load_example

deepinv.utils.load_example(name, **kwargs)

Load example image from the DeepInverse HuggingFace using deepinv.utils.load_url_image() if image file or deepinv.utils.load_torch_url() if torch tensor in .pt file or deepinv.utils.load_np_url() if numpy array in npy or npz file.

Available examples for name include (see the HuggingFace repo for full list):

Table 44 Example Images

Name	Origin	Image size	Domain
barbara.jpeg, butterfly.png	Set14	(3, 512, 512), (3, 256, 256)	natural
cameraman.png	Classic toy image	(1, 512, 512)	natural
CBSD_0010.png	CBSD68	(2, 481, 321)	natural
celeba_example.jpg	CelebA	(3, 1024, 1024)	natural
div2k_valid_hr_0877.png, div2k_valid_lr_bicubic_0877x4.png	GT and measurement from Div2k	(3, 1152, 2040), (3, 288, 510)	natural
leaves.png	Set3C dataset	(3, 256, 256)	natural
mbappe.jpg		(3, 443, 664)	natural
CT100_256x256_0.pt	CT100	(1, 256, 256)	medical
brainweb_t1_ICBM_1mm_subject_0.npy	BrainWeb 3D MRI data	(181, 217, 181)	medical
demo_mini_subset_fastmri_brain_0.pt	FastMRI	(2, 320, 320)	medical
SheppLogan.png	Shepp Logan phantom	(4, 512, 512)	medical
FMD_TwoPhoton_MICE_R_gt_12_avg50.png	FMD	(3, 512, 512)	microscopy
JAX_018_011_RGB.tif	Sample RGB patch from WorldView-3	(3, 1024, 1024)	satellite

Parameters:

• name (str) – filename of the image from the HuggingFace dataset.

• kwargs (dict) – keyword args to pass to deepinv.utils.load_url_image()

Returns:

torch.Tensor containing the image.

Examples using load_example:

Bring your own dataset

Bring your own dataset

Use iterative reconstruction algorithms

Use iterative reconstruction algorithms

Use a pretrained model

Use a pretrained model

5 minute quickstart tutorial

5 minute quickstart tutorial

Benchmarking pretrained denoisers

Benchmarking pretrained denoisers

Inference and fine-tune a foundation model

Inference and fine-tune a foundation model

Reconstructing an image using the deep image prior.

Reconstructing an image using the deep image prior.

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Expected Patch Log Likelihood (EPLL) for Denoising and Inpainting

Tour of blur operators

Tour of blur operators

Random phase retrieval and reconstruction methods.

Random phase retrieval and reconstruction methods.

Tour of forward sensing operators

Tour of forward sensing operators

Ptychography phase retrieval

Ptychography phase retrieval

Spatial unwrapping and modulo imaging

Spatial unwrapping and modulo imaging

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

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

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).

Building your custom MCMC sampling algorithm.

Building your custom MCMC sampling algorithm.

Image reconstruction with a diffusion model

Image reconstruction with a diffusion model

Implementing DiffPIR

Implementing DiffPIR

Building your diffusion posterior sampling method using SDEs

Building your diffusion posterior sampling method using SDEs

Implementing DPS

Implementing DPS

Uncertainty quantification with PnP-ULA.

Uncertainty quantification with PnP-ULA.

Image transformations for Equivariant Imaging

Image transformations for Equivariant Imaging

Image transforms for equivariance & augmentations

Image transforms for equivariance & augmentations