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
:
Imaging inverse problems with adversarial networks
Single photon lidar operator for depth ranging.
Stacking and concatenating forward operators.
Reconstructing an image using the deep image prior.
Training a reconstruction network.
A tour of forward sensing operators
Image deblurring with custom deep explicit prior.
Random phase retrieval and reconstruction methods.
Image deblurring with Total-Variation (TV) prior
Image inpainting with wavelet prior
Plug-and-Play algorithm with Mirror Descent for Poisson noise inverse problems.
Vanilla PnP for computed tomography (CT).
DPIR method for PnP image deblurring.
Regularization by Denoising (RED) for Super-Resolution.
PnP with custom optimization algorithm (Condat-Vu Primal-Dual)
Uncertainty quantification with PnP-ULA.
Image reconstruction with a diffusion model
Building your custom sampling algorithm.
Self-supervised learning with measurement splitting
Image transformations for Equivariant Imaging
Self-supervised MRI reconstruction with Artifact2Artifact
Self-supervised denoising with the UNSURE loss.
Self-supervised denoising with the SURE loss.
Self-supervised denoising with the Neighbor2Neighbor loss.
Self-supervised learning with Equivariant Imaging for MRI.
Self-supervised learning from incomplete measurements of multiple operators.
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
Vanilla Unfolded algorithm for super-resolution
Learned iterative custom prior
Deep Equilibrium (DEQ) algorithms for image deblurring
Learned Primal-Dual algorithm for CT scan.
Unfolded Chambolle-Pock for constrained image inpainting