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DeFlow Assets

There are two ways to setup the environment: conda in your desktop and docker container isolate environment.

Docker Environment

Build Docker Image

If you want to build docker with compile all things inside, there are some things need setup first in your own desktop environment:

  • NVIDIA-driver: which I believe most of people may already have it. Try nvidia-smi to check if you have it.
  • Docker: 
    # Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
$(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
  • nvidia-container-toolkit 
    sudo apt update && apt install nvidia-container-toolkit

Then follow this stackoverflow answers:

  1. Edit/create the /etc/docker/daemon.json with content:

    {
   "runtimes": {
      "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
         } 
   },
   "default-runtime": "nvidia" 
}

  2. Restart docker daemon:

    sudo systemctl restart docker

  3. Then you can build the docker image:

    cd DeFlow && docker build -t zhangkin/deflow .

Installation

We will use conda to manage the environment with mamba for faster package installation.

System

Install conda for package management and mamba for faster package installation:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh

Environment

Create base env: [~5 mins]

git clone https://github.com/KTH-RPL/DeFlow
cd DeFlow && git submodule update --init --recursive
mamba env create -f assets/environment.yml

CUDA package (nvcc compiler already installed through conda), the compile time is around 1-5 minutes:

mamba activate seflow
cd assets/cuda/mmcv && python ./setup.py install && cd ../../..

Checking the environment:

mamba activate seflow
python -c "import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.version.cuda)"
python -c "import lightning.pytorch as pl"
python -c "from assets.cuda.mmcv import Voxelization, DynamicScatter;print('success test on mmcv package')"

Other issues

  1. looks like open3d and fire package conflict, not sure

    • need install package like pip install --ignore-installed, ref: pip cannot install distutils installed project, my error: ERROR: Cannot uninstall 'blinker'.
    • need specific werkzeug version for open3d 0.16.0, otherwise error: ImportError: cannot import name 'url_quote' from 'werkzeug.urls'. But need update to solve the problem: pip install --upgrade Flask ref

  2. ImportError: libtorch_cuda.so: undefined symbol: cudaGraphInstantiateWithFlags, version libcudart.so.11.0 The cuda version: pytorch::pytorch-cuda and nvidia::cudatoolkit need be same. Reference link

  3. In cluster have error: pandas ImportError: /lib64/libstdc++.so.6: version GLIBCXX_3.4.29' not foundSolved byexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/proj/berzelius-2023-154/users/x_qinzh/mambaforge/lib`

Contribute

If you want to contribute to new model, here are tips you can follow:

  1. Dataloader: we believe all data could be process to .h5, we named as different scene and inside a scene, the key of each data is timestamp. Check dataprocess/README.md for more details.
  2. Model: All model files can be found here: src/models. You can view deflow and fastflow3d to know how to implement a new model. Don't forget to add to the __init__.py file to import class.
  3. Loss: All loss files can be found here: src/lossfuncs.py. There are three loss functions already inside the file, you can add a new one following the same pattern.
  4. Training: Once you have implemented the model, you can add the model to the config file here: conf/model and train the model using the command python train.py model=your_model_name. One more note here may: if your res_dict from model output is different, you may need add one pattern in def training_step and def validation_step.

All others like eval and vis will be changed according to the model you implemented as you follow the above steps.