deepreg.model.backbone.local_net.LocalNet.__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 2021-02-02 00:17 UTC

LocalNet.init() C

↳ Parent: deepreg.model.backbone.local_net

Complexity

Conditions

Size

Total Lines	126
Code Lines	83

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	83
dl	0
loc	126
rs	6.623
c	0
b	0
f	0
cc	6
nop	10

How to fix Long Method Many Parameters

# coding=utf-8


from typing import List

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

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


@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,
        control_points: (tuple, None) = None,
        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 control_points: specify the distance between control points (in voxels).
        :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._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 = tf.keras.Sequential(
                [
                    layer.Conv3dBlock(
                        filters=num_channels[i],
                        kernel_size=kernel_sizes[i],
                        padding="same",
                    ),
                    layer.ResidualConv3dBlock(
                        filters=num_channels[i],
                        kernel_size=kernel_sizes[i],
                        padding="same",
                    ),
                ]
            )
            downsample_pool = tfkl.MaxPool3D(pool_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._conv3d_block = layer.Conv3dBlock(
            filters=num_channels[-1], kernel_size=3, padding="same"
        )  # level E

        self._upsample_blocks = []
        for level in range(
            self._extract_max_level - 1, self._extract_min_level - 1, -1
        ):  # level D to E-1
            padding = layer.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_block = layer.LocalNetUpSampleResnetBlock(
                num_channels[level],
                output_padding=padding,
                output_shape=self._tensor_shapes[level],
            )
            self._upsample_blocks.append(upsample_block)

        self._extract_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 self._extract_levels
        ]

        self.resize = (
            layer.ResizeCPTransform(control_points)
            if control_points is not None
            else False
        )
        self.interpolate = (
            layer.BSplines3DTransform(control_points, image_size)
            if control_points is not None
            else False
        )

    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
        encoded = []
        h_in = inputs
        for level in range(self._extract_max_level):  # level 0 to E - 1
            skip = self._downsample_convs[level](inputs=h_in, training=training)
            h_in = self._downsample_pools[level](inputs=skip, training=training)
            encoded.append(skip)
        h_bottom = self._conv3d_block(
            inputs=h_in, training=training
        )  # level E of encoding/decoding

        # up sample from level E to D
        decoded = [h_bottom]  # level E
        for idx, level in enumerate(
            range(self._extract_max_level - 1, self._extract_min_level - 1, -1)
        ):  # level E-1 to D
            h_bottom = self._upsample_blocks[idx](
                inputs=[h_bottom, encoded[level]], training=training
            )
            decoded.append(h_bottom)

        # output
        output = tf.add_n(
            [
                self._extract_layers[idx](
                    inputs=decoded[self._extract_max_level - level]
                )
                for idx, level in enumerate(self._extract_levels)
            ]
        ) / len(self._extract_levels)

        if self.resize:
            output = self.resize(output)
            output = self.interpolate(output)

        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
4
5			import tensorflow as tf
6			import tensorflow.keras.layers as tfkl
7
8			from deepreg.model import layer
9			from deepreg.model.backbone.interface import Backbone
10			from deepreg.registry import REGISTRY
11
12
13			@REGISTRY.register_backbone(name="local")
14			class LocalNet(Backbone):
15			"""
16			Build LocalNet for image registration.
17
18			Reference:
19
20			- Hu, Yipeng, et al.
21			"Weakly-supervised convolutional neural networks
22			for multimodal image registration."
23			Medical image analysis 49 (2018): 1-13.
24			https://doi.org/10.1016/j.media.2018.07.002
25
26			- Hu, Yipeng, et al.
27			"Label-driven weakly-supervised learning
28			for multimodal deformable image registration,"
29			https://arxiv.org/abs/1711.01666
30			"""
31
32			def __init__(
33			self,
34			image_size: tuple,
35			out_channels: int,
36			num_channel_initial: int,
37			extract_levels: List[int],
38			out_kernel_initializer: str,
39			out_activation: str,
40			control_points: (tuple, None) = None,
41			name: str = "LocalNet",
42			**kwargs,
43			):
44			"""
45			Image is encoded gradually, i from level 0 to E,
46			then it is decoded gradually, j from level E to D.
47			Some of the decoded levels are used for generating extractions.
48
49			So, extract_levels are between [0, E] with E = max(extract_levels),
50			and D = min(extract_levels).
51
52			:param image_size: such as (dim1, dim2, dim3)
53			:param out_channels: number of channels for the extractions
54			:param num_channel_initial: number of initial channels.
55			:param extract_levels: number of extraction levels.
56			:param out_kernel_initializer: initializer to use for kernels.
57			:param out_activation: activation to use at end layer.
58			:param control_points: specify the distance between control points (in voxels).
59			:param name: name of the backbone.
60			:param kwargs: additional arguments.
61			"""
62			super().__init__(
63			image_size=image_size,
64			out_channels=out_channels,
65			num_channel_initial=num_channel_initial,
66			out_kernel_initializer=out_kernel_initializer,
67			out_activation=out_activation,
68			name=name,
69			**kwargs,
70			)
71
72			# save parameters
73			self._extract_levels = extract_levels
74			self._extract_max_level = max(self._extract_levels) # E
75			self._extract_min_level = min(self._extract_levels) # D
76
77			# init layer variables
78			num_channels = [
79			num_channel_initial * (2 ** level)
80			for level in range(self._extract_max_level + 1)
81			] # level 0 to E
82			kernel_sizes = [
83			7 if level == 0 else 3 for level in range(self._extract_max_level + 1)
84			]
85			self._downsample_convs = []
86			self._downsample_pools = []
87			tensor_shape = image_size
88			self._tensor_shapes = [tensor_shape]
89			for i in range(self._extract_max_level):
90			downsample_conv = tf.keras.Sequential(
91			[
92			layer.Conv3dBlock(
93			filters=num_channels[i],
94			kernel_size=kernel_sizes[i],
95			padding="same",
96			),
97			layer.ResidualConv3dBlock(
98			filters=num_channels[i],
99			kernel_size=kernel_sizes[i],
100			padding="same",
101			),
102			]
103			)
104			downsample_pool = tfkl.MaxPool3D(pool_size=2, strides=2, padding="same")
105			tensor_shape = tuple((x + 1) // 2 for x in tensor_shape)
106			self._downsample_convs.append(downsample_conv)
107			self._downsample_pools.append(downsample_pool)
108			self._tensor_shapes.append(tensor_shape)
109
110			self._conv3d_block = layer.Conv3dBlock(
111			filters=num_channels[-1], kernel_size=3, padding="same"
112			) # level E
113
114			self._upsample_blocks = []
115			for level in range(
116			self._extract_max_level - 1, self._extract_min_level - 1, -1
117			): # level D to E-1
118			padding = layer.deconv_output_padding(
119			input_shape=self._tensor_shapes[level + 1],
120			output_shape=self._tensor_shapes[level],
121			kernel_size=kernel_sizes[level],
122			stride=2,
123			padding="same",
124			)
125			upsample_block = layer.LocalNetUpSampleResnetBlock(
126			num_channels[level],
127			output_padding=padding,
128			output_shape=self._tensor_shapes[level],
129			)
130			self._upsample_blocks.append(upsample_block)
131
132			self._extract_layers = [
133			tf.keras.Sequential(
134			[
135			tfkl.Conv3D(
136			filters=out_channels,
137			kernel_size=3,
138			strides=1,
139			padding="same",
140			kernel_initializer=out_kernel_initializer,
141			activation=out_activation,
142			),
143			layer.Resize3d(shape=image_size),
144			]
145			)
146			for _ in self._extract_levels
147			]
148
149			self.resize = (
150			layer.ResizeCPTransform(control_points)
151			if control_points is not None
152			else False
153			)
154			self.interpolate = (
155			layer.BSplines3DTransform(control_points, image_size)
156			if control_points is not None
157			else False
158			)
159
160			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...
161			"""
162			Build LocalNet graph based on built layers.
163
164			:param inputs: image batch, shape = (batch, f_dim1, f_dim2, f_dim3, ch)
165			:param training: None or bool.
166			:param mask: None or tf.Tensor.
167			:return: shape = (batch, f_dim1, f_dim2, f_dim3, out_channels)
168			"""
169
170			# down sample from level 0 to E
171			# outputs used for decoding, encoded[i] corresponds -> level i
172			# stored only 0 to E-1
173			encoded = []
174			h_in = inputs
175			for level in range(self._extract_max_level): # level 0 to E - 1
176			skip = self._downsample_convs[level](inputs=h_in, training=training)
177			h_in = self._downsample_pools[level](inputs=skip, training=training)
178			encoded.append(skip)
179			h_bottom = self._conv3d_block(
180			inputs=h_in, training=training
181			) # level E of encoding/decoding
182
183			# up sample from level E to D
184			decoded = [h_bottom] # level E
185			for idx, level in enumerate(
186			range(self._extract_max_level - 1, self._extract_min_level - 1, -1)
187			): # level E-1 to D
188			h_bottom = self._upsample_blocks[idx](
189			inputs=[h_bottom, encoded[level]], training=training
190			)
191			decoded.append(h_bottom)
192
193			# output
194			output = tf.add_n(
195			[
196			self._extract_layers[idx](
197			inputs=decoded[self._extract_max_level - level]
198			)
199			for idx, level in enumerate(self._extract_levels)
200			]
201			) / len(self._extract_levels)
202
203			if self.resize:
204			output = self.resize(output)
205			output = self.interpolate(output)
206
207			return output
208

DeepRegNet / DeepReg

Pull Request — main (#656)

LocalNet.__init__() C

Complexity

Size

Duplication

Importance

How to fix Long Method Many Parameters

Long Method

Many Parameters

Duplication Side-by-Side

Filter issues like

LocalNet.init() C