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

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Push — master ( b06930...3ddbe5 )

by Fernando

created 2020-10-27 16:27 UTC

Transform.parse_subject() A

↳ Parent: torchio.transforms.transform

Complexity

Conditions

Size

Total Lines	8
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	6
nop	1
dl	0
loc	8
rs	10
c	0
b	0
f	0

import copy
import numbers
import warnings
from abc import ABC, abstractmethod
from typing import Optional, Union, Tuple, List

import torch
import numpy as np
import nibabel as nib
import SimpleITK as sitk

from .. import TypeData, DATA, AFFINE, TypeNumber
from ..data.subject import Subject
from ..data.image import Image, ScalarImage
from ..utils import nib_to_sitk, sitk_to_nib
from .interpolation import Interpolation


TypeTransformInput = Union[Subject, torch.Tensor, np.ndarray, dict, sitk.Image]


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

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

    The input can be an instance of
    :py:class:`torchio.Subject`,
    :py:class:`torchio.Image`,
    :py:class:`numpy.ndarray`,
    :py:class:`torch.Tensor`,
    :py:class:`SimpleITK.image`,
    or a Python dictionary.

    Args:
        p: Probability that this transform will be applied.
        copy: Make a shallow copy of the input before applying the transform.
        keys: Mandatory if the input is a Python dictionary. The transform will
            be applied only to the data in each key.
    """
    def __init__(
            self,
            p: float = 1,
            copy: bool = True,
            keys: Optional[List[str]] = None,
            ):
        self.probability = self.parse_probability(p)
        self.copy = copy
        self.keys = keys
        self.default_image_name = 'default_image_name'

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

        Args:
            data: Instance of :py:class:`~torchio.Subject`, 4D
                :py:class:`torch.Tensor` or 4D NumPy array 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 is an identity and a tensor will be
                also returned.
        """
        if torch.rand(1).item() > self.probability:
            return data

        is_tensor = is_array = is_dict = is_image = is_sitk = is_nib = False

        if isinstance(data, nib.Nifti1Image):
            tensor = data.get_fdata(dtype=np.float32)
            data = ScalarImage(tensor=tensor, affine=data.affine)
            subject = self._get_subject_from_image(data)
            is_nib = True
        elif isinstance(data, (np.ndarray, torch.Tensor)):
            subject = self.parse_tensor(data)
            is_array = isinstance(data, np.ndarray)
            is_tensor = True
        elif isinstance(data, Image):
            subject = self._get_subject_from_image(data)
            is_image = True
        elif isinstance(data, Subject):
            subject = data
        elif isinstance(data, sitk.Image):
            subject = self._get_subject_from_sitk_image(data)
            is_sitk = True
        elif isinstance(data, dict):  # e.g. Eisen or MONAI dicts
            if self.keys is None:
                message = (
                    'If input is a dictionary, a value for "keys" must be'
                    ' specified when instantiating the transform'
                )
                raise RuntimeError(message)
            subject = self._get_subject_from_dict(data, self.keys)
            is_dict = True
        else:
            raise ValueError(f'Input type not recognized: {type(data)}')
        self.parse_subject(subject)

        if self.copy:
            subject = copy.copy(subject)

        with np.errstate(all='raise'):
            transformed = self.apply_transform(subject)

        for image in transformed.get_images(intensity_only=False):
            ndim = image[DATA].ndim
            assert ndim == 4, f'Output of {self.name} is {ndim}D'

        if is_tensor or is_sitk:
            image = transformed[self.default_image_name]
            transformed = image[DATA]
            if is_array:
                transformed = transformed.numpy()
            elif is_sitk:
                transformed = nib_to_sitk(image[DATA], image[AFFINE])
        elif is_image:
            transformed = transformed[self.default_image_name]
        elif is_dict:
            transformed = dict(transformed)
            for key, value in transformed.items():
                if isinstance(value, Image):
                    transformed[key] = value.data
        elif is_nib:
            image = transformed[self.default_image_name]
            data = image[DATA]
            if len(data) > 1:
                message = (
                    'Multichannel images not supported for input of type'
                    ' nibabel.nifti.Nifti1Image'
                )
                raise RuntimeError(message)
            transformed = nib.Nifti1Image(data[0].numpy(), image[AFFINE])
        return transformed

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

    @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 ``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):
            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_degree, max_degree = 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_degree, numbers.Number)
        max_is_number = isinstance(max_degree, 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_degree > max_degree:
            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_degree < min_constraint:
            raise ValueError(
                f'If {name} is a sequence, the first value must be greater'
                f' than {min_constraint}, but it is {min_degree}'
            )

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

        if type_constraint is not None:
            min_type_ok = isinstance(min_degree, type_constraint)
            max_type_ok = isinstance(max_degree, 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_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_subject(subject: Subject) -> None:
        if not isinstance(subject, Subject):
            message = (
                'Input to a transform must be a tensor or an instance'
                f' of torchio.Subject, not "{type(subject)}"'
            )
            raise RuntimeError(message)

    def parse_tensor(self, data: TypeData) -> Subject:
        if data.ndim != 4:
            message = (
                'The input must be a 4D tensor with dimensions'
                f' (channels, x, y, z) but it has shape {tuple(data.shape)}'
            )
            raise ValueError(message)
        return self._get_subject_from_tensor(data)

    @staticmethod
    def parse_interpolation(interpolation: str) -> Interpolation:
        if isinstance(interpolation, Interpolation):
            message = (
                'Interpolation of type torchio.Interpolation'
                ' is deprecated, please use a string instead'
            )
            warnings.warn(message, FutureWarning)
        elif isinstance(interpolation, str):
            interpolation = interpolation.lower()
            supported_values = [key.name.lower() for key in Interpolation]
            if interpolation in supported_values:
                interpolation = getattr(Interpolation, interpolation.upper())
            else:
                message = (
                    f'Interpolation "{interpolation}" is not among'
                    f' the supported values: {supported_values}'
                )
                raise AttributeError(message)
        else:
            message = (
                'image_interpolation must be a string,'
                f' not {type(interpolation)}'
            )
            raise TypeError(message)
        return interpolation

    def _get_subject_from_tensor(self, tensor: torch.Tensor) -> Subject:
        image = ScalarImage(tensor=tensor)
        return self._get_subject_from_image(image)

    def _get_subject_from_image(self, image: Image) -> Subject:
        subject = Subject({self.default_image_name: image})
        return subject

    @staticmethod
    def _get_subject_from_dict(
            data: dict,
            image_keys: List[str],
            ) -> Subject:
        subject_dict = {}
        for key, value in data.items():
            if key in image_keys:
                value = ScalarImage(tensor=value)
            subject_dict[key] = value
        return Subject(subject_dict)

    def _get_subject_from_sitk_image(self, image):
        tensor, affine = sitk_to_nib(image)
        image = ScalarImage(tensor=tensor, affine=affine)
        return self._get_subject_from_image(image)

    @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)

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


1			import copy
2			import numbers
3			import warnings
4			from abc import ABC, abstractmethod
5			from typing import Optional, Union, Tuple, List
6
7			import torch
8			import numpy as np
9			import nibabel as nib
10			import SimpleITK as sitk
11
12			from .. import TypeData, DATA, AFFINE, TypeNumber
13			from ..data.subject import Subject
14			from ..data.image import Image, ScalarImage
15			from ..utils import nib_to_sitk, sitk_to_nib
16			from .interpolation import Interpolation
17
18
19			TypeTransformInput = Union[Subject, torch.Tensor, np.ndarray, dict, sitk.Image]
20
21
22			class Transform(ABC):
23			"""Abstract class for all TorchIO transforms.
24
25			All subclasses should overwrite
26			:py:meth:`torchio.tranforms.Transform.apply_transform`,
27			which takes data, applies some transformation and returns the result.
28
29			The input can be an instance of
30			:py:class:`torchio.Subject`,
31			:py:class:`torchio.Image`,
32			:py:class:`numpy.ndarray`,
33			:py:class:`torch.Tensor`,
34			:py:class:`SimpleITK.image`,
35			or a Python dictionary.
36
37			Args:
38			p: Probability that this transform will be applied.
39			copy: Make a shallow copy of the input before applying the transform.
40			keys: Mandatory if the input is a Python dictionary. The transform will
41			be applied only to the data in each key.
42			"""
43			def __init__(
44			self,
45			p: float = 1,
46			copy: bool = True,
47			keys: Optional[List[str]] = None,
48			):
49			self.probability = self.parse_probability(p)
50			self.copy = copy
51			self.keys = keys
52			self.default_image_name = 'default_image_name'
53
54			def __call__(self, data: TypeTransformInput) -> TypeTransformInput:
55			"""Transform data and return a result of the same type.
56
57			Args:
58			data: Instance of :py:class:`~torchio.Subject`, 4D
59			:py:class:`torch.Tensor` or 4D NumPy array with dimensions
60			:math:`(C, W, H, D)`, where :math:`C` is the number of channels
61			and :math:`W, H, D` are the spatial dimensions. If the input is
62			a tensor, the affine matrix is an identity and a tensor will be
63			also returned.
64			"""
65			if torch.rand(1).item() > self.probability:
66			return data
67
68			is_tensor = is_array = is_dict = is_image = is_sitk = is_nib = False
69
70			if isinstance(data, nib.Nifti1Image):
71			tensor = data.get_fdata(dtype=np.float32)
72			data = ScalarImage(tensor=tensor, affine=data.affine)
73			subject = self._get_subject_from_image(data)
74			is_nib = True
75			elif isinstance(data, (np.ndarray, torch.Tensor)):
76			subject = self.parse_tensor(data)
77			is_array = isinstance(data, np.ndarray)
78			is_tensor = True
79			elif isinstance(data, Image):
80			subject = self._get_subject_from_image(data)
81			is_image = True
82			elif isinstance(data, Subject):
83			subject = data
84			elif isinstance(data, sitk.Image):
85			subject = self._get_subject_from_sitk_image(data)
86			is_sitk = True
87			elif isinstance(data, dict): # e.g. Eisen or MONAI dicts
88			if self.keys is None:
89			message = (
90			'If input is a dictionary, a value for "keys" must be'
91			' specified when instantiating the transform'
92			)
93			raise RuntimeError(message)
94			subject = self._get_subject_from_dict(data, self.keys)
95			is_dict = True
96			else:
97			raise ValueError(f'Input type not recognized: {type(data)}')
98			self.parse_subject(subject)
99
100			if self.copy:
101			subject = copy.copy(subject)
102
103			with np.errstate(all='raise'):
104			transformed = self.apply_transform(subject)
105
106			for image in transformed.get_images(intensity_only=False):
107			ndim = image[DATA].ndim
108			assert ndim == 4, f'Output of {self.name} is {ndim}D'
109
110			if is_tensor or is_sitk:
111			image = transformed[self.default_image_name]
112			transformed = image[DATA]
113			if is_array:
114			transformed = transformed.numpy()
115			elif is_sitk:
116			transformed = nib_to_sitk(image[DATA], image[AFFINE])
117			elif is_image:
118			transformed = transformed[self.default_image_name]
119			elif is_dict:
120			transformed = dict(transformed)
121			for key, value in transformed.items():
122			if isinstance(value, Image):
123			transformed[key] = value.data
124			elif is_nib:
125			image = transformed[self.default_image_name]
126			data = image[DATA]
127			if len(data) > 1:
128			message = (
129			'Multichannel images not supported for input of type'
130			' nibabel.nifti.Nifti1Image'
131			)
132			raise RuntimeError(message)
133			transformed = nib.Nifti1Image(data[0].numpy(), image[AFFINE])
134			return transformed
135
136			@abstractmethod
137			def apply_transform(self, subject: Subject):
138			raise NotImplementedError
139
140			@staticmethod
141			def parse_range(
142			nums_range: Union[TypeNumber, Tuple[TypeNumber, TypeNumber]],
143			name: str,
144			min_constraint: TypeNumber = None,
145			max_constraint: TypeNumber = None,
146			type_constraint: type = None,
147			) -> Tuple[TypeNumber, TypeNumber]:
148			r"""Adapted from ``torchvision.transforms.RandomRotation``.
149
150			Args:
151			nums_range: Tuple of two numbers :math:`(n_{min}, n_{max})`,
152			where :math:`n_{min} \leq n_{max}`.
153			If a single positive number :math:`n` is provided,
154			:math:`n_{min} = -n` and :math:`n_{max} = n`.
155			name: Name of the parameter, so that an informative error message
156			can be printed.
157			min_constraint: Minimal value that :math:`n_{min}` can take,
158			default is None, i.e. there is no minimal value.
159			max_constraint: Maximal value that :math:`n_{max}` can take,
160			default is None, i.e. there is no maximal value.
161			type_constraint: Precise type that :math:`n_{max}` and
162			:math:`n_{min}` must take.
163
164			Returns:
165			A tuple of two numbers :math:`(n_{min}, n_{max})`.
166
167			Raises:
168			ValueError: if :attr:`nums_range` is negative
169			ValueError: if :math:`n_{max}` or :math:`n_{min}` is not a number
170			ValueError: if :math:`n_{max} \lt n_{min}`
171			ValueError: if :attr:`min_constraint` is not None and
172			:math:`n_{min}` is smaller than :attr:`min_constraint`
173			ValueError: if :attr:`max_constraint` is not None and
174			:math:`n_{max}` is greater than :attr:`max_constraint`
175			ValueError: if :attr:`type_constraint` is not None and
176			:math:`n_{max}` and :math:`n_{max}` are not of type
177			:attr:`type_constraint`.
178			"""
179			if isinstance(nums_range, numbers.Number):
180			if nums_range < 0:
181			raise ValueError(
182			f'If {name} is a single number,'
183			f' it must be positive, not {nums_range}')
184			if min_constraint is not None and nums_range < min_constraint:
185			raise ValueError(
186			f'If {name} is a single number, it must be greater'
187			f' than {min_constraint}, not {nums_range}'
188			)
189			if max_constraint is not None and nums_range > max_constraint:
190			raise ValueError(
191			f'If {name} is a single number, it must be smaller'
192			f' than {max_constraint}, not {nums_range}'
193			)
194			if type_constraint is not None:
195			if not isinstance(nums_range, type_constraint):
196			raise ValueError(
197			f'If {name} is a single number, it must be of'
198			f' type {type_constraint}, not {nums_range}'
199			)
200			min_range = -nums_range if min_constraint is None else nums_range
201			return (min_range, nums_range)
202
203			try:
204			min_degree, max_degree = nums_range
205			except (TypeError, ValueError):
206			raise ValueError(
207			f'If {name} is not a single number, it must be'
208			f' a sequence of len 2, not {nums_range}'
209			)
210
211			min_is_number = isinstance(min_degree, numbers.Number)
212			max_is_number = isinstance(max_degree, numbers.Number)
213			if not min_is_number or not max_is_number:
214			message = (
215			f'{name} values must be numbers, not {nums_range}')
216			raise ValueError(message)
217
218			if min_degree > max_degree:
219			raise ValueError(
220			f'If {name} is a sequence, the second value must be'
221			f' equal or greater than the first, but it is {nums_range}')
222
223			if min_constraint is not None and min_degree < min_constraint:
224			raise ValueError(
225			f'If {name} is a sequence, the first value must be greater'
226			f' than {min_constraint}, but it is {min_degree}'
227			)
228
229			if max_constraint is not None and max_degree > max_constraint:
230			raise ValueError(
231			f'If {name} is a sequence, the second value must be smaller'
232			f' than {max_constraint}, but it is {max_degree}'
233			)
234
235			if type_constraint is not None:
236			min_type_ok = isinstance(min_degree, type_constraint)
237			max_type_ok = isinstance(max_degree, type_constraint)
238			if not min_type_ok or not max_type_ok:
239			raise ValueError(
240			f'If "{name}" is a sequence, its values must be of'
241			f' type "{type_constraint}", not "{type(nums_range)}"'
242			)
243			return nums_range
244
245			@staticmethod
246			def parse_probability(probability: float) -> float:
247			is_number = isinstance(probability, numbers.Number)
248			if not (is_number and 0 <= probability <= 1):
249			message = (
250			'Probability must be a number in [0, 1],'
251			f' not {probability}'
252			)
253			raise ValueError(message)
254			return probability
255
256			@staticmethod
257			def parse_subject(subject: Subject) -> None:
258			if not isinstance(subject, Subject):
259			message = (
260			'Input to a transform must be a tensor or an instance'
261			f' of torchio.Subject, not "{type(subject)}"'
262			)
263			raise RuntimeError(message)
264
265			def parse_tensor(self, data: TypeData) -> Subject:
266			if data.ndim != 4:
267			message = (
268			'The input must be a 4D tensor with dimensions'
269			f' (channels, x, y, z) but it has shape {tuple(data.shape)}'
270			)
271			raise ValueError(message)
272			return self._get_subject_from_tensor(data)
273
274			@staticmethod
275			def parse_interpolation(interpolation: str) -> Interpolation:
276			if isinstance(interpolation, Interpolation):
277			message = (
278			'Interpolation of type torchio.Interpolation'
279			' is deprecated, please use a string instead'
280			)
281			warnings.warn(message, FutureWarning)
282			elif isinstance(interpolation, str):
283			interpolation = interpolation.lower()
284			supported_values = [key.name.lower() for key in Interpolation]
285			if interpolation in supported_values:
286			interpolation = getattr(Interpolation, interpolation.upper())
287			else:
288			message = (
289			f'Interpolation "{interpolation}" is not among'
290			f' the supported values: {supported_values}'
291			)
292			raise AttributeError(message)
293			else:
294			message = (
295			'image_interpolation must be a string,'
296			f' not {type(interpolation)}'
297			)
298			raise TypeError(message)
299			return interpolation
300
301			def _get_subject_from_tensor(self, tensor: torch.Tensor) -> Subject:
302			image = ScalarImage(tensor=tensor)
303			return self._get_subject_from_image(image)
304
305			def _get_subject_from_image(self, image: Image) -> Subject:
306			subject = Subject({self.default_image_name: image})
307			return subject
308
309			@staticmethod
310			def _get_subject_from_dict(
311			data: dict,
312			image_keys: List[str],
313			) -> Subject:
314			subject_dict = {}
315			for key, value in data.items():
316			if key in image_keys:
317			value = ScalarImage(tensor=value)
318			subject_dict[key] = value
319			return Subject(subject_dict)
320
321			def _get_subject_from_sitk_image(self, image):
322			tensor, affine = sitk_to_nib(image)
323			image = ScalarImage(tensor=tensor, affine=affine)
324			return self._get_subject_from_image(image)
325
326			@staticmethod
327			def nib_to_sitk(data: TypeData, affine: TypeData) -> sitk.Image:
328			return nib_to_sitk(data, affine)
329
330			@staticmethod
331			def sitk_to_nib(image: sitk.Image) -> Tuple[torch.Tensor, np.ndarray]:
332			return sitk_to_nib(image)
333
334			@property
335			def name(self):
336			return self.__class__.__name__
337

fepegar / torchio

Push — master ( b06930...3ddbe5 )

Transform.parse_subject() A

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