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Installation

1) Environment requirements

  • Python 3.x
  • Pytorch 1.11
  • CUDA 9.2 or higher

The following installation guild suppose python=3.7 pytorch=1.11 and cuda=10.2. You may change them according to your system.

Create a conda virtual environment and activate it.

conda create -n softgroup python=3.7
conda activate softgroup

2) Clone the following two repositories.

git clone https://github.com/thangvubk/SoftGroup.git
git clone https://github.com/dbolya/yolact.git

note: Please put the above two projects in the same directory.

3) Install the dependencies.

conda install pytorch cudatoolkit=10.2 -c pytorch
pip install spconv-cu102
pip install -r requirements.txt
pip install opencv-python
pip install pycocotools
pip install PyQt5
pip install opencv-contrib-python==4.5.2.52
pip install pybullet
pip install open3D==0.8.0

4) Install build requirement.

sudo apt-get install libsparsehash-dev

5) Setup

python setup.py build_ext develop

6) Replace files

The files that need to be replaced are as follows.

SoftGroup
├── configs
│   ├── softgroup_s3dis_backbone_fold5.yaml
│   ├── softgroup_s3dis_fold5.yaml
├── data
│   ├── coco.py
│   ├── config.py
├── dataset
│   ├── s3dis
│       ├── downsample.py
│       ├── prepare_data.sh
│       ├── prepare_data_inst.py
│       ├── prepare_data_inst_gttxt.py
├── train_softgroup.py
├── train.py
├── yolact.py

Prepare Data

Prepare your own 3D model files (urdf format)

1) Generate dataset

Please creates folders as follows.

dataset
├── sense_data
│   ├── depth
│   ├── ints_img
│   ├── label
│   ├── label_img
│   ├── rgb_img
│   ├── Stanford3dDataset_v1.2
│       ├── Area_1
│       ├── Area_2
│       ├── Area_3
│       ├── Area_4
│       ├── Area_5
│       ├── Area_6
│   ├── data
│       ├── banana
│       ├── bowl
│       ├── ...

Then run:

conda activate softgroup
cd create_data
python create_val_dataset.py

note: The 3D model path needs to be modified in create_val_dataset.py

2) Prepare 2D instance segmentation label

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install
pip install git+git://github.com/waspinator/[email protected]

Put ./create_data/create_json.py to ./cocoapi/PythonAPI

python create_name_list.py
python create_json.py

After running the script, you should get two files instances_train2017.json and instances_val2017.json.

Put the above two files to ./SoftGroup/data/coco/annotations.

Put the images under the ./dataset/sense_data/rbg_img folder into ./SoftGroup/data/coco/images.

3) Prepare 3D instance segmentation label

Put the ./dataset/sense_data/Stanford3dDataset_v1.2 to ./SoftGroup/dataset/s3dis/ folder.

Preprocess data

cd SoftGroup/dataset/s3dis
bash prepare_data.sh

4) Prepare shape estimation dataset

The generated dataset is stored in the ./dataset/sense_data/data directory

cd create_data
python create_scale_obj_dataset.py

Train and Test

1) Train 3D instance segmentation model

python train_softgroup.py --config=softgroup_s3dis_backbone_fold5.yaml
python train_softgroup.py --config=softgroup_s3dis_fold5.yaml

2) Train 2D instance segmentation model

python train.py --config=yolact_resnet50_config

3) Train 2D+3D instance segmentation model

First, Replace the following files with the files in the replace_files directory

SoftGroup
├── softgroup
│   ├── data
│       ├── custom.py
│       ├── s3dis.py
│       ├── __init__.py
│   ├── model
│       ├── softgroup.py
│   ├── util
│       ├── optim.py

Then run

python train_2D+3D.py

4) Train shape estimation model

python train_test_cp.py

5) Eval 2D+3D instance segmentation model

python eval_test.py

6) Eval overall_pipline

python eval_test_scale.py

7) Eval only 2D+shape-estimation

python eval_ints_scale.py

8) Eval only 3D+shape-estimation

python test_scale.py