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15 changes: 15 additions & 0 deletions .gitignore
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**__pycache__**
**build**
**egg-info**
**dist**
data/
*.pyc
venv/
*.idea/
*.so
*.yaml
*.sh
*.pth
*.pkl
*.zip
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201 changes: 201 additions & 0 deletions LICENSE
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7 changes: 7 additions & 0 deletions README.md
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# CenterPoint

This repo is an reimplementation of CenterPoint on the KITTI dataset. For nuScenes and Waymo, please refer to the [original repo](https://github.com/tianweiy/CenterPoint). We provide two configs, [centerpoint.yaml](tools/cfgs/kitti_models/centerpoint.yaml) for the vanilla centerpoint model and [centerpoint_rcnn.yaml](tools/cfgs/kitti_models/centerpoint_rcnn.yaml) which combines centerpoint with PVRCNN.

## Acknowledgement

Our code is based on [OpenPCDet](https://github.com/open-mmlab/OpenPCDet). Some util files are copied from [mmdetection](https://github.com/open-mmlab/mmdetection) and [mmdetection3d](https://github.com/open-mmlab/mmdetection3d). Thanks OpenMMLab Development Team for their awesome codebases.
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# Quick Demo

Here we provide a quick demo to test a pretrained model on the custom point cloud data and visualize the predicted results.

We suppose you already followed the [INSTALL.md](INSTALL.md) to install the `OpenPCDet` repo successfully.

1. Download the provided pretrained models as shown in the [README.md](../README.md).

2. Make sure you have already installed the `mayavi` visualization tools. If not, you could install it as follows:
```
pip install mayavi
```

3. Prepare you custom point cloud data (skip this step if you use the original KITTI data).
* You need to transform the coordinate of your custom point cloud to
the unified normative coordinate of `OpenPCDet`, that is, x-axis points towards to front direction,
y-axis points towards to the left direction, and z-axis points towards to the top direction.
* (Optional) the z-axis origin of your point cloud coordinate should be about 1.6m above the ground surface,
since currently the provided models are trained on the KITTI dataset.
* Set the intensity information, and save your transformed custom data to `numpy file`:
```python
# Transform your point cloud data
...

# Save it to the file.
# The shape of points should be (num_points, 4), that is [x, y, z, intensity],
# If you doesn't have the intensity information, just set them to zeros.
# If you have the intensity information, you should normalize them to [0, 1].
points[:, 3] = 0
np.save(`my_data.npy`, points)
```

4. Run the demo with a pretrained model (e.g. PV-RCNN) and your custom point cloud data as follows:
```shell
python demo.py --cfg_file cfgs/kitti_models/pv_rcnn.yaml \
--ckpt pv_rcnn_8369.pth \
--data_path ${POINT_CLOUD_DATA}
```
Here `${POINT_CLOUD_DATA}` could be the following format:
* Your transformed custom data with a single numpy file like `my_data.npy`.
* Your transformed custom data with a directory to test with multiple point cloud data.
* The original KITTI `.bin` data within `data/kitti`, like `data/kitti/training/velodyne/000008.bin`.

Then you could see the predicted results with visualized point cloud as follows:

<p align="center">
<img src="demo.png" width="99%">
</p>
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# Getting Started
The dataset configs are located within [tools/cfgs/dataset_configs](../tools/cfgs/dataset_configs),
and the model configs are located within [tools/cfgs](../tools/cfgs) for different datasets.


## Dataset Preparation

Currently we provide the dataloader of KITTI dataset and NuScenes dataset, and the supporting of more datasets are on the way.

### KITTI Dataset
* Please download the official [KITTI 3D object detection](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) dataset and organize the downloaded files as follows (the road planes could be downloaded from [[road plane]](https://drive.google.com/file/d/1d5mq0RXRnvHPVeKx6Q612z0YRO1t2wAp/view?usp=sharing), which are optional for data augmentation in the training):
* NOTE: if you already have the data infos from `pcdet v0.1`, you can choose to use the old infos and set the DATABASE_WITH_FAKELIDAR option in tools/cfgs/dataset_configs/kitti_dataset.yaml as True. The second choice is that you can create the infos and gt database again and leave the config unchanged.

```
OpenPCDet
├── data
│ ├── kitti
│ │ │── ImageSets
│ │ │── training
│ │ │ ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│ │ │── testing
│ │ │ ├──calib & velodyne & image_2
├── pcdet
├── tools
```

* Generate the data infos by running the following command:
```python
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
```

### NuScenes Dataset
* Please download the official [NuScenes 3D object detection dataset](https://www.nuscenes.org/download) and
organize the downloaded files as follows:
```
OpenPCDet
├── data
│ ├── nuscenes
│ │ │── v1.0-trainval (or v1.0-mini if you use mini)
│ │ │ │── samples
│ │ │ │── sweeps
│ │ │ │── maps
│ │ │ │── v1.0-trainval
├── pcdet
├── tools
```

* Install the `nuscenes-devkit` with version `1.0.5` by running the following command:
```shell script
pip install nuscenes-devkit==1.0.5
```

* Generate the data infos by running the following command (it may take several hours):
```python
python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \
--cfg_file tools/cfgs/dataset_configs/nuscenes_dataset.yaml \
--version v1.0-trainval
```

## Training & Testing


### Test and evaluate the pretrained models
* Test with a pretrained model:
```shell script
python test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --ckpt ${CKPT}
```

* To test all the saved checkpoints of a specific training setting and draw the performance curve on the Tensorboard, add the `--eval_all` argument:
```shell script
python test.py --cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --eval_all
```

* To test with multiple GPUs:
```shell script
sh scripts/dist_test.sh ${NUM_GPUS} \
--cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE}

# or

sh scripts/slurm_test_mgpu.sh ${PARTITION} ${NUM_GPUS} \
--cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE}
```


### Train a model
You could optionally add extra command line parameters `--batch_size ${BATCH_SIZE}` and `--epochs ${EPOCHS}` to specify your preferred parameters.


* Train with multiple GPUs or multiple machines
```shell script
sh scripts/dist_train.sh ${NUM_GPUS} --cfg_file ${CONFIG_FILE}

# or

sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} ${NUM_GPUS} --cfg_file ${CONFIG_FILE}
```

* Train with a single GPU:
```shell script
python train.py --cfg_file ${CONFIG_FILE}
```
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