ImageDataset#
- class deepinv.datasets.ImageDataset[source]#
Bases:
DatasetBase class for imaging datasets in DeepInverse.
All datasets used with DeepInverse should inherit from this class.
We provide the function
check_dataset()to automatically check that__getitem__returns the correct format out of the following options:xi.e a dataset that returns only ground truth;(x, y)i.e. a dataset that returns pairs of ground truth and measurement.xcan be equal totorch.nanif your dataset is ground-truth-free.(x, params)i.e. a dataset of ground truth and dict of physics parameters. Useful for training with online measurements.(x, y, params)i.e. a dataset that returns ground truth, measurements and dict of physics params.
This check is also available for datasets using the method
ImageDataset.check_dataset().Datasets should ideally return
torch.Tensorordeepinv.utils.TensorListso that they are batchable and can be used withdeepinv.If using DeepInverse with your own custom dataset, you should inherit from this class and use
check_dataset()to check your dataset is compatible.
Examples using ImageDataset:#
Imaging inverse problems with adversarial networks
Patch priors for limited-angle computed tomography
Regularization by Denoising (RED) for Super-Resolution.
Self-supervised MRI reconstruction with Artifact2Artifact
Self-supervised learning with Equivariant Imaging for MRI.
Self-supervised learning from incomplete measurements of multiple operators.
Self-supervised denoising with the Neighbor2Neighbor loss.
Self-supervised denoising with the Generalized R2R loss.
Self-supervised learning with measurement splitting
Deep Equilibrium (DEQ) algorithms for image deblurring
Learned Iterative Soft-Thresholding Algorithm (LISTA) for compressed sensing
Unfolded Chambolle-Pock for constrained image inpainting
