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import copy |
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import collections |
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from typing import Sequence, Optional, Callable |
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from torch.utils.data import Dataset |
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from .subject import Subject |
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class SubjectsDataset(Dataset): |
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"""Base TorchIO dataset. |
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:class:`~torchio.data.dataset.SubjectsDataset` |
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is a reader of 3D medical images that directly |
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inherits from :class:`torch.utils.data.Dataset`. |
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It can be used with a :class:`torch.utils.data.DataLoader` |
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for efficient loading and augmentation. |
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It receives a list of instances of |
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:class:`torchio.data.subject.Subject`. |
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Args: |
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subjects: List of instances of |
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:class:`~torchio.data.subject.Subject`. |
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transform: An instance of :class:`torchio.transforms.Transform` |
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that will be applied to each subject. |
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Example: |
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>>> import torchio as tio |
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>>> subject_a = tio.Subject( |
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... t1=tio.ScalarImage('t1.nrrd',), |
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... t2=tio.ScalarImage('t2.mha',), |
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... label=tio.LabelMap('t1_seg.nii.gz'), |
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... age=31, |
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... name='Fernando Perez', |
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... ) |
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>>> subject_b = tio.Subject( |
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... t1=tio.ScalarImage('colin27_t1_tal_lin.minc',), |
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... t2=tio.ScalarImage('colin27_t2_tal_lin_dicom',), |
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... label=tio.LabelMap('colin27_seg1.nii.gz'), |
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... age=56, |
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... name='Colin Holmes', |
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... ) |
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>>> subjects_list = [subject_a, subject_b] |
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>>> transforms = [ |
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... tio.RescaleIntensity((0, 1)), |
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... tio.RandomAffine(), |
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... ] |
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>>> transform = tio.Compose(transforms) |
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>>> subjects_dataset = tio.SubjectsDataset(subjects_list, transform=transform) |
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>>> subject = subjects_dataset[0] |
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.. _NiBabel: https://nipy.org/nibabel/#nibabel |
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.. _SimpleITK: https://itk.org/Wiki/ITK/FAQ#What_3D_file_formats_can_ITK_import_and_export.3F |
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.. _DICOM: https://www.dicomstandard.org/ |
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.. _affine matrix: https://nipy.org/nibabel/coordinate_systems.html |
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""" |
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def __init__( |
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self, |
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subjects: Sequence[Subject], |
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transform: Optional[Callable] = None, |
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): |
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self._parse_subjects_list(subjects) |
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self.subjects = subjects |
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self._transform: Optional[Callable] |
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self.set_transform(transform) |
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def __len__(self): |
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return len(self.subjects) |
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def __getitem__(self, index: int) -> Subject: |
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if not isinstance(index, int): |
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raise ValueError(f'Index "{index}" must be int, not {type(index)}') |
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subject = self.subjects[index] |
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subject = copy.deepcopy(subject) # cheap since images not loaded yet |
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subject.load() |
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# Apply transform (this is usually the bottleneck) |
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if self._transform is not None: |
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subject = self._transform(subject) |
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return subject |
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def set_transform(self, transform: Optional[Callable]) -> None: |
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"""Set the :attr:`transform` attribute. |
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Args: |
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transform: An instance of :class:`torchio.transforms.Transform`. |
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""" |
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if transform is not None and not callable(transform): |
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raise ValueError( |
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f'The transform must be a callable object, not {transform}') |
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self._transform = transform |
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@staticmethod |
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def _parse_subjects_list(subjects_list: Sequence[Subject]) -> None: |
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# Check that it's list or tuple |
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if not isinstance(subjects_list, collections.abc.Sequence): |
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raise TypeError( |
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f'Subject list must be a sequence, not {type(subjects_list)}') |
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# Check that it's not empty |
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if not subjects_list: |
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raise ValueError('Subjects list is empty') |
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# Check each element |
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for subject in subjects_list: |
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if not isinstance(subject, Subject): |
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message = ( |
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'Subjects list must contain instances of torchio.Subject,' |
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f' not "{type(subject)}"' |
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) |
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raise TypeError(message) |
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fepegar /
torchio
