Deep Inversion Validation Library’s documentation

This is the documentation of DIVal, a library for testing and comparing deep learning based methods for inverse problems written in python.

To get started, we recommend having a look at the example scripts.

Below is a list of some fundamental functions and classes.

get_standard_dataset(name, **kwargs)

Return a standard dataset by name.

Dataset([space])

Dataset base class.

get_reference_reconstructor(...[, pretrained])

Return a reference reconstructor.

Reconstructor([reco_space, ...])

Abstract reconstructor base class.

Measure([short_name])

Abstract base class for measures used for evaluation.

TaskTable([name])

Task table containing reconstruction tasks to evaluate.

Standard datasets

One main goal of this library is to provide public standard datasets suitable for deep learning. Currently, the following datasets are included:

  • 'ellipses':

    A typical synthetical CT dataset with ellipse phantoms.

  • 'lodopab':

    The public LoDoPaB-CT dataset, based on real CT reconstructions from the public LIDC-IDRI dataset. See also the LoDoPaBDataset class.

These datasets can be accessed by calling dival.get_standard_dataset(), e.g.

from dival import get_standard_dataset
dataset = get_standard_dataset('ellipses')

Note on astra and CUDA: The CT datasets come with a ray_trafo attribute providing the forward operator. There are different backend implementations for it, the default is 'astra_cuda'. This requires both the astra-toolbox and a CUDA-enabled GPU being available. In order to use the (slow) scikit-image backend instead, you can pass impl='skimage' to get_standard_dataset. If astra is available but CUDA is not, impl='astra_cpu' is preferred. The latest development version of astra can be installed with conda install astra-toolbox -c astra-toolbox/label/dev.

Reference reconstructors

For some reconstructor classes on some standard datasets reference configurations are provided. These configurations include hyper parameters and pretrained learned parameters for a LearnedReconstructor.

A reference reconstructor can be retrieved by calling dival.get_reference_reconstructor(), e.g.

from dival import get_reference_reconstructor
reconstructor = get_reference_reconstructor('fbp', 'ellipses')

We are happy to receive your feedback (e.g. via github issues) if you think a reference configuration should be improved or another reconstructor should be included.

Metrics for image reconstruction

To evaluate image reconstruction performance on datasets that include ground truth images, full-reference methods like PSNR and SSIM can be used.

Both PSNR and SSIM depend on the data range, which is the difference between the maximum and the minimum possible values. While e.g. scikit-image uses a global data range of 1.0 for PSNR and 2.0 for SSIM by default (in case of a floating point data type), an image-dependent default value is used in dival. If no data range is specified explicitly, it is determined as np.max(gt)-np.min(gt) from the respective ground truth image gt. While we consider it to be a drawback of this approach that the metric differs for different ground truth images, it has the benefit to be quite flexible in absence of a priori knowledge. If known, we recommend specifying the data range explicitly.

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