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- #! /usr/bin/python
- # -*- coding: utf-8 -*-
-
- import tensorlayer as tl
- from tensorlayer import logging
- from tensorlayer.layers.core import Module
- from tensorlayer.backend import BACKEND
-
- __all__ = [
- 'SeparableConv1d',
- 'SeparableConv2d',
- ]
-
-
- 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
- ------------
- 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.
- 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_rate 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_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 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 TensorLayer
-
- >>> net = tl.layers.Input([8, 50, 64], name='input')
- >>> separableconv1d = tl.layers.SeparableConv1d(n_filter=32, filter_size=3, stride=2, padding='SAME', act=tl.ReLU, name='separable_1d')(net)
- >>> print(separableconv1d)
- >>> output shape : (8, 25, 32)
-
- """
-
- def __init__(
- self, n_filter=32, filter_size=1, stride=1, act=None, padding="SAME", data_format="channels_last",
- dilation_rate=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.n_filter = n_filter
- self.filter_size = filter_size
- self.stride = stride
- self.padding = padding
- self.data_format = data_format
- self.dilation_rate = dilation_rate
- 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: n_filter: %d filter_size: %s strides: %s depth_multiplier: %d act: %s" % (
- self.name, n_filter, str(filter_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={n_filter}, kernel_size={filter_size}'
- ', stride={stride}, padding={padding}'
- )
- if self.dilation_rate != 1:
- s += ', dilation={dilation_rate}'
- 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 = 'NWC'
- if self.in_channels is None:
- self.in_channels = inputs_shape[-1]
- elif self.data_format == 'channels_first':
- self.data_format = 'NCW'
- 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.filter_size, self.in_channels, self.depth_multiplier)
- elif BACKEND == 'mindspore':
- self.depthwise_filter_shape = (self.filter_size, 1, self.depth_multiplier * self.in_channels)
- elif BACKEND == 'paddle':
- self.depthwise_filter_shape = (self.filter_size, 1, self.depth_multiplier * self.in_channels)
-
- self.pointwise_filter_shape = (1, self.depth_multiplier * self.in_channels, self.n_filter)
-
- 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.n_filter, ), init=self.b_init)
- self.bias_add = tl.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 = tl.ops.SeparableConv1D(
- stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation_rate,
- out_channel=self.n_filter, k_size=self.filter_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(tl.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)
- return outputs
-
-
- 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
- ------------
- n_filter : int
- The dimensionality of the output space (i.e. the number of filters in the convolution).
- filter_size : tuple of int
- Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
- strides : tuple of 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.
- 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_rate : tuple of 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 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 TensorLayer
-
- >>> net = tl.layers.Input([8, 50, 50, 64], name='input')
- >>> separableconv2d = tl.layers.SeparableConv2d(n_filter=32, filter_size=(3,3), strides=(2,2), depth_multiplier = 3 , padding='SAME', act=tl.ReLU, name='separable_2d')(net)
- >>> print(separableconv2d)
- >>> output shape : (8, 24, 24, 32)
-
- """
-
- def __init__(
- self, n_filter=32, filter_size=(1, 1), strides=(1, 1), act=None, padding="VALID", data_format="channels_last",
- dilation_rate=(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.n_filter = n_filter
- self.filter_size = filter_size
- self._strides = self.strides = strides
- self.padding = padding
- self.data_format = data_format
- self._dilation_rate = self.dilation_rate = dilation_rate
- 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: n_filter: %d filter_size: %s strides: %s depth_multiplier: %d act: %s" % (
- self.name, n_filter, str(filter_size), str(strides), 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={n_filter}, kernel_size={filter_size}'
- ', stride={strides }, padding={padding}'
- )
- if self.dilation_rate != (1, ) * len(self.dilation_rate):
- s += ', dilation={dilation_rate}'
- 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.filter_size[0], self.filter_size[1], self.in_channels, self.depth_multiplier
- )
- self.pointwise_filter_shape = (1, 1, self.depth_multiplier * self.in_channels, self.n_filter)
-
- elif BACKEND == 'mindspore' or BACKEND == 'paddle':
- self.depthwise_filter_shape = (
- self.filter_size[0], self.filter_size[1], 1, self.depth_multiplier * self.in_channels
- )
- self.pointwise_filter_shape = (1, 1, self.depth_multiplier * self.in_channels, self.n_filter)
-
- 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.n_filter, ), init=self.b_init)
- self.bias_add = tl.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 = tl.ops.SeparableConv2D(
- strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate,
- out_channel=self.n_filter, k_size=self.filter_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(tl.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)
- return outputs
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