Source code for torchio.data.dataset

import copy
from typing import Sequence, Optional, Callable, Iterable

from torch.utils.data import Dataset

from .subject import Subject


[docs]class SubjectsDataset(Dataset): """Base TorchIO dataset. Reader of 3D medical images that directly inherits from the PyTorch :class:`~torch.utils.data.Dataset`. It can be used with a PyTorch :class:`~torch.utils.data.DataLoader` for efficient loading and augmentation. It receives a list of instances of :class:`~torchio.Subject` and an optional transform applied to the volumes after loading. Args: subjects: List of instances of :class:`~torchio.Subject`. transform: An instance of :class:`~torchio.transforms.Transform` that will be applied to each subject. load_getitem: Load all subject images before returning it in :meth:`__getitem__`. Set it to ``False`` if some of the images will not be needed during training. Example: >>> import torchio as tio >>> subject_a = tio.Subject( ... t1=tio.ScalarImage('t1.nrrd',), ... t2=tio.ScalarImage('t2.mha',), ... label=tio.LabelMap('t1_seg.nii.gz'), ... age=31, ... name='Fernando Perez', ... ) >>> subject_b = tio.Subject( ... t1=tio.ScalarImage('colin27_t1_tal_lin.minc',), ... t2=tio.ScalarImage('colin27_t2_tal_lin_dicom',), ... label=tio.LabelMap('colin27_seg1.nii.gz'), ... age=56, ... name='Colin Holmes', ... ) >>> subjects_list = [subject_a, subject_b] >>> transforms = [ ... tio.RescaleIntensity(out_min_max=(0, 1)), ... tio.RandomAffine(), ... ] >>> transform = tio.Compose(transforms) >>> subjects_dataset = tio.SubjectsDataset(subjects_list, transform=transform) >>> subject = subjects_dataset[0] .. _NiBabel: https://nipy.org/nibabel/#nibabel .. _SimpleITK: https://itk.org/Wiki/ITK/FAQ#What_3D_file_formats_can_ITK_import_and_export.3F .. _DICOM: https://www.dicomstandard.org/ .. _affine matrix: https://nipy.org/nibabel/coordinate_systems.html .. tip:: To quickly iterate over the subjects without loading the images, use :meth:`dry_iter()`. """ # noqa: E501 def __init__( self, subjects: Sequence[Subject], transform: Optional[Callable] = None, load_getitem: bool = True, ): self._parse_subjects_list(subjects) self._subjects = subjects self._transform: Optional[Callable] self.set_transform(transform) self.load_getitem = load_getitem def __len__(self): return len(self._subjects) def __getitem__(self, index: int) -> Subject: if not isinstance(index, int): raise ValueError(f'Index "{index}" must be int, not {type(index)}') subject = self._subjects[index] subject = copy.deepcopy(subject) # cheap since images not loaded yet if self.load_getitem: subject.load() # Apply transform (this is usually the bottleneck) if self._transform is not None: subject = self._transform(subject) return subject
[docs] def dry_iter(self): """Return the internal list of subjects. This can be used to iterate over the subjects without loading the data and applying any transforms:: >>> names = [subject.name for subject in dataset.dry_iter()] """ return self._subjects
[docs] def set_transform(self, transform: Optional[Callable]) -> None: """Set the :attr:`transform` attribute. Args: transform: Callable object, typically an subclass of :class:`torchio.transforms.Transform`. """ if transform is not None and not callable(transform): message = ( 'The transform must be a callable object,' f' but it has type {type(transform)}' ) raise ValueError(message) self._transform = transform
@staticmethod def _parse_subjects_list(subjects_list: Iterable[Subject]) -> None: # Check that it's an iterable try: iter(subjects_list) except TypeError as e: message = ( f'Subject list must be an iterable, not {type(subjects_list)}' ) raise TypeError(message) from e # Check that it's not empty if not subjects_list: raise ValueError('Subjects list is empty') # Check each element for subject in subjects_list: if not isinstance(subject, Subject): message = ( 'Subjects list must contain instances of torchio.Subject,' f' not "{type(subject)}"' ) raise TypeError(message)