refactor(lidar_centerpoint): add training docs (autowarefoundation#5570)

Signed-off-by: Kaan Çolak <kaancolak95@gmail.com>
Signed-off-by: Simon Eisenmann <simon.eisenmann@driveblocks.ai>

perception/lidar_centerpoint/README.md

@@ -64,12 +64,207 @@ ros2 launch lidar_centerpoint lidar_centerpoint.launch.xml model_name:=centerpoi
 
 You can download the onnx format of trained models by clicking on the links below.
 
-- Centerpoint : [pts_voxel_encoder_centerpoint.onnx](https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint.onnx), [pts_backbone_neck_head_centerpoint.onnx](https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint.onnx)
+- Centerpoint: [pts_voxel_encoder_centerpoint.onnx](https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint.onnx), [pts_backbone_neck_head_centerpoint.onnx](https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint.onnx)
 - Centerpoint tiny: [pts_voxel_encoder_centerpoint_tiny.onnx](https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_voxel_encoder_centerpoint_tiny.onnx), [pts_backbone_neck_head_centerpoint_tiny.onnx](https://awf.ml.dev.web.auto/perception/models/centerpoint/v2/pts_backbone_neck_head_centerpoint_tiny.onnx)
 
 `Centerpoint` was trained in `nuScenes` (~28k lidar frames) [8] and TIER IV's internal database (~11k lidar frames) for 60 epochs.
 `Centerpoint tiny` was trained in `Argoverse 2` (~110k lidar frames) [9] and TIER IV's internal database (~11k lidar frames) for 20 epochs.
 
+## Training CenterPoint Model and Deploying to the Autoware
+
+### Overview
+
+This guide provides instructions on training a CenterPoint model using the **mmdetection3d** repository
+and seamlessly deploying it within Autoware.
+
+### Installation
+
+#### Install prerequisites
+
+**Step 1.** Download and install Miniconda from the [official website](https://mmpretrain.readthedocs.io/en/latest/get_started.html).
+
+**Step 2.** Create a conda virtual environment and activate it
+
+```bash
+conda create --name train-centerpoint python=3.8 -y
+conda activate train-centerpoint
+```
+
+**Step 3.** Install PyTorch
+
+Please ensure you have PyTorch installed, and compatible with CUDA 11.6, as it is a requirement for current Autoware.
+
+```bash
+conda install pytorch==1.13.1 torchvision==0.14.1 pytorch-cuda=11.6 -c pytorch -c nvidia
+```
+
+#### Install mmdetection3d
+
+**Step 1.** Install MMEngine, MMCV, and MMDetection using MIM
+
+```bash
+pip install -U openmim
+mim install mmengine
+mim install 'mmcv>=2.0.0rc4'
+mim install 'mmdet>=3.0.0rc5, <3.3.0'
+```
+
+**Step 2.** Install mmdetection3d forked repository
+
+Introduced several valuable enhancements in our fork of the mmdetection3d repository.
+Notably, we've made the PointPillar z voxel feature input optional to maintain compatibility with the original paper.
+In addition, we've integrated a PyTorch to ONNX converter and a T4 format reader for added functionality.
+
+```bash
+git clone https://github.com/autowarefoundation/mmdetection3d.git
+cd mmdetection3d
+pip install -v -e .
+```
+
+#### Use Training Repository with Docker
+
+Alternatively, you can use Docker to run the mmdetection3d repository. We provide a Dockerfile to build a Docker image with the mmdetection3d repository and its dependencies.
+
+Clone fork of the mmdetection3d repository
+
+```bash
+git clone https://github.com/autowarefoundation/mmdetection3d.git
+```
+
+Build the Docker image by running the following command:
+
+```bash
+cd mmdetection3d
+docker build -t mmdetection3d -f docker/Dockerfile .
+```
+
+Run the Docker container:
+
+```bash
+docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection3d/data mmdetection3d
+```
+
+### Preparing NuScenes dataset for training
+
+**Step 1.** Download the NuScenes dataset from the [official website](https://www.nuscenes.org/download) and extract the dataset to a folder of your choice.
+
+**Note:** The NuScenes dataset is large and requires significant disk space. Ensure you have enough storage available before proceeding.
+
+**Step 2.** Create a symbolic link to the dataset folder
+
+```bash
+ln -s /path/to/nuscenes/dataset/ /path/to/mmdetection3d/data/nuscenes/
+```
+
+**Step 3.** Prepare the NuScenes data by running:
+
+```bash
+cd mmdetection3d
+python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes
+```
+
+### Training CenterPoint with NuScenes Dataset
+
+#### Prepare the config file
+
+The configuration file that illustrates how to train the CenterPoint model with the NuScenes dataset is
+located at `mmdetection3d/projects/AutowareCenterPoint/configs`. This configuration file is a derived version of
+[this centerpoint configuration file](https://github.com/autowarefoundation/mmdetection3d/blob/5c0613be29bd2e51771ec5e046d89ba3089887c7/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py)
+from mmdetection3D.
+In this custom configuration, the **use_voxel_center_z parameter** is set as **False** to deactivate the z coordinate of the voxel center,
+aligning with the original paper's specifications and making the model compatible with Autoware. Additionally, the filter size is set as **[32, 32]**.
+
+The CenterPoint model can be tailored to your specific requirements by modifying various parameters within the configuration file.
+This includes adjustments related to preprocessing operations, training, testing, model architecture, dataset, optimizer, learning rate scheduler, and more.
+
+#### Start training
+
+```bash
+python tools/train.py projects/AutowareCenterPoint/configs/centerpoint_custom.py --work-dir ./work_dirs/centerpoint_custom
+```
+
+#### Evaluation of the trained model
+
+For evaluation purposes, we have included a sample dataset captured from the vehicle which consists of the following LiDAR sensors:
+1 x Velodyne VLS128, 4 x Velodyne VLP16, and 1 x Robosense RS Bpearl. This dataset comprises 600 LiDAR frames and encompasses 5 distinct classes, 6905 cars, 3951 pedestrians,
+75 cyclists, 162 buses, and 326 trucks 3D annotation. In the sample dataset, frames are annotated as 2 frames for each second. You can employ this dataset for a wide range of purposes,
+including training, evaluation, and fine-tuning of models. It is organized in the T4 format.
+
+##### Download the sample dataset
+
+```bash
+wget https://autoware-files.s3.us-west-2.amazonaws.com/dataset/lidar_detection_sample_dataset.tar.gz
+#Extract the dataset to a folder of your choice
+tar -xvf lidar_detection_sample_dataset.tar.gz
+#Create a symbolic link to the dataset folder
+ln -s /PATH/TO/DATASET/ /PATH/TO/mmdetection3d/data/tier4_dataset/
+```
+
+##### Prepare dataset and evaluate trained model
+
+Create `.pkl` files for training, evaluation, and testing.
+
+The dataset was formatted according to T4Dataset specifications, with 'sample_dataset' designated as one of its versions.
+
+```bash
+python tools/create_data.py T4Dataset --root-path data/sample_dataset/ --out-dir data/sample_dataset/ --extra-tag T4Dataset --version sample_dataset --annotation-hz 2
+```
+
+Run evaluation
+
+```bash
+python tools/test.py projects/AutowareCenterPoint/configs/centerpoint_custom_test.py /PATH/OF/THE/CHECKPOINT  --task lidar_det
+```
+
+Evaluation results could be relatively low because of the e to variations in sensor modalities between the sample dataset
+and the training dataset. The model's training parameters are originally tailored to the NuScenes dataset, which employs a single lidar
+sensor positioned atop the vehicle. In contrast, the provided sample dataset comprises concatenated point clouds positioned at
+the base link location of the vehicle.
+
+### Deploying CenterPoint model to Autoware
+
+#### Convert CenterPoint PyTorch model to ONNX Format
+
+The lidar_centerpoint implementation requires two ONNX models as input the voxel encoder and the backbone-neck-head of the CenterPoint model, other aspects of the network,
+such as preprocessing operations, are implemented externally. Under the fork of the mmdetection3d repository,
+we have included a script that converts the CenterPoint model to Autoware compatible ONNX format.
+You can find it in `mmdetection3d/projects/AutowareCenterPoint` file.
+
+```bash
+python projects/AutowareCenterPoint/centerpoint_onnx_converter.py --cfg projects/AutowareCenterPoint/configs/centerpoint_custom.py --ckpt work_dirs/centerpoint_custom/YOUR_BEST_MODEL.pth --work-dir ./work_dirs/onnx_models
+```
+
+#### Create the config file for the custom model
+
+Create a new config file named **centerpoint_custom.param.yaml** under the config file directory of the lidar_centerpoint node. Sets the parameters of the config file like
+point_cloud_range, point_feature_size, voxel_size, etc. according to the training config file.
+
+```yaml
+/**:
+  ros__parameters:
+    class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
+    point_feature_size: 4
+    max_voxel_size: 40000
+    point_cloud_range: [-51.2, -51.2, -3.0, 51.2, 51.2, 5.0]
+    voxel_size: [0.2, 0.2, 8.0]
+    downsample_factor: 1
+    encoder_in_feature_size: 9
+    # post-process params
+    circle_nms_dist_threshold: 0.5
+    iou_nms_target_class_names: ["CAR"]
+    iou_nms_search_distance_2d: 10.0
+    iou_nms_threshold: 0.1
+    yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
+```
+
+#### Launch the lidar_centerpoint node
+
+```bash
+cd /YOUR/AUTOWARE/PATH/Autoware
+source install/setup.bash
+ros2 launch lidar_centerpoint lidar_centerpoint.launch.xml  model_name:=centerpoint_custom  model_path:=/PATH/TO/ONNX/FILE/
+```
+
 ### Changelog
 
 #### v1 (2022/07/06)
@@ -144,3 +339,14 @@ Example:
 [v1-head-centerpoint]: https://awf.ml.dev.web.auto/perception/models/centerpoint/v1/pts_backbone_neck_head_centerpoint.onnx
 [v1-encoder-centerpoint-tiny]: https://awf.ml.dev.web.auto/perception/models/centerpoint/v1/pts_voxel_encoder_centerpoint_tiny.onnx
 [v1-head-centerpoint-tiny]: https://awf.ml.dev.web.auto/perception/models/centerpoint/v1/pts_backbone_neck_head_centerpoint_tiny.onnx
+
+## Acknowledgment: deepen.ai's 3D Annotation Tools Contribution
+
+Special thanks to [Deepen AI](https://www.deepen.ai/) for providing their 3D Annotation tools, which have been instrumental in creating our sample dataset.
+
+## Legal Notice
+
+_The nuScenes dataset is released publicly for non-commercial use under the Creative
+Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License.
+Additional Terms of Use can be found at <https://www.nuscenes.org/terms-of-use>.
+To inquire about a commercial license please contact nuscenes@motional.com._