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https://github.com/SmallMunich/nutonomy_pointpillars
Convert pointpillars Pytorch Model To ONNX for TensorRT Inference
https://github.com/SmallMunich/nutonomy_pointpillars
nutonomy onnx pointpillars pytorch tensorrt
Last synced: 14 days ago
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Convert pointpillars Pytorch Model To ONNX for TensorRT Inference
- Host: GitHub
- URL: https://github.com/SmallMunich/nutonomy_pointpillars
- Owner: SmallMunich
- License: mit
- Created: 2019-09-28T07:40:43.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-11-11T08:04:32.000Z (about 4 years ago)
- Last Synced: 2024-08-01T03:45:30.206Z (3 months ago)
- Topics: nutonomy, onnx, pointpillars, pytorch, tensorrt
- Language: Python
- Homepage:
- Size: 4.5 MB
- Stars: 374
- Watchers: 5
- Forks: 85
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PointPillars Pytorch Model Convert To ONNX, And Using TensorRT to Load this IR(ONNX) for Fast Speeding Inference
Welcome to PointPillars(This is origin from nuTonomy/second.pytorch ReadMe.txt).
This repo demonstrates how to reproduce the results from
[_PointPillars: Fast Encoders for Object Detection from Point Clouds_](https://arxiv.org/abs/1812.05784) (to be published at CVPR 2019) on the
[KITTI dataset](http://www.cvlibs.net/datasets/kitti/) by making the minimum required changes from the preexisting
open source codebase [SECOND](https://github.com/traveller59/second.pytorch).
Meanwhile, This part of the code also refers to the open source k0suke-murakami (https://github.com/k0suke-murakami/train_point_pillars) this code.
This is not an official nuTonomy codebase, but it can be used to match the published PointPillars results.
**WARNING: This code is not being actively maintained. This code can be used to reproduce the results in the first version of the paper, https://arxiv.org/abs/1812.05784v1. For an actively maintained repository that can also reproduce PointPillars results on nuScenes, we recommend using [SECOND](https://github.com/traveller59/second.pytorch). We are not the owners of the repository, but we have worked with the author and endorse his code.**
## Getting Started
This is a fork of [SECOND for KITTI object detection](https://github.com/traveller59/second.pytorch) and the relevant
subset of the original README is reproduced here.
### Docker Environments
If you do not waste time on pointpillars envs, please pull my docker virtual environments :
```bash
docker pull smallmunich/suke_pointpillars:v1
```
Attention: when you launch this docker envs, please run this command :
```bash
conda activate pointpillars
```
And Then, you can run train or evaluation or onnx model generate command line.
### Install
#### 1. Clone code
```bash
git clone https://github.com/SmallMunich/nutonomy_pointpillars.git
```
#### 2. Install Python packages
It is recommend to use the Anaconda package manager.
First, use Anaconda to configure as many packages as possible.
```bash
conda create -n pointpillars python=3.6 anaconda
source activate pointpillars
conda install shapely pybind11 protobuf scikit-image numba pillow
conda install pytorch torchvision -c pytorch
conda install google-sparsehash -c bioconda
```
Then use pip for the packages missing from Anaconda.
```bash
pip install --upgrade pip
pip install fire tensorboardX
```
Finally, install SparseConvNet. This is not required for PointPillars, but the general SECOND code base expects this
to be correctly configured. However, I suggest you install the spconv instead of SparseConvNet.
```bash
git clone [email protected]:facebookresearch/SparseConvNet.git
cd SparseConvNet/
bash build.sh
# NOTE: if bash build.sh fails, try bash develop.sh instead
```
Additionally, you may need to install Boost geometry:
```bash
sudo apt-get install libboost-all-dev
```
#### 3. Setup cuda for numba
You need to add following environment variables for numba to ~/.bashrc:
```bash
export NUMBAPRO_CUDA_DRIVER=/usr/lib/x86_64-linux-gnu/libcuda.so
export NUMBAPRO_NVVM=/usr/local/cuda/nvvm/lib64/libnvvm.so
export NUMBAPRO_LIBDEVICE=/usr/local/cuda/nvvm/libdevice
```
#### 4. PYTHONPATH
Add nutonomy_pointpillars/ to your PYTHONPATH.
```bash
export PYTHONPATH=$PYTHONPATH:/your_root_path/nutonomy_pointpillars/
```
### Prepare dataset
#### 1. Dataset preparation
Download KITTI dataset and create some directories first:
```plain
└── KITTI_DATASET_ROOT
├── training <-- 7481 train data
| ├── image_2 <-- for visualization
| ├── calib
| ├── label_2
| ├── velodyne
| └── velodyne_reduced <-- empty directory
└── testing <-- 7580 test data
├── image_2 <-- for visualization
├── calib
├── velodyne
└── velodyne_reduced <-- empty directory
```
Note: PointPillar's protos use ```KITTI_DATASET_ROOT=/data/sets/kitti_second/```.
#### 2. Create kitti infos:
```bash
python create_data.py create_kitti_info_file --data_path=KITTI_DATASET_ROOT
```
#### 3. Create reduced point cloud:
```bash
python create_data.py create_reduced_point_cloud --data_path=KITTI_DATASET_ROOT
```
#### 4. Create groundtruth-database infos:
```bash
python create_data.py create_groundtruth_database --data_path=KITTI_DATASET_ROOT
```
#### 5. Modify config file
The config file needs to be edited to point to the above datasets:
```bash
train_input_reader: {
...
database_sampler {
database_info_path: "/path/to/kitti_dbinfos_train.pkl"
...
}
kitti_info_path: "/path/to/kitti_infos_train.pkl"
kitti_root_path: "KITTI_DATASET_ROOT"
}
...
eval_input_reader: {
...
kitti_info_path: "/path/to/kitti_infos_val.pkl"
kitti_root_path: "KITTI_DATASET_ROOT"
}
```
### Train
```bash
cd ~/second.pytorch/second
python ./pytorch/train.py train --config_path=./configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir
```
* If you want to train a new model, make sure "/path/to/model_dir" doesn't exist.
* If "/path/to/model_dir" does exist, training will be resumed from the last checkpoint.
* Training only supports a single GPU.
* Training uses a batchsize=2 which should fit in memory on most standard GPUs.
* On a single 1080Ti, training xyres_16 requires approximately 20 hours for 160 epochs.
### Evaluate
```bash
cd ~/second.pytorch/second/
python pytorch/train.py evaluate --config_path= configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir
```
* Detection result will saved in model_dir/eval_results/step_xxx.
* By default, results are stored as a result.pkl file. To save as official KITTI label format use --pickle_result=False.
### ONNX IR Generate
### pointpillars pytorch model convert to IR onnx, you should verify some code as follows:
this python file is : second/pyotrch/models/voxelnet.py
```bash
voxel_features = self.voxel_feature_extractor(pillar_x, pillar_y, pillar_z, pillar_i,
num_points, x_sub_shaped, y_sub_shaped, mask)
###################################################################################
# return voxel_features ### onnx voxel_features export
# middle_feature_extractor for trim shape
voxel_features = voxel_features.squeeze()
voxel_features = voxel_features.permute(1, 0)
```
UNCOMMENT this line: return voxel_features
And Then, you can run convert IR command.
```bash
cd ~/second.pytorch/second/
python pytorch/train.py onnx_model_generate --config_path= configs/pointpillars/car/xyres_16.proto --model_dir=/path/to/model_dir
```
### Compare ONNX model With Pytorch Origin model predicts
* If you want to check this convert model about pfe.onnx and rpn.onnx model, please refer to this py-file: check_onnx_valid.py
* Now, we can compare onnx results with pytorch origin model predicts as follows :
* the pfe.onnx and rpn.onnx predicts file is located: "second/pytorch/onnx_predict_outputs", you can see it carefully.
```bash
eval_voxel_features.txt
eval_voxel_features_onnx.txt
eval_rpn_features.txt
eval_rpn_onnx_features.txt
```
* pfe.onnx model compare with origin pfe-layer :
* rpn.onnx model compare with origin rpn-layer :
### Compare ONNX with TensorRT Fast Speed Inference
* First you needs this environments(onnx_tensorrt envs):
```bash
docker pull smallmunich/onnx_tensorrt:latest
```
* If you want to use pfe.onnx and rpn.onnx model for tensorrt inference, please refer to this py-file: tensorrt_onnx_infer.py
* Now, we can compare onnx results with pytorch origin model predicts as follows :
* the pfe.onnx and rpn.onnx predicts file is located: "second/pytorch/onnx_predict_outputs", you can see it carefully.
```bash
pfe_rpn_onnx_outputs.txt
pfe_tensorrt_outputs.txt
rpn_onnx_outputs.txt
rpn_tensorrt_outputs.txt
```
* pfe.onnx model compare with tensorrt pfe-layer :
* rpn.onnx model compare with tensorrt rpn-layer :
### Blog Address
* More Details will be update on my chinese blog:
* export from pytorch to onnx IR blog : https://blog.csdn.net/Small_Munich/article/details/101559424
* onnx compare blog : https://blog.csdn.net/Small_Munich/article/details/102073540
* tensorrt compare blog : https://blog.csdn.net/Small_Munich/article/details/102489147
* wait for update & best wishes.