87 lines
4.3 KiB
Markdown
Executable File
87 lines
4.3 KiB
Markdown
Executable File
# GraspNet graspness
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This project aims to address issues encountered during the migration of the repository [GS-Net](https://github.com/graspnet/graspness_unofficial) to an RTX 4090 GPU.
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The original repo is a fork of paper "Graspness Discovery in Clutters for Fast and Accurate Grasp Detection" (ICCV 2021) by [Zibo Chen](https://github.com/rhett-chen).
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[[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Wang_Graspness_Discovery_in_Clutters_for_Fast_and_Accurate_Grasp_Detection_ICCV_2021_paper.pdf)]
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[[dataset](https://graspnet.net/)]
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[[API](https://github.com/graspnet/graspnetAPI)]
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## Requirements
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- Python 3
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- PyTorch 1.8
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- Open3d 0.8
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- TensorBoard 2.3
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- NumPy
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- SciPy
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- Pillow
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- tqdm
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- MinkowskiEngine
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## Installation
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Get the code.
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```bash
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git clone https://github.com/graspnet/graspness_unofficial.git
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cd graspness_unofficial
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```
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Install packages via Pip.
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```bash
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pip install -r requirements.txt
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```
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Compile and install pointnet2 operators (code adapted from [votenet](https://github.com/facebookresearch/votenet)).
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```bash
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cd pointnet2
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python setup.py install
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```
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Compile and install knn operator (code adapted from [pytorch_knn_cuda](https://github.com/chrischoy/pytorch_knn_cuda)).
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```bash
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cd knn
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python setup.py install
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```
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Install graspnetAPI for evaluation.
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```bash
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git clone https://github.com/graspnet/graspnetAPI.git
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cd graspnetAPI
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pip install .
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```
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For MinkowskiEngine, please refer https://github.com/NVIDIA/MinkowskiEngine
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## Point level Graspness Generation
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Point level graspness label 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_graspness.py](dataset/generate_graspness.py).
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```bash
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cd dataset
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python generate_graspness.py --dataset_root /data3/graspnet --camera_type kinect
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```
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## Simplify dataset
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original dataset grasp_label files have redundant data, We can significantly save the memory cost. The code is in [dataset/simplify_dataset.py](dataset/simplify_dataset.py)
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```bash
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cd dataset
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python simplify_dataset.py --dataset_root /data3/graspnet
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```
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## Training and Testing
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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.
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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.
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## Model Weights
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We provide trained model weights. The model trained with RealSense data is available at [Google drive](https://drive.google.com/file/d/1RfdpEM2y0x98rV28d7B2Dg8LLFKnBkfL/view?usp=sharing) (this model is recommended for real-world application). The model trained with Kinect data is available at [Google drive](https://drive.google.com/file/d/10o5fc8LQsbI8H0pIC2RTJMNapW9eczqF/view?usp=sharing).
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## Results
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Results "In repo" report the model performance of my results without collision detection.
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Evaluation results on Kinect camera:
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| | | Seen | | | Similar | | | Novel | |
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|:--------:|:------:|:----------------:|:----------------:|:------:|:----------------:|:----------------:|:------:|:----------------:|:----------------:|
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| | __AP__ | AP<sub>0.8</sub> | AP<sub>0.4</sub> | __AP__ | AP<sub>0.8</sub> | AP<sub>0.4</sub> | __AP__ | AP<sub>0.8</sub> | AP<sub>0.4</sub> |
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| In paper | 61.19 | 71.46 | 56.04 | 47.39 | 56.78 | 40.43 | 19.01 | 23.73 | 10.60 |
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| In repo | 61.83 | 73.28 | 54.14 | 51.13 | 62.53 | 41.57 | 19.94 | 24.90 | 11.02 |
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## Troubleshooting
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If you meet the torch.floor error in MinkowskiEngine, you can simply solve it by changing the source code of MinkowskiEngine:
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MinkowskiEngine/utils/quantization.py 262，from discrete_coordinates =_auto_floor(coordinates) to discrete_coordinates = coordinates
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## Acknowledgement
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My code is mainly based on Graspnet-baseline https://github.com/graspnet/graspnet-baseline.
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