bob.med.tb.data.utils¶

Common utilities

Classes

SampleListDataset(samples[, transforms])

PyTorch dataset wrapper around Sample lists

class bob.med.tb.data.utils.SampleListDataset(samples, transforms=[])[source]¶

Bases: Generic[torch.utils.data.dataset.T_co]

PyTorch dataset wrapper around Sample lists

A transform object can be passed that will be applied to the image, ground truth and mask (if present).

It supports indexing such that dataset[i] can be used to get ith sample.

Parameters

samples (list) – A list of bob.med.tb.data.sample.Sample objects
transforms (list, Optional) – a list of transformations to be applied to both image and ground-truth data. Notice a last transform (torchvision.transforms.transforms.ToTensor) is always applied - you do not need to add that.

copy(transforms=None)[source]¶

Returns a deep copy of itself, optionally resetting transforms

Parameters: transforms (list, Optional) – An optional list of transforms to set in the copy. If not specified, use self.transforms.

random_permute(feature)[source]¶

Randomly permute feature values from all samples

Useful for permutation feature importance computation