universome / Inr Gan
Adversarial Generation of Continuous Images [CVPR 2021]
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Official implementation of Adversarial Generation of Continuous Images
Disclaimer
Right now, the repo is being rewritten/refactored from firelab to pytorch-lightning, so you may like to wait a week or two for the update before starting building it.
INR-based GAN
Generating images in their implicit form.
To run the reconstruction model, you should first install firelab library:
pip install firelab
To run the experiment on a single GPU, use the following command:
CUDA_VISIBLE_DEVICES=0,1,2,3 horovodrun -np 4 --mpi-args="--oversubscribe" python src/run.py -c configs/inr-gan.yml --config.dataset lsun_church_outdoor --config.distributed_training.enabled true --config.logging.freqs.images 1000 --config.logging.freqs.fid 10000
To run a multi-gpu training, we use horovod which is launched via:
horovodrun -np NUM_GPUS --mpi-args=--oversubscribe python src/run.py -c configs/inr-gan.yml --config.distributed_training.enabled true --config.dataset lsun_256 --config.hp.batch_size BATCH_SIZE
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