Skip to content

Latest commit

 

History

History
197 lines (147 loc) · 4.86 KB

CONTRIBUTING.md

File metadata and controls

197 lines (147 loc) · 4.86 KB
Raw

Contributing

Thank you for interest in contributing to Hysia!

V2 is under active development currently. You are welcome to create a issue, pull request here. We will credit them into V2.

Setup Environment

Setup the developing environment by downloading data and install environments locally. All script is running from the project root.

1. Download Data

Here is a summary of required data / packed libraries.

File name Description File ID Unzipped directory
hysia-decoder-lib-linux-x86-64.tar.gz Hysia Decoder dependent lib 1fi-MSLLsJ4ALeoIP4ZjUQv9DODc1Ha6O hysia/core/HysiaDecode
weights.tar.gz Pretrained model weights 1O1-QT8HJRL1hHfkRqprIw24ahiEMkfrX .
object-detection-data.tar.gz Object detection data 1an7KGVer6WC3Xt2yUTATCznVyoSZSlJG third/object_detection

For users without Google Drive access, you can download from Baidu Wangpan and unzip files correspondingly.

Option 1: auto-download

bash scripts/download-data.sh

Option 2: manually download

1. Download Hysia Decoder dependent libraries and unzip it:

deocder_path=hysia/core/HysiaDecode
mv hysia-decoder-lib-linux-x86-64.tar.gz "${deocder_path}"
cd "${deocder_path}"
tar xvzf hysia-decoder-lib-linux-x86-64.tar.gz
rm -f hysia-decoder-lib-linux-x86-64.tar.gz
cd -

2. Download pretrained model weights and unzip it:

tar xvzf weights.tar.gz
# and remove the weights zip
rm -f weights.tar.gz

3. Download object detection data in third-party library and unzip it:

mv object-detection-data.tar.gz third/object_detection
cd third/object_detection
tar xvzf object-detction-data.tar.gz
rm object-detection-data.tar.gz
cd -

2. Installation

These scripts are tested on Ubuntu 16.04 x86-64 with CUDA9.0 and CUDNN7. Docker is recommended for other system version.

Requirements:

  • Conda
  • Nvidia driver
  • CUDA = 9
  • CUDNN
  • g++
  • zlib1g-dev

Option 1: auto-installation

Run the following script:

# Execute this script at project root
bash scripts/install-build.sh

Option 2: step-by-step installation

# Firstly, make sure that your Conda is setup correctly and have CUDA,
# CUDNN installed on your system.

# Install Conda virtual environment
conda env create -f environment.yml

conda activate V2O

export BASE_DIR=${PWD}

# Compile HysiaDecoder
cd "${BASE_DIR}"/hysia/core/HysiaDecode
make clean
# If nvidia driver is higher than 396, set NV_VERSION=<your nvidia major version>
make NV_VERSION=<your nvidia driver major version>

# Build mmdetect
# ROI align op
cd "${BASE_DIR}"/third/
cd mmdet/ops/roi_align
rm -rf build
python setup.py build_ext --inplace

# ROI pool op
cd ../roi_pool
rm -rf build
python setup.py build_ext --inplace

# NMS op
cd ../nms
make clean
make PYTHON=python

# Initialize Django
# This will prompt some input from you
cd "${BASE_DIR}"/server
python -m grpc_tools.protoc -I . --python_out=. --grpc_python_out=. protos/api2msl.proto

python manage.py makemigrations restapi
python manage.py migrate
python manage.py loaddata dlmodels.json
python manage.py createsuperuser

unset BASE_DIR

(Optional) Rebuild the frontend

If the frontend is updated, please run the following:

Option 1: auto-build

cd server/react-build
bash ./build.sh
cd ..

Option 2: manually build

cd server/react-front

# Install dependencies
npm i
npm audit fix

# Build static files
npm run-script build

# fix js path
python fix_js_path.py build

# create a copy of build static files
mkdir -p tmp
cp -r build/* tmp/

# move static folder to static common
mv tmp/*html ../templates/
mv tmp/* ../static/
cp -rfl ../static/static/* ../static/
rm -r ../static/static/

# clear temp
rm -r tmp

Testing

Demo

Start model server:

cd server

# Start model server
python start_model_servers.py

Start Django server

cd server

# Run Django
python manage.py runserver 0.0.0.0:8000