depth_wise_3D.DepthwiseConv3D.compute_output_shape() - Code Metrics - Inspection of "Issue 678: Add EfficientNet" - DeepRegNet/DeepReg - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — main (#785)

unknown

created 2021-06-25 14:41 UTC

DepthwiseConv3D.compute_output_shape() B

↳ Parent: depth_wise_3D

Complexity

Conditions

Size

Total Lines	29
Code Lines	24

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	24
dl	0
loc	29
rs	8.8373
c	0
b	0
f	0
cc	5
nop	2

# https://github.com/alexandrosstergiou/keras-DepthwiseConv3D
# -*- coding: utf-8 -*-

'''
This is a modification of the SeparableConv3D code in Keras,
to perform just the Depthwise Convolution (1st step) of the
Depthwise Separable Convolution layer.
'''
from __future__ import absolute_import

from keras import backend as K
from keras import initializers
from keras import regularizers
from keras import constraints
from keras import layers
from keras.engine import InputSpec
from keras.utils import conv_utils
from keras.legacy.interfaces import conv3d_args_preprocessor, generate_legacy_interface
from keras.layers import Conv3D
from keras.backend.tensorflow_backend import _preprocess_padding, _preprocess_conv3d_input

import tensorflow as tf


def depthwise_conv3d_args_preprocessor(args, kwargs):
    converted = []

    if 'init' in kwargs:
        init = kwargs.pop('init')
        kwargs['depthwise_initializer'] = init
        converted.append(('init', 'depthwise_initializer'))

    args, kwargs, _converted = conv3d_args_preprocessor(args, kwargs)
    return args, kwargs, converted + _converted

    legacy_depthwise_conv3d_support = generate_legacy_interface(
    allowed_positional_args=['filters', 'kernel_size'],
    conversions=[('nb_filter', 'filters'),
                 ('subsample', 'strides'),
                 ('border_mode', 'padding'),
                 ('dim_ordering', 'data_format'),
                 ('b_regularizer', 'bias_regularizer'),
                 ('b_constraint', 'bias_constraint'),
                 ('bias', 'use_bias')],
    value_conversions={'dim_ordering': {'tf': 'channels_last',
                                        'th': 'channels_first',
                                        'default': None}},
    preprocessor=depthwise_conv3d_args_preprocessor)


class DepthwiseConv3D(Conv3D):
    """Depthwise 3D convolution.
    Depth-wise part of separable convolutions consist in performing
    just the first step/operation
    (which acts on each input channel separately).
    It does not perform the pointwise convolution (second step).
    The `depth_multiplier` argument controls how many
    output channels are generated per input channel in the depthwise step.
    # Arguments
        kernel_size: An integer or tuple/list of 3 integers, specifying the
            depth, width and height of the 3D convolution window.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        strides: An integer or tuple/list of 3 integers,
            specifying the strides of the convolution along the depth, width and height.
            Can be a single integer to specify the same value for
            all spatial dimensions.
        padding: one of `"valid"` or `"same"` (case-insensitive).
        depth_multiplier: The number of depthwise convolution output channels
            for each input channel.
            The total number of depthwise convolution output
            channels will be equal to `filterss_in * depth_multiplier`.
        groups: The depth size of the convolution (as a variant of the original Depthwise conv)
        data_format: A string,
            one of `channels_last` (default) or `channels_first`.
            The ordering of the dimensions in the inputs.
            `channels_last` corresponds to inputs with shape
            `(batch, height, width, channels)` while `channels_first`
            corresponds to inputs with shape
            `(batch, channels, height, width)`.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        depthwise_initializer: Initializer for the depthwise kernel matrix
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        depthwise_regularizer: Regularizer function applied to
            the depthwise kernel matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        dialation_rate: List of ints.
                        Defines the dilation factor for each dimension in the
                        input. Defaults to (1,1,1)
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        depthwise_constraint: Constraint function applied to
            the depthwise kernel matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).
    # Input shape
        5D tensor with shape:
        `(batch, depth, channels, rows, cols)` if data_format='channels_first'
        or 5D tensor with shape:
        `(batch, depth, rows, cols, channels)` if data_format='channels_last'.
    # Output shape
        5D tensor with shape:
        `(batch, filters * depth, new_depth, new_rows, new_cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(batch, new_depth, new_rows, new_cols, filters * depth)` if data_format='channels_last'.
        `rows` and `cols` values might have changed due to padding.
    """

    #@legacy_depthwise_conv3d_support
    def __init__(self,
                 kernel_size,
                 strides=(1, 1, 1),
                 padding='valid',
                 depth_multiplier=1,
                 groups=None,
                 data_format=None,
                 activation=None,
                 use_bias=True,
                 depthwise_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 dilation_rate = (1, 1, 1),
                 depthwise_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 depthwise_constraint=None,
                 bias_constraint=None,
                 **kwargs):
        super(DepthwiseConv3D, self).__init__(
            filters=None,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            activation=activation,
            use_bias=use_bias,
            bias_regularizer=bias_regularizer,
            dilation_rate=dilation_rate,
            activity_regularizer=activity_regularizer,
            bias_constraint=bias_constraint,
            **kwargs)
        self.depth_multiplier = depth_multiplier
        self.groups = groups
        self.depthwise_initializer = initializers.get(depthwise_initializer)
        self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
        self.depthwise_constraint = constraints.get(depthwise_constraint)
        self.bias_initializer = initializers.get(bias_initializer)
        self.dilation_rate = dilation_rate
        self._padding = _preprocess_padding(self.padding)
        self._strides = (1,) + self.strides + (1,)
        self._data_format = "NDHWC"
        self.input_dim = None

    def build(self, input_shape):
        if len(input_shape) < 5:
            raise ValueError('Inputs to `DepthwiseConv3D` should have rank 5. '
                             'Received input shape:', str(input_shape))
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs to '
                             '`DepthwiseConv3D` '
                             'should be defined. Found `None`.')
        self.input_dim = int(input_shape[channel_axis])

        if self.groups is None:
            self.groups = self.input_dim

        if self.groups > self.input_dim:
            raise ValueError('The number of groups cannot exceed the number of channels')

        if self.input_dim % self.groups != 0:
            raise ValueError('Warning! The channels dimension is not divisible by the group size chosen')

        depthwise_kernel_shape = (self.kernel_size[0],
                                  self.kernel_size[1],
                                  self.kernel_size[2],
                                  self.input_dim,
                                  self.depth_multiplier)

        self.depthwise_kernel = self.add_weight(
            shape=depthwise_kernel_shape,
            initializer=self.depthwise_initializer,
            name='depthwise_kernel',
            regularizer=self.depthwise_regularizer,
            constraint=self.depthwise_constraint)

        if self.use_bias:
            self.bias = self.add_weight(shape=(self.groups * self.depth_multiplier,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
        # Set input spec.
        self.input_spec = InputSpec(ndim=5, axes={channel_axis: self.input_dim})
        self.built = True

    def call(self, inputs, training=None):
        inputs = _preprocess_conv3d_input(inputs, self.data_format)

        if self.data_format == 'channels_last':
            dilation = (1,) + self.dilation_rate + (1,)
        else:
            dilation = self.dilation_rate + (1,) + (1,)

        if self._data_format == 'NCDHW':
            outputs = tf.concat(
                [tf.nn.conv3d(inputs[0][:, i:i+self.input_dim//self.groups, :, :, :], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
                    strides=self._strides,
                    padding=self._padding,
                    dilations=dilation,
                    data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=1)

        else:
            outputs = tf.concat(
                [tf.nn.conv3d(inputs[0][:, :, :, :, i:i+self.input_dim//self.groups], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
                    strides=self._strides,
                    padding=self._padding,
                    dilations=dilation,
                    data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=-1)

        if self.bias is not None:
            outputs = K.bias_add(
                outputs,
                self.bias,
                data_format=self.data_format)

        if self.activation is not None:
            return self.activation(outputs)

        return outputs

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            depth = input_shape[2]
            rows = input_shape[3]
            cols = input_shape[4]
            out_filters = self.groups * self.depth_multiplier
        elif self.data_format == 'channels_last':
            depth = input_shape[1]
            rows = input_shape[2]
            cols = input_shape[3]
            out_filters = self.groups * self.depth_multiplier

        depth = conv_utils.conv_output_length(depth, self.kernel_size[0],

                                             self.padding,
                                             self.strides[0])

        rows = conv_utils.conv_output_length(rows, self.kernel_size[1],

                                             self.padding,
                                             self.strides[1])

        cols = conv_utils.conv_output_length(cols, self.kernel_size[2],

                                             self.padding,
                                             self.strides[2])

        if self.data_format == 'channels_first':
            return (input_shape[0], out_filters, depth, rows, cols)


        elif self.data_format == 'channels_last':
            return (input_shape[0], depth, rows, cols, out_filters)

    def get_config(self):
        config = super(DepthwiseConv3D, self).get_config()
        config.pop('filters')
        config.pop('kernel_initializer')
        config.pop('kernel_regularizer')
        config.pop('kernel_constraint')
        config['depth_multiplier'] = self.depth_multiplier
        config['depthwise_initializer'] = initializers.serialize(self.depthwise_initializer)
        config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
        config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
        return config

DepthwiseConvolution3D = DepthwiseConv3D

1			# https://github.com/alexandrosstergiou/keras-DepthwiseConv3D
2			# -- coding: utf-8 --
3
4			'''
5			This is a modification of the SeparableConv3D code in Keras,
6			to perform just the Depthwise Convolution (1st step) of the
7			Depthwise Separable Convolution layer.
8			'''
9			from __future__ import absolute_import
10
11			from keras import backend as K
12			from keras import initializers
13			from keras import regularizers
14			from keras import constraints
15			from keras import layers
16			from keras.engine import InputSpec
17			from keras.utils import conv_utils
18			from keras.legacy.interfaces import conv3d_args_preprocessor, generate_legacy_interface
19			from keras.layers import Conv3D
20			from keras.backend.tensorflow_backend import _preprocess_padding, _preprocess_conv3d_input
21
22			import tensorflow as tf
23
24
25			def depthwise_conv3d_args_preprocessor(args, kwargs):
26			converted = []
27
28			if 'init' in kwargs:
29			init = kwargs.pop('init')
30			kwargs['depthwise_initializer'] = init
31			converted.append(('init', 'depthwise_initializer'))
32
33			args, kwargs, _converted = conv3d_args_preprocessor(args, kwargs)
34			return args, kwargs, converted + _converted
35
36			legacy_depthwise_conv3d_support = generate_legacy_interface(
37			allowed_positional_args=['filters', 'kernel_size'],
38			conversions=[('nb_filter', 'filters'),
39			('subsample', 'strides'),
40			('border_mode', 'padding'),
41			('dim_ordering', 'data_format'),
42			('b_regularizer', 'bias_regularizer'),
43			('b_constraint', 'bias_constraint'),
44			('bias', 'use_bias')],
45			value_conversions={'dim_ordering': {'tf': 'channels_last',
46			'th': 'channels_first',
47			'default': None}},
48			preprocessor=depthwise_conv3d_args_preprocessor)
49
50
51			class DepthwiseConv3D(Conv3D):
52			"""Depthwise 3D convolution.
53			Depth-wise part of separable convolutions consist in performing
54			just the first step/operation
55			(which acts on each input channel separately).
56			It does not perform the pointwise convolution (second step).
57			The `depth_multiplier` argument controls how many
58			output channels are generated per input channel in the depthwise step.
59			# Arguments
60			kernel_size: An integer or tuple/list of 3 integers, specifying the
61			depth, width and height of the 3D convolution window.
62			Can be a single integer to specify the same value for
63			all spatial dimensions.
64			strides: An integer or tuple/list of 3 integers,
65			specifying the strides of the convolution along the depth, width and height.
66			Can be a single integer to specify the same value for
67			all spatial dimensions.
68			padding: one of `"valid"` or `"same"` (case-insensitive).
69			depth_multiplier: The number of depthwise convolution output channels
70			for each input channel.
71			The total number of depthwise convolution output
72			channels will be equal to `filterss_in * depth_multiplier`.
73			groups: The depth size of the convolution (as a variant of the original Depthwise conv)
74			data_format: A string,
75			one of `channels_last` (default) or `channels_first`.
76			The ordering of the dimensions in the inputs.
77			`channels_last` corresponds to inputs with shape
78			`(batch, height, width, channels)` while `channels_first`
79			corresponds to inputs with shape
80			`(batch, channels, height, width)`.
81			It defaults to the `image_data_format` value found in your
82			Keras config file at `~/.keras/keras.json`.
83			If you never set it, then it will be "channels_last".
84			activation: Activation function to use
85			(see [activations](../activations.md)).
86			If you don't specify anything, no activation is applied
87			(ie. "linear" activation: `a(x) = x`).
88			use_bias: Boolean, whether the layer uses a bias vector.
89			depthwise_initializer: Initializer for the depthwise kernel matrix
90			(see [initializers](../initializers.md)).
91			bias_initializer: Initializer for the bias vector
92			(see [initializers](../initializers.md)).
93			depthwise_regularizer: Regularizer function applied to
94			the depthwise kernel matrix
95			(see [regularizer](../regularizers.md)).
96			bias_regularizer: Regularizer function applied to the bias vector
97			(see [regularizer](../regularizers.md)).
98			dialation_rate: List of ints.
99			Defines the dilation factor for each dimension in the
100			input. Defaults to (1,1,1)
101			activity_regularizer: Regularizer function applied to
102			the output of the layer (its "activation").
103			(see [regularizer](../regularizers.md)).
104			depthwise_constraint: Constraint function applied to
105			the depthwise kernel matrix
106			(see [constraints](../constraints.md)).
107			bias_constraint: Constraint function applied to the bias vector
108			(see [constraints](../constraints.md)).
109			# Input shape
110			5D tensor with shape:
111			`(batch, depth, channels, rows, cols)` if data_format='channels_first'
112			or 5D tensor with shape:
113			`(batch, depth, rows, cols, channels)` if data_format='channels_last'.
114			# Output shape
115			5D tensor with shape:
116			`(batch, filters * depth, new_depth, new_rows, new_cols)` if data_format='channels_first'
117			or 4D tensor with shape:
118			`(batch, new_depth, new_rows, new_cols, filters * depth)` if data_format='channels_last'.
119			`rows` and `cols` values might have changed due to padding.
120			"""
121
122			#@legacy_depthwise_conv3d_support
123			def __init__(self,
124			kernel_size,
125			strides=(1, 1, 1),
126			padding='valid',
127			depth_multiplier=1,
128			groups=None,
129			data_format=None,
130			activation=None,
131			use_bias=True,
132			depthwise_initializer='glorot_uniform',
133			bias_initializer='zeros',
134			dilation_rate = (1, 1, 1),
135			depthwise_regularizer=None,
136			bias_regularizer=None,
137			activity_regularizer=None,
138			depthwise_constraint=None,
139			bias_constraint=None,
140			**kwargs):
141			super(DepthwiseConv3D, self).__init__(
142			filters=None,
143			kernel_size=kernel_size,
144			strides=strides,
145			padding=padding,
146			data_format=data_format,
147			activation=activation,
148			use_bias=use_bias,
149			bias_regularizer=bias_regularizer,
150			dilation_rate=dilation_rate,
151			activity_regularizer=activity_regularizer,
152			bias_constraint=bias_constraint,
153			**kwargs)
154			self.depth_multiplier = depth_multiplier
155			self.groups = groups
156			self.depthwise_initializer = initializers.get(depthwise_initializer)
157			self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
158			self.depthwise_constraint = constraints.get(depthwise_constraint)
159			self.bias_initializer = initializers.get(bias_initializer)
160			self.dilation_rate = dilation_rate
161			self._padding = _preprocess_padding(self.padding)
162			self._strides = (1,) + self.strides + (1,)
163			self._data_format = "NDHWC"
164			self.input_dim = None
165
166			def build(self, input_shape):
167			if len(input_shape) < 5:
168			raise ValueError('Inputs to `DepthwiseConv3D` should have rank 5. '
169			'Received input shape:', str(input_shape))
170			if self.data_format == 'channels_first':
171			channel_axis = 1
172			else:
173			channel_axis = -1
174			if input_shape[channel_axis] is None:
175			raise ValueError('The channel dimension of the inputs to '
176			'`DepthwiseConv3D` '
177			'should be defined. Found `None`.')
178			self.input_dim = int(input_shape[channel_axis])
179
180			if self.groups is None:
181			self.groups = self.input_dim
182
183			if self.groups > self.input_dim:
184			raise ValueError('The number of groups cannot exceed the number of channels')
185
186			if self.input_dim % self.groups != 0:
187			raise ValueError('Warning! The channels dimension is not divisible by the group size chosen')
188
189			depthwise_kernel_shape = (self.kernel_size[0],
190			self.kernel_size[1],
191			self.kernel_size[2],
192			self.input_dim,
193			self.depth_multiplier)
194
195			self.depthwise_kernel = self.add_weight(
196			shape=depthwise_kernel_shape,
197			initializer=self.depthwise_initializer,
198			name='depthwise_kernel',
199			regularizer=self.depthwise_regularizer,
200			constraint=self.depthwise_constraint)
201
202			if self.use_bias:
203			self.bias = self.add_weight(shape=(self.groups * self.depth_multiplier,),
204			initializer=self.bias_initializer,
205			name='bias',
206			regularizer=self.bias_regularizer,
207			constraint=self.bias_constraint)
208			else:
209			self.bias = None
210			# Set input spec.
211			self.input_spec = InputSpec(ndim=5, axes={channel_axis: self.input_dim})
212			self.built = True
213
214			def call(self, inputs, training=None):
215			inputs = _preprocess_conv3d_input(inputs, self.data_format)
216
217			if self.data_format == 'channels_last':
218			dilation = (1,) + self.dilation_rate + (1,)
219			else:
220			dilation = self.dilation_rate + (1,) + (1,)
221
222			if self._data_format == 'NCDHW':
223			outputs = tf.concat(
224			[tf.nn.conv3d(inputs[0][:, i:i+self.input_dim//self.groups, :, :, :], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
225			strides=self._strides,
226			padding=self._padding,
227			dilations=dilation,
228			data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=1)
229
230			else:
231			outputs = tf.concat(
232			[tf.nn.conv3d(inputs[0][:, :, :, :, i:i+self.input_dim//self.groups], self.depthwise_kernel[:, :, :, i:i+self.input_dim//self.groups, :],
233			strides=self._strides,
234			padding=self._padding,
235			dilations=dilation,
236			data_format=self._data_format) for i in range(0, self.input_dim, self.input_dim//self.groups)], axis=-1)
237
238			if self.bias is not None:
239			outputs = K.bias_add(
240			outputs,
241			self.bias,
242			data_format=self.data_format)
243
244			if self.activation is not None:
245			return self.activation(outputs)
246
247			return outputs
248
249			def compute_output_shape(self, input_shape):
250			if self.data_format == 'channels_first':
251			depth = input_shape[2]
252			rows = input_shape[3]
253			cols = input_shape[4]
254			out_filters = self.groups * self.depth_multiplier
255			elif self.data_format == 'channels_last':
256			depth = input_shape[1]
257			rows = input_shape[2]
258			cols = input_shape[3]
259			out_filters = self.groups * self.depth_multiplier
260
261			depth = conv_utils.conv_output_length(depth, self.kernel_size[0],
			0 ignored issues – show introduced 2021-06-25 14:45 UTC by Report Bug Copy Issue Report The variable `depth` does not seem to be defined for all execution paths. Loading history...
262			self.padding,
263			self.strides[0])
264
265			rows = conv_utils.conv_output_length(rows, self.kernel_size[1],
			0 ignored issues – show introduced 2021-06-25 14:45 UTC by Report Bug Copy Issue Report The variable `rows` does not seem to be defined for all execution paths. Loading history...
266			self.padding,
267			self.strides[1])
268
269			cols = conv_utils.conv_output_length(cols, self.kernel_size[2],
			0 ignored issues – show introduced 2021-06-25 14:45 UTC by Report Bug Copy Issue Report The variable `cols` does not seem to be defined for all execution paths. Loading history...
270			self.padding,
271			self.strides[2])
272
273			if self.data_format == 'channels_first':
274			return (input_shape[0], out_filters, depth, rows, cols)
			0 ignored issues – show introduced 2021-06-25 14:45 UTC by Report Bug Copy Issue Report The variable `out_filters` does not seem to be defined for all execution paths. Loading history...
275
276			elif self.data_format == 'channels_last':
277			return (input_shape[0], depth, rows, cols, out_filters)
278
279			def get_config(self):
280			config = super(DepthwiseConv3D, self).get_config()
281			config.pop('filters')
282			config.pop('kernel_initializer')
283			config.pop('kernel_regularizer')
284			config.pop('kernel_constraint')
285			config['depth_multiplier'] = self.depth_multiplier
286			config['depthwise_initializer'] = initializers.serialize(self.depthwise_initializer)
287			config['depthwise_regularizer'] = regularizers.serialize(self.depthwise_regularizer)
288			config['depthwise_constraint'] = constraints.serialize(self.depthwise_constraint)
289			return config
290
291			DepthwiseConvolution3D = DepthwiseConv3D

DeepRegNet / DeepReg

Pull Request — main (#785)

DepthwiseConv3D.compute_output_shape() B

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