#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import get_collection_trainable
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'SeparableConv1d',
'SeparableConv2d',
]
[docs]class SeparableConv1d(Layer):
"""The :class:`SeparableConv1d` class is a 1D depthwise separable convolutional layer, see `tf.layers.separable_conv1d <https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv1d>`__.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
Parameters
------------
prev_layer : :class:`Layer`
Previous layer.
n_filter : int
The dimensionality of the output space (i.e. the number of filters in the convolution).
filter_size : int
Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides : int
Specifying the stride 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 : str
One of "valid" or "same" (case-insensitive).
data_format : str
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 : int
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 : int
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.
depthwise_init : initializer
for the depthwise convolution kernel.
pointwise_init : initializer
For the pointwise convolution kernel.
b_init : initializer
For the bias vector. If None, ignore bias in the pointwise part only.
name : a str
A unique layer name.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
n_filter=100,
filter_size=3,
strides=1,
act=None,
padding='valid',
data_format='channels_last',
dilation_rate=1,
depth_multiplier=1,
# activation=None,
# use_bias=True,
depthwise_init=None,
pointwise_init=None,
b_init=tf.zeros_initializer(),
# depthwise_regularizer=None,
# pointwise_regularizer=None,
# bias_regularizer=None,
# activity_regularizer=None,
# depthwise_constraint=None,
# pointwise_constraint=None,
# W_init=tf.truncated_normal_initializer(stddev=0.1),
# b_init=tf.constant_initializer(value=0.0),
W_init_args=None, # TODO: Remove when TF <1.3 not supported
b_init_args=None, # TODO: Remove when TF <1.3 not supported
name='seperable1d',
):
super(SeparableConv1d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"SeparableConv1d %s: n_filter: %d filter_size: %s filter_size: %s depth_multiplier: %d act: %s" % (
self.name, n_filter, str(filter_size), str(strides), depth_multiplier,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
# with tf.variable_scope(name) as vs:
nn = tf.layers.SeparableConv1D(
filters=n_filter,
kernel_size=filter_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=self.act,
use_bias=(True if b_init is not None else False),
depthwise_initializer=depthwise_init,
pointwise_initializer=pointwise_init,
bias_initializer=b_init,
# depthwise_regularizer=None,
# pointwise_regularizer=None,
# bias_regularizer=None,
# activity_regularizer=None,
# depthwise_constraint=None,
# pointwise_constraint=None,
# bias_constraint=None,
trainable=True,
name=name
)
self.outputs = nn(self.inputs)
# new_variables = nn.weights
# new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=self.name) #vs.name)
new_variables = get_collection_trainable(self.name)
self._add_layers(self.outputs)
self._add_params(new_variables)
[docs]class SeparableConv2d(Layer):
"""The :class:`SeparableConv2d` class is a 2D depthwise separable convolutional layer, see `tf.layers.separable_conv2d <https://www.tensorflow.org/api_docs/python/tf/layers/separable_conv2d>`__.
This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
While :class:`DepthwiseConv2d` performs depthwise convolution only, which allow us to add batch normalization between depthwise and pointwise convolution.
Parameters
------------
prev_layer : :class:`Layer`
Previous layer.
n_filter : int
The dimensionality of the output space (i.e. the number of filters in the convolution).
filter_size : tuple/list of 2 int
Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
strides : tuple/list of 2 int
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 : str
One of "valid" or "same" (case-insensitive).
data_format : str
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 : integer or tuple/list of 2 int
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 : int
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.
depthwise_init : initializer
for the depthwise convolution kernel.
pointwise_init : initializer
For the pointwise convolution kernel.
b_init : initializer
For the bias vector. If None, ignore bias in the pointwise part only.
name : a str
A unique layer name.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
n_filter=100,
filter_size=(3, 3),
strides=(1, 1),
act=None,
padding='valid',
data_format='channels_last',
dilation_rate=(1, 1),
depth_multiplier=1,
# activation=None,
# use_bias=True,
depthwise_init=None,
pointwise_init=None,
b_init=tf.zeros_initializer(),
# depthwise_regularizer=None,
# pointwise_regularizer=None,
# bias_regularizer=None,
# activity_regularizer=None,
# depthwise_constraint=None,
# pointwise_constraint=None,
# W_init=tf.truncated_normal_initializer(stddev=0.1),
# b_init=tf.constant_initializer(value=0.0),
W_init_args=None, # TODO: Remove when TF <1.3 not supported
b_init_args=None, # TODO: Remove when TF <1.3 not supported
name='seperable',
):
# if W_init_args is None:
# W_init_args = {}
# if b_init_args is None:
# b_init_args = {}
super(SeparableConv2d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"SeparableConv2d %s: n_filter: %d filter_size: %s filter_size: %s depth_multiplier: %d act: %s" % (
self.name, n_filter, str(filter_size), str(strides), depth_multiplier,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
# with tf.variable_scope(name) as vs:
nn = tf.layers.SeparableConv2D(
filters=n_filter,
kernel_size=filter_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
depth_multiplier=depth_multiplier,
activation=self.act,
use_bias=(True if b_init is not None else False),
depthwise_initializer=depthwise_init,
pointwise_initializer=pointwise_init,
bias_initializer=b_init,
# depthwise_regularizer=None,
# pointwise_regularizer=None,
# bias_regularizer=None,
# activity_regularizer=None,
# depthwise_constraint=None,
# pointwise_constraint=None,
# bias_constraint=None,
trainable=True,
name=name
)
self.outputs = nn(self.inputs)
# new_variables = nn.weights
# new_variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=self.name) #vs.name)
new_variables = get_collection_trainable(self.name)
self._add_layers(self.outputs)
self._add_params(new_variables)