To setup the environment, we use conda to manage our dependencies.
Our developers use CUDA 10.1 to do experiments.
You can specify the appropriate cudatoolkit version to install on your machine in the environment.yml file, and then run the following to create the conda environment:
conda env create -f environment.ymlYou shall manually install the following dependencies.
# Install mmcv
## CAUTION: The latest versions of mmcv 1.5.3, mmdet 2.25.0 are not well supported, due to bugs in mmdet.
pip install mmcv-full==1.4.3 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html
# Install mmdet
pip install openmim
mim install mmdet==2.20.0
# Install coco panopticapi
pip install git+https://github.com/cocodataset/panopticapi.git
# For visualization
conda install -c conda-forge pycocotools
pip install detectron2==0.5 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html
# If you're using wandb for logging
pip install wandb
wandb login
# If you develop and run openpsg directly, install it from source:
pip install -v -e .
# "-v" means verbose, or more output
# "-e" means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.To explore and download their original Dataset: PSG Dataset
Or you can directly download their original dataset using the link they provided: PSG Website
And some pre-trained models that possibly be used: Weights
Our codebase accesses the datasets from ./data/ and pre-trained models from ./work_dirs/checkpoints/ by default.
Our checkpoint for PSGTR is available at:
Users should change the json filename in config/base/datasets/psg.py (Line 4-5) to './data/psg/psg.json'.
Run the scripts below:
PYTHONPATH='.':$PYTHONPATH \
python tools/test.py \
configs/psgtr/psgtr_r50_psg.py \
work_dirs/PATH_TO_PSGTR_CHECKPOINT \
--eval sgdet
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