deepreg.model.backbone.local_net.LocalNet.build_layers() - 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 22:08 UTC

LocalNet.build_layers() C

↳ Parent: deepreg.model.backbone.local_net

Complexity

Conditions

Size

Total Lines	111
Code Lines	77

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	77
dl	0
loc	111
rs	6.8193
c	0
b	0
f	0
cc	6
nop	12

How to fix Long Method Many Parameters

# coding=utf-8


from typing import List, Tuple, Union

import tensorflow as tf
import tensorflow.keras.layers as tfkl
from tensorflow.python.keras.utils import conv_utils

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,
        num_channel_initial: int,
        extract_levels: List[int],
        out_kernel_initializer: str,
        out_activation: str,
        out_channels: int,
        use_additive_upsampling: bool = True,
        name: str = "LocalNet",
        **kwargs,
    ):
        """
        Init.

        Image is encoded gradually, i from level 0 to D,
        then it is decoded gradually, j from level D to 0.
        Some of the decoded levels are used for generating extractions.

        So, extract_levels are between [0, D].

        :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._depth = max(self._extract_levels)  # D

        # init layers
        # all lists start with d = 0
        self._downsample_convs = None
        self._downsample_pools = None
        self._bottom_block = None
        self._upsample_deconvs = None
        self._upsample_convs = None
        self._output_block = None

        # build layers
        self.build_layers(
            image_size=image_size,
            num_channel_initial=num_channel_initial,
            depth=self._depth,
            extract_levels=self._extract_levels,
            downsample_kernel_sizes=[7] + [3] * self._depth,
            upsample_kernel_sizes=3,
            strides=2,
            padding="same",
            out_kernel_initializer=out_kernel_initializer,
            out_activation=out_activation,
            out_channels=out_channels,
        )

    def build_layers(
        self,
        image_size: tuple,
        num_channel_initial: int,
        depth: int,
        extract_levels: List[int],
        downsample_kernel_sizes: Union[int, List[int]],
        upsample_kernel_sizes: Union[int, List[int]],
        strides: int,
        padding: str,
        out_kernel_initializer: str,
        out_activation: str,
        out_channels: int,
    ):
        """
        Build layers that will be used in call.

        :param image_size: (dim1, dim2, dim3).
        :param num_channel_initial: number of initial channels.
        :param depth: network starts with d = 0, and the bottom has d = depth.
        :param extract_levels: from which depths the output will be built.
        :param downsample_kernel_sizes: kernel size for down-sampling
        :param upsample_kernel_sizes: kernel size for up-sampling
        :param strides: strides for down-sampling
        :param padding: padding mode for all conv layers
        :param out_kernel_initializer: initializer to use for kernels.
        :param out_activation: activation to use at end layer.
        :param out_channels: number of channels for the extractions
        """
        # init params
        min_extract_level = min(extract_levels)
        num_channels = [num_channel_initial * (2 ** d) for d in range(depth + 1)]
        if isinstance(downsample_kernel_sizes, int):
            downsample_kernel_sizes = [downsample_kernel_sizes] * (depth + 1)
        assert len(downsample_kernel_sizes) == depth + 1
        if isinstance(upsample_kernel_sizes, int):
            upsample_kernel_sizes = [upsample_kernel_sizes] * depth
        assert len(upsample_kernel_sizes) == depth

        # down-sampling
        self._downsample_convs = []
        self._downsample_pools = []
        tensor_shape = image_size
        tensor_shapes = [tensor_shape]
        for d in range(depth):
            downsample_conv = self.build_conv_block(
                filters=num_channels[d],
                kernel_size=downsample_kernel_sizes[d],
                padding=padding,
            )
            downsample_pool = self.build_down_sampling_block(
                kernel_size=strides, strides=strides, padding=padding
            )
            tensor_shape = tuple(
                conv_utils.conv_output_length(
                    input_length=x,
                    filter_size=strides,
                    padding=padding,
                    stride=strides,
                    dilation=1,
                )
                for x in tensor_shape
            )
            self._downsample_convs.append(downsample_conv)
            self._downsample_pools.append(downsample_pool)
            tensor_shapes.append(tensor_shape)

        # bottom layer
        self._bottom_block = self.build_bottom_block(
            filters=num_channels[depth],
            kernel_size=downsample_kernel_sizes[depth],
            padding=padding,
        )

        # up-sampling
        self._upsample_deconvs = []
        self._upsample_convs = []
        for d in range(depth - 1, min_extract_level - 1, -1):
            kernel_size = upsample_kernel_sizes[d]
            output_padding = layer_util.deconv_output_padding(
                input_shape=tensor_shapes[d + 1],
                output_shape=tensor_shapes[d],
                kernel_size=kernel_size,
                stride=strides,
                padding=padding,
            )
            upsample_deconv = self.build_up_sampling_block(
                filters=num_channels[d],
                output_padding=output_padding,
                kernel_size=kernel_size,
                strides=strides,
                padding=padding,
                output_shape=tensor_shapes[d],
            )
            upsample_conv = self.build_conv_block(
                filters=num_channels[d], kernel_size=kernel_size, padding=padding
            )
            self._upsample_deconvs = [upsample_deconv] + self._upsample_deconvs
            self._upsample_convs = [upsample_conv] + self._upsample_convs
        if min_extract_level > 0:
            # add Nones to make lists have length depth - 1
            self._upsample_deconvs = [None] * min_extract_level + self._upsample_deconvs
            self._upsample_convs = [None] * min_extract_level + self._upsample_convs

        # extraction
        self._output_block = 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-sampling
        skips = []
        down_sampled = inputs
        for d in range(self._depth):
            skip = self._downsample_convs[d](inputs=down_sampled, training=training)
            down_sampled = self._downsample_pools[d](inputs=skip, training=training)
            skips.append(skip)

        # bottom
        up_sampled = self._bottom_block(inputs=down_sampled, training=training)

        # up-sampling
        outs = [up_sampled]
        for d in range(self._depth - 1, min(self._extract_levels) - 1, -1):
            up_sampled = self._upsample_deconvs[d](inputs=up_sampled, training=training)
            up_sampled = self.build_skip_block()([up_sampled, skips[d]])
            up_sampled = self._upsample_convs[d](inputs=up_sampled, training=training)
            outs.append(up_sampled)

        # output
        output = self._output_block(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			from tensorflow.python.keras.utils import conv_utils
8
9			from deepreg.model import layer, layer_util
10			from deepreg.model.backbone.interface import Backbone
11			from deepreg.registry import REGISTRY
12
13
14			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...
15			def __init__(
16			self,
17			filters: int,
18			output_padding: int,
19			kernel_size: int,
20			padding: str,
21			strides: int,
22			output_shape: tuple,
23			name: str = "AdditiveUpsampling",
24			):
25			"""
26			Addictive up-sampling layer.
27
28			:param filters: number of channels for output
29			:param output_padding: padding for output
30			:param kernel_size: arg for deconv3d
31			:param padding: arg for deconv3d
32			:param strides: arg for deconv3d
33			:param output_shape: shape of the output tensor
34			:param name: name of the layer.
35			"""
36			super().__init__(name=name)
37			self.deconv3d = layer.Deconv3dBlock(
38			filters=filters,
39			output_padding=output_padding,
40			kernel_size=kernel_size,
41			strides=strides,
42			padding=padding,
43			)
44			self.resize = layer.Resize3d(shape=output_shape)
45
46			def call(self, inputs, **kwargs):
47			deconved = self.deconv3d(inputs)
48			resized = self.resize(inputs)
49			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...
50			return deconved + resized
51
52
53			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...
54			def __init__(
55			self,
56			image_size: Tuple[int],
57			extract_levels: List[int],
58			out_channels: int,
59			out_kernel_initializer: str,
60			out_activation: str,
61			name: str = "Extraction",
62			):
63			"""
64			:param image_size: such as (dim1, dim2, dim3)
65			:param extract_levels: number of extraction levels.
66			:param out_channels: number of channels for the extractions
67			:param out_kernel_initializer: initializer to use for kernels.
68			:param out_activation: activation to use at end layer.
69			:param name: name of the layer
70			"""
71			super().__init__(name=name)
72			self.extract_levels = extract_levels
73			self.max_level = max(extract_levels)
74			self.layers = [
75			tf.keras.Sequential(
76			[
77			tfkl.Conv3D(
78			filters=out_channels,
79			kernel_size=3,
80			strides=1,
81			padding="same",
82			kernel_initializer=out_kernel_initializer,
83			activation=out_activation,
84			),
85			layer.Resize3d(shape=image_size),
86			]
87			)
88			for _ in extract_levels
89			]
90
91			def call(self, inputs: List[tf.Tensor], **kwargs) -> tf.Tensor:
92			"""
93
94			:param inputs: a list of tensors
95			:param kwargs:
96			:return:
97			"""
98
99			return tf.add_n(
100			[
101			self.layers[idx](inputs=inputs[self.max_level - level])
102			for idx, level in enumerate(self.extract_levels)
103			]
104			) / len(self.extract_levels)
105
106
107			@REGISTRY.register_backbone(name="local")
108			class LocalNet(Backbone):
109			"""
110			Build LocalNet for image registration.
111
112			Reference:
113
114			- Hu, Yipeng, et al.
115			"Weakly-supervised convolutional neural networks
116			for multimodal image registration."
117			Medical image analysis 49 (2018): 1-13.
118			https://doi.org/10.1016/j.media.2018.07.002
119
120			- Hu, Yipeng, et al.
121			"Label-driven weakly-supervised learning
122			for multimodal deformable image registration,"
123			https://arxiv.org/abs/1711.01666
124			"""
125
126			def __init__(
127			self,
128			image_size: tuple,
129			num_channel_initial: int,
130			extract_levels: List[int],
131			out_kernel_initializer: str,
132			out_activation: str,
133			out_channels: int,
134			use_additive_upsampling: bool = True,
135			name: str = "LocalNet",
136			**kwargs,
137			):
138			"""
139			Init.
140
141			Image is encoded gradually, i from level 0 to D,
142			then it is decoded gradually, j from level D to 0.
143			Some of the decoded levels are used for generating extractions.
144
145			So, extract_levels are between [0, D].
146
147			:param image_size: such as (dim1, dim2, dim3)
148			:param out_channels: number of channels for the extractions
149			:param num_channel_initial: number of initial channels.
150			:param extract_levels: number of extraction levels.
151			:param out_kernel_initializer: initializer to use for kernels.
152			:param out_activation: activation to use at end layer.
153			:param use_additive_upsampling: whether use additive up-sampling.
154			:param name: name of the backbone.
155			:param kwargs: additional arguments.
156			"""
157			super().__init__(
158			image_size=image_size,
159			out_channels=out_channels,
160			num_channel_initial=num_channel_initial,
161			out_kernel_initializer=out_kernel_initializer,
162			out_activation=out_activation,
163			name=name,
164			**kwargs,
165			)
166
167			# save parameters
168			self._extract_levels = extract_levels
169			self._use_additive_upsampling = use_additive_upsampling
170			self._depth = max(self._extract_levels) # D
171
172			# init layers
173			# all lists start with d = 0
174			self._downsample_convs = None
175			self._downsample_pools = None
176			self._bottom_block = None
177			self._upsample_deconvs = None
178			self._upsample_convs = None
179			self._output_block = None
180
181			# build layers
182			self.build_layers(
183			image_size=image_size,
184			num_channel_initial=num_channel_initial,
185			depth=self._depth,
186			extract_levels=self._extract_levels,
187			downsample_kernel_sizes=[7] + [3] * self._depth,
188			upsample_kernel_sizes=3,
189			strides=2,
190			padding="same",
191			out_kernel_initializer=out_kernel_initializer,
192			out_activation=out_activation,
193			out_channels=out_channels,
194			)
195
196			def build_layers(
197			self,
198			image_size: tuple,
199			num_channel_initial: int,
200			depth: int,
201			extract_levels: List[int],
202			downsample_kernel_sizes: Union[int, List[int]],
203			upsample_kernel_sizes: Union[int, List[int]],
204			strides: int,
205			padding: str,
206			out_kernel_initializer: str,
207			out_activation: str,
208			out_channels: int,
209			):
210			"""
211			Build layers that will be used in call.
212
213			:param image_size: (dim1, dim2, dim3).
214			:param num_channel_initial: number of initial channels.
215			:param depth: network starts with d = 0, and the bottom has d = depth.
216			:param extract_levels: from which depths the output will be built.
217			:param downsample_kernel_sizes: kernel size for down-sampling
218			:param upsample_kernel_sizes: kernel size for up-sampling
219			:param strides: strides for down-sampling
220			:param padding: padding mode for all conv layers
221			:param out_kernel_initializer: initializer to use for kernels.
222			:param out_activation: activation to use at end layer.
223			:param out_channels: number of channels for the extractions
224			"""
225			# init params
226			min_extract_level = min(extract_levels)
227			num_channels = [num_channel_initial * (2 ** d) for d in range(depth + 1)]
228			if isinstance(downsample_kernel_sizes, int):
229			downsample_kernel_sizes = [downsample_kernel_sizes] * (depth + 1)
230			assert len(downsample_kernel_sizes) == depth + 1
231			if isinstance(upsample_kernel_sizes, int):
232			upsample_kernel_sizes = [upsample_kernel_sizes] * depth
233			assert len(upsample_kernel_sizes) == depth
234
235			# down-sampling
236			self._downsample_convs = []
237			self._downsample_pools = []
238			tensor_shape = image_size
239			tensor_shapes = [tensor_shape]
240			for d in range(depth):
241			downsample_conv = self.build_conv_block(
242			filters=num_channels[d],
243			kernel_size=downsample_kernel_sizes[d],
244			padding=padding,
245			)
246			downsample_pool = self.build_down_sampling_block(
247			kernel_size=strides, strides=strides, padding=padding
248			)
249			tensor_shape = tuple(
250			conv_utils.conv_output_length(
251			input_length=x,
252			filter_size=strides,
253			padding=padding,
254			stride=strides,
255			dilation=1,
256			)
257			for x in tensor_shape
258			)
259			self._downsample_convs.append(downsample_conv)
260			self._downsample_pools.append(downsample_pool)
261			tensor_shapes.append(tensor_shape)
262
263			# bottom layer
264			self._bottom_block = self.build_bottom_block(
265			filters=num_channels[depth],
266			kernel_size=downsample_kernel_sizes[depth],
267			padding=padding,
268			)
269
270			# up-sampling
271			self._upsample_deconvs = []
272			self._upsample_convs = []
273			for d in range(depth - 1, min_extract_level - 1, -1):
274			kernel_size = upsample_kernel_sizes[d]
275			output_padding = layer_util.deconv_output_padding(
276			input_shape=tensor_shapes[d + 1],
277			output_shape=tensor_shapes[d],
278			kernel_size=kernel_size,
279			stride=strides,
280			padding=padding,
281			)
282			upsample_deconv = self.build_up_sampling_block(
283			filters=num_channels[d],
284			output_padding=output_padding,
285			kernel_size=kernel_size,
286			strides=strides,
287			padding=padding,
288			output_shape=tensor_shapes[d],
289			)
290			upsample_conv = self.build_conv_block(
291			filters=num_channels[d], kernel_size=kernel_size, padding=padding
292			)
293			self._upsample_deconvs = [upsample_deconv] + self._upsample_deconvs
294			self._upsample_convs = [upsample_conv] + self._upsample_convs
295			if min_extract_level > 0:
296			# add Nones to make lists have length depth - 1
297			self._upsample_deconvs = [None] * min_extract_level + self._upsample_deconvs
298			self._upsample_convs = [None] * min_extract_level + self._upsample_convs
299
300			# extraction
301			self._output_block = self.build_output_block(
302			image_size=image_size,
303			extract_levels=extract_levels,
304			out_channels=out_channels,
305			out_kernel_initializer=out_kernel_initializer,
306			out_activation=out_activation,
307			)
308
309			def build_conv_block(
310			self, filters: int, kernel_size: int, padding: str
311			) -> Union[tf.keras.Model, tfkl.Layer]:
312			"""
313			Build a conv block for down-sampling or up-sampling.
314
315			This block do not change the tensor shape (width, height, depth),
316			it only changes the number of channels.
317
318			:param filters: number of channels for output
319			:param kernel_size: arg for conv3d
320			:param padding: arg for conv3d
321			:return: a block consists of one or multiple layers
322			"""
323			return tf.keras.Sequential(
324			[
325			layer.Conv3dBlock(
326			filters=filters,
327			kernel_size=kernel_size,
328			padding=padding,
329			),
330			layer.ResidualConv3dBlock(
331			filters=filters,
332			kernel_size=kernel_size,
333			padding=padding,
334			),
335			]
336			)
337
338			def build_down_sampling_block(
339			self, kernel_size: int, padding: str, strides: int
340			) -> Union[tf.keras.Model, tfkl.Layer]:
341			"""
342			Build a block for down-sampling.
343
344			This block changes the tensor shape (width, height, depth),
345			but it does not changes the number of channels.
346
347			:param kernel_size: arg for pool3d
348			:param padding: arg for pool3d
349			:param strides: arg for pool3d
350			:return: a block consists of one or multiple layers
351			"""
352			return tfkl.MaxPool3D(pool_size=kernel_size, strides=strides, padding=padding)
353
354			def build_bottom_block(
355			self, filters: int, kernel_size: int, padding: str
356			) -> Union[tf.keras.Model, tfkl.Layer]:
357			"""
358			Build a block for bottom layer.
359
360			This block do not change the tensor shape (width, height, depth),
361			it only changes the number of channels.
362
363			:param filters: number of channels for output
364			:param kernel_size: arg for conv3d
365			:param padding: arg for conv3d
366			:return: a block consists of one or multiple layers
367			"""
368			return layer.Conv3dBlock(
369			filters=filters, kernel_size=kernel_size, padding=padding
370			)
371
372			def build_up_sampling_block(
373			self,
374			filters: int,
375			output_padding: int,
376			kernel_size: int,
377			padding: str,
378			strides: int,
379			output_shape: tuple,
380			) -> Union[tf.keras.Model, tfkl.Layer]:
381			"""
382			Build a block for up-sampling.
383
384			This block changes the tensor shape (width, height, depth),
385			but it does not changes the number of channels.
386
387			:param filters: number of channels for output
388			:param output_padding: padding for output
389			:param kernel_size: arg for deconv3d
390			:param padding: arg for deconv3d
391			:param strides: arg for deconv3d
392			:param output_shape: shape of the output tensor
393			:return: a block consists of one or multiple layers
394			"""
395
396			if self._use_additive_upsampling:
397			return AdditiveUpsampling(
398			filters=filters,
399			output_padding=output_padding,
400			kernel_size=kernel_size,
401			strides=strides,
402			padding=padding,
403			output_shape=output_shape,
404			)
405
406			return layer.Deconv3dBlock(
407			filters=filters,
408			output_padding=output_padding,
409			kernel_size=kernel_size,
410			strides=strides,
411			padding=padding,
412			)
413
414			def build_skip_block(self) -> Union[tf.keras.Model, tfkl.Layer]:
415			"""
416			Build a block for combining skipped tensor and up-sampled one.
417
418			This block do not change the tensor shape (width, height, depth),
419			it only changes the number of channels.
420
421			The input to this block is a list of tensors.
422
423			:return: a block consists of one or multiple layers
424			"""
425			return tfkl.Add()
426
427			def build_output_block(
428			self,
429			image_size: Tuple[int],
430			extract_levels: List[int],
431			out_channels: int,
432			out_kernel_initializer: str,
433			out_activation: str,
434			) -> Union[tf.keras.Model, tfkl.Layer]:
435			"""
436			Build a block for output.
437
438			The input to this block is a list of tensors.
439
440			:param image_size: such as (dim1, dim2, dim3)
441			:param extract_levels: number of extraction levels.
442			:param out_channels: number of channels for the extractions
443			:param out_kernel_initializer: initializer to use for kernels.
444			:param out_activation: activation to use at end layer.
445			:return: a block consists of one or multiple layers
446			"""
447			return Extraction(
448			image_size=image_size,
449			extract_levels=extract_levels,
450			out_channels=out_channels,
451			out_kernel_initializer=out_kernel_initializer,
452			out_activation=out_activation,
453			)
454
455			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...
456			"""
457			Build LocalNet graph based on built layers.
458
459			:param inputs: image batch, shape = (batch, f_dim1, f_dim2, f_dim3, ch)
460			:param training: None or bool.
461			:param mask: None or tf.Tensor.
462			:return: shape = (batch, f_dim1, f_dim2, f_dim3, out_channels)
463			"""
464
465			# down-sampling
466			skips = []
467			down_sampled = inputs
468			for d in range(self._depth):
469			skip = self._downsample_convs[d](inputs=down_sampled, training=training)
470			down_sampled = self._downsample_pools[d](inputs=skip, training=training)
471			skips.append(skip)
472
473			# bottom
474			up_sampled = self._bottom_block(inputs=down_sampled, training=training)
475
476			# up-sampling
477			outs = [up_sampled]
478			for d in range(self._depth - 1, min(self._extract_levels) - 1, -1):
479			up_sampled = self._upsample_deconvs[d](inputs=up_sampled, training=training)
480			up_sampled = self.build_skip_block()([up_sampled, skips[d]])
481			up_sampled = self._upsample_convs[d](inputs=up_sampled, training=training)
482			outs.append(up_sampled)
483
484			# output
485			output = self._output_block(outs)
486
487			return output
488

DeepRegNet / DeepReg

Pull Request — main (#656)

LocalNet.build_layers() C

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