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Panoptic Scene Graph Biased Annotation
When I was attempting to replicate the work in this paper, I encountered many compatibility issues with CUDA on the RTX 3090 server. After some adjustments, I finally managed to replicate the test code. Therefore, I will showcase the process below.
name: openpsg
channels:
- pytorch
- conda-forge
dependencies:
- python=3.7
- pytorch=1.7.0
- torchvision=0.8.0
- torchaudio==0.7.0
- cudatoolkit=11.0
- pip
- pip:
- openmim
- pre-commit
conda env create -f environment.yml
conda activate openpsg
# Install mmcv. Here you should notice that this link is based on cu110
pip install mmcv-full==1.4.3 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html
# Install mmdet
pip install openmim
pip install mmdet==2.22.0
# Here when I use mim install, it cannot stop. I don't know the reason. Anyway, pip install works.
# mim install mmdet==2.20.0
# Install coco panopticapi
# When you cannot install based on pip install, I recommend you to git clone this git and install it by hand.
git clone [email protected]:cocodataset/panopticapi.git
cd panopticapi
pip install -v -e .
cd ../
# For visualization
conda install -c conda-forge pycocotools
# here for the same reason, you should find the package based on cuda11.0
pip install detectron2==0.5 -f \
https://dl.fbaipublicfiles.com/detectron2/wheels/cu110/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 .
# finally, here is one dataclasses that terminal shows the error to me. I don't know the effect, but I recommend you to install it.
pip install dataclasses
Based on these two method, I hope you can replicate this code :)
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