get_freer_gpu#
- deepinv.utils.get_freer_gpu(verbose=True)[source]#
Returns the GPU device with the most free memory.
Use in conjunction with
torch.cuda.is_available()
. Attempts to usenvidia-smi
withbash
, if these don’t exist then uses torch commands to get free memory.- Parameters:
verbose (bool) – print selected GPU index and memory
- Return torch.device device:
selected torch cuda device.
Examples using get_freer_gpu
:#

Reconstructing an image using the deep image prior.

Random phase retrieval and reconstruction methods.

Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.

Regularization by Denoising (RED) for Super-Resolution.

PnP with custom optimization algorithm (Condat-Vu Primal-Dual)

Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing

Deep Equilibrium (DEQ) algorithms for image deblurring

Unfolded Chambolle-Pock for constrained image inpainting

Self-supervised learning with measurement splitting

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised denoising with the Generalized R2R loss.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised MRI reconstruction with Artifact2Artifact

Imaging inverse problems with adversarial networks