get_freer_gpu

deepinv.utils.get_freer_gpu(verbose=True)

Returns the GPU device with the most free memory.

Use in conjunction with torch.cuda.is_available(). Attempts to use nvidia-smi with bash, 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:

Single photon lidar operator for depth ranging.

Single photon lidar operator for depth ranging.

Stacking and concatenating forward operators.

Stacking and concatenating forward operators.

Reconstructing an image using the deep image prior.

Reconstructing an image using the deep image prior.

Creating your own dataset

Creating your own dataset

Training a reconstruction network.

Training a reconstruction network.

3D diffraction PSF

3D diffraction PSF

A tour of forward sensing operators

A tour of forward sensing operators

Image deblurring with custom deep explicit prior.

Image deblurring with custom deep explicit prior.

Saving and loading models

Saving and loading models

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

Image deblurring with Total-Variation (TV) prior

Image deblurring with Total-Variation (TV) prior

Image inpainting with wavelet prior

Image inpainting with wavelet prior

3D wavelet denoising

3D wavelet denoising

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

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

Vanilla PnP for computed tomography (CT).

Vanilla PnP for computed tomography (CT).

DPIR method for PnP image deblurring.

DPIR method for PnP image deblurring.

Regularization by Denoising (RED) for Super-Resolution.

Regularization by Denoising (RED) for Super-Resolution.

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

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

Uncertainty quantification with PnP-ULA.

Uncertainty quantification with PnP-ULA.

Image reconstruction with a diffusion model

Image reconstruction with a diffusion model

Building your custom sampling algorithm.

Building your custom sampling algorithm.

Implementing DPS

Implementing DPS

Implementing DiffPIR

Implementing DiffPIR

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

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

Vanilla Unfolded algorithm for super-resolution

Vanilla Unfolded algorithm for super-resolution

Learned iterative custom prior

Learned iterative custom prior

Deep Equilibrium (DEQ) algorithms for image deblurring

Deep Equilibrium (DEQ) algorithms for image deblurring

Learned Primal-Dual algorithm for CT scan.

Learned Primal-Dual algorithm for CT scan.

Unfolded Chambolle-Pock for constrained image inpainting

Unfolded Chambolle-Pock for constrained image inpainting

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

Self-supervised learning with measurement splitting

Self-supervised learning with measurement splitting

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised MRI reconstruction with Artifact2Artifact

Self-supervised denoising with the UNSURE loss.

Self-supervised denoising with the UNSURE loss.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the SURE loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised denoising with the Neighbor2Neighbor loss.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning with Equivariant Imaging for MRI.

Self-supervised learning from incomplete measurements of multiple operators.

Self-supervised learning from incomplete measurements of multiple operators.

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks