders in [README.md](https://github.com/open-mmlab/mmdetection3d/blob/master/README.md)(#1272, #1283)
- Migrate all the mmdet3d ops to mmcv (#1240, #1286, #1290, #1333)
- Add `with_plane` flag in the KITTI data conversion (#1278)
- Update instructions and links in the documentation (#1300, 1309, #1319)
- Support parallel Waymo dataset converter and ground truth database generator (#1327)
- Add quick installation commands to [getting_started.md](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/getting_started.md)(#1366)
#### Bug Fixes
- Update nuimages configs to use new nms config style (#1258)
- Fix the usage of np.long for windows compatibility (#1270)
- Fix the incorrect indexing in `BasePoints` (#1274)
- Fix the incorrect indexing in the [pillar_scatter.forward_single](https://github.com/open-mmlab/mmdetection3d/blob/dev/mmdet3d/models/middle_encoders/pillar_scatter.py#L38)(#1280)
- Fix unit tests that use GPUs (#1301)
- Fix incorrect feature dimensions in `DynamicPillarFeatureNet` caused by previous upgrading of `PillarFeatureNet` (#1302)
- Remove the `CameraPoints` constraint in `PointSample` (#1314)
- Fix imprecise timestamps saving of Waymo dataset (#1327)
#### Contributors
A total of 9 developers contributed to this release.
We have adopted CUDA operators compiled from [mmcv](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/__init__.py) and removed all the CUDA operators in mmdet3d. We now do not need to compile the CUDA operators in mmdet3d anymore.
### Waymo dataset converter refactoring
In this version we did a major code refactoring that boosted the performance of waymo dataset conversion by multiprocessing.
Meanwhile, we also fixed the imprecise timestamps saving issue in waymo dataset conversion. This change introduces following backward compatibility breaks:
- The point cloud .bin files of waymo dataset need to be regenerated.
In the .bin files each point occupies 6 `float32` and the meaning of the last `float32` now changed from **imprecise timestamps** to **range frame offset**.
The **range frame offset** for each point is calculated as`ri * h * w + row * w + col` if the point is from the **TOP** lidar or `-1` otherwise.
The `h`, `w` denote the height and width of the TOP lidar's range frame.
The `ri`, `row`, `col` denote the return index, the row and the column of the range frame where each point locates.
Following tables show the difference across the change:
| Meaning | x | y | z | intensity | elongation | **range frame offset** |
- The objects' point cloud .bin files in the GT-database of waymo dataset need to be regenerated because we also dumped the range frame offset for each point into it.
Following tables show the difference across the change:
| Meaning | x | y | z | intensity | elongation | **range frame offset** |
- Any configuration that uses waymo dataset with GT Augmentation should change the `db_sampler.points_loader.load_dim` from `5` to `6`.
## v1.0.0rc0
### Coordinate system refactoring
### Coordinate system refactoring
...
@@ -17,11 +69,11 @@ Since definitions of box representation have changed, the annotation data of mos
...
@@ -17,11 +69,11 @@ Since definitions of box representation have changed, the annotation data of mos
- nuScenes: For LiDAR boxes in training/validation data and GT databases, (x_size, y_size, z_size, yaw) out of (x, y, z, x_size, y_size, z_size) should be converted.
- nuScenes: For LiDAR boxes in training/validation data and GT databases, (x_size, y_size, z_size, yaw) out of (x, y, z, x_size, y_size, z_size) should be converted.
- Lyft: Same as nuScenes.
- Lyft: Same as nuScenes.
Please regenerate the data annotation/GT database files or use [`update_data_coords.py`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/tools/update_data_coords.py) to update the data.
Please regenerate the data annotation/GT database files or use [`update_data_coords.py`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/tools/update_data_coords.py) to update the data.
To use boxes under Depth and LiDAR coordinate systems, or to convert boxes between different coordinate systems, users should be aware of the difference between the old and new definitions. For example, the rotation, flipping, and bev functions of [`DepthInstance3DBoxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/depth_box3d.py) and [`LiDARInstance3DBoxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mdet3d/core/bbox/structures/lidar_box3d.py) and box conversion [functions](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/box_3d_mode.py) have all been reimplemented in the refactoring.
To use boxes under Depth and LiDAR coordinate systems, or to convert boxes between different coordinate systems, users should be aware of the difference between the old and new definitions. For example, the rotation, flipping, and bev functions of [`DepthInstance3DBoxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/depth_box3d.py) and [`LiDARInstance3DBoxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mdet3d/core/bbox/structures/lidar_box3d.py) and box conversion [functions](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/box_3d_mode.py) have all been reimplemented in the refactoring.
Consequently, functions like [`output_to_lyft_box`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/datasets/lyft_dataset.py) undergo small modification to adapt to the new LiDAR/Depth box.
Consequently, functions like [`output_to_lyft_box`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/datasets/lyft_dataset.py) undergo small modification to adapt to the new LiDAR/Depth box.
Since the LiDAR system `(x_size, y_size, z_size)` now corresponds to `(l, w, h)` instead of `(w, l, h)`, the anchor sizes for LiDAR boxes are also changed, e.g., from `[1.6, 3.9, 1.56]` to `[3.9, 1.6, 1.56]`.
Since the LiDAR system `(x_size, y_size, z_size)` now corresponds to `(l, w, h)` instead of `(w, l, h)`, the anchor sizes for LiDAR boxes are also changed, e.g., from `[1.6, 3.9, 1.56]` to `[3.9, 1.6, 1.56]`.
...
@@ -29,12 +81,12 @@ Functions only involving points are generally unaffected except if they rely on
...
@@ -29,12 +81,12 @@ Functions only involving points are generally unaffected except if they rely on
#### Other BC-breaking or new features:
#### Other BC-breaking or new features:
-`array_converter`: Please refer to [array_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/utils/array_converter.py). Functions wrapped with `array_converter` can convert array-like input types of `torch.Tensor`, `np.ndarray`, and `list/tuple/float` to `torch.Tensor` to process in an unified PyTorch pipeline. The result may finally be converted back to the input type. Most functions in [utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/utils.py) are wrapped with `array_converter`.
-`array_converter`: Please refer to [array_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/utils/array_converter.py). Functions wrapped with `array_converter` can convert array-like input types of `torch.Tensor`, `np.ndarray`, and `list/tuple/float` to `torch.Tensor` to process in an unified PyTorch pipeline. The result may finally be converted back to the input type. Most functions in [utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/utils.py) are wrapped with `array_converter`.
-[`points_in_boxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/base_box3d.py) and [`points_in_boxes_batch`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/base_box3d.py) will be deprecated soon. They are renamed to `points_in_boxes_part` and `points_in_boxes_all` respectively, with more detailed docstrings. The major difference of the two functions is that if a point is enclosed by multiple boxes, `points_in_boxes_part` will only return the index of the first enclosing box while `points_in_boxes_all` will return all the indices of enclosing boxes.
-[`points_in_boxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/base_box3d.py) and [`points_in_boxes_batch`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/base_box3d.py) will be deprecated soon. They are renamed to `points_in_boxes_part` and `points_in_boxes_all` respectively, with more detailed docstrings. The major difference of the two functions is that if a point is enclosed by multiple boxes, `points_in_boxes_part` will only return the index of the first enclosing box while `points_in_boxes_all` will return all the indices of enclosing boxes.
-`rotation_3d_in_axis`: Please refer to [utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/utils.py). Now this function supports multiple input types and more options. The function with the same name in [box_np_ops.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/box_np_ops.py) is deleted since we do not need another function to tackle with NumPy data. `rotation_2d`, `points_cam2img`, and `limit_period` in box_np_ops.py are also deleted for the same reason.
-`rotation_3d_in_axis`: Please refer to [utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/utils.py). Now this function supports multiple input types and more options. The function with the same name in [box_np_ops.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/box_np_ops.py) is deleted since we do not need another function to tackle with NumPy data. `rotation_2d`, `points_cam2img`, and `limit_period` in box_np_ops.py are also deleted for the same reason.
-`bev` method of [`CameraInstance3DBoxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/core/bbox/structures/cam_box3d.py): Changed it to be consistent with the definition of bev in Depth and LiDAR coordinate systems.
-`bev` method of [`CameraInstance3DBoxes`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/core/bbox/structures/cam_box3d.py): Changed it to be consistent with the definition of bev in Depth and LiDAR coordinate systems.
- Data augmentation utils in [data_augment_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/datasets/pipelines/data_augment_utils.py) now follow the rules of a right-handed system.
- Data augmentation utils in [data_augment_utils.py](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/datasets/pipelines/data_augment_utils.py) now follow the rules of a right-handed system.
- We do not need the yaw hacking in KITTI anymore after refining [`get_direction_target`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0.dev0/mmdet3d/models/dense_heads/train_mixins.py). Interested users may refer to PR [#677](https://github.com/open-mmlab/mmdetection3d/pull/677) .
- We do not need the yaw hacking in KITTI anymore after refining [`get_direction_target`](https://github.com/open-mmlab/mmdetection3d/blob/v1.0.0rc0/mmdet3d/models/dense_heads/train_mixins.py). Interested users may refer to PR [#677](https://github.com/open-mmlab/mmdetection3d/pull/677) .
@@ -100,4 +100,4 @@ Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0
...
@@ -100,4 +100,4 @@ Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0
### Mixed Precision (FP16) Training
### Mixed Precision (FP16) Training
Please refer [Mixed Precision (FP16) Training] on PointPillars(https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py) for details.
Please refer to [Mixed Precision (FP16) Training on PointPillars](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py) for details.
We support different file client backends: Disk, Ceph and LMDB, etc. Here is an example of how to modify configs for Ceph-based data loading and saving.
## Load data and annotations from Ceph
We support loading data and generated annotation info files (pkl and json) from Ceph:
```python
# set file client backends as Ceph
file_client_args=dict(
backend='petrel',
path_mapping=dict({
'./data/nuscenes/':
's3://openmmlab/datasets/detection3d/nuscenes/',# replace the path with your data path on Ceph
'data/nuscenes/':
's3://openmmlab/datasets/detection3d/nuscenes/'# replace the path with your data path on Ceph
