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Unverified Commit 1f5dec4c authored by Jingwei Zhang's avatar Jingwei Zhang Committed by GitHub
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[Enhance] Provide offline annotations and polish docs (#2457) 

* init commit

* add zh-cn docs

* polish docs

* polish docs

* refine docs and update description about S3DIS, ScanNet

* polish docs
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README.md
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......@@ -66,7 +66,7 @@ Documentation: https://mmdetection3d.readthedocs.io/
English | [简体中文](README_zh-CN.md)
The master branch works with **PyTorch 1.6+**.
The main branch works with **PyTorch 1.8+**.
MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is
a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk/).
......
docs/en/get_started.md
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......@@ -4,7 +4,7 @@
In this section, we demonstrate how to prepare an environment with PyTorch.
MMDetection3D works on Linux, Windows (experimental support) and macOS. It requires Python 3.7+, CUDA 9.2+, and PyTorch 1.6+.
MMDetection3D works on Linux, Windows (experimental support) and macOS. It requires Python 3.7+, CUDA 10.0+, and PyTorch 1.8+.
```{note}
If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
......@@ -67,7 +67,7 @@ pip install -v -e .
Case b: If you use mmdet3d as a dependency or third-party package, install it with MIM:
```shell
mim install "mmdet3d>=1.1.0rc0"
mim install "mmdet3d>=1.1.0"
```
Note:
......@@ -256,7 +256,7 @@ pip install "mmcv>=2.0.0rc4" -f https://download.openmmlab.com/mmcv/dist/cu116/t
```python
import mmdet3d
print(mmdet3d.__version__)
# Example output: 1.1.0rc0, or an another version.
# Example output: 1.1.0, or an another version.
```
```{note}
......
docs/en/user_guides/dataset_prepare.md
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......@@ -102,9 +102,17 @@ In an environment using slurm, users may run the following command instead:
sh tools/create_data.sh <partition> kitti
```
**Tips**:
- **Ready-made Annotations**. We have also provided kitti data annotation files generated offline [here](#summary-of-annotation-files). You could download them and place them under `data/kitti/`. However, if you want to use `ObjectSample` Augmentation in LiDAR-based detection methods, you should additionally generate groundtruth database files and annotations.
```bash
python tools/create_data.py kitti --root-path ./data/kitti --out-dir ./data/kitti --extra-tag kitti --only-gt-databse
```
### Waymo
Download Waymo open dataset V1.2 [HERE](https://waymo.com/open/download/) and its data split [HERE](https://drive.google.com/drive/folders/18BVuF_RYJF0NjZpt8SnfzANiakoRMf0o?usp=sharing). Then put `.tfrecord` files into corresponding folders in `data/waymo/waymo_format/` and put the data split `.txt` files into `data/waymo/kitti_format/ImageSets`. Download ground truth `.bin` file for validation set [HERE](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_2_0/validation/ground_truth_objects) and put it into `data/waymo/waymo_format/`. A tip is that you can use `gsutil` to download the large-scale dataset with commands. You can take this [tool](https://github.com/RalphMao/Waymo-Dataset-Tool) as an example for more details. Subsequently, prepare waymo data by running:
Download Waymo open dataset V1.4.1 [HERE](https://waymo.com/open/download/) and its data split [HERE](https://drive.google.com/drive/folders/18BVuF_RYJF0NjZpt8SnfzANiakoRMf0o?usp=sharing). Then put `.tfrecord` files into corresponding folders in `data/waymo/waymo_format/` and put the data split `.txt` files into `data/waymo/kitti_format/ImageSets`. Download ground truth `.bin` file for validation set [HERE](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_4_1/validation/ground_truth_objects) and put it into `data/waymo/waymo_format/`. A tip is that you can use `gsutil` to download the large-scale dataset with commands. You can take this [tool](https://github.com/RalphMao/Waymo-Dataset-Tool) as an example for more details. Subsequently, prepare waymo data by running:
```bash
python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/waymo/ --workers 128 --extra-tag waymo
......@@ -112,9 +120,21 @@ python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/way
Note that:
- In case the preprocessing of Waymo dataset is slow or blocked, consider reducing the value of `--workers`. If this doesn't resolve the issue, you could set `--workers` as 0 to avoid using multiprocess.
- If your local disk does not have enough space for saving converted data, you can change the `--out-dir` to anywhere else. Just remember to create folders and prepare data there in advance and link them back to `data/waymo/kitti_format` after the data conversion.
- If you want faster evaluation on Waymo, you can download the preprocessed [metainfo](https://download.openmmlab.com/mmdetection3d/data/waymo/idx2metainfo.pkl) containing `contextname` and `timestamp` to the directory `data/waymo/waymo_format/`. Then, the dataset config is modified like the following:
**Tips**:
- **Ready-made Annotations**. We have provided the annotation files generated offline [here](#summary-of-annotation-files). However, the original Waymo data still needs to be converted to `kitti-format` data by yourself.
- **Waymo-mini**. If you just want to use a part of Waymo Dataset to verify some methods or debug quickly, you could use our provided [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_kitti_format.tar.gz) which only contains two segments in train split and one segment in val split from the original dataset. All the images, point clouds and annotations in this compressed file have been processed offline so that you can directly download and unzip it to `data/waymo/`:
```bash
tar -xzvf waymo_mini_kitti_format.tar.gz -C ./data/waymo
```
- **Faster evaluation**. If you want faster evaluation on Waymo, you can download the preprocessed [metainfo](https://download.openmmlab.com/mmdetection3d/data/waymo/idx2metainfo.pkl) containing `contextname` and `timestamp` to the directory `data/waymo/waymo_format/` and then modify the dataset config as the following:
```python
val_evaluator = dict(
......@@ -138,6 +158,14 @@ Download nuScenes V1.0 full dataset data [HERE](https://www.nuscenes.org/downloa
python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes
```
**Tips**:
- **Ready-made Annotations**. We have also provided NuScenes data annotation files generated offline [here](#summary-of-annotation-files). You could download them and place them under `data/nuscenes/`. However, if you want to use `ObjectSample` Augmentation in LiDAR-based detection methods, you should additionally generate groundtruth database files and annotations.
```bash
python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes --only-gt-databse
```
### Lyft
Download Lyft 3D detection data [HERE](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/data). Prepare Lyft data by running:
......@@ -157,6 +185,8 @@ To prepare ScanNet data, please see its [README](https://github.com/open-mmlab/m
To prepare SUN RGB-D data, please see its [README](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/data/sunrgbd/README.md).
**Tips**: For S3DIS, ScanNet and SUN RGB-D datasets, we have also provided data annotation files generated offline [here](#summary-of-annotation-files). You could download them and place them under `data/${DATASET}/`. However, you also need to generate point cloud files and semantic/instance masks files (if it has) by yourself.
### Customized Datasets
For using custom datasets, please refer to [Customize Datasets](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/en/advanced_guides/customize_dataset.md).
......@@ -178,3 +208,17 @@ Example:
```bash
python tools/dataset_converters/update_infos_to_v2.py --dataset kitti --pkl-path ./data/kitti/kitti_infos_trainval.pkl --out-dir ./data/kitti
```
### Summary of annotation files
We provide ready-made annotation files we generated offline for reference. You can directly use these files for convenice.
| Dataset | Train annotation file | Val annotation file | Test information file |
| :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------: |
| KITTI | [kitti_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/kitti/kitti_infos_train.pkl) | [kitti_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/kitti/kitti_infos_val.pkl) | [kitti_infos_test](https://download.openmmlab.com/mmdetection3d/data/kitti/kitti_infos_test.pkl) |
| NuScenes | [nuscenes_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_infos_train.pkl) [nuscenes_mini_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_mini_infos_train.pkl) | [nuscenes_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_infos_val.pkl) [nuscenes_mini_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_mini_infos_val.pkl) | |
| Waymo | [waymo_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_infos_train.pkl) [waymo_mini_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_infos_train.pkl) | [waymo_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_infos_val.pkl) [waymo_mini_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_infos_val.pkl) | [waymo_infos_test.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_infos_test.pkl) |
| [Waymo-mini kitti-format data](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_kitti_format.tar.gz) | | | |
| SUN RGB-D | [sunrgbd_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/sunrgbd/sunrgbd_infos_train.pkl) | [sunrgbd_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/sunrgbd/sunrgbd_infos_val.pkl) | |
| ScanNet | [scannet_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/scannet/scannet_infos_train.pkl) | [scannet_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/scannet/scannet_infos_val.pkl) | [scannet_infos_test.pkl](https://download.openmmlab.com/mmdetection3d/data/scannet/scannet_infos_test.pkl) |
| SemanticKitti | [semantickitti_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/semantickitti/semantickitti_infos_train.pkl) | [semantickitti_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/semantickitti/semantickitti_infos_val.pkl) | [semantickitti_infos_test.pkl](https://download.openmmlab.com/mmdetection3d/data/semantickitti/semantickitti_infos_test.pkl) |
docs/zh_cn/stat.py
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......@@ -6,7 +6,7 @@ from os import path as osp
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection3d/blob/master/'
url_prefix = 'https://github.com/open-mmlab/mmdetection3d/blob/main/'
files = sorted(glob.glob('../configs/*/README.md'))
......
docs/zh_cn/user_guides/dataset_prepare.md
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......@@ -101,6 +101,14 @@ python tools/create_data.py kitti --root-path ./data/kitti --out-dir ./data/kitt
sh tools/create_data.sh <partition> kitti
```
**小贴士**：
- **现成的标注文件**：我们已经提供了离线处理好的 [KITTI 标注文件](#数据集标注文件列表)。您直接下载他们并放到 `data/kitti/` 目录下。然而，如果你想在点云检测方法中使用 `ObjectSample` 这一数据增强，你可以再额外使用以下命令来生成物体标注框数据库：
```bash
python tools/create_data.py kitti --root-path ./data/kitti --out-dir ./data/kitti --extra-tag kitti --only-gt-databse
```
### Waymo
在[这里](https://waymo.com/open/download/)下载 Waymo 公开数据集 1.2 版本，在[这里](https://drive.google.com/drive/folders/18BVuF_RYJF0NjZpt8SnfzANiakoRMf0o?usp=sharing)下载其数据划分文件。然后，将 `.tfrecord` 文件置于 `data/waymo/waymo_format/` 目录下的相应位置，并将数据划分的 `.txt` 文件置于 `data/waymo/kitti_format/ImageSets` 目录下。在[这里](https://console.cloud.google.com/storage/browser/waymo_open_dataset_v_1_2_0/validation/ground_truth_objects)下载验证集的真实标签（`.bin` 文件）并将其置于 `data/waymo/waymo_format/`。提示：你可以使用 `gsutil` 来用命令下载大规模的数据集。更多细节请参考此[工具](https://github.com/RalphMao/Waymo-Dataset-Tool)。完成以上各步后，可以通过运行以下指令对 Waymo 数据进行预处理：
......@@ -113,7 +121,17 @@ python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/way
- 如果你的硬盘空间大小不足以存储转换后的数据，你可以将 `--out-dir` 参数设定为别的路径。你只需要记得在那个路径下创建文件夹并下载数据，然后在数据预处理完成后将其链接回 `data/waymo/kitti_format` 即可。
- 如果你想在 Waymo 上进行更快的评估，你可以下载已经预处理好的[元信息文件](https://download.openmmlab.com/mmdetection3d/data/waymo/idx2metainfo.pkl)并将其放置在 `data/waymo/waymo_format/` 目录下。接着，你可以按照以下来修改数据集的配置：
**小贴士**：
- **现成的标注文件**: 我们已经提供了离线处理好的 [Waymo 标注文件](#数据集标注文件列表)。您直接下载他们并放到 `data/waymo/kitti_format/` 目录下。然而，您还是需要自己使用上面的脚本将 Waymo 的原始数据还需要转成 kitti 格式。
- **Waymo-mini**： 如果你只是为了验证某些方法或者 debug, 你可以使用我们提供的 [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_kitti_format.tar.gz)。它只包含原始数据集中训练集中的 2 个 segments 和 验证集中的 1 个 segment。您只需要下载并且解压到 `data/waymo/`，即可使用它：
```bash
tar -xzvf waymo_mini_kitti_format.tar.gz -C ./data/waymo
```
- **更快的评估**： 如果你想在 Waymo 上进行更快的评估，你可以下载已经预处理好的[元信息文件](https://download.openmmlab.com/mmdetection3d/data/waymo/idx2metainfo.pkl)并将其放置在 `data/waymo/waymo_format/` 目录下。接着，你可以按照以下来修改数据集的配置：
```python
val_evaluator = dict(
......@@ -137,6 +155,14 @@ python tools/create_data.py waymo --root-path ./data/waymo/ --out-dir ./data/way
python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes
```
**小贴士**：
- **现成的标注文件**：我们已经提供了离线处理好的 [NuScenes 标注文件](#数据集标注文件列表)。您直接下载他们并放到 `data/nuscenes/` 目录下。然而，如果你想在点云检测方法中使用 `ObjectSample` 这一数据增强，你可以再额外使用以下命令来生成物体标注框数据库：
```bash
python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes --only-gt-databse
```
### Lyft
在[这里](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/data)下载 Lyft 3D 检测数据。通过运行以下指令对 Lyft 数据进行预处理：
......@@ -156,6 +182,8 @@ python tools/data_converter/lyft_data_fixer.py --version v1.01 --root-folder ./d
请参考 SUN RGB-D [README](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/data/sunrgbd/README.md) 文件以对其进行数据预处理。
**小贴士**：对于 S3DIS, ScanNet 和 SUN RGB-D 数据集，我们已经提供了离线处理好的 [标注文件](#数据集标注文件列表)。您可以直接下载他们并放到 `data/${DATASET}/` 目录下。然而，您还是需要自己利用我们的脚本来生成点云文件以及语义掩膜文件(如果该数据集有的话)。
### 自定义数据集
关于如何使用自定义数据集，请参考[自定义数据集](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/zh_cn/advanced_guides/customize_dataset.md)。
......@@ -177,3 +205,17 @@ python tools/dataset_converters/update_infos_to_v2.py --dataset ${DATA_SET} --pk
```bash
python tools/dataset_converters/update_infos_to_v2.py --dataset kitti --pkl-path ./data/kitti/kitti_infos_trainval.pkl --out-dir ./data/kitti
```
### 数据集标注文件列表
我们提供了离线生成好的数据集标注文件以供参考。为了方便，您也可以直接使用他们。
| 数据集 | 训练集标注文件 | 验证集标注文件 | 测试集标注文件 |
| :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------: |
| KITTI | [kitti_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/kitti/kitti_infos_train.pkl) | [kitti_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/kitti/kitti_infos_val.pkl) | [kitti_infos_test](https://download.openmmlab.com/mmdetection3d/data/kitti/kitti_infos_test.pkl) |
| NuScenes | [nuscenes_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_infos_train.pkl) [nuscenes_mini_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_mini_infos_train.pkl) | [nuscenes_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_infos_val.pkl) [nuscenes_mini_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/nuscenes/nuscenes_mini_infos_val.pkl) | |
| Waymo | [waymo_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_infos_train.pkl) [waymo_mini_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_infos_train.pkl) | [waymo_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_infos_val.pkl) [waymo_mini_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_infos_val.pkl) | [waymo_infos_test.pkl](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_infos_test.pkl) |
| [Waymo-mini kitti-format data](https://download.openmmlab.com/mmdetection3d/data/waymo/waymo_mini_kitti_format.tar.gz) | | | |
| SUN RGB-D | [sunrgbd_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/sunrgbd/sunrgbd_infos_train.pkl) | [sunrgbd_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/sunrgbd/sunrgbd_infos_val.pkl) | |
| ScanNet | [scannet_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/scannet/scannet_infos_train.pkl) | [scannet_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/scannet/scannet_infos_val.pkl) | [scannet_infos_test.pkl](https://download.openmmlab.com/mmdetection3d/data/scannet/scannet_infos_test.pkl) |
| SemanticKitti | [semantickitti_infos_train.pkl](https://download.openmmlab.com/mmdetection3d/data/semantickitti/semantickitti_infos_train.pkl) | [semantickitti_infos_val.pkl](https://download.openmmlab.com/mmdetection3d/data/semantickitti/semantickitti_infos_val.pkl) | [semantickitti_infos_test.pkl](https://download.openmmlab.com/mmdetection3d/data/semantickitti/semantickitti_infos_test.pkl) |
tools/create_data.py
View file @ 1f5dec4c
......@@ -271,6 +271,10 @@ parser.add_argument(
parser.add_argument('--extra-tag', type=str, default='kitti')
parser.add_argument(
'--workers', type=int, default=4, help='number of threads to be used')
parser.add_argument(
'--only-gt-databse',
action='store_true',
help='Whether to only generate ground truth database.')
args = parser.parse_args()
if __name__ == '__main__':
......@@ -278,38 +282,58 @@ if __name__ == '__main__':
register_all_modules()
if args.dataset == 'kitti':
kitti_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=args.version,
out_dir=args.out_dir,
with_plane=args.with_plane)
if args.only_gt_databse:
create_groundtruth_database(
'KittiDataset',
args.root_path,
args.extra_tag,
f'{args.extra_tag}_infos_train.pkl',
relative_path=False,
mask_anno_path='instances_train.json',
with_mask=(args.version == 'mask'))
else:
kitti_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=args.version,
out_dir=args.out_dir,
with_plane=args.with_plane)
elif args.dataset == 'nuscenes' and args.version != 'v1.0-mini':
train_version = f'{args.version}-trainval'
nuscenes_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=train_version,
dataset_name='NuScenesDataset',
out_dir=args.out_dir,
max_sweeps=args.max_sweeps)
test_version = f'{args.version}-test'
nuscenes_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=test_version,
dataset_name='NuScenesDataset',
out_dir=args.out_dir,
max_sweeps=args.max_sweeps)
if args.only_gt_databse:
create_groundtruth_database('NuScenesDataset', args.root_path,
args.extra_tag,
f'{args.extra_tag}_infos_train.pkl')
else:
train_version = f'{args.version}-trainval'
nuscenes_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=train_version,
dataset_name='NuScenesDataset',
out_dir=args.out_dir,
max_sweeps=args.max_sweeps)
test_version = f'{args.version}-test'
nuscenes_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=test_version,
dataset_name='NuScenesDataset',
out_dir=args.out_dir,
max_sweeps=args.max_sweeps)
elif args.dataset == 'nuscenes' and args.version == 'v1.0-mini':
train_version = f'{args.version}'
nuscenes_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=train_version,
dataset_name='NuScenesDataset',
out_dir=args.out_dir,
max_sweeps=args.max_sweeps)
if args.only_gt_databse:
create_groundtruth_database('NuScenesDataset', args.root_path,
args.extra_tag,
f'{args.extra_tag}_infos_train.pkl')
else:
train_version = f'{args.version}'
nuscenes_data_prep(
root_path=args.root_path,
info_prefix=args.extra_tag,
version=train_version,
dataset_name='NuScenesDataset',
out_dir=args.out_dir,
max_sweeps=args.max_sweeps)
elif args.dataset == 'lyft':
train_version = f'{args.version}-train'
lyft_data_prep(
......
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