## Requirements
- Python 3
- PyTorch 1.6
- Open3d >=0.8
- TensorBoard 2.3
- NumPy
- SciPy
- Pillow
- tqdm
## Installation
Get the code.
```bash
git clone https://github.com/graspnet/graspnet-baseline.git
cd graspnet-baseline
```
Install packages via Pip.
```bash
pip install -r requirements.txt
```
Compile and install pointnet2 operators (code adapted from [votenet](https://github.com/facebookresearch/votenet)).
```bash
cd pointnet2
python setup.py install
```
Compile and install knn operator (code adapted from [pytorch_knn_cuda](https://github.com/chrischoy/pytorch_knn_cuda)).
```bash
cd knn
python setup.py install
```
Install graspnetAPI for evaluation.
```bash
git clone https://github.com/graspnet/graspnetAPI.git
cd graspnetAPI
pip install .
```
## Tolerance Label Generation
Tolerance labels are not included in the original dataset, and need additional generation. Make sure you have downloaded the orginal dataset from [GraspNet](https://graspnet.net/). The generation code is in [dataset/generate_tolerance_label.py](dataset/generate_tolerance_label.py). You can simply generate tolerance label by running the script: (`--dataset_root` and `--num_workers` should be specified according to your settings)
```bash
cd dataset
sh command_generate_tolerance_label.sh
```
Or you can download the tolerance labels from [Google Drive](https://drive.google.com/file/d/1DcjGGhZIJsxd61719N0iWA7L6vNEK0ci/view?usp=sharing)/[Baidu Pan](https://pan.baidu.com/s/1HN29P-csHavJF-R_wec6SQ) and run:
```bash
mv tolerance.tar dataset/
cd dataset
tar -xvf tolerance.tar
```
## Training and Testing
Training examples are shown in [command_train.sh](command_train.sh). `--dataset_root`, `--camera` and `--log_dir` should be specified according to your settings. You can use TensorBoard to visualize training process.
Testing examples are shown in [command_test.sh](command_test.sh), which contains inference and result evaluation. `--dataset_root`, `--camera`, `--checkpoint_path` and `--dump_dir` should be specified according to your settings. Set `--collision_thresh` to -1 for fast inference.
The pretrained weights can be downloaded from:
- `checkpoint-rs.tar`
[[Google Drive](https://drive.google.com/file/d/1hd0G8LN6tRpi4742XOTEisbTXNZ-1jmk/view?usp=sharing)]
[[Baidu Pan](https://pan.baidu.com/s/1Eme60l39tTZrilF0I86R5A)]
- `checkpoint-kn.tar`
[[Google Drive](https://drive.google.com/file/d/1vK-d0yxwyJwXHYWOtH1bDMoe--uZ2oLX/view?usp=sharing)]
[[Baidu Pan](https://pan.baidu.com/s/1QpYzzyID-aG5CgHjPFNB9g)]
`checkpoint-rs.tar` and `checkpoint-kn.tar` are trained using RealSense data and Kinect data respectively.
## Demo
A demo program is provided for grasp detection and visualization using RGB-D images. You can refer to [command_demo.sh](command_demo.sh) to run the program. `--checkpoint_path` should be specified according to your settings (make sure you have downloaded the pretrained weights). The output should be similar to the following example:
