# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# =============================================================================

# pylint: disable=unused-import,g-bad-import-order

"""Contains the convolutional layer classes and their functional aliases.

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

from tensorflow.python.eager import context

from tensorflow.python.framework import ops

from tensorflow.python.framework import tensor_shape

from tensorflow.python.layers import base

from tensorflow.python.layers import utils

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import init_ops

from tensorflow.python.ops import nn

from tensorflow.python.ops import nn_ops

class _Conv(base.Layer):

"""Abstract nD convolution layer (private, used as implementation base).

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution.

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of n integers, specifying the

length of the convolution window.

strides: An integer or tuple/list of n integers,

specifying the stride length of the convolution.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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, ..., channels)` while `channels_first` corresponds to

inputs with shape `(batch, channels, ...)`.

dilation_rate: An integer or tuple/list of n integers, specifying

the dilation rate to use for dilated convolution.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any `strides` value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

"""

def __init__(self, rank,

filters,

kernel_size,

strides=1,

padding='valid',

data_format='channels_last',

dilation_rate=1,

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

**kwargs):

super(_Conv, self).__init__(trainable=trainable, name=name,

activity_regularizer=activity_regularizer,

**kwargs)

self.rank = rank

self.filters = filters

self.kernel_size = utils.normalize_tuple(kernel_size, rank, 'kernel_size')

self.strides = utils.normalize_tuple(strides, rank, 'strides')

self.padding = utils.normalize_padding(padding)

self.data_format = utils.normalize_data_format(data_format)

self.dilation_rate = utils.normalize_tuple(

dilation_rate, rank, 'dilation_rate')

self.activation = activation

self.use_bias = use_bias

self.kernel_initializer = kernel_initializer

self.bias_initializer = bias_initializer

self.kernel_regularizer = kernel_regularizer

self.bias_regularizer = bias_regularizer

self.kernel_constraint = kernel_constraint

self.bias_constraint = bias_constraint

self.input_spec = base.InputSpec(ndim=self.rank + 2)

def build(self, input_shape):

input_shape = tensor_shape.TensorShape(input_shape)

if self.data_format == 'channels_first':

channel_axis = 1

else:

channel_axis = -1

if input_shape[channel_axis].value is None:

raise ValueError('The channel dimension of the inputs '

'should be defined. Found `None`.')

input_dim = input_shape[channel_axis].value

kernel_shape = self.kernel_size + (input_dim, self.filters)

self.kernel = self.add_variable(name='kernel',

shape=kernel_shape,

initializer=self.kernel_initializer,

regularizer=self.kernel_regularizer,

constraint=self.kernel_constraint,

trainable=True,

dtype=self.dtype)

if self.use_bias:

self.bias = self.add_variable(name='bias',

shape=(self.filters,),

initializer=self.bias_initializer,

regularizer=self.bias_regularizer,

constraint=self.bias_constraint,

trainable=True,

dtype=self.dtype)

else:

self.bias = None

self.input_spec = base.InputSpec(ndim=self.rank + 2,

axes={channel_axis: input_dim})

self._convolution_op = nn_ops.Convolution(

input_shape,

filter_shape=self.kernel.get_shape(),

dilation_rate=self.dilation_rate,

strides=self.strides,

padding=self.padding.upper(),

data_format=utils.convert_data_format(self.data_format,

self.rank + 2))

self.built = True

def call(self, inputs):

outputs = self._convolution_op(inputs, self.kernel)

if self.use_bias:

if self.data_format == 'channels_first':

if self.rank == 1:

# nn.bias_add does not accept a 1D input tensor.

bias = array_ops.reshape(self.bias, (1, self.filters, 1))

outputs += bias

if self.rank == 2:

outputs = nn.bias_add(outputs, self.bias, data_format='NCHW')

if self.rank == 3:

# As of Mar 2017, direct addition is significantly slower than

# bias_add when computing gradients. To use bias_add, we collapse Z

# and Y into a single dimension to obtain a 4D input tensor.

outputs_shape = outputs.shape.as_list()

outputs_4d = array_ops.reshape(outputs,

[outputs_shape[0], outputs_shape[1],

outputs_shape[2] * outputs_shape[3],

outputs_shape[4]])

outputs_4d = nn.bias_add(outputs_4d, self.bias, data_format='NCHW')

outputs = array_ops.reshape(outputs_4d, outputs_shape)

else:

outputs = nn.bias_add(outputs, self.bias, data_format='NHWC')

if self.activation is not None:

return self.activation(outputs)

return outputs

def _compute_output_shape(self, input_shape):

input_shape = tensor_shape.TensorShape(input_shape).as_list()

if self.data_format == 'channels_last':

space = input_shape[1:-1]

new_space = []

for i in range(len(space)):

new_dim = utils.conv_output_length(

space[i],

self.kernel_size[i],

padding=self.padding,

stride=self.strides[i],

dilation=self.dilation_rate[i])

new_space.append(new_dim)

return tensor_shape.TensorShape([input_shape[0]] + new_space +

[self.filters])

else:

space = input_shape[2:]

new_space = []

for i in range(len(space)):

new_dim = utils.conv_output_length(

space[i],

self.kernel_size[i],

padding=self.padding,

stride=self.strides[i],

dilation=self.dilation_rate[i])

new_space.append(new_dim)

return tensor_shape.TensorShape([input_shape[0], self.filters] +

new_space)

class Conv1D(_Conv):

"""1D convolution layer (e.g. temporal convolution).

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of a single integer, specifying the

length of the 1D convolution window.

strides: An integer or tuple/list of a single integer,

specifying the stride length of the convolution.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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, length, channels)` while `channels_first` corresponds to

inputs with shape `(batch, channels, length)`.

dilation_rate: An integer or tuple/list of a single integer, specifying

the dilation rate to use for dilated convolution.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any `strides` value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

"""

def __init__(self, filters,

kernel_size,

strides=1,

padding='valid',

data_format='channels_last',

dilation_rate=1,

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

**kwargs):

super(Convolution1D, self).__init__(

rank=1,

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

kernel_initializer=kernel_initializer,

bias_initializer=bias_initializer,

kernel_regularizer=kernel_regularizer,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

kernel_constraint=kernel_constraint,

bias_constraint=bias_constraint,

trainable=trainable,

name=name, **kwargs)

def conv1d(inputs,

filters,

kernel_size,

strides=1,

padding='valid',

data_format='channels_last',

dilation_rate=1,

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

reuse=None):

"""Functional interface for 1D convolution layer (e.g. temporal convolution).

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

inputs: Tensor input.

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of a single integer, specifying the

length of the 1D convolution window.

strides: An integer or tuple/list of a single integer,

specifying the stride length of the convolution.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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, length, channels)` while `channels_first` corresponds to

inputs with shape `(batch, channels, length)`.

dilation_rate: An integer or tuple/list of a single integer, specifying

the dilation rate to use for dilated convolution.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any `strides` value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

reuse: Boolean, whether to reuse the weights of a previous layer

by the same name.

Returns:

Output tensor.

Raises:

ValueError: if eager execution is enabled.

"""

layer = Conv1D(

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

kernel_initializer=kernel_initializer,

bias_initializer=bias_initializer,

kernel_regularizer=kernel_regularizer,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

kernel_constraint=kernel_constraint,

bias_constraint=bias_constraint,

trainable=trainable,

name=name,

dtype=inputs.dtype.base_dtype,

_reuse=reuse,

_scope=name)

return layer.apply(inputs)

class Conv2D(_Conv):

"""2D convolution layer (e.g. spatial convolution over images).

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of 2 integers, specifying the

height and width of the 2D convolution window.

Can be a single integer to specify the same value for

all spatial dimensions.

strides: An integer or tuple/list of 2 integers,

specifying the strides of the convolution along the height and width.

Can be a single integer to specify the same value for

all spatial dimensions.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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)`.

dilation_rate: An integer or tuple/list of 2 integers, specifying

the dilation rate to use for dilated convolution.

Can be a single integer to specify the same value for

all spatial dimensions.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any stride value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

"""

def __init__(self, filters,

kernel_size,

strides=(1, 1),

padding='valid',

data_format='channels_last',

dilation_rate=(1, 1),

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

**kwargs):

super(Conv2D, self).__init__(

rank=2,

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

kernel_initializer=kernel_initializer,

bias_initializer=bias_initializer,

kernel_regularizer=kernel_regularizer,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

kernel_constraint=kernel_constraint,

bias_constraint=bias_constraint,

trainable=trainable,

name=name, **kwargs)

def conv2d(inputs,

filters,

kernel_size,

strides=(1, 1),

padding='valid',

data_format='channels_last',

dilation_rate=(1, 1),

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

reuse=None):

"""Functional interface for the 2D convolution layer.

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

inputs: Tensor input.

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of 2 integers, specifying the

height and width of the 2D convolution window.

Can be a single integer to specify the same value for

all spatial dimensions.

strides: An integer or tuple/list of 2 integers,

specifying the strides of the convolution along the height and width.

Can be a single integer to specify the same value for

all spatial dimensions.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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)`.

dilation_rate: An integer or tuple/list of 2 integers, specifying

the dilation rate to use for dilated convolution.

Can be a single integer to specify the same value for

all spatial dimensions.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any stride value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

reuse: Boolean, whether to reuse the weights of a previous layer

by the same name.

Returns:

Output tensor.

Raises:

ValueError: if eager execution is enabled.

"""

layer = Conv2D(

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

kernel_initializer=kernel_initializer,

bias_initializer=bias_initializer,

kernel_regularizer=kernel_regularizer,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

kernel_constraint=kernel_constraint,

bias_constraint=bias_constraint,

trainable=trainable,

name=name,

dtype=inputs.dtype.base_dtype,

_reuse=reuse,

_scope=name)

return layer.apply(inputs)

class Conv3D(_Conv):

"""3D convolution layer (e.g. spatial convolution over volumes).

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of 3 integers, specifying the

depth, height and width 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,

height and width.

Can be a single integer to specify the same value for

all spatial dimensions.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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, depth, height, width, channels)` while `channels_first`

corresponds to inputs with shape

`(batch, channels, depth, height, width)`.

dilation_rate: An integer or tuple/list of 3 integers, specifying

the dilation rate to use for dilated convolution.

Can be a single integer to specify the same value for

all spatial dimensions.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any stride value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

"""

def __init__(self, filters,

kernel_size,

strides=(1, 1, 1),

padding='valid',

data_format='channels_last',

dilation_rate=(1, 1, 1),

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

**kwargs):

super(Conv3D, self).__init__(

rank=3,

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

kernel_initializer=kernel_initializer,

bias_initializer=bias_initializer,

kernel_regularizer=kernel_regularizer,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

kernel_constraint=kernel_constraint,

bias_constraint=bias_constraint,

trainable=trainable,

name=name, **kwargs)

def conv3d(inputs,

filters,

kernel_size,

strides=(1, 1, 1),

padding='valid',

data_format='channels_last',

dilation_rate=(1, 1, 1),

activation=None,

use_bias=True,

kernel_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

kernel_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

kernel_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

reuse=None):

"""Functional interface for the 3D convolution layer.

This layer creates a convolution kernel that is convolved

(actually cross-correlated) with the layer input to produce a tensor of

outputs. If `use_bias` is True (and a `bias_initializer` is provided),

a bias vector is created and added to the outputs. Finally, if

`activation` is not `None`, it is applied to the outputs as well.

Arguments:

inputs: Tensor input.

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: An integer or tuple/list of 3 integers, specifying the

depth, height and width 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,

height and width.

Can be a single integer to specify the same value for

all spatial dimensions.

Specifying any stride value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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, depth, height, width, channels)` while `channels_first`

corresponds to inputs with shape

`(batch, channels, depth, height, width)`.

dilation_rate: An integer or tuple/list of 3 integers, specifying

the dilation rate to use for dilated convolution.

Can be a single integer to specify the same value for

all spatial dimensions.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any stride value != 1.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

kernel_initializer: An initializer for the convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

kernel_regularizer: Optional regularizer for the convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

kernel_constraint: Optional projection function to be applied to the

kernel after being updated by an `Optimizer` (e.g. used to implement

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

reuse: Boolean, whether to reuse the weights of a previous layer

by the same name.

Returns:

Output tensor.

Raises:

ValueError: if eager execution is enabled.

"""

layer = Conv3D(

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

kernel_initializer=kernel_initializer,

bias_initializer=bias_initializer,

kernel_regularizer=kernel_regularizer,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

kernel_constraint=kernel_constraint,

bias_constraint=bias_constraint,

trainable=trainable,

name=name,

dtype=inputs.dtype.base_dtype,

_reuse=reuse,

_scope=name)

return layer.apply(inputs)

class SeparableConv2D(Conv2D):

"""Depthwise separable 2D convolution.

This layer performs a depthwise convolution that acts separately on

channels, followed by a pointwise convolution that mixes channels.

If `use_bias` is True and a bias initializer is provided,

it adds a bias vector to the output.

It then optionally applies an activation function to produce the final output.

Arguments:

filters: Integer, the dimensionality of the output space (i.e. the number

of filters in the convolution).

kernel_size: A tuple or list of 2 integers specifying the spatial

dimensions of the filters. Can be a single integer to specify the same

value for all spatial dimensions.

strides: A tuple or list of 2 positive integers specifying the strides

of the convolution. Can be a single integer to specify the same value for

all spatial dimensions.

Specifying any `stride` value != 1 is incompatible with specifying

any `dilation_rate` value != 1.

padding: One of `"valid"` or `"same"` (case-insensitive).

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)`.

dilation_rate: An integer or tuple/list of 2 integers, specifying

the dilation rate to use for dilated convolution.

Can be a single integer to specify the same value for

all spatial dimensions.

Currently, specifying any `dilation_rate` value != 1 is

incompatible with specifying any stride value != 1.

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 `num_filters_in * depth_multiplier`.

activation: Activation function. Set it to None to maintain a

linear activation.

use_bias: Boolean, whether the layer uses a bias.

depthwise_initializer: An initializer for the depthwise convolution kernel.

pointwise_initializer: An initializer for the pointwise convolution kernel.

bias_initializer: An initializer for the bias vector. If None, the default

initializer will be used.

depthwise_regularizer: Optional regularizer for the depthwise

convolution kernel.

pointwise_regularizer: Optional regularizer for the pointwise

convolution kernel.

bias_regularizer: Optional regularizer for the bias vector.

activity_regularizer: Optional regularizer function for the output.

depthwise_constraint: Optional projection function to be applied to the

depthwise kernel after being updated by an `Optimizer` (e.g. used for

norm constraints or value constraints for layer weights). The function

must take as input the unprojected variable and must return the

projected variable (which must have the same shape). Constraints are

not safe to use when doing asynchronous distributed training.

pointwise_constraint: Optional projection function to be applied to the

pointwise kernel after being updated by an `Optimizer`.

bias_constraint: Optional projection function to be applied to the

bias after being updated by an `Optimizer`.

trainable: Boolean, if `True` also add variables to the graph collection

`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).

name: A string, the name of the layer.

"""

def __init__(self, filters,

kernel_size,

strides=(1, 1),

padding='valid',

data_format='channels_last',

dilation_rate=(1, 1),

depth_multiplier=1,

activation=None,

use_bias=True,

depthwise_initializer=None,

pointwise_initializer=None,

bias_initializer=init_ops.zeros_initializer(),

depthwise_regularizer=None,

pointwise_regularizer=None,

bias_regularizer=None,

activity_regularizer=None,

depthwise_constraint=None,

pointwise_constraint=None,

bias_constraint=None,

trainable=True,

name=None,

**kwargs):

super(SeparableConv2D, self).__init__(

filters=filters,

kernel_size=kernel_size,

strides=strides,

padding=padding,

data_format=data_format,

dilation_rate=dilation_rate,

activation=activation,

use_bias=use_bias,

bias_regularizer=bias_regularizer,

activity_regularizer=activity_regularizer,

bias_constraint=bias_constraint,

trainable=trainable,

name=name,

**kwargs)

self.data_format = data_format

self.depth_multiplier = depth_multiplier

self.depthwise_initializer = depthwise_initializer

self.pointwise_initializer = pointwise_initializer

self.depthwise_regularizer = depthwise_regularizer

self.pointwise_regularizer = pointwise_regularizer

self.depthwise_constraint = depthwise_constraint

self.pointwise_constraint = pointwise_constraint

def build(self, input_shape):

if len(input_shape) < 4:

raise ValueError('Inputs to `SeparableConv2D` should have rank 4. '

'Received input shape:', str(input_shape))

if self.data_format == 'channels_first':

channel_axis = 1

else:

channel_axis = 3

if input_shape[channel_axis] is None:

raise ValueError('The channel dimension of the inputs to '

'`SeparableConv2D` '

'should be defined. Found `None`.')

input_dim = int(input_shape[channel_axis])

self.input_spec = base.InputSpec(ndim=4, axes={channel_axis: input_dim})

depthwise_kernel_shape = (self.kernel_size[0],

self.kernel_size[1],

input_dim,

self.depth_multiplier)

pointwise_kernel_shape = (1, 1,

self.depth_multiplier * input_dim,

self.filters)

self.depthwise_kernel = self.add_variable(

name='depthwise_kernel',

shape=depthwise_kernel_shape,

initializer=self.depthwise_initializer,

regularizer=self.depthwise_regularizer,

constraint=self.depthwise_constraint,

trainable=True,

dtype=self.dtype)

self.pointwise_kernel = self.add_variable(

name='pointwise_kernel',

shape=pointwise_kernel_shape,

initializer=self.pointwise_initializer,

regularizer=self.pointwise_regularizer,

constraint=self.pointwise_constraint,

trainable=True,

dtype=self.dtype)

if self.use_bias:

self.bias = self.add_variable(name='bias',

shape=(self.filters,),

initializer=self.bias_initializer,

regularizer=self.bias_regularizer,

constraint=self.bias_constraint,

trainable=True,

dtype=self.dtype)

else:

self.bias = None

self.built = True

def call(self, inputs):

# Apply the actual ops.

if self.data_format == 'channels_last':

strides = (1,) + self.strides + (1,)

else:

strides = (1, 1) + self.strides

outputs = nn.separable_conv2d(

inputs,

self.depthwise_kernel,

self.pointwise_kernel,

strides=strides,

padding=self.padding.upper(),

rate=self.dilation_rate,

data_format=utils.convert_data_format(self.data_format, ndim=4))

if self.use_bias:

outputs = nn.bias_add(

outputs,

self.bias,