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import warnings |
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from typing import Union, Sequence, Dict |
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import torch |
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import numpy as np |
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from ...data.subject import Subject |
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from .. import Transform |
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from . import RandomTransform |
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TypeTransformsDict = Union[Dict[Transform, float], Sequence[Transform]] |
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class Compose(Transform): |
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"""Compose several transforms together. |
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Args: |
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transforms: Sequence of instances of |
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:class:`~torchio.transforms.Transform`. |
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**kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. |
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""" |
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def __init__(self, transforms: Sequence[Transform], **kwargs): |
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super().__init__(**kwargs) |
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if not transforms: |
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raise ValueError('The list of transforms is empty') |
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for transform in transforms: |
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if not callable(transform): |
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message = ( |
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'One or more of the objects passed to the Compose transform' |
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f' are not callable: "{transform}"' |
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) |
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raise TypeError(message) |
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self.transforms = transforms |
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def __len__(self): |
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return len(self.transforms) |
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def __getitem__(self, index) -> Transform: |
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return self.transforms[index] |
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def __repr__(self) -> str: |
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return repr(self.transforms) |
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def apply_transform(self, subject: Subject) -> Subject: |
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for transform in self.transforms: |
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subject = transform(subject) |
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return subject |
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def is_invertible(self) -> bool: |
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return all(t.is_invertible() for t in self.transforms) |
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def inverse(self, warn: bool = True) -> Transform: |
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"""Return a composed transform with inverted order and transforms. |
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Args: |
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warn: Issue a warning if some transforms are not invertible. |
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""" |
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transforms = [] |
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for transform in self.transforms: |
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if transform.is_invertible(): |
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transforms.append(transform.inverse()) |
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elif warn: |
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message = f'Skipping {transform.name} as it is not invertible' |
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warnings.warn(message, RuntimeWarning) |
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transforms.reverse() |
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if transforms: |
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result = Compose(transforms) |
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else: # return noop if no invertible transforms are found |
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def result(x): return x # noqa: E704 |
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if warn: |
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warnings.warn('No invertible transforms found', RuntimeWarning) |
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return result |
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class OneOf(RandomTransform): |
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"""Apply only one of the given transforms. |
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Args: |
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transforms: Dictionary with instances of |
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:class:`~torchio.transforms.Transform` as keys and |
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probabilities as values. Probabilities are normalized so they sum |
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to one. If a sequence is given, the same probability will be |
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assigned to each transform. |
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**kwargs: See :class:`~torchio.transforms.Transform` for additional keyword arguments. |
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Example: |
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>>> import torchio as tio |
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>>> colin = tio.datasets.Colin27() |
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>>> transforms_dict = { |
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... tio.RandomAffine(): 0.75, |
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... tio.RandomElasticDeformation(): 0.25, |
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... } # Using 3 and 1 as probabilities would have the same effect |
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>>> transform = tio.OneOf(transforms_dict) |
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>>> transformed = transform(colin) |
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""" |
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def __init__( |
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self, |
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transforms: TypeTransformsDict, |
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**kwargs |
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): |
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super().__init__(**kwargs) |
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self.transforms_dict = self._get_transforms_dict(transforms) |
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def apply_transform(self, subject: Subject) -> Subject: |
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weights = torch.Tensor(list(self.transforms_dict.values())) |
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index = torch.multinomial(weights, 1) |
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transforms = list(self.transforms_dict.keys()) |
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transform = transforms[index] |
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transformed = transform(subject) |
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return transformed |
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def _get_transforms_dict( |
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self, |
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transforms: TypeTransformsDict, |
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) -> Dict[Transform, float]: |
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if isinstance(transforms, dict): |
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transforms_dict = dict(transforms) |
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self._normalize_probabilities(transforms_dict) |
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else: |
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try: |
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p = 1 / len(transforms) |
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except TypeError as e: |
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message = ( |
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'Transforms argument must be a dictionary or a sequence,' |
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f' not {type(transforms)}' |
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) |
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raise ValueError(message) from e |
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transforms_dict = {transform: p for transform in transforms} |
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for transform in transforms_dict: |
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if not isinstance(transform, Transform): |
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message = ( |
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'All keys in transform_dict must be instances of' |
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f'torchio.Transform, not "{type(transform)}"' |
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) |
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raise ValueError(message) |
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return transforms_dict |
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@staticmethod |
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def _normalize_probabilities( |
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transforms_dict: Dict[Transform, float], |
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) -> None: |
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probabilities = np.array(list(transforms_dict.values()), dtype=float) |
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if np.any(probabilities < 0): |
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message = ( |
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'Probabilities must be greater or equal to zero,' |
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f' not "{probabilities}"' |
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) |
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raise ValueError(message) |
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if np.all(probabilities == 0): |
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message = ( |
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'At least one probability must be greater than zero,' |
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f' but they are "{probabilities}"' |
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) |
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raise ValueError(message) |
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for transform, probability in transforms_dict.items(): |
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transforms_dict[transform] = probability / probabilities.sum() |
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fepegar /
torchio
