deepreg.model.network.DDFModel._resize_interpolate() - Code Metrics - Inspection of "BSplines interp. independent of the backbone" - DeepRegNet/DeepReg - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — main (#663)

by Yunguan

created 2021-02-02 21:25 UTC

DDFModel._resize_interpolate() A

↳ Parent: deepreg.model.network

Complexity

Conditions

Size

Total Lines	8
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	6
dl	0
loc	8
rs	10
c	0
b	0
f	0
cc	1
nop	3

import logging

from abc import abstractmethod
from copy import deepcopy
from typing import Dict, Optional

import tensorflow as tf

from deepreg.model import layer, layer_util
from deepreg.model.backbone import GlobalNet
from deepreg.registry import REGISTRY


def dict_without(d: dict, key) -> dict:

    """
    Return a copy of the given dict without a certain key.

    :param d: dict to be copied.
    :param key: key to be removed.
    :return: the copy without a key
    """
    copied = deepcopy(d)
    copied.pop(key)
    return copied


class RegistrationModel(tf.keras.Model):
    """Interface for registration model."""

    def __init__(
        self,
        moving_image_size: tuple,
        fixed_image_size: tuple,
        index_size: int,
        labeled: bool,
        batch_size: int,
        config: dict,
        num_devices: int = 1,
        name: str = "RegistrationModel",
    ):
        """
        Init.

        :param moving_image_size: (m_dim1, m_dim2, m_dim3)
        :param fixed_image_size: (f_dim1, f_dim2, f_dim3)
        :param index_size: number of indices for identify each sample
        :param labeled: if the data is labeled
        :param batch_size: size of mini-batch
        :param config: config for method, backbone, and loss.
        :param num_devices: number of GPU used,
            global_batch_size = batch_size*num_devices
        :param name: name of the model
        """
        super().__init__(name=name)
        self.moving_image_size = moving_image_size
        self.fixed_image_size = fixed_image_size
        self.index_size = index_size
        self.labeled = labeled
        self.batch_size = batch_size
        self.config = config
        self.num_devices = num_devices
        self.global_batch_size = num_devices * batch_size

        self._inputs = None  # save inputs of self._model as dict
        self._outputs = None  # save outputs of self._model as dict
        self._model = self.build_model()
        self.build_loss()

    def get_config(self) -> dict:
        """Return the config dictionary for recreating this class."""
        return dict(
            moving_image_size=self.moving_image_size,
            fixed_image_size=self.fixed_image_size,
            index_size=self.index_size,
            labeled=self.labeled,
            batch_size=self.batch_size,
            config=self.config,
            num_devices=self.num_devices,
            name=self.name,
        )

    @abstractmethod
    def build_model(self):
        """Build the model to be saved as self._model."""

    def build_inputs(self) -> Dict[str, tf.keras.layers.Input]:
        """
        Build input tensors.

        :return: dict of inputs.
        """
        # (batch, m_dim1, m_dim2, m_dim3, 1)
        moving_image = tf.keras.Input(
            shape=self.moving_image_size,
            batch_size=self.batch_size,
            name="moving_image",
        )
        # (batch, f_dim1, f_dim2, f_dim3, 1)
        fixed_image = tf.keras.Input(
            shape=self.fixed_image_size,
            batch_size=self.batch_size,
            name="fixed_image",
        )
        # (batch, index_size)
        indices = tf.keras.Input(
            shape=(self.index_size,),
            batch_size=self.batch_size,
            name="indices",
        )

        if not self.labeled:
            return dict(
                moving_image=moving_image, fixed_image=fixed_image, indices=indices
            )

        # (batch, m_dim1, m_dim2, m_dim3, 1)
        moving_label = tf.keras.Input(
            shape=self.moving_image_size,
            batch_size=self.batch_size,
            name="moving_label",
        )
        # (batch, m_dim1, m_dim2, m_dim3, 1)
        fixed_label = tf.keras.Input(
            shape=self.fixed_image_size,
            batch_size=self.batch_size,
            name="fixed_label",
        )
        return dict(
            moving_image=moving_image,
            fixed_image=fixed_image,
            moving_label=moving_label,
            fixed_label=fixed_label,
            indices=indices,
        )

    def concat_images(
        self,
        moving_image: tf.Tensor,
        fixed_image: tf.Tensor,
        moving_label: Optional[tf.Tensor] = None,
    ) -> tf.Tensor:
        """
        Adjust image shape and concatenate them together.

        :param moving_image: registration source
        :param fixed_image: registration target
        :param moving_label: optional, only used for conditional model.
        :return:
        """
        images = []

        # (batch, m_dim1, m_dim2, m_dim3, 1)
        moving_image = tf.expand_dims(moving_image, axis=4)
        moving_image = layer_util.resize3d(
            image=moving_image, size=self.fixed_image_size
        )
        images.append(moving_image)

        # (batch, m_dim1, m_dim2, m_dim3, 1)
        fixed_image = tf.expand_dims(fixed_image, axis=4)
        images.append(fixed_image)

        # (batch, m_dim1, m_dim2, m_dim3, 1)
        if moving_label is not None:
            moving_label = tf.expand_dims(moving_label, axis=4)
            moving_label = layer_util.resize3d(
                image=moving_label, size=self.fixed_image_size
            )
            images.append(moving_label)

        # (batch, f_dim1, f_dim2, f_dim3, 2 or 3)
        images = tf.concat(images, axis=4)
        return images

    def _build_loss(self, name: str, inputs_dict: dict):
        """
        Build and add one weighted loss together with the metrics.

        :param name: name of loss
        :param inputs_dict: inputs for loss function
        """
        if name not in self.config["loss"]:
            # loss config is not defined
            logging.warning(

                f"The configuration for loss {name} is not defined."
                f"Loss is not used."
            )
            return

        loss_config = self.config["loss"][name]

        if "weight" not in loss_config:
            # default loss weight 1
            logging.warning(

                f"The weight for loss {name} is not defined."
                f"Default weight = 1.0 is used."
            )
            loss_config["weight"] = 1.0

        # build loss
        weight = loss_config["weight"]

        if weight == 0:
            logging.warning(f"The weight for loss {name} is zero." f"Loss is not used.")

            return

        loss_cls = REGISTRY.build_loss(config=dict_without(d=loss_config, key="weight"))
        loss = loss_cls(**inputs_dict) / self.global_batch_size
        weighted_loss = loss * weight

        # add loss
        self._model.add_loss(weighted_loss)

        # add metric
        self._model.add_metric(
            loss, name=f"loss/{name}_{loss_cls.name}", aggregation="mean"
        )
        self._model.add_metric(
            weighted_loss,
            name=f"loss/{name}_{loss_cls.name}_weighted",
            aggregation="mean",
        )

    @abstractmethod
    def build_loss(self):
        """Build losses according to configs."""

    def call(

        self, inputs: Dict[str, tf.Tensor], training=None, mask=None
    ) -> Dict[str, tf.Tensor]:
        """
        Call the self._model.

        :param inputs: a dict of tensors.
        :param training: training or not.
        :param mask: maks for inputs.
        :return:
        """
        return self._model(inputs, training=training, mask=mask)  # pragma: no cover

    @abstractmethod
    def postprocess(
        self,
        inputs: Dict[str, tf.Tensor],
        outputs: Dict[str, tf.Tensor],
    ) -> (tf.Tensor, Dict):
        """
        Return a dict used for saving inputs and outputs.

        :param inputs: dict of model inputs
        :param outputs: dict of model outputs
        :return: tuple, indices and a dict.
            In the dict, each value is (tensor, normalize, on_label), where
            - normalize = True if the tensor need to be normalized to [0, 1]
            - on_label = True if the tensor depends on label
        """


@REGISTRY.register_model(name="ddf")
class DDFModel(RegistrationModel):
    """
    A registration model predicts DDF.

    When using global net as backbone,
    the model predicts an affine transformation parameters,
    and a DDF is calculated based on that.
    """

    def _resize_interpolate(self, field, control_points):

        resize = layer.ResizeCPTransform(control_points)
        field = resize(field)

        interpolate = layer.BSplines3DTransform(control_points, self.fixed_image_size)
        field = interpolate(field)

        return field

    def build_model(self):
        """Build the model to be saved as self._model."""
        # build inputs
        self._inputs = self.build_inputs()
        moving_image = self._inputs["moving_image"]
        fixed_image = self._inputs["fixed_image"]

        # build ddf
        control_points = self.config["backbone"].pop("control_points", False)
        backbone_inputs = self.concat_images(moving_image, fixed_image)
        backbone = REGISTRY.build_backbone(
            config=self.config["backbone"],
            default_args=dict(
                image_size=self.fixed_image_size,
                out_channels=3,
                out_kernel_initializer="zeros",
                out_activation=None,
            ),
        )

        if isinstance(backbone, GlobalNet):
            # (f_dim1, f_dim2, f_dim3, 3), (4, 3)
            ddf, theta = backbone(inputs=backbone_inputs)
            self._outputs = dict(ddf=ddf, theta=theta)
        else:
            # (f_dim1, f_dim2, f_dim3, 3)
            ddf = backbone(inputs=backbone_inputs)
            ddf = (
                self._resize_interpolate(ddf, control_points) if control_points else ddf
            )
            self._outputs = dict(ddf=ddf)

        # build outputs
        warping = layer.Warping(fixed_image_size=self.fixed_image_size)
        # (f_dim1, f_dim2, f_dim3, 3)
        pred_fixed_image = warping(inputs=[ddf, moving_image])
        self._outputs["pred_fixed_image"] = pred_fixed_image

        if not self.labeled:
            return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)

        # (f_dim1, f_dim2, f_dim3, 3)
        moving_label = self._inputs["moving_label"]
        pred_fixed_label = warping(inputs=[ddf, moving_label])

        self._outputs["pred_fixed_label"] = pred_fixed_label
        return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)

    def build_loss(self):
        """Build losses according to configs."""
        fixed_image = self._inputs["fixed_image"]
        ddf = self._outputs["ddf"]
        pred_fixed_image = self._outputs["pred_fixed_image"]

        # ddf
        self._build_loss(name="regularization", inputs_dict=dict(inputs=ddf))

        # image
        self._build_loss(
            name="image", inputs_dict=dict(y_true=fixed_image, y_pred=pred_fixed_image)
        )

        # label
        if self.labeled:
            fixed_label = self._inputs["fixed_label"]
            pred_fixed_label = self._outputs["pred_fixed_label"]
            self._build_loss(
                name="label",
                inputs_dict=dict(y_true=fixed_label, y_pred=pred_fixed_label),
            )

    def postprocess(
        self,
        inputs: Dict[str, tf.Tensor],
        outputs: Dict[str, tf.Tensor],
    ) -> (tf.Tensor, Dict):
        """
        Return a dict used for saving inputs and outputs.

        :param inputs: dict of model inputs
        :param outputs: dict of model outputs
        :return: tuple, indices and a dict.
            In the dict, each value is (tensor, normalize, on_label), where
            - normalize = True if the tensor need to be normalized to [0, 1]
            - on_label = True if the tensor depends on label
        """
        indices = inputs["indices"]
        processed = dict(
            moving_image=(inputs["moving_image"], True, False),
            fixed_image=(inputs["fixed_image"], True, False),
            ddf=(outputs["ddf"], True, False),
            pred_fixed_image=(outputs["pred_fixed_image"], True, False),
        )

        # save theta for affine model
        if "theta" in outputs:
            processed["theta"] = (outputs["theta"], None, None)

        if not self.labeled:
            return indices, processed

        processed = {
            **dict(
                moving_label=(inputs["moving_label"], False, True),
                fixed_label=(inputs["fixed_label"], False, True),
                pred_fixed_label=(outputs["pred_fixed_label"], False, True),
            ),
            **processed,
        }

        return indices, processed


@REGISTRY.register_model(name="dvf")
class DVFModel(DDFModel):
    """
    A registration model predicts DVF.

    DDF is calculated based on DVF.
    """

    def build_model(self):
        """Build the model to be saved as self._model."""
        # build inputs
        self._inputs = self.build_inputs()
        moving_image = self._inputs["moving_image"]
        fixed_image = self._inputs["fixed_image"]
        control_points = self.config["backbone"].pop("control_points", False)

        # build ddf
        backbone_inputs = self.concat_images(moving_image, fixed_image)
        backbone = REGISTRY.build_backbone(
            config=self.config["backbone"],
            default_args=dict(
                image_size=self.fixed_image_size,
                out_channels=3,
                out_kernel_initializer="zeros",
                out_activation=None,
            ),
        )
        dvf = backbone(inputs=backbone_inputs)
        dvf = self._resize_interpolate(dvf, control_points) if control_points else dvf
        ddf = layer.IntDVF(fixed_image_size=self.fixed_image_size)(dvf)

        # build outputs
        warping = layer.Warping(fixed_image_size=self.fixed_image_size)
        # (f_dim1, f_dim2, f_dim3, 3)
        pred_fixed_image = warping(inputs=[ddf, moving_image])

        self._outputs = dict(dvf=dvf, ddf=ddf, pred_fixed_image=pred_fixed_image)

        if not self.labeled:
            return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)

        # (f_dim1, f_dim2, f_dim3, 3)
        moving_label = self._inputs["moving_label"]
        pred_fixed_label = warping(inputs=[ddf, moving_label])

        self._outputs["pred_fixed_label"] = pred_fixed_label
        return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)

    def postprocess(
        self,
        inputs: Dict[str, tf.Tensor],
        outputs: Dict[str, tf.Tensor],
    ) -> (tf.Tensor, Dict):
        """
        Return a dict used for saving inputs and outputs.

        :param inputs: dict of model inputs
        :param outputs: dict of model outputs
        :return: tuple, indices and a dict.
            In the dict, each value is (tensor, normalize, on_label), where
            - normalize = True if the tensor need to be normalized to [0, 1]
            - on_label = True if the tensor depends on label
        """
        indices, processed = super().postprocess(inputs=inputs, outputs=outputs)
        processed["dvf"] = (outputs["dvf"], True, False)
        return indices, processed


@REGISTRY.register_model(name="conditional")
class ConditionalModel(RegistrationModel):
    """
    A registration model predicts fixed image label without DDF or DVF.
    """

    def build_model(self):
        """Build the model to be saved as self._model."""
        assert self.labeled

        # build inputs
        self._inputs = self.build_inputs()
        moving_image = self._inputs["moving_image"]
        fixed_image = self._inputs["fixed_image"]
        moving_label = self._inputs["moving_label"]

        # build ddf
        backbone_inputs = self.concat_images(moving_image, fixed_image, moving_label)
        backbone = REGISTRY.build_backbone(
            config=self.config["backbone"],
            default_args=dict(
                image_size=self.fixed_image_size,
                out_channels=1,
                out_kernel_initializer="glorot_uniform",
                out_activation="sigmoid",
            ),
        )
        # (batch, f_dim1, f_dim2, f_dim3)
        pred_fixed_label = backbone(inputs=backbone_inputs)
        pred_fixed_label = tf.squeeze(pred_fixed_label, axis=4)

        self._outputs = dict(pred_fixed_label=pred_fixed_label)
        return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)

    def build_loss(self):
        """Build losses according to configs."""
        fixed_label = self._inputs["fixed_label"]
        pred_fixed_label = self._outputs["pred_fixed_label"]

        self._build_loss(
            name="label",
            inputs_dict=dict(y_true=fixed_label, y_pred=pred_fixed_label),
        )

    def postprocess(
        self,
        inputs: Dict[str, tf.Tensor],
        outputs: Dict[str, tf.Tensor],
    ) -> (tf.Tensor, Dict):
        """
        Return a dict used for saving inputs and outputs.

        :param inputs: dict of model inputs
        :param outputs: dict of model outputs
        :return: tuple, indices and a dict.
            In the dict, each value is (tensor, normalize, on_label), where
            - normalize = True if the tensor need to be normalized to [0, 1]
            - on_label = True if the tensor depends on label
        """
        indices = inputs["indices"]
        processed = dict(
            moving_image=(inputs["moving_image"], True, False),
            fixed_image=(inputs["fixed_image"], True, False),
            pred_fixed_label=(outputs["pred_fixed_label"], True, True),
            moving_label=(inputs["moving_label"], False, True),
            fixed_label=(inputs["fixed_label"], False, True),
        )

        return indices, processed


1			import logging
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report Missing module docstring Loading history...
2			from abc import abstractmethod
3			from copy import deepcopy
4			from typing import Dict, Optional
5
6			import tensorflow as tf
7
8			from deepreg.model import layer, layer_util
9			from deepreg.model.backbone import GlobalNet
10			from deepreg.registry import REGISTRY
11
12
13			def dict_without(d: dict, key) -> dict:
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report "key" missing in parameter type documentation Loading history...
14			"""
15			Return a copy of the given dict without a certain key.
16
17			:param d: dict to be copied.
18			:param key: key to be removed.
19			:return: the copy without a key
20			"""
21			copied = deepcopy(d)
22			copied.pop(key)
23			return copied
24
25
26			class RegistrationModel(tf.keras.Model):
27			"""Interface for registration model."""
28
29			def __init__(
30			self,
31			moving_image_size: tuple,
32			fixed_image_size: tuple,
33			index_size: int,
34			labeled: bool,
35			batch_size: int,
36			config: dict,
37			num_devices: int = 1,
38			name: str = "RegistrationModel",
39			):
40			"""
41			Init.
42
43			:param moving_image_size: (m_dim1, m_dim2, m_dim3)
44			:param fixed_image_size: (f_dim1, f_dim2, f_dim3)
45			:param index_size: number of indices for identify each sample
46			:param labeled: if the data is labeled
47			:param batch_size: size of mini-batch
48			:param config: config for method, backbone, and loss.
49			:param num_devices: number of GPU used,
50			global_batch_size = batch_size*num_devices
51			:param name: name of the model
52			"""
53			super().__init__(name=name)
54			self.moving_image_size = moving_image_size
55			self.fixed_image_size = fixed_image_size
56			self.index_size = index_size
57			self.labeled = labeled
58			self.batch_size = batch_size
59			self.config = config
60			self.num_devices = num_devices
61			self.global_batch_size = num_devices * batch_size
62
63			self._inputs = None # save inputs of self._model as dict
64			self._outputs = None # save outputs of self._model as dict
65			self._model = self.build_model()
66			self.build_loss()
67
68			def get_config(self) -> dict:
69			"""Return the config dictionary for recreating this class."""
70			return dict(
71			moving_image_size=self.moving_image_size,
72			fixed_image_size=self.fixed_image_size,
73			index_size=self.index_size,
74			labeled=self.labeled,
75			batch_size=self.batch_size,
76			config=self.config,
77			num_devices=self.num_devices,
78			name=self.name,
79			)
80
81			@abstractmethod
82			def build_model(self):
83			"""Build the model to be saved as self._model."""
84
85			def build_inputs(self) -> Dict[str, tf.keras.layers.Input]:
86			"""
87			Build input tensors.
88
89			:return: dict of inputs.
90			"""
91			# (batch, m_dim1, m_dim2, m_dim3, 1)
92			moving_image = tf.keras.Input(
93			shape=self.moving_image_size,
94			batch_size=self.batch_size,
95			name="moving_image",
96			)
97			# (batch, f_dim1, f_dim2, f_dim3, 1)
98			fixed_image = tf.keras.Input(
99			shape=self.fixed_image_size,
100			batch_size=self.batch_size,
101			name="fixed_image",
102			)
103			# (batch, index_size)
104			indices = tf.keras.Input(
105			shape=(self.index_size,),
106			batch_size=self.batch_size,
107			name="indices",
108			)
109
110			if not self.labeled:
111			return dict(
112			moving_image=moving_image, fixed_image=fixed_image, indices=indices
113			)
114
115			# (batch, m_dim1, m_dim2, m_dim3, 1)
116			moving_label = tf.keras.Input(
117			shape=self.moving_image_size,
118			batch_size=self.batch_size,
119			name="moving_label",
120			)
121			# (batch, m_dim1, m_dim2, m_dim3, 1)
122			fixed_label = tf.keras.Input(
123			shape=self.fixed_image_size,
124			batch_size=self.batch_size,
125			name="fixed_label",
126			)
127			return dict(
128			moving_image=moving_image,
129			fixed_image=fixed_image,
130			moving_label=moving_label,
131			fixed_label=fixed_label,
132			indices=indices,
133			)
134
135			def concat_images(
136			self,
137			moving_image: tf.Tensor,
138			fixed_image: tf.Tensor,
139			moving_label: Optional[tf.Tensor] = None,
140			) -> tf.Tensor:
141			"""
142			Adjust image shape and concatenate them together.
143
144			:param moving_image: registration source
145			:param fixed_image: registration target
146			:param moving_label: optional, only used for conditional model.
147			:return:
148			"""
149			images = []
150
151			# (batch, m_dim1, m_dim2, m_dim3, 1)
152			moving_image = tf.expand_dims(moving_image, axis=4)
153			moving_image = layer_util.resize3d(
154			image=moving_image, size=self.fixed_image_size
155			)
156			images.append(moving_image)
157
158			# (batch, m_dim1, m_dim2, m_dim3, 1)
159			fixed_image = tf.expand_dims(fixed_image, axis=4)
160			images.append(fixed_image)
161
162			# (batch, m_dim1, m_dim2, m_dim3, 1)
163			if moving_label is not None:
164			moving_label = tf.expand_dims(moving_label, axis=4)
165			moving_label = layer_util.resize3d(
166			image=moving_label, size=self.fixed_image_size
167			)
168			images.append(moving_label)
169
170			# (batch, f_dim1, f_dim2, f_dim3, 2 or 3)
171			images = tf.concat(images, axis=4)
172			return images
173
174			def _build_loss(self, name: str, inputs_dict: dict):
175			"""
176			Build and add one weighted loss together with the metrics.
177
178			:param name: name of loss
179			:param inputs_dict: inputs for loss function
180			"""
181			if name not in self.config["loss"]:
182			# loss config is not defined
183			logging.warning(
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report Use lazy % formatting in logging functions Loading history...
184			f"The configuration for loss {name} is not defined."
185			f"Loss is not used."
186			)
187			return
188
189			loss_config = self.config["loss"][name]
190
191			if "weight" not in loss_config:
192			# default loss weight 1
193			logging.warning(
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report Use lazy % formatting in logging functions Loading history...
194			f"The weight for loss {name} is not defined."
195			f"Default weight = 1.0 is used."
196			)
197			loss_config["weight"] = 1.0
198
199			# build loss
200			weight = loss_config["weight"]
201
202			if weight == 0:
203			logging.warning(f"The weight for loss {name} is zero." f"Loss is not used.")
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report Use lazy % formatting in logging functions Loading history...
204			return
205
206			loss_cls = REGISTRY.build_loss(config=dict_without(d=loss_config, key="weight"))
207			loss = loss_cls(**inputs_dict) / self.global_batch_size
208			weighted_loss = loss * weight
209
210			# add loss
211			self._model.add_loss(weighted_loss)
212
213			# add metric
214			self._model.add_metric(
215			loss, name=f"loss/{name}_{loss_cls.name}", aggregation="mean"
216			)
217			self._model.add_metric(
218			weighted_loss,
219			name=f"loss/{name}_{loss_cls.name}_weighted",
220			aggregation="mean",
221			)
222
223			@abstractmethod
224			def build_loss(self):
225			"""Build losses according to configs."""
226
227			def call(
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report "mask, training" missing in parameter type documentation Loading history...
228			self, inputs: Dict[str, tf.Tensor], training=None, mask=None
229			) -> Dict[str, tf.Tensor]:
230			"""
231			Call the self._model.
232
233			:param inputs: a dict of tensors.
234			:param training: training or not.
235			:param mask: maks for inputs.
236			:return:
237			"""
238			return self._model(inputs, training=training, mask=mask) # pragma: no cover
239
240			@abstractmethod
241			def postprocess(
242			self,
243			inputs: Dict[str, tf.Tensor],
244			outputs: Dict[str, tf.Tensor],
245			) -> (tf.Tensor, Dict):
246			"""
247			Return a dict used for saving inputs and outputs.
248
249			:param inputs: dict of model inputs
250			:param outputs: dict of model outputs
251			:return: tuple, indices and a dict.
252			In the dict, each value is (tensor, normalize, on_label), where
253			- normalize = True if the tensor need to be normalized to [0, 1]
254			- on_label = True if the tensor depends on label
255			"""
256
257
258			@REGISTRY.register_model(name="ddf")
259			class DDFModel(RegistrationModel):
260			"""
261			A registration model predicts DDF.
262
263			When using global net as backbone,
264			the model predicts an affine transformation parameters,
265			and a DDF is calculated based on that.
266			"""
267
268			def _resize_interpolate(self, field, control_points):
			0 ignored issues – show introduced 2021-02-02 11:29 UTC by Report Bug Copy Issue Report Missing function or method docstring Loading history...
269			resize = layer.ResizeCPTransform(control_points)
270			field = resize(field)
271
272			interpolate = layer.BSplines3DTransform(control_points, self.fixed_image_size)
273			field = interpolate(field)
274
275			return field
276
277			def build_model(self):
278			"""Build the model to be saved as self._model."""
279			# build inputs
280			self._inputs = self.build_inputs()
281			moving_image = self._inputs["moving_image"]
282			fixed_image = self._inputs["fixed_image"]
283
284			# build ddf
285			control_points = self.config["backbone"].pop("control_points", False)
286			backbone_inputs = self.concat_images(moving_image, fixed_image)
287			backbone = REGISTRY.build_backbone(
288			config=self.config["backbone"],
289			default_args=dict(
290			image_size=self.fixed_image_size,
291			out_channels=3,
292			out_kernel_initializer="zeros",
293			out_activation=None,
294			),
295			)
296
297			if isinstance(backbone, GlobalNet):
298			# (f_dim1, f_dim2, f_dim3, 3), (4, 3)
299			ddf, theta = backbone(inputs=backbone_inputs)
300			self._outputs = dict(ddf=ddf, theta=theta)
301			else:
302			# (f_dim1, f_dim2, f_dim3, 3)
303			ddf = backbone(inputs=backbone_inputs)
304			ddf = (
305			self._resize_interpolate(ddf, control_points) if control_points else ddf
306			)
307			self._outputs = dict(ddf=ddf)
308
309			# build outputs
310			warping = layer.Warping(fixed_image_size=self.fixed_image_size)
311			# (f_dim1, f_dim2, f_dim3, 3)
312			pred_fixed_image = warping(inputs=[ddf, moving_image])
313			self._outputs["pred_fixed_image"] = pred_fixed_image
314
315			if not self.labeled:
316			return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)
317
318			# (f_dim1, f_dim2, f_dim3, 3)
319			moving_label = self._inputs["moving_label"]
320			pred_fixed_label = warping(inputs=[ddf, moving_label])
321
322			self._outputs["pred_fixed_label"] = pred_fixed_label
323			return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)
324
325			def build_loss(self):
326			"""Build losses according to configs."""
327			fixed_image = self._inputs["fixed_image"]
328			ddf = self._outputs["ddf"]
329			pred_fixed_image = self._outputs["pred_fixed_image"]
330
331			# ddf
332			self._build_loss(name="regularization", inputs_dict=dict(inputs=ddf))
333
334			# image
335			self._build_loss(
336			name="image", inputs_dict=dict(y_true=fixed_image, y_pred=pred_fixed_image)
337			)
338
339			# label
340			if self.labeled:
341			fixed_label = self._inputs["fixed_label"]
342			pred_fixed_label = self._outputs["pred_fixed_label"]
343			self._build_loss(
344			name="label",
345			inputs_dict=dict(y_true=fixed_label, y_pred=pred_fixed_label),
346			)
347
348			def postprocess(
349			self,
350			inputs: Dict[str, tf.Tensor],
351			outputs: Dict[str, tf.Tensor],
352			) -> (tf.Tensor, Dict):
353			"""
354			Return a dict used for saving inputs and outputs.
355
356			:param inputs: dict of model inputs
357			:param outputs: dict of model outputs
358			:return: tuple, indices and a dict.
359			In the dict, each value is (tensor, normalize, on_label), where
360			- normalize = True if the tensor need to be normalized to [0, 1]
361			- on_label = True if the tensor depends on label
362			"""
363			indices = inputs["indices"]
364			processed = dict(
365			moving_image=(inputs["moving_image"], True, False),
366			fixed_image=(inputs["fixed_image"], True, False),
367			ddf=(outputs["ddf"], True, False),
368			pred_fixed_image=(outputs["pred_fixed_image"], True, False),
369			)
370
371			# save theta for affine model
372			if "theta" in outputs:
373			processed["theta"] = (outputs["theta"], None, None)
374
375			if not self.labeled:
376			return indices, processed
377
378			processed = {
379			**dict(
380			moving_label=(inputs["moving_label"], False, True),
381			fixed_label=(inputs["fixed_label"], False, True),
382			pred_fixed_label=(outputs["pred_fixed_label"], False, True),
383			),
384			**processed,
385			}
386
387			return indices, processed
388
389
390			@REGISTRY.register_model(name="dvf")
391			class DVFModel(DDFModel):
392			"""
393			A registration model predicts DVF.
394
395			DDF is calculated based on DVF.
396			"""
397
398			def build_model(self):
399			"""Build the model to be saved as self._model."""
400			# build inputs
401			self._inputs = self.build_inputs()
402			moving_image = self._inputs["moving_image"]
403			fixed_image = self._inputs["fixed_image"]
404			control_points = self.config["backbone"].pop("control_points", False)
405
406			# build ddf
407			backbone_inputs = self.concat_images(moving_image, fixed_image)
408			backbone = REGISTRY.build_backbone(
409			config=self.config["backbone"],
410			default_args=dict(
411			image_size=self.fixed_image_size,
412			out_channels=3,
413			out_kernel_initializer="zeros",
414			out_activation=None,
415			),
416			)
417			dvf = backbone(inputs=backbone_inputs)
418			dvf = self._resize_interpolate(dvf, control_points) if control_points else dvf
419			ddf = layer.IntDVF(fixed_image_size=self.fixed_image_size)(dvf)
420
421			# build outputs
422			warping = layer.Warping(fixed_image_size=self.fixed_image_size)
423			# (f_dim1, f_dim2, f_dim3, 3)
424			pred_fixed_image = warping(inputs=[ddf, moving_image])
425
426			self._outputs = dict(dvf=dvf, ddf=ddf, pred_fixed_image=pred_fixed_image)
427
428			if not self.labeled:
429			return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)
430
431			# (f_dim1, f_dim2, f_dim3, 3)
432			moving_label = self._inputs["moving_label"]
433			pred_fixed_label = warping(inputs=[ddf, moving_label])
434
435			self._outputs["pred_fixed_label"] = pred_fixed_label
436			return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)
437
438			def postprocess(
439			self,
440			inputs: Dict[str, tf.Tensor],
441			outputs: Dict[str, tf.Tensor],
442			) -> (tf.Tensor, Dict):
443			"""
444			Return a dict used for saving inputs and outputs.
445
446			:param inputs: dict of model inputs
447			:param outputs: dict of model outputs
448			:return: tuple, indices and a dict.
449			In the dict, each value is (tensor, normalize, on_label), where
450			- normalize = True if the tensor need to be normalized to [0, 1]
451			- on_label = True if the tensor depends on label
452			"""
453			indices, processed = super().postprocess(inputs=inputs, outputs=outputs)
454			processed["dvf"] = (outputs["dvf"], True, False)
455			return indices, processed
456
457
458			@REGISTRY.register_model(name="conditional")
459			class ConditionalModel(RegistrationModel):
460			"""
461			A registration model predicts fixed image label without DDF or DVF.
462			"""
463
464			def build_model(self):
465			"""Build the model to be saved as self._model."""
466			assert self.labeled
467
468			# build inputs
469			self._inputs = self.build_inputs()
470			moving_image = self._inputs["moving_image"]
471			fixed_image = self._inputs["fixed_image"]
472			moving_label = self._inputs["moving_label"]
473
474			# build ddf
475			backbone_inputs = self.concat_images(moving_image, fixed_image, moving_label)
476			backbone = REGISTRY.build_backbone(
477			config=self.config["backbone"],
478			default_args=dict(
479			image_size=self.fixed_image_size,
480			out_channels=1,
481			out_kernel_initializer="glorot_uniform",
482			out_activation="sigmoid",
483			),
484			)
485			# (batch, f_dim1, f_dim2, f_dim3)
486			pred_fixed_label = backbone(inputs=backbone_inputs)
487			pred_fixed_label = tf.squeeze(pred_fixed_label, axis=4)
488
489			self._outputs = dict(pred_fixed_label=pred_fixed_label)
490			return tf.keras.Model(inputs=self._inputs, outputs=self._outputs)
491
492			def build_loss(self):
493			"""Build losses according to configs."""
494			fixed_label = self._inputs["fixed_label"]
495			pred_fixed_label = self._outputs["pred_fixed_label"]
496
497			self._build_loss(
498			name="label",
499			inputs_dict=dict(y_true=fixed_label, y_pred=pred_fixed_label),
500			)
501
502			def postprocess(
503			self,
504			inputs: Dict[str, tf.Tensor],
505			outputs: Dict[str, tf.Tensor],
506			) -> (tf.Tensor, Dict):
507			"""
508			Return a dict used for saving inputs and outputs.
509
510			:param inputs: dict of model inputs
511			:param outputs: dict of model outputs
512			:return: tuple, indices and a dict.
513			In the dict, each value is (tensor, normalize, on_label), where
514			- normalize = True if the tensor need to be normalized to [0, 1]
515			- on_label = True if the tensor depends on label
516			"""
517			indices = inputs["indices"]
518			processed = dict(
519			moving_image=(inputs["moving_image"], True, False),
520			fixed_image=(inputs["fixed_image"], True, False),
521			pred_fixed_label=(outputs["pred_fixed_label"], True, True),
522			moving_label=(inputs["moving_label"], False, True),
523			fixed_label=(inputs["fixed_label"], False, True),
524			)
525
526			return indices, processed
527

DeepRegNet / DeepReg

Pull Request — main (#663)

DDFModel._resize_interpolate() A

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like