Create a Python 3.10.9 environement with CUDA 11.6.2. Then, install PyTorch 1.5.1+ and torchvision 0.6.1+:
conda create -n gal-dino python==3.10.9
conda activate gal-dino
For Pawsey Setonix continue with (as of 22.4.2024):
module load rocm/5.6.1 gcc/12.2.0 gromacs-amd-gfx90a/2023.2
pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6
Otherwise:
conda install -c pytorch pytorch torchvision
In both cases install packages in requirements.txt.
conda install conda-forge::opencv
pip install -r requirements.txt
cd models/dino/ops
CPLUS_INCLUDE_PATH=$CONDA_PREFIX/targets/x86_64-linux/include/ python setup.py build
python setup.py install
# unit test (should see all checking is True). Note you may run out of GPU memory so adjust 'for channels in [..]' in test.py accordingly
python test.py
cd -
Download and extract RadioGalaxyNET data from here (for Pawsey Setonix follow the link and select s3 rclone method after clicking download. You will get a command to paste into Setonix term). We expect the directory structure to be the following:
./RadioGalaxyNET/
annotations/ # annotation json files
train/ # train images
val/ # val images
test/ # test images
To train on a single node with single gpu run:
python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py -c config/DINO/DINO_4scale.py
To ease reproduction of our results we provide model checkpoint here.
Place the model in ./outputs_gal/ directory.
To evaluate on test images with a single GPU run:
python -m torch.distributed.launch --nproc_per_node=1 --use_env main.py -c config/DINO/DINO_4scale.py --eval --resume outputs_gal/checkpoint.pth
Apache 2.0 license.