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INSTALL.md

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Installation

Docker Installation (recommended)

Docker is a self-contained environment that can be used to run the code without having to install the dependencies. We have built a docker image with Ubuntu 20.04, ROS Noetic, and PyTorch 1.13.1. Make sure you have nvidia-docker installed. The newer version of PyTorch means that the host machine must have a CUDA 11.7 compatible driver. At the root of the repository, run the following command to build the container:

docker compose build base

Run the container:

docker compose run --rm gpu

The base container can build the code without having access to a GPU, and is useful for exploring the structure of the project and environment. The actual code must be run from a container that has access to a GPU.

The docker-compose file at the root of the repository is configured to mount the current directory into the container. This means that any changes you make to the code on your host machine will be reflected in the container.

Manual Installation

The code was tested on Ubuntu 20.04 using Python 3.8 and PyTorch v1.13.1. NVIDIA GPUs are needed for both training and testing. Reference the Dockerfile for build instructions and dependencies.

  1. [Optional but recommended] create a new virtual environment and activate it.

    python -m venv venv
source venv/bin/activate

  2. Install pytorch 1.13.1 with CUDA 11.7

    pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu117

  3. Install COCOAPI:

    cd /opt
git clone https://github.com/cocodataset/cocoapi.git && \
   cd cocoapi &&\
   git checkout 8c9bcc3 && \
   cd PythonAPI && \
   make && \
   python setup.py install

  4. Compile deformable convolutional (forked from DCNv2).

    git clone https://github.com/acmiyaguchi/DCNv2.git && \
   cd DCNv2 && \
   git checkout pytorch_1.11 && \
   # https://github.com/facebookresearch/pytorch3d/issues/318
   FORCE_CUDA=1 \
   # https://github.com/pytorch/extension-cpp/issues/71
   TORCH_CUDA_ARCH_LIST="3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX" \
   python setup.py build develop

  5. Clone this repo:

    GKNet_ROOT=<path to clone>
git clone https://github.com/ivalab/GraspKpNet.git $GKNet_ROOT

  6. Install the requirements

    pip install -r requirements.txt

  7. [Optional, only required if you are using extremenet or multi-scale testing] Compile NMS if your want to use multi-scale testing or test ExtremeNet.

    cd $GKNet_ROOT/vendor/nms
make

Downloading models

In addition to the model weight and dataset links referenced in the main README, we have hosted the following on a public mirror. The purpose is to provide a stable link for a subset of models used during model inference. In general, prefix the filename with https://f004.backblazeb2.com/file/acm-ivalab/GraspKpNet/ to download the file. Please use discretion when downloading the larger files.