torchio.transforms.transform.Transform.name() - Code Metrics - Inspection of "Fix flake8 length" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — master (#403)

by Fernando

created 2021-01-01 13:42 UTC

torchio.transforms.transform.Transform.name() A

↳ Parent: torchio.transforms.transform

Complexity

Conditions

Size

Total Lines	3
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	3
nop	1
dl	0
loc	3
rs	10
c	0
b	0
f	0

import copy
import numbers
import warnings
from typing import Union, Tuple
from abc import ABC, abstractmethod
from contextlib import contextmanager

import torch
import numpy as np
import SimpleITK as sitk

from ..utils import to_tuple
from ..data.subject import Subject
from ..data.io import nib_to_sitk, sitk_to_nib
from ..data.image import LabelMap
from ..typing import (
    TypeKeys,
    TypeData,
    TypeNumber,
    TypeCallable,
    TypeTripletInt,
)
from .interpolation import Interpolation, get_sitk_interpolator
from .data_parser import DataParser, TypeTransformInput

TypeSixBounds = Tuple[int, int, int, int, int, int]
TypeBounds = Union[
    int,
    TypeTripletInt,
    TypeSixBounds,
]
TypeMaskingMethod = Union[str, TypeCallable, TypeBounds, None]
anat_axes = 'Left', 'Right', 'Anterior', 'Posterior', 'Inferior', 'Superior'


class Transform(ABC):
    """Abstract class for all TorchIO transforms.

    All subclasses must overwrite
    :meth:`Transform.apply_transform`,
    which takes data, applies some transformation and returns the result.

    The input can be an instance of
    :class:`torchio.Subject`,
    :class:`torchio.Image`,
    :class:`numpy.ndarray`,
    :class:`torch.Tensor`,
    :class:`SimpleITK.Image`,
    or :class:`dict`.

    Args:
        p: Probability that this transform will be applied.
        copy: Make a shallow copy of the input before applying the transform.
        include: Sequence of strings with the names of the only images to which
            the transform will be applied.
            Mandatory if the input is a :class:`dict`.
        exclude: Sequence of strings with the names of the images to which the
            the transform will not be applied, apart from the ones that are
            excluded because of the transform type.
            For example, if a subject includes an MRI, a CT and a label map,
            and the CT is added to the list of exclusions of an intensity
            transform such as :class:`~torchio.transforms.RandomBlur`,
            the transform will be only applied to the MRI, as the label map is
            excluded by default by spatial transforms.
    """
    def __init__(
            self,
            p: float = 1,
            copy: bool = True,
            include: TypeKeys = None,
            exclude: TypeKeys = None,
            keys: TypeKeys = None,
            ):
        self.probability = self.parse_probability(p)
        self.copy = copy
        if keys is not None:
            message = (
                'The "keys" argument is deprecated and will be removed in the'
                ' future. Use "include" instead.'
            )
            warnings.warn(message, DeprecationWarning)
            include = keys
        self.include, self.exclude = self.parse_include_and_exclude(
            include, exclude)

    def __call__(
            self,
            data: TypeTransformInput,
            ) -> TypeTransformInput:
        """Transform data and return a result of the same type.

        Args:
            data: Instance of 1) :class:`~torchio.Subject`, 4D
                :class:`torch.Tensor` or :class:`numpy.ndarray` with dimensions
                :math:`(C, W, H, D)`, where :math:`C` is the number of channels
                and :math:`W, H, D` are the spatial dimensions. If the input is
                a tensor, the affine matrix will be set to identity. Other
                valid input types are a SimpleITK image, a
                :class:`torchio.Image`, a NiBabel Nifti1 image or a
                :class:`dict`. The output type is the same as the input type.
        """
        if torch.rand(1).item() > self.probability:
            return data
        data_parser = DataParser(data, keys=self.include)
        subject = data_parser.get_subject()
        if self.copy:
            subject = copy.copy(subject)
        with np.errstate(all='raise'):
            transformed = self.apply_transform(subject)
        self.add_transform_to_subject_history(transformed)
        for image in transformed.get_images(intensity_only=False):
            ndim = image.data.ndim
            assert ndim == 4, f'Output of {self.name} is {ndim}D'

        output = data_parser.get_output(transformed)
        return output

    def __repr__(self):
        if hasattr(self, 'args_names'):
            names = self.args_names
            args_strings = [f'{arg}={getattr(self, arg)}' for arg in names]
            if hasattr(self, 'invert_transform') and self.invert_transform:
                args_strings.append('invert=True')
            args_string = ', '.join(args_strings)
            return f'{self.name}({args_string})'
        else:
            return super().__repr__()

    @property
    def name(self):
        return self.__class__.__name__

    @abstractmethod
    def apply_transform(self, subject: Subject):
        raise NotImplementedError

    def add_transform_to_subject_history(self, subject):
        from .augmentation import RandomTransform
        from . import Compose, OneOf, CropOrPad, EnsureShapeMultiple
        from .preprocessing.label import SequentialLabels
        call_others = (
            RandomTransform,
            Compose,
            OneOf,
            CropOrPad,
            EnsureShapeMultiple,
            SequentialLabels,
        )
        if not isinstance(self, call_others):
            subject.add_transform(self, self._get_reproducing_arguments())

    @staticmethod
    def to_range(n, around):
        if around is None:
            return 0, n
        else:
            return around - n, around + n

    def parse_params(self, params, around, name, make_ranges=True, **kwargs):
        params = to_tuple(params)
        # d or (a, b)
        if len(params) == 1 or (len(params) == 2 and make_ranges):
            params *= 3  # (d, d, d) or (a, b, a, b, a, b)
        if len(params) == 3 and make_ranges:  # (a, b, c)
            items = [self.to_range(n, around) for n in params]
            # (-a, a, -b, b, -c, c) or (1-a, 1+a, 1-b, 1+b, 1-c, 1+c)
            params = [n for prange in items for n in prange]
        if make_ranges:
            if len(params) != 6:
                message = (
                    f'If "{name}" is a sequence, it must have length 2, 3 or'
                    f' 6, not {len(params)}'
                )
                raise ValueError(message)
            for param_range in zip(params[::2], params[1::2]):
                self._parse_range(param_range, name, **kwargs)
        return tuple(params)

    @staticmethod
    def _parse_range(
            nums_range: Union[TypeNumber, Tuple[TypeNumber, TypeNumber]],
            name: str,
            min_constraint: TypeNumber = None,
            max_constraint: TypeNumber = None,
            type_constraint: type = None,
            ) -> Tuple[TypeNumber, TypeNumber]:
        r"""Adapted from :class:`torchvision.transforms.RandomRotation`.

        Args:
            nums_range: Tuple of two numbers :math:`(n_{min}, n_{max})`,
                where :math:`n_{min} \leq n_{max}`.
                If a single positive number :math:`n` is provided,
                :math:`n_{min} = -n` and :math:`n_{max} = n`.
            name: Name of the parameter, so that an informative error message
                can be printed.
            min_constraint: Minimal value that :math:`n_{min}` can take,
                default is None, i.e. there is no minimal value.
            max_constraint: Maximal value that :math:`n_{max}` can take,
                default is None, i.e. there is no maximal value.
            type_constraint: Precise type that :math:`n_{max}` and
                :math:`n_{min}` must take.

        Returns:
            A tuple of two numbers :math:`(n_{min}, n_{max})`.

        Raises:
            ValueError: if :attr:`nums_range` is negative
            ValueError: if :math:`n_{max}` or :math:`n_{min}` is not a number
            ValueError: if :math:`n_{max} \lt n_{min}`
            ValueError: if :attr:`min_constraint` is not None and
                :math:`n_{min}` is smaller than :attr:`min_constraint`
            ValueError: if :attr:`max_constraint` is not None and
                :math:`n_{max}` is greater than :attr:`max_constraint`
            ValueError: if :attr:`type_constraint` is not None and
                :math:`n_{max}` and :math:`n_{max}` are not of type
                :attr:`type_constraint`.
        """
        if isinstance(nums_range, numbers.Number):  # single number given
            if nums_range < 0:
                raise ValueError(
                    f'If {name} is a single number,'
                    f' it must be positive, not {nums_range}')
            if min_constraint is not None and nums_range < min_constraint:
                raise ValueError(
                    f'If {name} is a single number, it must be greater'
                    f' than {min_constraint}, not {nums_range}'
                )
            if max_constraint is not None and nums_range > max_constraint:
                raise ValueError(
                    f'If {name} is a single number, it must be smaller'
                    f' than {max_constraint}, not {nums_range}'
                )
            if type_constraint is not None:
                if not isinstance(nums_range, type_constraint):
                    raise ValueError(
                        f'If {name} is a single number, it must be of'
                        f' type {type_constraint}, not {nums_range}'
                    )
            min_range = -nums_range if min_constraint is None else nums_range
            return (min_range, nums_range)

        try:
            min_value, max_value = nums_range
        except (TypeError, ValueError):
            raise ValueError(
                f'If {name} is not a single number, it must be'
                f' a sequence of len 2, not {nums_range}'
            )

        min_is_number = isinstance(min_value, numbers.Number)
        max_is_number = isinstance(max_value, numbers.Number)
        if not min_is_number or not max_is_number:
            message = (
                f'{name} values must be numbers, not {nums_range}')
            raise ValueError(message)

        if min_value > max_value:
            raise ValueError(
                f'If {name} is a sequence, the second value must be'
                f' equal or greater than the first, but it is {nums_range}')

        if min_constraint is not None and min_value < min_constraint:
            raise ValueError(
                f'If {name} is a sequence, the first value must be greater'
                f' than {min_constraint}, but it is {min_value}'
            )

        if max_constraint is not None and max_value > max_constraint:
            raise ValueError(
                f'If {name} is a sequence, the second value must be smaller'
                f' than {max_constraint}, but it is {max_value}'
            )

        if type_constraint is not None:
            min_type_ok = isinstance(min_value, type_constraint)
            max_type_ok = isinstance(max_value, type_constraint)
            if not min_type_ok or not max_type_ok:
                raise ValueError(
                    f'If "{name}" is a sequence, its values must be of'
                    f' type "{type_constraint}", not "{type(nums_range)}"'
                )
        return nums_range

    @staticmethod
    def parse_interpolation(interpolation: str) -> str:
        if not isinstance(interpolation, str):
            itype = type(interpolation)
            raise TypeError(f'Interpolation must be a string, not {itype}')
        interpolation = interpolation.lower()
        is_string = isinstance(interpolation, str)
        supported_values = [key.name.lower() for key in Interpolation]
        is_supported = interpolation.lower() in supported_values
        if is_string and is_supported:
            return interpolation
        message = (
            f'Interpolation "{interpolation}" of type {type(interpolation)}'
            f' must be a string among the supported values: {supported_values}'
        )
        raise ValueError(message)

    @staticmethod
    def parse_probability(probability: float) -> float:
        is_number = isinstance(probability, numbers.Number)
        if not (is_number and 0 <= probability <= 1):
            message = (
                'Probability must be a number in [0, 1],'
                f' not {probability}'
            )
            raise ValueError(message)
        return probability

    @staticmethod
    def parse_include_and_exclude(
            include: TypeKeys = None,
            exclude: TypeKeys = None,
            ) -> Tuple[TypeKeys, TypeKeys]:
        if include is not None and exclude is not None:
            raise ValueError('Include and exclude cannot both be specified')
        return include, exclude

    @staticmethod
    def nib_to_sitk(data: TypeData, affine: TypeData) -> sitk.Image:
        return nib_to_sitk(data, affine)

    @staticmethod
    def sitk_to_nib(image: sitk.Image) -> Tuple[torch.Tensor, np.ndarray]:
        return sitk_to_nib(image)

    def _get_reproducing_arguments(self):
        """
        Return a dictionary with the arguments that would be necessary to
        reproduce the transform exactly.
        """
        reproducing_arguments = {
            'include': self.include,
            'exclude': self.exclude,
            'copy': self.copy,
        }
        args_names = {name: getattr(self, name) for name in self.args_names}
        reproducing_arguments.update(args_names)
        return reproducing_arguments

    def is_invertible(self):
        return hasattr(self, 'invert_transform')

    def inverse(self):
        if not self.is_invertible():
            raise RuntimeError(f'{self.name} is not invertible')
        new = copy.deepcopy(self)
        new.invert_transform = not self.invert_transform
        return new

    @staticmethod
    @contextmanager
    def _use_seed(seed):
        """Perform an operation using a specific seed for the PyTorch RNG"""
        torch_rng_state = torch.random.get_rng_state()
        torch.manual_seed(seed)
        yield
        torch.random.set_rng_state(torch_rng_state)

    @staticmethod
    def get_sitk_interpolator(interpolation: str) -> int:
        return get_sitk_interpolator(interpolation)

    @staticmethod
    def parse_bounds(bounds_parameters: TypeBounds) -> TypeSixBounds:
        try:
            bounds_parameters = tuple(bounds_parameters)
        except TypeError:
            bounds_parameters = (bounds_parameters,)

        # Check that numbers are integers
        for number in bounds_parameters:
            if not isinstance(number, (int, np.integer)) or number < 0:
                message = (
                    'Bounds values must be integers greater or equal to zero,'
                    f' not "{bounds_parameters}" of type {type(number)}'
                )
                raise ValueError(message)
        bounds_parameters = tuple(int(n) for n in bounds_parameters)
        bounds_parameters_length = len(bounds_parameters)
        if bounds_parameters_length == 6:
            return bounds_parameters
        if bounds_parameters_length == 1:
            return 6 * bounds_parameters
        if bounds_parameters_length == 3:
            return tuple(np.repeat(bounds_parameters, 2).tolist())
        message = (
            'Bounds parameter must be an integer or a tuple of'
            f' 3 or 6 integers, not {bounds_parameters}'
        )
        raise ValueError(message)

    @staticmethod
    def ones(tensor: torch.Tensor) -> torch.Tensor:
        return torch.ones_like(tensor, dtype=torch.bool)

    @staticmethod
    def mean(tensor: torch.Tensor) -> torch.Tensor:
        mask = tensor > tensor.mean()
        return mask

    @staticmethod
    def get_mask_from_masking_method(
            masking_method: TypeMaskingMethod,
            subject: Subject,
            tensor: torch.Tensor,
            ) -> torch.Tensor:
        if masking_method is None:
            return Transform.ones(tensor)
        elif callable(masking_method):
            return masking_method(tensor)
        elif type(masking_method) is str:
            in_subject = masking_method in subject
            if in_subject and isinstance(subject[masking_method], LabelMap):
                return subject[masking_method].data.bool()
            masking_method = masking_method.capitalize()
            if masking_method in anat_axes:
                return Transform.get_mask_from_anatomical_label(
                    masking_method, tensor)
        elif type(masking_method) in (tuple, list, int):
            return Transform.get_mask_from_bounds(masking_method, tensor)
        message = (
            'Masking method parameter must be a function, a label map name,'
            f' an anatomical label: {anat_axes}, or a bounds parameter'
            ' (an int, tuple of 3 ints, or tuple of 6 ints),'
            f' not {masking_method} of type {type(masking_method)}'
        )
        raise ValueError(message)

    @staticmethod
    def get_mask_from_anatomical_label(
            anatomical_label: str,
            tensor: torch.Tensor,
            ) -> torch.Tensor:
        anatomical_label = anatomical_label.title()
        if anatomical_label.title() not in anat_axes:
            message = (
                f'Anatomical label must be one of {anat_axes}'
                f' not {anatomical_label}'
            )
            raise ValueError(message)
        mask = torch.zeros_like(tensor, dtype=torch.bool)
        _, width, height, depth = tensor.shape
        if anatomical_label == 'Right':
            mask[:, width // 2:] = True
        elif anatomical_label == 'Left':
            mask[:, :width // 2] = True
        elif anatomical_label == 'Anterior':
            mask[:, :, height // 2:] = True
        elif anatomical_label == 'Posterior':
            mask[:, :, :height // 2] = True
        elif anatomical_label == 'Superior':
            mask[:, :, :, depth // 2:] = True
        elif anatomical_label == 'Inferior':
            mask[:, :, :, :depth // 2] = True
        return mask

    @staticmethod
    def get_mask_from_bounds(
            bounds_parameters: TypeBounds,
            tensor: torch.Tensor,
            ) -> torch.Tensor:
        bounds_parameters = Transform.parse_bounds(bounds_parameters)
        low = bounds_parameters[::2]
        high = bounds_parameters[1::2]
        i0, j0, k0 = low
        i1, j1, k1 = np.array(tensor.shape[1:]) - high
        mask = torch.zeros_like(tensor, dtype=torch.bool)
        mask[:, i0:i1, j0:j1, k0:k1] = True
        return mask


1			import copy
2			import numbers
3			import warnings
4			from typing import Union, Tuple
5			from abc import ABC, abstractmethod
6			from contextlib import contextmanager
7
8			import torch
9			import numpy as np
10			import SimpleITK as sitk
11
12			from ..utils import to_tuple
13			from ..data.subject import Subject
14			from ..data.io import nib_to_sitk, sitk_to_nib
15			from ..data.image import LabelMap
16			from ..typing import (
17			TypeKeys,
18			TypeData,
19			TypeNumber,
20			TypeCallable,
21			TypeTripletInt,
22			)
23			from .interpolation import Interpolation, get_sitk_interpolator
24			from .data_parser import DataParser, TypeTransformInput
25
26			TypeSixBounds = Tuple[int, int, int, int, int, int]
27			TypeBounds = Union[
28			int,
29			TypeTripletInt,
30			TypeSixBounds,
31			]
32			TypeMaskingMethod = Union[str, TypeCallable, TypeBounds, None]
33			anat_axes = 'Left', 'Right', 'Anterior', 'Posterior', 'Inferior', 'Superior'
34
35
36			class Transform(ABC):
37			"""Abstract class for all TorchIO transforms.
38
39			All subclasses must overwrite
40			:meth:`Transform.apply_transform`,
41			which takes data, applies some transformation and returns the result.
42
43			The input can be an instance of
44			:class:`torchio.Subject`,
45			:class:`torchio.Image`,
46			:class:`numpy.ndarray`,
47			:class:`torch.Tensor`,
48			:class:`SimpleITK.Image`,
49			or :class:`dict`.
50
51			Args:
52			p: Probability that this transform will be applied.
53			copy: Make a shallow copy of the input before applying the transform.
54			include: Sequence of strings with the names of the only images to which
55			the transform will be applied.
56			Mandatory if the input is a :class:`dict`.
57			exclude: Sequence of strings with the names of the images to which the
58			the transform will not be applied, apart from the ones that are
59			excluded because of the transform type.
60			For example, if a subject includes an MRI, a CT and a label map,
61			and the CT is added to the list of exclusions of an intensity
62			transform such as :class:`~torchio.transforms.RandomBlur`,
63			the transform will be only applied to the MRI, as the label map is
64			excluded by default by spatial transforms.
65			"""
66			def __init__(
67			self,
68			p: float = 1,
69			copy: bool = True,
70			include: TypeKeys = None,
71			exclude: TypeKeys = None,
72			keys: TypeKeys = None,
73			):
74			self.probability = self.parse_probability(p)
75			self.copy = copy
76			if keys is not None:
77			message = (
78			'The "keys" argument is deprecated and will be removed in the'
79			' future. Use "include" instead.'
80			)
81			warnings.warn(message, DeprecationWarning)
82			include = keys
83			self.include, self.exclude = self.parse_include_and_exclude(
84			include, exclude)
85
86			def __call__(
87			self,
88			data: TypeTransformInput,
89			) -> TypeTransformInput:
90			"""Transform data and return a result of the same type.
91
92			Args:
93			data: Instance of 1) :class:`~torchio.Subject`, 4D
94			:class:`torch.Tensor` or :class:`numpy.ndarray` with dimensions
95			:math:`(C, W, H, D)`, where :math:`C` is the number of channels
96			and :math:`W, H, D` are the spatial dimensions. If the input is
97			a tensor, the affine matrix will be set to identity. Other
98			valid input types are a SimpleITK image, a
99			:class:`torchio.Image`, a NiBabel Nifti1 image or a
100			:class:`dict`. The output type is the same as the input type.
101			"""
102			if torch.rand(1).item() > self.probability:
103			return data
104			data_parser = DataParser(data, keys=self.include)
105			subject = data_parser.get_subject()
106			if self.copy:
107			subject = copy.copy(subject)
108			with np.errstate(all='raise'):
109			transformed = self.apply_transform(subject)
110			self.add_transform_to_subject_history(transformed)
111			for image in transformed.get_images(intensity_only=False):
112			ndim = image.data.ndim
113			assert ndim == 4, f'Output of {self.name} is {ndim}D'
114
115			output = data_parser.get_output(transformed)
116			return output
117
118			def __repr__(self):
119			if hasattr(self, 'args_names'):
120			names = self.args_names
121			args_strings = [f'{arg}={getattr(self, arg)}' for arg in names]
122			if hasattr(self, 'invert_transform') and self.invert_transform:
123			args_strings.append('invert=True')
124			args_string = ', '.join(args_strings)
125			return f'{self.name}({args_string})'
126			else:
127			return super().__repr__()
128
129			@property
130			def name(self):
131			return self.__class__.__name__
132
133			@abstractmethod
134			def apply_transform(self, subject: Subject):
135			raise NotImplementedError
136
137			def add_transform_to_subject_history(self, subject):
138			from .augmentation import RandomTransform
139			from . import Compose, OneOf, CropOrPad, EnsureShapeMultiple
140			from .preprocessing.label import SequentialLabels
141			call_others = (
142			RandomTransform,
143			Compose,
144			OneOf,
145			CropOrPad,
146			EnsureShapeMultiple,
147			SequentialLabels,
148			)
149			if not isinstance(self, call_others):
150			subject.add_transform(self, self._get_reproducing_arguments())
151
152			@staticmethod
153			def to_range(n, around):
154			if around is None:
155			return 0, n
156			else:
157			return around - n, around + n
158
159			def parse_params(self, params, around, name, make_ranges=True, **kwargs):
160			params = to_tuple(params)
161			# d or (a, b)
162			if len(params) == 1 or (len(params) == 2 and make_ranges):
163			params *= 3 # (d, d, d) or (a, b, a, b, a, b)
164			if len(params) == 3 and make_ranges: # (a, b, c)
165			items = [self.to_range(n, around) for n in params]
166			# (-a, a, -b, b, -c, c) or (1-a, 1+a, 1-b, 1+b, 1-c, 1+c)
167			params = [n for prange in items for n in prange]
168			if make_ranges:
169			if len(params) != 6:
170			message = (
171			f'If "{name}" is a sequence, it must have length 2, 3 or'
172			f' 6, not {len(params)}'
173			)
174			raise ValueError(message)
175			for param_range in zip(params[::2], params[1::2]):
176			self._parse_range(param_range, name, **kwargs)
177			return tuple(params)
178
179			@staticmethod
180			def _parse_range(
181			nums_range: Union[TypeNumber, Tuple[TypeNumber, TypeNumber]],
182			name: str,
183			min_constraint: TypeNumber = None,
184			max_constraint: TypeNumber = None,
185			type_constraint: type = None,
186			) -> Tuple[TypeNumber, TypeNumber]:
187			r"""Adapted from :class:`torchvision.transforms.RandomRotation`.
188
189			Args:
190			nums_range: Tuple of two numbers :math:`(n_{min}, n_{max})`,
191			where :math:`n_{min} \leq n_{max}`.
192			If a single positive number :math:`n` is provided,
193			:math:`n_{min} = -n` and :math:`n_{max} = n`.
194			name: Name of the parameter, so that an informative error message
195			can be printed.
196			min_constraint: Minimal value that :math:`n_{min}` can take,
197			default is None, i.e. there is no minimal value.
198			max_constraint: Maximal value that :math:`n_{max}` can take,
199			default is None, i.e. there is no maximal value.
200			type_constraint: Precise type that :math:`n_{max}` and
201			:math:`n_{min}` must take.
202
203			Returns:
204			A tuple of two numbers :math:`(n_{min}, n_{max})`.
205
206			Raises:
207			ValueError: if :attr:`nums_range` is negative
208			ValueError: if :math:`n_{max}` or :math:`n_{min}` is not a number
209			ValueError: if :math:`n_{max} \lt n_{min}`
210			ValueError: if :attr:`min_constraint` is not None and
211			:math:`n_{min}` is smaller than :attr:`min_constraint`
212			ValueError: if :attr:`max_constraint` is not None and
213			:math:`n_{max}` is greater than :attr:`max_constraint`
214			ValueError: if :attr:`type_constraint` is not None and
215			:math:`n_{max}` and :math:`n_{max}` are not of type
216			:attr:`type_constraint`.
217			"""
218			if isinstance(nums_range, numbers.Number): # single number given
219			if nums_range < 0:
220			raise ValueError(
221			f'If {name} is a single number,'
222			f' it must be positive, not {nums_range}')
223			if min_constraint is not None and nums_range < min_constraint:
224			raise ValueError(
225			f'If {name} is a single number, it must be greater'
226			f' than {min_constraint}, not {nums_range}'
227			)
228			if max_constraint is not None and nums_range > max_constraint:
229			raise ValueError(
230			f'If {name} is a single number, it must be smaller'
231			f' than {max_constraint}, not {nums_range}'
232			)
233			if type_constraint is not None:
234			if not isinstance(nums_range, type_constraint):
235			raise ValueError(
236			f'If {name} is a single number, it must be of'
237			f' type {type_constraint}, not {nums_range}'
238			)
239			min_range = -nums_range if min_constraint is None else nums_range
240			return (min_range, nums_range)
241
242			try:
243			min_value, max_value = nums_range
244			except (TypeError, ValueError):
245			raise ValueError(
246			f'If {name} is not a single number, it must be'
247			f' a sequence of len 2, not {nums_range}'
248			)
249
250			min_is_number = isinstance(min_value, numbers.Number)
251			max_is_number = isinstance(max_value, numbers.Number)
252			if not min_is_number or not max_is_number:
253			message = (
254			f'{name} values must be numbers, not {nums_range}')
255			raise ValueError(message)
256
257			if min_value > max_value:
258			raise ValueError(
259			f'If {name} is a sequence, the second value must be'
260			f' equal or greater than the first, but it is {nums_range}')
261
262			if min_constraint is not None and min_value < min_constraint:
263			raise ValueError(
264			f'If {name} is a sequence, the first value must be greater'
265			f' than {min_constraint}, but it is {min_value}'
266			)
267
268			if max_constraint is not None and max_value > max_constraint:
269			raise ValueError(
270			f'If {name} is a sequence, the second value must be smaller'
271			f' than {max_constraint}, but it is {max_value}'
272			)
273
274			if type_constraint is not None:
275			min_type_ok = isinstance(min_value, type_constraint)
276			max_type_ok = isinstance(max_value, type_constraint)
277			if not min_type_ok or not max_type_ok:
278			raise ValueError(
279			f'If "{name}" is a sequence, its values must be of'
280			f' type "{type_constraint}", not "{type(nums_range)}"'
281			)
282			return nums_range
283
284			@staticmethod
285			def parse_interpolation(interpolation: str) -> str:
286			if not isinstance(interpolation, str):
287			itype = type(interpolation)
288			raise TypeError(f'Interpolation must be a string, not {itype}')
289			interpolation = interpolation.lower()
290			is_string = isinstance(interpolation, str)
291			supported_values = [key.name.lower() for key in Interpolation]
292			is_supported = interpolation.lower() in supported_values
293			if is_string and is_supported:
294			return interpolation
295			message = (
296			f'Interpolation "{interpolation}" of type {type(interpolation)}'
297			f' must be a string among the supported values: {supported_values}'
298			)
299			raise ValueError(message)
300
301			@staticmethod
302			def parse_probability(probability: float) -> float:
303			is_number = isinstance(probability, numbers.Number)
304			if not (is_number and 0 <= probability <= 1):
305			message = (
306			'Probability must be a number in [0, 1],'
307			f' not {probability}'
308			)
309			raise ValueError(message)
310			return probability
311
312			@staticmethod
313			def parse_include_and_exclude(
314			include: TypeKeys = None,
315			exclude: TypeKeys = None,
316			) -> Tuple[TypeKeys, TypeKeys]:
317			if include is not None and exclude is not None:
318			raise ValueError('Include and exclude cannot both be specified')
319			return include, exclude
320
321			@staticmethod
322			def nib_to_sitk(data: TypeData, affine: TypeData) -> sitk.Image:
323			return nib_to_sitk(data, affine)
324
325			@staticmethod
326			def sitk_to_nib(image: sitk.Image) -> Tuple[torch.Tensor, np.ndarray]:
327			return sitk_to_nib(image)
328
329			def _get_reproducing_arguments(self):
330			"""
331			Return a dictionary with the arguments that would be necessary to
332			reproduce the transform exactly.
333			"""
334			reproducing_arguments = {
335			'include': self.include,
336			'exclude': self.exclude,
337			'copy': self.copy,
338			}
339			args_names = {name: getattr(self, name) for name in self.args_names}
340			reproducing_arguments.update(args_names)
341			return reproducing_arguments
342
343			def is_invertible(self):
344			return hasattr(self, 'invert_transform')
345
346			def inverse(self):
347			if not self.is_invertible():
348			raise RuntimeError(f'{self.name} is not invertible')
349			new = copy.deepcopy(self)
350			new.invert_transform = not self.invert_transform
351			return new
352
353			@staticmethod
354			@contextmanager
355			def _use_seed(seed):
356			"""Perform an operation using a specific seed for the PyTorch RNG"""
357			torch_rng_state = torch.random.get_rng_state()
358			torch.manual_seed(seed)
359			yield
360			torch.random.set_rng_state(torch_rng_state)
361
362			@staticmethod
363			def get_sitk_interpolator(interpolation: str) -> int:
364			return get_sitk_interpolator(interpolation)
365
366			@staticmethod
367			def parse_bounds(bounds_parameters: TypeBounds) -> TypeSixBounds:
368			try:
369			bounds_parameters = tuple(bounds_parameters)
370			except TypeError:
371			bounds_parameters = (bounds_parameters,)
372
373			# Check that numbers are integers
374			for number in bounds_parameters:
375			if not isinstance(number, (int, np.integer)) or number < 0:
376			message = (
377			'Bounds values must be integers greater or equal to zero,'
378			f' not "{bounds_parameters}" of type {type(number)}'
379			)
380			raise ValueError(message)
381			bounds_parameters = tuple(int(n) for n in bounds_parameters)
382			bounds_parameters_length = len(bounds_parameters)
383			if bounds_parameters_length == 6:
384			return bounds_parameters
385			if bounds_parameters_length == 1:
386			return 6 * bounds_parameters
387			if bounds_parameters_length == 3:
388			return tuple(np.repeat(bounds_parameters, 2).tolist())
389			message = (
390			'Bounds parameter must be an integer or a tuple of'
391			f' 3 or 6 integers, not {bounds_parameters}'
392			)
393			raise ValueError(message)
394
395			@staticmethod
396			def ones(tensor: torch.Tensor) -> torch.Tensor:
397			return torch.ones_like(tensor, dtype=torch.bool)
398
399			@staticmethod
400			def mean(tensor: torch.Tensor) -> torch.Tensor:
401			mask = tensor > tensor.mean()
402			return mask
403
404			@staticmethod
405			def get_mask_from_masking_method(
406			masking_method: TypeMaskingMethod,
407			subject: Subject,
408			tensor: torch.Tensor,
409			) -> torch.Tensor:
410			if masking_method is None:
411			return Transform.ones(tensor)
412			elif callable(masking_method):
413			return masking_method(tensor)
414			elif type(masking_method) is str:
415			in_subject = masking_method in subject
416			if in_subject and isinstance(subject[masking_method], LabelMap):
417			return subject[masking_method].data.bool()
418			masking_method = masking_method.capitalize()
419			if masking_method in anat_axes:
420			return Transform.get_mask_from_anatomical_label(
421			masking_method, tensor)
422			elif type(masking_method) in (tuple, list, int):
423			return Transform.get_mask_from_bounds(masking_method, tensor)
424			message = (
425			'Masking method parameter must be a function, a label map name,'
426			f' an anatomical label: {anat_axes}, or a bounds parameter'
427			' (an int, tuple of 3 ints, or tuple of 6 ints),'
428			f' not {masking_method} of type {type(masking_method)}'
429			)
430			raise ValueError(message)
431
432			@staticmethod
433			def get_mask_from_anatomical_label(
434			anatomical_label: str,
435			tensor: torch.Tensor,
436			) -> torch.Tensor:
437			anatomical_label = anatomical_label.title()
438			if anatomical_label.title() not in anat_axes:
439			message = (
440			f'Anatomical label must be one of {anat_axes}'
441			f' not {anatomical_label}'
442			)
443			raise ValueError(message)
444			mask = torch.zeros_like(tensor, dtype=torch.bool)
445			_, width, height, depth = tensor.shape
446			if anatomical_label == 'Right':
447			mask[:, width // 2:] = True
448			elif anatomical_label == 'Left':
449			mask[:, :width // 2] = True
450			elif anatomical_label == 'Anterior':
451			mask[:, :, height // 2:] = True
452			elif anatomical_label == 'Posterior':
453			mask[:, :, :height // 2] = True
454			elif anatomical_label == 'Superior':
455			mask[:, :, :, depth // 2:] = True
456			elif anatomical_label == 'Inferior':
457			mask[:, :, :, :depth // 2] = True
458			return mask
459
460			@staticmethod
461			def get_mask_from_bounds(
462			bounds_parameters: TypeBounds,
463			tensor: torch.Tensor,
464			) -> torch.Tensor:
465			bounds_parameters = Transform.parse_bounds(bounds_parameters)
466			low = bounds_parameters[::2]
467			high = bounds_parameters[1::2]
468			i0, j0, k0 = low
469			i1, j1, k1 = np.array(tensor.shape[1:]) - high
470			mask = torch.zeros_like(tensor, dtype=torch.bool)
471			mask[:, i0:i1, j0:j1, k0:k1] = True
472			return mask
473

fepegar / torchio

Pull Request — master (#403)

torchio.transforms.transform.Transform.name() A

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