deepreg.loss.label.SumSquaredDifference.__init__() - Code Metrics - Inspection of "Merge pull request #740 from DeepRegNet/739-refact..." - DeepRegNet/DeepReg - Measure and Improve Code Quality continuously with Scrutinizer

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by Yunguan

created 2021-04-11 15:29 UTC

deepreg.loss.label.SumSquaredDifference.init() A

↳ Parent: deepreg.loss.label

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Total Lines	13
Code Lines	6

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eloc	6
dl	0
loc	13
rs	10
c	0
b	0
f	0
cc	1
nop	3

"""Provide different loss or metrics classes for labels."""

import tensorflow as tf

from deepreg.constant import EPS
from deepreg.loss.util import MultiScaleMixin, NegativeLossMixin
from deepreg.registry import REGISTRY


class SumSquaredDifference(tf.keras.losses.Loss):
    """
    Actually, mean of squared distance between y_true and y_pred.

    The inconsistent name was for convention.

    y_true and y_pred have to be at least 1d tensor, including batch axis.
    """

    def __init__(
        self,
        name: str = "SumSquaredDifference",
        **kwargs,
    ):
        """
        Init.

        :param name: name of the loss.
        :param kwargs: additional arguments.
        """
        super().__init__(name=name, **kwargs)
        self.flatten = tf.keras.layers.Flatten()

    def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
        """
        Return mean squared different for a batch.

        :param y_true: shape = (batch, ...)
        :param y_pred: shape = (batch, ...)
        :return: shape = (batch,)
        """
        loss = tf.math.squared_difference(y_true, y_pred)
        loss = self.flatten(loss)
        return tf.reduce_mean(loss, axis=1)


@REGISTRY.register_loss(name="ssd")
class SumSquaredDifferenceLoss(MultiScaleMixin, SumSquaredDifference):
    """Define loss with multi-scaling options."""


class DiceScore(tf.keras.losses.Loss):
    """
    Define dice score.

    The formulation is:

        0. w_fg + w_bg = 1
        1. let y_prod = y_true * y_pred and y_sum  = y_true + y_pred
        2. num = 2 *  (w_fg * y_true * y_pred + w_bg * (1−y_true) * (1−y_pred))
               = 2 *  ((w_fg+w_bg) * y_prod - w_bg * y_sum + w_bg)
               = 2 *  (y_prod - w_bg * y_sum + w_bg)
        3. denom = (w_fg * (y_true + y_pred) + w_bg * (1−y_true + 1−y_pred))
                 = (w_fg-w_bg) * y_sum + 2 * w_bg
                 = (1-2*w_bg) * y_sum + 2 * w_bg
        4. dice score = num / denom

    where num and denom are summed over all axes except the batch axis.

    Reference:
        Sudre, Carole H., et al. "Generalised dice overlap as a deep learning loss
        function for highly unbalanced segmentations." Deep learning in medical image
        analysis and multimodal learning for clinical decision support.
        Springer, Cham, 2017. 240-248.
    """

    def __init__(
        self,
        binary: bool = False,
        background_weight: float = 0.0,
        smooth_nr: float = EPS,
        smooth_dr: float = EPS,
        name: str = "DiceScore",
        **kwargs,
    ):
        """
        Init.

        :param binary: if True, project y_true, y_pred to 0 or 1.
        :param background_weight: weight for background, where y == 0.
        :param smooth_nr: small constant added to numerator in case of zero covariance.
        :param smooth_dr: small constant added to denominator in case of zero variance.
        :param name: name of the loss.
        :param kwargs: additional arguments.
        """
        super().__init__(name=name, **kwargs)
        if background_weight < 0 or background_weight > 1:
            raise ValueError(
                "The background weight for Dice Score must be "
                f"within [0, 1], got {background_weight}."
            )

        self.binary = binary
        self.background_weight = background_weight
        self.smooth_nr = smooth_nr
        self.smooth_dr = smooth_dr
        self.flatten = tf.keras.layers.Flatten()

    def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
        """
        Return loss for a batch.

        :param y_true: shape = (batch, ...)
        :param y_pred: shape = (batch, ...)
        :return: shape = (batch,)
        """
        if self.binary:
            y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype)
            y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype)

        # (batch, ...) -> (batch, d)
        y_true = self.flatten(y_true)
        y_pred = self.flatten(y_pred)

        # for foreground class
        y_prod = tf.reduce_sum(y_true * y_pred, axis=1)
        y_sum = tf.reduce_sum(y_true + y_pred, axis=1)

        if self.background_weight > 0:
            # generalized
            vol = tf.reduce_sum(tf.ones_like(y_true), axis=1)
            numerator = 2 * (
                y_prod - self.background_weight * y_sum + self.background_weight * vol
            )
            denominator = (
                1 - 2 * self.background_weight
            ) * y_sum + 2 * self.background_weight * vol
        else:
            # foreground only
            numerator = 2 * y_prod
            denominator = y_sum

        return (numerator + self.smooth_nr) / (denominator + self.smooth_dr)

    def get_config(self) -> dict:
        """Return the config dictionary for recreating this class."""
        config = super().get_config()
        config.update(
            binary=self.binary,
            background_weight=self.background_weight,
            smooth_nr=self.smooth_nr,
            smooth_dr=self.smooth_dr,
        )
        return config


@REGISTRY.register_loss(name="dice")
class DiceLoss(NegativeLossMixin, MultiScaleMixin, DiceScore):
    """Revert the sign of DiceScore and support multi-scaling options."""


class CrossEntropy(tf.keras.losses.Loss):
    """
    Define weighted cross-entropy.

    The formulation is:
        loss = − w_fg * y_true log(y_pred) - w_bg * (1−y_true) log(1−y_pred)
    """

    def __init__(
        self,
        binary: bool = False,
        background_weight: float = 0.0,
        smooth: float = EPS,
        name: str = "CrossEntropy",
        **kwargs,
    ):
        """
        Init.

        :param binary: if True, project y_true, y_pred to 0 or 1
        :param background_weight: weight for background, where y == 0.
        :param smooth: smooth constant for log.
        :param name: name of the loss.
        :param kwargs: additional arguments.
        """
        super().__init__(name=name, **kwargs)
        if background_weight < 0 or background_weight > 1:
            raise ValueError(
                "The background weight for Cross Entropy must be "
                f"within [0, 1], got {background_weight}."
            )
        self.binary = binary
        self.background_weight = background_weight
        self.smooth = smooth
        self.flatten = tf.keras.layers.Flatten()

    def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
        """
        Return loss for a batch.

        :param y_true: shape = (batch, ...)
        :param y_pred: shape = (batch, ...)
        :return: shape = (batch,)
        """
        if self.binary:
            y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype)
            y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype)

        # (batch, ...) -> (batch, d)
        y_true = self.flatten(y_true)
        y_pred = self.flatten(y_pred)

        loss_fg = -tf.reduce_mean(y_true * tf.math.log(y_pred + self.smooth), axis=1)
        if self.background_weight > 0:
            loss_bg = -tf.reduce_mean(
                (1 - y_true) * tf.math.log(1 - y_pred + self.smooth), axis=1
            )
            return (
                1 - self.background_weight
            ) * loss_fg + self.background_weight * loss_bg
        else:
            return loss_fg

    def get_config(self) -> dict:
        """Return the config dictionary for recreating this class."""
        config = super().get_config()
        config.update(
            binary=self.binary,
            background_weight=self.background_weight,
            smooth=self.smooth,
        )
        return config


@REGISTRY.register_loss(name="cross-entropy")
class CrossEntropyLoss(MultiScaleMixin, CrossEntropy):
    """Define loss with multi-scaling options."""


class JaccardIndex(DiceScore):
    """
    Define Jaccard index.

    The formulation is:
    1. num = y_true * y_pred
    2. denom = y_true + y_pred - y_true * y_pred
    3. Jaccard index = num / denom

        0. w_fg + w_bg = 1
        1. let y_prod = y_true * y_pred and y_sum  = y_true + y_pred
        2. num = (w_fg * y_true * y_pred + w_bg * (1−y_true) * (1−y_pred))
               = ((w_fg+w_bg) * y_prod - w_bg * y_sum + w_bg)
               = (y_prod - w_bg * y_sum + w_bg)
        3. denom = (w_fg * (y_true + y_pred - y_true * y_pred)
                  + w_bg * (1−y_true + 1−y_pred - (1−y_true) * (1−y_pred)))
                 = w_fg * (y_sum - y_prod) + w_bg * (1-y_prod)
                 = (1-w_bg) * y_sum - y_prod + w_bg
        4. dice score = num / denom
    """

    def __init__(
        self,
        binary: bool = False,
        background_weight: float = 0.0,
        smooth_nr: float = EPS,
        smooth_dr: float = EPS,
        name: str = "JaccardIndex",
        **kwargs,
    ):
        """
        Init.

        :param binary: if True, project y_true, y_pred to 0 or 1.
        :param background_weight: weight for background, where y == 0.
        :param smooth_nr: small constant added to numerator in case of zero covariance.
        :param smooth_dr: small constant added to denominator in case of zero variance.
        :param name: name of the loss.
        :param kwargs: additional arguments.
        """
        super().__init__(
            binary=binary,
            background_weight=background_weight,
            smooth_nr=smooth_nr,
            smooth_dr=smooth_dr,
            name=name,
            **kwargs,
        )

    def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
        """
        Return loss for a batch.

        :param y_true: shape = (batch, ...)
        :param y_pred: shape = (batch, ...)
        :return: shape = (batch,)
        """
        if self.binary:
            y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype)
            y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype)

        # (batch, ...) -> (batch, d)
        y_true = self.flatten(y_true)
        y_pred = self.flatten(y_pred)

        # for foreground class
        y_prod = tf.reduce_sum(y_true * y_pred, axis=1)
        y_sum = tf.reduce_sum(y_true + y_pred, axis=1)

        if self.background_weight > 0:
            # generalized
            vol = tf.reduce_sum(tf.ones_like(y_true), axis=1)
            numerator = (
                y_prod - self.background_weight * y_sum + self.background_weight * vol
            )
            denominator = (
                (1 - self.background_weight) * y_sum
                - y_prod
                + self.background_weight * vol
            )
        else:
            # foreground only
            numerator = y_prod
            denominator = y_sum - y_prod

        return (numerator + self.smooth_nr) / (denominator + self.smooth_dr)


@REGISTRY.register_loss(name="jaccard")
class JaccardLoss(NegativeLossMixin, MultiScaleMixin, JaccardIndex):
    """Revert the sign of JaccardIndex."""


def compute_centroid(mask: tf.Tensor, grid: tf.Tensor) -> tf.Tensor:
    """
    Calculate the centroid of the mask.
    :param mask: shape = (batch, dim1, dim2, dim3)
    :param grid: shape = (1, dim1, dim2, dim3, 3)
    :return: shape = (batch, 3), batch of vectors denoting
             location of centroids.
    """
    assert len(mask.shape) == 4
    assert len(grid.shape) == 5
    bool_mask = tf.expand_dims(
        tf.cast(mask >= 0.5, dtype=tf.float32), axis=4
    )  # (batch, dim1, dim2, dim3, 1)
    masked_grid = bool_mask * grid  # (batch, dim1, dim2, dim3, 3)
    numerator = tf.reduce_sum(masked_grid, axis=[1, 2, 3])  # (batch, 3)
    denominator = tf.reduce_sum(bool_mask, axis=[1, 2, 3])  # (batch, 1)
    return (numerator + EPS) / (denominator + EPS)  # (batch, 3)


def compute_centroid_distance(
    y_true: tf.Tensor, y_pred: tf.Tensor, grid: tf.Tensor
) -> tf.Tensor:
    """
    Calculate the L2-distance between two tensors' centroids.
    :param y_true: tensor, shape = (batch, dim1, dim2, dim3)
    :param y_pred: tensor, shape = (batch, dim1, dim2, dim3)
    :param grid: tensor, shape = (1, dim1, dim2, dim3, 3)
    :return: shape = (batch,)
    """
    centroid_1 = compute_centroid(mask=y_pred, grid=grid)  # (batch, 3)
    centroid_2 = compute_centroid(mask=y_true, grid=grid)  # (batch, 3)
    return tf.sqrt(tf.reduce_sum((centroid_1 - centroid_2) ** 2, axis=1))


def foreground_proportion(y: tf.Tensor) -> tf.Tensor:
    """
    Calculate the percentage of foreground vs background per 3d volume.
    :param y: shape = (batch, dim1, dim2, dim3), a 3D label tensor
    :return: shape = (batch,)
    """
    y = tf.cast(y >= 0.5, dtype=tf.float32)
    return tf.reduce_sum(y, axis=[1, 2, 3]) / tf.reduce_sum(
        tf.ones_like(y), axis=[1, 2, 3]
    )


1			"""Provide different loss or metrics classes for labels."""
2
3			import tensorflow as tf
4
5			from deepreg.constant import EPS
6			from deepreg.loss.util import MultiScaleMixin, NegativeLossMixin
7			from deepreg.registry import REGISTRY
8
9
10			class SumSquaredDifference(tf.keras.losses.Loss):
11			"""
12			Actually, mean of squared distance between y_true and y_pred.
13
14			The inconsistent name was for convention.
15
16			y_true and y_pred have to be at least 1d tensor, including batch axis.
17			"""
18
19			def __init__(
20			self,
21			name: str = "SumSquaredDifference",
22			**kwargs,
23			):
24			"""
25			Init.
26
27			:param name: name of the loss.
28			:param kwargs: additional arguments.
29			"""
30			super().__init__(name=name, **kwargs)
31			self.flatten = tf.keras.layers.Flatten()
32
33			def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
34			"""
35			Return mean squared different for a batch.
36
37			:param y_true: shape = (batch, ...)
38			:param y_pred: shape = (batch, ...)
39			:return: shape = (batch,)
40			"""
41			loss = tf.math.squared_difference(y_true, y_pred)
42			loss = self.flatten(loss)
43			return tf.reduce_mean(loss, axis=1)
44
45
46			@REGISTRY.register_loss(name="ssd")
47			class SumSquaredDifferenceLoss(MultiScaleMixin, SumSquaredDifference):
48			"""Define loss with multi-scaling options."""
49
50
51			class DiceScore(tf.keras.losses.Loss):
52			"""
53			Define dice score.
54
55			The formulation is:
56
57			0. w_fg + w_bg = 1
58			1. let y_prod = y_true * y_pred and y_sum = y_true + y_pred
59			2. num = 2 * (w_fg * y_true * y_pred + w_bg * (1−y_true) * (1−y_pred))
60			= 2 * ((w_fg+w_bg) * y_prod - w_bg * y_sum + w_bg)
61			= 2 * (y_prod - w_bg * y_sum + w_bg)
62			3. denom = (w_fg * (y_true + y_pred) + w_bg * (1−y_true + 1−y_pred))
63			= (w_fg-w_bg) * y_sum + 2 * w_bg
64			= (1-2w_bg) y_sum + 2 * w_bg
65			4. dice score = num / denom
66
67			where num and denom are summed over all axes except the batch axis.
68
69			Reference:
70			Sudre, Carole H., et al. "Generalised dice overlap as a deep learning loss
71			function for highly unbalanced segmentations." Deep learning in medical image
72			analysis and multimodal learning for clinical decision support.
73			Springer, Cham, 2017. 240-248.
74			"""
75
76			def __init__(
77			self,
78			binary: bool = False,
79			background_weight: float = 0.0,
80			smooth_nr: float = EPS,
81			smooth_dr: float = EPS,
82			name: str = "DiceScore",
83			**kwargs,
84			):
85			"""
86			Init.
87
88			:param binary: if True, project y_true, y_pred to 0 or 1.
89			:param background_weight: weight for background, where y == 0.
90			:param smooth_nr: small constant added to numerator in case of zero covariance.
91			:param smooth_dr: small constant added to denominator in case of zero variance.
92			:param name: name of the loss.
93			:param kwargs: additional arguments.
94			"""
95			super().__init__(name=name, **kwargs)
96			if background_weight < 0 or background_weight > 1:
97			raise ValueError(
98			"The background weight for Dice Score must be "
99			f"within [0, 1], got {background_weight}."
100			)
101
102			self.binary = binary
103			self.background_weight = background_weight
104			self.smooth_nr = smooth_nr
105			self.smooth_dr = smooth_dr
106			self.flatten = tf.keras.layers.Flatten()
107
108			def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
109			"""
110			Return loss for a batch.
111
112			:param y_true: shape = (batch, ...)
113			:param y_pred: shape = (batch, ...)
114			:return: shape = (batch,)
115			"""
116			if self.binary:
117			y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype)
118			y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype)
119
120			# (batch, ...) -> (batch, d)
121			y_true = self.flatten(y_true)
122			y_pred = self.flatten(y_pred)
123
124			# for foreground class
125			y_prod = tf.reduce_sum(y_true * y_pred, axis=1)
126			y_sum = tf.reduce_sum(y_true + y_pred, axis=1)
127
128			if self.background_weight > 0:
129			# generalized
130			vol = tf.reduce_sum(tf.ones_like(y_true), axis=1)
131			numerator = 2 * (
132			y_prod - self.background_weight * y_sum + self.background_weight * vol
133			)
134			denominator = (
135			1 - 2 * self.background_weight
136			) * y_sum + 2 * self.background_weight * vol
137			else:
138			# foreground only
139			numerator = 2 * y_prod
140			denominator = y_sum
141
142			return (numerator + self.smooth_nr) / (denominator + self.smooth_dr)
143
144			def get_config(self) -> dict:
145			"""Return the config dictionary for recreating this class."""
146			config = super().get_config()
147			config.update(
148			binary=self.binary,
149			background_weight=self.background_weight,
150			smooth_nr=self.smooth_nr,
151			smooth_dr=self.smooth_dr,
152			)
153			return config
154
155
156			@REGISTRY.register_loss(name="dice")
157			class DiceLoss(NegativeLossMixin, MultiScaleMixin, DiceScore):
158			"""Revert the sign of DiceScore and support multi-scaling options."""
159
160
161			class CrossEntropy(tf.keras.losses.Loss):
162			"""
163			Define weighted cross-entropy.
164
165			The formulation is:
166			loss = − w_fg * y_true log(y_pred) - w_bg * (1−y_true) log(1−y_pred)
167			"""
168
169			def __init__(
170			self,
171			binary: bool = False,
172			background_weight: float = 0.0,
173			smooth: float = EPS,
174			name: str = "CrossEntropy",
175			**kwargs,
176			):
177			"""
178			Init.
179
180			:param binary: if True, project y_true, y_pred to 0 or 1
181			:param background_weight: weight for background, where y == 0.
182			:param smooth: smooth constant for log.
183			:param name: name of the loss.
184			:param kwargs: additional arguments.
185			"""
186			super().__init__(name=name, **kwargs)
187			if background_weight < 0 or background_weight > 1:
188			raise ValueError(
189			"The background weight for Cross Entropy must be "
190			f"within [0, 1], got {background_weight}."
191			)
192			self.binary = binary
193			self.background_weight = background_weight
194			self.smooth = smooth
195			self.flatten = tf.keras.layers.Flatten()
196
197			def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
198			"""
199			Return loss for a batch.
200
201			:param y_true: shape = (batch, ...)
202			:param y_pred: shape = (batch, ...)
203			:return: shape = (batch,)
204			"""
205			if self.binary:
206			y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype)
207			y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype)
208
209			# (batch, ...) -> (batch, d)
210			y_true = self.flatten(y_true)
211			y_pred = self.flatten(y_pred)
212
213			loss_fg = -tf.reduce_mean(y_true * tf.math.log(y_pred + self.smooth), axis=1)
214			if self.background_weight > 0:
215			loss_bg = -tf.reduce_mean(
216			(1 - y_true) * tf.math.log(1 - y_pred + self.smooth), axis=1
217			)
218			return (
219			1 - self.background_weight
220			) * loss_fg + self.background_weight * loss_bg
221			else:
222			return loss_fg
223
224			def get_config(self) -> dict:
225			"""Return the config dictionary for recreating this class."""
226			config = super().get_config()
227			config.update(
228			binary=self.binary,
229			background_weight=self.background_weight,
230			smooth=self.smooth,
231			)
232			return config
233
234
235			@REGISTRY.register_loss(name="cross-entropy")
236			class CrossEntropyLoss(MultiScaleMixin, CrossEntropy):
237			"""Define loss with multi-scaling options."""
238
239
240			class JaccardIndex(DiceScore):
241			"""
242			Define Jaccard index.
243
244			The formulation is:
245			1. num = y_true * y_pred
246			2. denom = y_true + y_pred - y_true * y_pred
247			3. Jaccard index = num / denom
248
249			0. w_fg + w_bg = 1
250			1. let y_prod = y_true * y_pred and y_sum = y_true + y_pred
251			2. num = (w_fg * y_true * y_pred + w_bg * (1−y_true) * (1−y_pred))
252			= ((w_fg+w_bg) * y_prod - w_bg * y_sum + w_bg)
253			= (y_prod - w_bg * y_sum + w_bg)
254			3. denom = (w_fg * (y_true + y_pred - y_true * y_pred)
255			+ w_bg * (1−y_true + 1−y_pred - (1−y_true) * (1−y_pred)))
256			= w_fg * (y_sum - y_prod) + w_bg * (1-y_prod)
257			= (1-w_bg) * y_sum - y_prod + w_bg
258			4. dice score = num / denom
259			"""
260
261			def __init__(
262			self,
263			binary: bool = False,
264			background_weight: float = 0.0,
265			smooth_nr: float = EPS,
266			smooth_dr: float = EPS,
267			name: str = "JaccardIndex",
268			**kwargs,
269			):
270			"""
271			Init.
272
273			:param binary: if True, project y_true, y_pred to 0 or 1.
274			:param background_weight: weight for background, where y == 0.
275			:param smooth_nr: small constant added to numerator in case of zero covariance.
276			:param smooth_dr: small constant added to denominator in case of zero variance.
277			:param name: name of the loss.
278			:param kwargs: additional arguments.
279			"""
280			super().__init__(
281			binary=binary,
282			background_weight=background_weight,
283			smooth_nr=smooth_nr,
284			smooth_dr=smooth_dr,
285			name=name,
286			**kwargs,
287			)
288
289			def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor:
290			"""
291			Return loss for a batch.
292
293			:param y_true: shape = (batch, ...)
294			:param y_pred: shape = (batch, ...)
295			:return: shape = (batch,)
296			"""
297			if self.binary:
298			y_true = tf.cast(y_true >= 0.5, dtype=y_true.dtype)
299			y_pred = tf.cast(y_pred >= 0.5, dtype=y_pred.dtype)
300
301			# (batch, ...) -> (batch, d)
302			y_true = self.flatten(y_true)
303			y_pred = self.flatten(y_pred)
304
305			# for foreground class
306			y_prod = tf.reduce_sum(y_true * y_pred, axis=1)
307			y_sum = tf.reduce_sum(y_true + y_pred, axis=1)
308
309			if self.background_weight > 0:
310			# generalized
311			vol = tf.reduce_sum(tf.ones_like(y_true), axis=1)
312			numerator = (
313			y_prod - self.background_weight * y_sum + self.background_weight * vol
314			)
315			denominator = (
316			(1 - self.background_weight) * y_sum
317			- y_prod
318			+ self.background_weight * vol
319			)
320			else:
321			# foreground only
322			numerator = y_prod
323			denominator = y_sum - y_prod
324
325			return (numerator + self.smooth_nr) / (denominator + self.smooth_dr)
326
327
328			@REGISTRY.register_loss(name="jaccard")
329			class JaccardLoss(NegativeLossMixin, MultiScaleMixin, JaccardIndex):
330			"""Revert the sign of JaccardIndex."""
331
332
333			def compute_centroid(mask: tf.Tensor, grid: tf.Tensor) -> tf.Tensor:
334			"""
335			Calculate the centroid of the mask.
336			:param mask: shape = (batch, dim1, dim2, dim3)
337			:param grid: shape = (1, dim1, dim2, dim3, 3)
338			:return: shape = (batch, 3), batch of vectors denoting
339			location of centroids.
340			"""
341			assert len(mask.shape) == 4
342			assert len(grid.shape) == 5
343			bool_mask = tf.expand_dims(
344			tf.cast(mask >= 0.5, dtype=tf.float32), axis=4
345			) # (batch, dim1, dim2, dim3, 1)
346			masked_grid = bool_mask * grid # (batch, dim1, dim2, dim3, 3)
347			numerator = tf.reduce_sum(masked_grid, axis=[1, 2, 3]) # (batch, 3)
348			denominator = tf.reduce_sum(bool_mask, axis=[1, 2, 3]) # (batch, 1)
349			return (numerator + EPS) / (denominator + EPS) # (batch, 3)
350
351
352			def compute_centroid_distance(
353			y_true: tf.Tensor, y_pred: tf.Tensor, grid: tf.Tensor
354			) -> tf.Tensor:
355			"""
356			Calculate the L2-distance between two tensors' centroids.
357			:param y_true: tensor, shape = (batch, dim1, dim2, dim3)
358			:param y_pred: tensor, shape = (batch, dim1, dim2, dim3)
359			:param grid: tensor, shape = (1, dim1, dim2, dim3, 3)
360			:return: shape = (batch,)
361			"""
362			centroid_1 = compute_centroid(mask=y_pred, grid=grid) # (batch, 3)
363			centroid_2 = compute_centroid(mask=y_true, grid=grid) # (batch, 3)
364			return tf.sqrt(tf.reduce_sum((centroid_1 - centroid_2) ** 2, axis=1))
365
366
367			def foreground_proportion(y: tf.Tensor) -> tf.Tensor:
368			"""
369			Calculate the percentage of foreground vs background per 3d volume.
370			:param y: shape = (batch, dim1, dim2, dim3), a 3D label tensor
371			:return: shape = (batch,)
372			"""
373			y = tf.cast(y >= 0.5, dtype=tf.float32)
374			return tf.reduce_sum(y, axis=[1, 2, 3]) / tf.reduce_sum(
375			tf.ones_like(y), axis=[1, 2, 3]
376			)
377

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