deepreg.model.backbone.local_net.Extraction.__init__() - Code Metrics - Inspection of "WIP 640 refactor model layers" - DeepRegNet/DeepReg - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Pull Request — main (#656)

by Yunguan

created 2021-02-17 00:27 UTC

Extraction.init() A

↳ Parent: deepreg.model.backbone.local_net

Complexity

Conditions

Size

Total Lines	35
Code Lines	23

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	23
dl	0
loc	35
rs	9.328
c	0
b	0
f	0
cc	1
nop	7

# coding=utf-8


from typing import List, Tuple, Union

import tensorflow as tf
import tensorflow.keras.layers as tfkl

from deepreg.model import layer, layer_util
from deepreg.model.backbone.interface import Backbone
from deepreg.registry import REGISTRY


class AdditiveUpsampling(tfkl.Layer):

    def __init__(
        self,
        filters: int,
        output_padding: int,
        kernel_size: int,
        padding: str,
        strides: int,
        output_shape: tuple,
        name: str = "AdditiveUpsampling",
    ):
        """
        Addictive up-sampling layer.

        :param filters: number of channels for output
        :param output_padding: padding for output
        :param kernel_size: arg for deconv3d
        :param padding: arg for deconv3d
        :param strides: arg for deconv3d
        :param output_shape: shape of the output tensor
        :param name: name of the layer.
        """
        super().__init__(name=name)
        self.deconv3d = layer.Deconv3dBlock(
            filters=filters,
            output_padding=output_padding,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
        )
        self.resize = layer.Resize3d(shape=output_shape)

    def call(self, inputs, **kwargs):
        deconved = self.deconv3d(inputs)
        resized = self.resize(inputs)
        resized = tf.add_n(tf.split(resized, num_or_size_splits=2, axis=4))

        return deconved + resized


class Extraction(tfkl.Layer):

    def __init__(
        self,
        image_size: Tuple[int],
        extract_levels: List[int],
        out_channels: int,
        out_kernel_initializer: str,
        out_activation: str,
        name: str = "Extraction",
    ):
        """
        :param image_size: such as (dim1, dim2, dim3)
        :param extract_levels: number of extraction levels.
        :param out_channels: number of channels for the extractions
        :param out_kernel_initializer: initializer to use for kernels.
        :param out_activation: activation to use at end layer.
        :param name: name of the layer
        """
        super().__init__(name=name)
        self.extract_levels = extract_levels
        self.max_level = max(extract_levels)
        self.layers = [
            tf.keras.Sequential(
                [
                    tfkl.Conv3D(
                        filters=out_channels,
                        kernel_size=3,
                        strides=1,
                        padding="same",
                        kernel_initializer=out_kernel_initializer,
                        activation=out_activation,
                    ),
                    layer.Resize3d(shape=image_size),
                ]
            )
            for _ in extract_levels
        ]

    def call(self, inputs: List[tf.Tensor], **kwargs) -> tf.Tensor:
        """

        :param inputs: a list of tensors
        :param kwargs:
        :return:
        """

        return tf.add_n(
            [
                self.layers[idx](inputs=inputs[self.max_level - level])
                for idx, level in enumerate(self.extract_levels)
            ]
        ) / len(self.extract_levels)


@REGISTRY.register_backbone(name="local")
class LocalNet(Backbone):
    """
    Build LocalNet for image registration.

    Reference:

    - Hu, Yipeng, et al.
      "Weakly-supervised convolutional neural networks
      for multimodal image registration."
      Medical image analysis 49 (2018): 1-13.
      https://doi.org/10.1016/j.media.2018.07.002

    - Hu, Yipeng, et al.
      "Label-driven weakly-supervised learning
      for multimodal deformable image registration,"
      https://arxiv.org/abs/1711.01666
    """

    def __init__(
        self,
        image_size: tuple,
        out_channels: int,
        num_channel_initial: int,
        extract_levels: List[int],
        out_kernel_initializer: str,
        out_activation: str,
        use_additive_upsampling: bool = True,
        name: str = "LocalNet",
        **kwargs,
    ):
        """
        Image is encoded gradually, i from level 0 to E,
        then it is decoded gradually, j from level E to D.
        Some of the decoded levels are used for generating extractions.

        So, extract_levels are between [0, E] with E = max(extract_levels),
        and D = min(extract_levels).

        :param image_size: such as (dim1, dim2, dim3)
        :param out_channels: number of channels for the extractions
        :param num_channel_initial: number of initial channels.
        :param extract_levels: number of extraction levels.
        :param out_kernel_initializer: initializer to use for kernels.
        :param out_activation: activation to use at end layer.
        :param use_additive_upsampling: whether use additive up-sampling.
        :param name: name of the backbone.
        :param kwargs: additional arguments.
        """
        super().__init__(
            image_size=image_size,
            out_channels=out_channels,
            num_channel_initial=num_channel_initial,
            out_kernel_initializer=out_kernel_initializer,
            out_activation=out_activation,
            name=name,
            **kwargs,
        )

        # save parameters
        self._extract_levels = extract_levels
        self._use_additive_upsampling = use_additive_upsampling
        self._extract_max_level = max(self._extract_levels)  # E
        self._extract_min_level = min(self._extract_levels)  # D

        # init layer variables
        num_channels = [
            num_channel_initial * (2 ** level)
            for level in range(self._extract_max_level + 1)
        ]  # level 0 to E
        kernel_sizes = [
            7 if level == 0 else 3 for level in range(self._extract_max_level + 1)
        ]
        self._downsample_convs = []
        self._downsample_pools = []
        tensor_shape = image_size
        self._tensor_shapes = [tensor_shape]
        for i in range(self._extract_max_level):
            downsample_conv = self.build_conv_block(
                filters=num_channels[i], kernel_size=kernel_sizes[i], padding="same"
            )
            downsample_pool = self.build_down_sampling_block(
                kernel_size=2, strides=2, padding="same"
            )
            tensor_shape = tuple((x + 1) // 2 for x in tensor_shape)
            self._downsample_convs.append(downsample_conv)
            self._downsample_pools.append(downsample_pool)
            self._tensor_shapes.append(tensor_shape)

        self._bottom_block = self.build_bottom_block(
            filters=num_channels[-1], kernel_size=3, padding="same"
        )  # level E

        self._upsample_deconvs = []
        self._upsample_convs = []
        for level in range(
            self._extract_max_level - 1, self._extract_min_level - 1, -1
        ):  # level D to E-1
            padding = layer_util.deconv_output_padding(
                input_shape=self._tensor_shapes[level + 1],
                output_shape=self._tensor_shapes[level],
                kernel_size=kernel_sizes[level],
                stride=2,
                padding="same",
            )
            upsample_deconv = self.build_up_sampling_block(
                filters=num_channels[level],
                output_padding=padding,
                kernel_size=3,
                strides=2,
                padding="same",
                output_shape=self._tensor_shapes[level],
            )
            upsample_conv = self.build_conv_block(
                filters=num_channels[level], kernel_size=3, padding="same"
            )
            self._upsample_deconvs.append(upsample_deconv)
            self._upsample_convs.append(upsample_conv)
        self._output = self.build_output_block(
            image_size=image_size,
            extract_levels=extract_levels,
            out_channels=out_channels,
            out_kernel_initializer=out_kernel_initializer,
            out_activation=out_activation,
        )

    def build_conv_block(
        self, filters: int, kernel_size: int, padding: str
    ) -> Union[tf.keras.Model, tfkl.Layer]:
        """
        Build a conv block for down-sampling or up-sampling.

        This block do not change the tensor shape (width, height, depth),
        it only changes the number of channels.

        :param filters: number of channels for output
        :param kernel_size: arg for conv3d
        :param padding: arg for conv3d
        :return: a block consists of one or multiple layers
        """
        return tf.keras.Sequential(
            [
                layer.Conv3dBlock(
                    filters=filters,
                    kernel_size=kernel_size,
                    padding=padding,
                ),
                layer.ResidualConv3dBlock(
                    filters=filters,
                    kernel_size=kernel_size,
                    padding=padding,
                ),
            ]
        )

    def build_down_sampling_block(
        self, kernel_size: int, padding: str, strides: int
    ) -> Union[tf.keras.Model, tfkl.Layer]:
        """
        Build a block for down-sampling.

        This block changes the tensor shape (width, height, depth),
        but it does not changes the number of channels.

        :param kernel_size: arg for pool3d
        :param padding: arg for pool3d
        :param strides: arg for pool3d
        :return: a block consists of one or multiple layers
        """
        return tfkl.MaxPool3D(pool_size=kernel_size, strides=strides, padding=padding)

    def build_bottom_block(
        self, filters: int, kernel_size: int, padding: str
    ) -> Union[tf.keras.Model, tfkl.Layer]:
        """
        Build a block for bottom layer.

        This block do not change the tensor shape (width, height, depth),
        it only changes the number of channels.

        :param filters: number of channels for output
        :param kernel_size: arg for conv3d
        :param padding: arg for conv3d
        :return: a block consists of one or multiple layers
        """
        return layer.Conv3dBlock(
            filters=filters, kernel_size=kernel_size, padding=padding
        )

    def build_up_sampling_block(
        self,
        filters: int,
        output_padding: int,
        kernel_size: int,
        padding: str,
        strides: int,
        output_shape: tuple,
    ) -> Union[tf.keras.Model, tfkl.Layer]:
        """
        Build a block for up-sampling.

        This block changes the tensor shape (width, height, depth),
        but it does not changes the number of channels.

        :param filters: number of channels for output
        :param output_padding: padding for output
        :param kernel_size: arg for deconv3d
        :param padding: arg for deconv3d
        :param strides: arg for deconv3d
        :param output_shape: shape of the output tensor
        :return: a block consists of one or multiple layers
        """

        if self._use_additive_upsampling:
            return AdditiveUpsampling(
                filters=filters,
                output_padding=output_padding,
                kernel_size=kernel_size,
                strides=strides,
                padding=padding,
                output_shape=output_shape,
            )

        return layer.Deconv3dBlock(
            filters=filters,
            output_padding=output_padding,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
        )

    def build_skip_block(self) -> Union[tf.keras.Model, tfkl.Layer]:
        """
        Build a block for combining skipped tensor and up-sampled one.

        This block do not change the tensor shape (width, height, depth),
        it only changes the number of channels.

        The input to this block is a list of tensors.

        :return: a block consists of one or multiple layers
        """
        return tfkl.Add()

    def build_output_block(
        self,
        image_size: Tuple[int],
        extract_levels: List[int],
        out_channels: int,
        out_kernel_initializer: str,
        out_activation: str,
    ) -> Union[tf.keras.Model, tfkl.Layer]:
        """
        Build a block for output.

        The input to this block is a list of tensors.

        :param image_size: such as (dim1, dim2, dim3)
        :param extract_levels: number of extraction levels.
        :param out_channels: number of channels for the extractions
        :param out_kernel_initializer: initializer to use for kernels.
        :param out_activation: activation to use at end layer.
        :return: a block consists of one or multiple layers
        """
        return Extraction(
            image_size=image_size,
            extract_levels=extract_levels,
            out_channels=out_channels,
            out_kernel_initializer=out_kernel_initializer,
            out_activation=out_activation,
        )

    def call(self, inputs: tf.Tensor, training=None, mask=None) -> tf.Tensor:

        """
        Build LocalNet graph based on built layers.

        :param inputs: image batch, shape = (batch, f_dim1, f_dim2, f_dim3, ch)
        :param training: None or bool.
        :param mask: None or tf.Tensor.
        :return: shape = (batch, f_dim1, f_dim2, f_dim3, out_channels)
        """

        # down sample from level 0 to E
        # outputs used for decoding, encoded[i] corresponds -> level i
        # stored only 0 to E-1
        skips = []
        down_sampled = inputs
        for level in range(self._extract_max_level):  # level 0 to E - 1
            skip = self._downsample_convs[level](inputs=down_sampled, training=training)
            down_sampled = self._downsample_pools[level](inputs=skip, training=training)
            skips.append(skip)
        up_sampled = self._bottom_block(
            inputs=down_sampled, training=training
        )  # level E of encoding/decoding

        # up sample from level E to D
        outs = [up_sampled]  # level E
        for idx, level in enumerate(
            range(self._extract_max_level - 1, self._extract_min_level - 1, -1)
        ):  # level E-1 to D
            up_sampled = self._upsample_deconvs[idx](
                inputs=up_sampled, training=training
            )
            up_sampled = self.build_skip_block()([up_sampled, skips[level]])
            up_sampled = self._upsample_convs[idx](inputs=up_sampled, training=training)
            outs.append(up_sampled)

        # output
        output = self._output(outs)

        return output


1			# coding=utf-8
			0 ignored issues – show introduced 2021-01-26 01:41 UTC by Report Bug Copy Issue Report Missing module docstring Loading history...
2
3			from typing import List, Tuple, Union
4
5			import tensorflow as tf
6			import tensorflow.keras.layers as tfkl
7
8			from deepreg.model import layer, layer_util
9			from deepreg.model.backbone.interface import Backbone
10			from deepreg.registry import REGISTRY
11
12
13			class AdditiveUpsampling(tfkl.Layer):
			0 ignored issues – show introduced 2021-01-29 00:08 UTC by Report Bug Copy Issue Report Missing class docstring Loading history...
14			def __init__(
15			self,
16			filters: int,
17			output_padding: int,
18			kernel_size: int,
19			padding: str,
20			strides: int,
21			output_shape: tuple,
22			name: str = "AdditiveUpsampling",
23			):
24			"""
25			Addictive up-sampling layer.
26
27			:param filters: number of channels for output
28			:param output_padding: padding for output
29			:param kernel_size: arg for deconv3d
30			:param padding: arg for deconv3d
31			:param strides: arg for deconv3d
32			:param output_shape: shape of the output tensor
33			:param name: name of the layer.
34			"""
35			super().__init__(name=name)
36			self.deconv3d = layer.Deconv3dBlock(
37			filters=filters,
38			output_padding=output_padding,
39			kernel_size=kernel_size,
40			strides=strides,
41			padding=padding,
42			)
43			self.resize = layer.Resize3d(shape=output_shape)
44
45			def call(self, inputs, **kwargs):
46			deconved = self.deconv3d(inputs)
47			resized = self.resize(inputs)
48			resized = tf.add_n(tf.split(resized, num_or_size_splits=2, axis=4))
			0 ignored issues – show Unused Code introduced 2021-02-17 00:58 UTC by Report Bug Copy Issue Report Argument 'axis' passed by position and keyword in function call Loading history...
49			return deconved + resized
50
51
52			class Extraction(tfkl.Layer):
			0 ignored issues – show introduced 2021-02-17 00:58 UTC by Report Bug Copy Issue Report Missing class docstring Loading history...
53			def __init__(
54			self,
55			image_size: Tuple[int],
56			extract_levels: List[int],
57			out_channels: int,
58			out_kernel_initializer: str,
59			out_activation: str,
60			name: str = "Extraction",
61			):
62			"""
63			:param image_size: such as (dim1, dim2, dim3)
64			:param extract_levels: number of extraction levels.
65			:param out_channels: number of channels for the extractions
66			:param out_kernel_initializer: initializer to use for kernels.
67			:param out_activation: activation to use at end layer.
68			:param name: name of the layer
69			"""
70			super().__init__(name=name)
71			self.extract_levels = extract_levels
72			self.max_level = max(extract_levels)
73			self.layers = [
74			tf.keras.Sequential(
75			[
76			tfkl.Conv3D(
77			filters=out_channels,
78			kernel_size=3,
79			strides=1,
80			padding="same",
81			kernel_initializer=out_kernel_initializer,
82			activation=out_activation,
83			),
84			layer.Resize3d(shape=image_size),
85			]
86			)
87			for _ in extract_levels
88			]
89
90			def call(self, inputs: List[tf.Tensor], **kwargs) -> tf.Tensor:
91			"""
92
93			:param inputs: a list of tensors
94			:param kwargs:
95			:return:
96			"""
97
98			return tf.add_n(
99			[
100			self.layers[idx](inputs=inputs[self.max_level - level])
101			for idx, level in enumerate(self.extract_levels)
102			]
103			) / len(self.extract_levels)
104
105
106			@REGISTRY.register_backbone(name="local")
107			class LocalNet(Backbone):
108			"""
109			Build LocalNet for image registration.
110
111			Reference:
112
113			- Hu, Yipeng, et al.
114			"Weakly-supervised convolutional neural networks
115			for multimodal image registration."
116			Medical image analysis 49 (2018): 1-13.
117			https://doi.org/10.1016/j.media.2018.07.002
118
119			- Hu, Yipeng, et al.
120			"Label-driven weakly-supervised learning
121			for multimodal deformable image registration,"
122			https://arxiv.org/abs/1711.01666
123			"""
124
125			def __init__(
126			self,
127			image_size: tuple,
128			out_channels: int,
129			num_channel_initial: int,
130			extract_levels: List[int],
131			out_kernel_initializer: str,
132			out_activation: str,
133			use_additive_upsampling: bool = True,
134			name: str = "LocalNet",
135			**kwargs,
136			):
137			"""
138			Image is encoded gradually, i from level 0 to E,
139			then it is decoded gradually, j from level E to D.
140			Some of the decoded levels are used for generating extractions.
141
142			So, extract_levels are between [0, E] with E = max(extract_levels),
143			and D = min(extract_levels).
144
145			:param image_size: such as (dim1, dim2, dim3)
146			:param out_channels: number of channels for the extractions
147			:param num_channel_initial: number of initial channels.
148			:param extract_levels: number of extraction levels.
149			:param out_kernel_initializer: initializer to use for kernels.
150			:param out_activation: activation to use at end layer.
151			:param use_additive_upsampling: whether use additive up-sampling.
152			:param name: name of the backbone.
153			:param kwargs: additional arguments.
154			"""
155			super().__init__(
156			image_size=image_size,
157			out_channels=out_channels,
158			num_channel_initial=num_channel_initial,
159			out_kernel_initializer=out_kernel_initializer,
160			out_activation=out_activation,
161			name=name,
162			**kwargs,
163			)
164
165			# save parameters
166			self._extract_levels = extract_levels
167			self._use_additive_upsampling = use_additive_upsampling
168			self._extract_max_level = max(self._extract_levels) # E
169			self._extract_min_level = min(self._extract_levels) # D
170
171			# init layer variables
172			num_channels = [
173			num_channel_initial * (2 ** level)
174			for level in range(self._extract_max_level + 1)
175			] # level 0 to E
176			kernel_sizes = [
177			7 if level == 0 else 3 for level in range(self._extract_max_level + 1)
178			]
179			self._downsample_convs = []
180			self._downsample_pools = []
181			tensor_shape = image_size
182			self._tensor_shapes = [tensor_shape]
183			for i in range(self._extract_max_level):
184			downsample_conv = self.build_conv_block(
185			filters=num_channels[i], kernel_size=kernel_sizes[i], padding="same"
186			)
187			downsample_pool = self.build_down_sampling_block(
188			kernel_size=2, strides=2, padding="same"
189			)
190			tensor_shape = tuple((x + 1) // 2 for x in tensor_shape)
191			self._downsample_convs.append(downsample_conv)
192			self._downsample_pools.append(downsample_pool)
193			self._tensor_shapes.append(tensor_shape)
194
195			self._bottom_block = self.build_bottom_block(
196			filters=num_channels[-1], kernel_size=3, padding="same"
197			) # level E
198
199			self._upsample_deconvs = []
200			self._upsample_convs = []
201			for level in range(
202			self._extract_max_level - 1, self._extract_min_level - 1, -1
203			): # level D to E-1
204			padding = layer_util.deconv_output_padding(
205			input_shape=self._tensor_shapes[level + 1],
206			output_shape=self._tensor_shapes[level],
207			kernel_size=kernel_sizes[level],
208			stride=2,
209			padding="same",
210			)
211			upsample_deconv = self.build_up_sampling_block(
212			filters=num_channels[level],
213			output_padding=padding,
214			kernel_size=3,
215			strides=2,
216			padding="same",
217			output_shape=self._tensor_shapes[level],
218			)
219			upsample_conv = self.build_conv_block(
220			filters=num_channels[level], kernel_size=3, padding="same"
221			)
222			self._upsample_deconvs.append(upsample_deconv)
223			self._upsample_convs.append(upsample_conv)
224			self._output = self.build_output_block(
225			image_size=image_size,
226			extract_levels=extract_levels,
227			out_channels=out_channels,
228			out_kernel_initializer=out_kernel_initializer,
229			out_activation=out_activation,
230			)
231
232			def build_conv_block(
233			self, filters: int, kernel_size: int, padding: str
234			) -> Union[tf.keras.Model, tfkl.Layer]:
235			"""
236			Build a conv block for down-sampling or up-sampling.
237
238			This block do not change the tensor shape (width, height, depth),
239			it only changes the number of channels.
240
241			:param filters: number of channels for output
242			:param kernel_size: arg for conv3d
243			:param padding: arg for conv3d
244			:return: a block consists of one or multiple layers
245			"""
246			return tf.keras.Sequential(
247			[
248			layer.Conv3dBlock(
249			filters=filters,
250			kernel_size=kernel_size,
251			padding=padding,
252			),
253			layer.ResidualConv3dBlock(
254			filters=filters,
255			kernel_size=kernel_size,
256			padding=padding,
257			),
258			]
259			)
260
261			def build_down_sampling_block(
262			self, kernel_size: int, padding: str, strides: int
263			) -> Union[tf.keras.Model, tfkl.Layer]:
264			"""
265			Build a block for down-sampling.
266
267			This block changes the tensor shape (width, height, depth),
268			but it does not changes the number of channels.
269
270			:param kernel_size: arg for pool3d
271			:param padding: arg for pool3d
272			:param strides: arg for pool3d
273			:return: a block consists of one or multiple layers
274			"""
275			return tfkl.MaxPool3D(pool_size=kernel_size, strides=strides, padding=padding)
276
277			def build_bottom_block(
278			self, filters: int, kernel_size: int, padding: str
279			) -> Union[tf.keras.Model, tfkl.Layer]:
280			"""
281			Build a block for bottom layer.
282
283			This block do not change the tensor shape (width, height, depth),
284			it only changes the number of channels.
285
286			:param filters: number of channels for output
287			:param kernel_size: arg for conv3d
288			:param padding: arg for conv3d
289			:return: a block consists of one or multiple layers
290			"""
291			return layer.Conv3dBlock(
292			filters=filters, kernel_size=kernel_size, padding=padding
293			)
294
295			def build_up_sampling_block(
296			self,
297			filters: int,
298			output_padding: int,
299			kernel_size: int,
300			padding: str,
301			strides: int,
302			output_shape: tuple,
303			) -> Union[tf.keras.Model, tfkl.Layer]:
304			"""
305			Build a block for up-sampling.
306
307			This block changes the tensor shape (width, height, depth),
308			but it does not changes the number of channels.
309
310			:param filters: number of channels for output
311			:param output_padding: padding for output
312			:param kernel_size: arg for deconv3d
313			:param padding: arg for deconv3d
314			:param strides: arg for deconv3d
315			:param output_shape: shape of the output tensor
316			:return: a block consists of one or multiple layers
317			"""
318
319			if self._use_additive_upsampling:
320			return AdditiveUpsampling(
321			filters=filters,
322			output_padding=output_padding,
323			kernel_size=kernel_size,
324			strides=strides,
325			padding=padding,
326			output_shape=output_shape,
327			)
328
329			return layer.Deconv3dBlock(
330			filters=filters,
331			output_padding=output_padding,
332			kernel_size=kernel_size,
333			strides=strides,
334			padding=padding,
335			)
336
337			def build_skip_block(self) -> Union[tf.keras.Model, tfkl.Layer]:
338			"""
339			Build a block for combining skipped tensor and up-sampled one.
340
341			This block do not change the tensor shape (width, height, depth),
342			it only changes the number of channels.
343
344			The input to this block is a list of tensors.
345
346			:return: a block consists of one or multiple layers
347			"""
348			return tfkl.Add()
349
350			def build_output_block(
351			self,
352			image_size: Tuple[int],
353			extract_levels: List[int],
354			out_channels: int,
355			out_kernel_initializer: str,
356			out_activation: str,
357			) -> Union[tf.keras.Model, tfkl.Layer]:
358			"""
359			Build a block for output.
360
361			The input to this block is a list of tensors.
362
363			:param image_size: such as (dim1, dim2, dim3)
364			:param extract_levels: number of extraction levels.
365			:param out_channels: number of channels for the extractions
366			:param out_kernel_initializer: initializer to use for kernels.
367			:param out_activation: activation to use at end layer.
368			:return: a block consists of one or multiple layers
369			"""
370			return Extraction(
371			image_size=image_size,
372			extract_levels=extract_levels,
373			out_channels=out_channels,
374			out_kernel_initializer=out_kernel_initializer,
375			out_activation=out_activation,
376			)
377
378			def call(self, inputs: tf.Tensor, training=None, mask=None) -> tf.Tensor:
			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...
379			"""
380			Build LocalNet graph based on built layers.
381
382			:param inputs: image batch, shape = (batch, f_dim1, f_dim2, f_dim3, ch)
383			:param training: None or bool.
384			:param mask: None or tf.Tensor.
385			:return: shape = (batch, f_dim1, f_dim2, f_dim3, out_channels)
386			"""
387
388			# down sample from level 0 to E
389			# outputs used for decoding, encoded[i] corresponds -> level i
390			# stored only 0 to E-1
391			skips = []
392			down_sampled = inputs
393			for level in range(self._extract_max_level): # level 0 to E - 1
394			skip = self._downsample_convs[level](inputs=down_sampled, training=training)
395			down_sampled = self._downsample_pools[level](inputs=skip, training=training)
396			skips.append(skip)
397			up_sampled = self._bottom_block(
398			inputs=down_sampled, training=training
399			) # level E of encoding/decoding
400
401			# up sample from level E to D
402			outs = [up_sampled] # level E
403			for idx, level in enumerate(
404			range(self._extract_max_level - 1, self._extract_min_level - 1, -1)
405			): # level E-1 to D
406			up_sampled = self._upsample_deconvs[idx](
407			inputs=up_sampled, training=training
408			)
409			up_sampled = self.build_skip_block()([up_sampled, skips[level]])
410			up_sampled = self._upsample_convs[idx](inputs=up_sampled, training=training)
411			outs.append(up_sampled)
412
413			# output
414			output = self._output(outs)
415
416			return output
417

DeepRegNet / DeepReg

Pull Request — main (#656)

Extraction.__init__() A

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Extraction.init() A