Source code for tensorlayerx.nn.layers.convolution.separable_conv

#! /usr/bin/python
# -*- coding: utf-8 -*-

import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module
from tensorlayerx.backend import BACKEND

__all__ = [
    'SeparableConv1d',
    'SeparableConv2d',
]


[docs]class SeparableConv1d(Module): """The :class:`SeparableConv1d` class is a 1D depthwise separable convolutional layer. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. Parameters ------------ out_channels : int The dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size : int Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. stride : 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 value != 1. act : activation function The activation function of this layer. 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 : 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 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 or str for the depthwise convolution kernel. pointwise_init : initializer or str For the pointwise convolution kernel. b_init : initializer or str For the bias vector. If None, ignore bias in the pointwise part only. in_channels : int The number of in channels. name : None or str A unique layer name. Examples -------- With TensorLayerX >>> net = tlx.nn.Input([8, 50, 64], name='input') >>> separableconv1d = tlx.nn.SeparableConv1d(out_channels=32, kernel_size=3, stride=2, padding='SAME', act=tlx.ReLU, name='separable_1d')(net) >>> print(separableconv1d) >>> output shape : (8, 25, 32) """ def __init__( self, out_channels=32, kernel_size=1, stride=1, act=None, padding="SAME", data_format="channels_last", dilation=1, depth_multiplier=1, depthwise_init='truncated_normal', pointwise_init='truncated_normal', b_init='constant', in_channels=None, name=None ): super(SeparableConv1d, self).__init__(name, act=act) self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.data_format = data_format self.dilation = dilation self.depth_multiplier = depth_multiplier self.depthwise_init = self.str_to_init(depthwise_init) self.pointwise_init = self.str_to_init(pointwise_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "SeparableConv1d %s: out_channels: %d kernel_size: %s strides: %s depth_multiplier: %d act: %s" % ( self.name, out_channels, str(kernel_size), str(stride), depth_multiplier, self.act.__class__.__name__ if self.act is not None else 'No Activation' ) ) def __repr__(self): actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation' s = ( '{classname}(in_channels={in_channels}, out_channels={out_channels}, kernel_size={kernel_size}' ', stride={stride}, padding={padding}' ) if self.dilation != 1: s += ', dilation={dilation}' if self.b_init is None: s += ', bias=False' s += (', ' + actstr) if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): if self.data_format == 'channels_last': if self.in_channels is None: self.in_channels = inputs_shape[-1] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] else: raise Exception("data_format should be either channels_last or channels_first") if BACKEND == 'tensorflow': self.depthwise_filter_shape = (self.kernel_size, self.in_channels, self.depth_multiplier) elif BACKEND in ['mindspore', 'paddle', 'torch']: self.depthwise_filter_shape = (self.kernel_size, 1, self.depth_multiplier * self.in_channels) self.pointwise_filter_shape = (1, self.depth_multiplier * self.in_channels, self.out_channels) self.depthwise_W = self._get_weights( 'depthwise_filters', shape=self.depthwise_filter_shape, init=self.depthwise_init ) self.pointwise_W = self._get_weights( 'pointwise_filters', shape=self.pointwise_filter_shape, init=self.pointwise_init ) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels, ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.act_init_flag = False if self.act: self.activate = self.act self.act_init_flag = True self.separable_conv1d = tlx.ops.SeparableConv1D( stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation, out_channel=self.out_channels, k_size=self.kernel_size, in_channel=self.in_channels, depth_multiplier=self.depth_multiplier ) def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.separable_conv1d(inputs, self.depthwise_W, self.pointwise_W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs
[docs]class SeparableConv2d(Module): """The :class:`SeparableConv2d` class is a 2D depthwise separable convolutional layer. This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels. Parameters ------------ out_channels : int The dimensionality of the output space (i.e. the number of filters in the convolution). kernel_size : tuple or int Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. stride : tuple or 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 value != 1. act : activation function The activation function of this layer. 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 : tuple or 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 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 or str for the depthwise convolution kernel. pointwise_init : initializer or str For the pointwise convolution kernel. b_init : initializer or str For the bias vector. If None, ignore bias in the pointwise part only. in_channels : int The number of in channels. name : None or str A unique layer name. Examples -------- With TensorLayerX >>> net = tlx.nn.Input([8, 50, 50, 64], name='input') >>> separableconv2d = tlx.nn.SeparableConv2d(out_channels=32, kernel_size=(3,3), stride=(2,2), depth_multiplier = 3 , padding='SAME', act=tlx.ReLU, name='separable_2d')(net) >>> print(separableconv2d) >>> output shape : (8, 24, 24, 32) """ def __init__( self, out_channels=32, kernel_size=(1, 1), stride=(1, 1), act=None, padding="VALID", data_format="channels_last", dilation=(1, 1), depth_multiplier=1, depthwise_init='truncated_normal', pointwise_init='truncated_normal', b_init='constant', in_channels=None, name=None ): super(SeparableConv2d, self).__init__(name, act=act) self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size) self._strides = self.stride = self.check_param(stride) self.padding = padding self.data_format = data_format self._dilation_rate = self.dilation = self.check_param(dilation) self.depth_multiplier = depth_multiplier self.depthwise_init = self.str_to_init(depthwise_init) self.pointwise_init = self.str_to_init(pointwise_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "SeparableConv2d %s: out_channels: %d kernel_size: %s strides: %s depth_multiplier: %d act: %s" % ( self.name, out_channels, str(kernel_size), str(stride), depth_multiplier, self.act.__class__.__name__ if self.act is not None else 'No Activation' ) ) def __repr__(self): actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation' s = ( '{classname}(in_channels={in_channels}, out_channels={out_channels}, kernel_size={kernel_size}' ', stride={strides }, padding={padding}' ) if self.dilation != (1, ) * len(self.dilation): s += ', dilation={dilation}' if self.b_init is None: s += ', bias=False' s += (', ' + actstr) if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): if self.data_format == 'channels_last': self.data_format = 'NHWC' if self.in_channels is None: self.in_channels = inputs_shape[-1] self._strides = [1, self._strides[0], self._strides[1], 1] self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1] elif self.data_format == 'channels_first': self.data_format = 'NCHW' if self.in_channels is None: self.in_channels = inputs_shape[1] self._strides = [1, 1, self._strides[0], self._strides[1]] self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]] else: raise Exception("data_format should be either channels_last or channels_first") if BACKEND == 'tensorflow': self.depthwise_filter_shape = ( self.kernel_size[0], self.kernel_size[1], self.in_channels, self.depth_multiplier ) self.pointwise_filter_shape = (1, 1, self.depth_multiplier * self.in_channels, self.out_channels) elif BACKEND in ['mindspore' , 'paddle', 'torch']: self.depthwise_filter_shape = ( self.kernel_size[0], self.kernel_size[1], 1, self.depth_multiplier * self.in_channels ) self.pointwise_filter_shape = (1, 1, self.depth_multiplier * self.in_channels, self.out_channels) self.depthwise_W = self._get_weights( 'depthwise_filters', shape=self.depthwise_filter_shape, init=self.depthwise_init ) self.pointwise_W = self._get_weights( 'pointwise_filters', shape=self.pointwise_filter_shape, init=self.pointwise_init ) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels, ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.act_init_flag = False if self.act: self.act_init_flag = True self.separable_conv2d = tlx.ops.SeparableConv2D( strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate, out_channel=self.out_channels, k_size=self.kernel_size, in_channel=self.in_channels, depth_multiplier=self.depth_multiplier ) def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.separable_conv2d(inputs, self.depthwise_W, self.pointwise_W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs