forked from TensorLayer/tensorlayer3
316 lines
14 KiB
Python
316 lines
14 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import tensorlayer as tl
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from tensorlayer import logging
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from tensorlayer.layers.core import Module
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from tensorlayer.backend import BACKEND
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__all__ = [
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'SeparableConv1d',
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'SeparableConv2d',
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]
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class SeparableConv1d(Module):
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"""The :class:`SeparableConv1d` class is a 1D depthwise separable convolutional layer.
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This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
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Parameters
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------------
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n_filter : int
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The dimensionality of the output space (i.e. the number of filters in the convolution).
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filter_size : int
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Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
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strides : int
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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.
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act : activation function
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The activation function of this layer.
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padding : str
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One of "valid" or "same" (case-insensitive).
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data_format : str
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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).
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dilation_rate : int
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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.
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depth_multiplier : int
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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.
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depthwise_init : initializer
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for the depthwise convolution kernel.
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pointwise_init : initializer
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For the pointwise convolution kernel.
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b_init : initializer
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For the bias vector. If None, ignore bias in the pointwise part only.
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in_channels : int
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The number of in channels.
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name : None or str
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A unique layer name.
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Examples
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--------
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With TensorLayer
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>>> net = tl.layers.Input([8, 50, 64], name='input')
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>>> separableconv1d = tl.layers.SeparableConv1d(n_filter=32, filter_size=3, strides=2, padding='SAME', act=tl.ReLU, name='separable_1d')(net)
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>>> print(separableconv1d)
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>>> output shape : (8, 25, 32)
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"""
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def __init__(
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self, n_filter=32, filter_size=1, stride=1, act=None, padding="SAME", data_format="channels_last",
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dilation_rate=1, depth_multiplier=1, depthwise_init=tl.initializers.truncated_normal(stddev=0.02),
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pointwise_init=tl.initializers.truncated_normal(stddev=0.02), b_init=tl.initializers.constant(value=0.0),
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in_channels=None, name=None
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):
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super(SeparableConv1d, self).__init__(name, act=act)
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self.n_filter = n_filter
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self.filter_size = filter_size
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self.stride = stride
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self.padding = padding
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self.data_format = data_format
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self.dilation_rate = dilation_rate
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self.depth_multiplier = depth_multiplier
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self.depthwise_init = depthwise_init
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self.pointwise_init = pointwise_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels:
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self.build(None)
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self._built = True
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logging.info(
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"SeparableConv1d %s: n_filter: %d filter_size: %s strides: %s depth_multiplier: %d act: %s" % (
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self.name, n_filter, str(filter_size), str(stride), depth_multiplier,
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self.act.__class__.__name__ if self.act is not None else 'No Activation'
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)
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)
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def __repr__(self):
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actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
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s = (
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'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
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', stride={strides}, padding={padding}'
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)
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if self.dilation_rate != 1:
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s += ', dilation={dilation_rate}'
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if self.b_init is None:
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s += ', bias=False'
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s += (', ' + actstr)
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if self.data_format == 'channels_last':
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self.data_format = 'NWC'
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if self.in_channels is None:
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self.in_channels = inputs_shape[-1]
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elif self.data_format == 'channels_first':
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self.data_format = 'NCW'
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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else:
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raise Exception("data_format should be either channels_last or channels_first")
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if BACKEND == 'tensorflow':
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self.depthwise_filter_shape = (self.filter_size, self.in_channels, self.depth_multiplier)
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elif BACKEND == 'mindspore':
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self.depthwise_filter_shape = (self.filter_size, 1, self.depth_multiplier * self.in_channels)
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self.pointwise_filter_shape = (1, self.depth_multiplier * self.in_channels, self.n_filter)
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self.depthwise_W = self._get_weights(
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'depthwise_filters', shape=self.depthwise_filter_shape, init=self.depthwise_init
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)
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self.pointwise_W = self._get_weights(
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'pointwise_filters', shape=self.pointwise_filter_shape, init=self.pointwise_init
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)
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self.b_init_flag = False
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if self.b_init:
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self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
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self.bias_add = tl.ops.BiasAdd(self.data_format)
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self.b_init_flag = True
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self.act_init_flag = False
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if self.act:
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self.activate = self.act
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self.act_init_flag = True
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self.separable_conv1d = tl.ops.SeparableConv1D(
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stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation_rate,
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out_channel=self.n_filter, k_size=self.filter_size, in_channel=self.in_channels,
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depth_multiplier=self.depth_multiplier
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)
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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outputs = self.separable_conv1d(inputs, self.depthwise_W, self.pointwise_W)
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if self.b_init_flag:
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outputs = self.bias_add(outputs, self.b)
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if self.act_init_flag:
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outputs = self.act(outputs)
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return outputs
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class SeparableConv2d(Module):
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"""The :class:`SeparableConv2d` class is a 2D depthwise separable convolutional layer.
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This layer performs a depthwise convolution that acts separately on channels, followed by a pointwise convolution that mixes channels.
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Parameters
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------------
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n_filter : int
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The dimensionality of the output space (i.e. the number of filters in the convolution).
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filter_size : tuple of int
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Specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.
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strides : tuple of int
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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.
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act : activation function
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The activation function of this layer.
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padding : str
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One of "valid" or "same" (case-insensitive).
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data_format : str
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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).
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dilation_rate : tuple of int
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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.
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depth_multiplier : int
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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.
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depthwise_init : initializer
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for the depthwise convolution kernel.
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pointwise_init : initializer
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For the pointwise convolution kernel.
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b_init : initializer
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For the bias vector. If None, ignore bias in the pointwise part only.
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in_channels : int
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The number of in channels.
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name : None or str
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A unique layer name.
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Examples
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--------
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With TensorLayer
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>>> net = tl.layers.Input([8, 50, 50, 64], name='input')
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>>> separableconv2d = tl.layers.SeparableConv2d(n_filter=32, filter_size=3, strides=2, depth_multiplier = 3 , padding='SAME', act=tl.ReLU, name='separable_2d')(net)
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>>> print(separableconv2d)
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>>> output shape : (8, 24, 24, 32)
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"""
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def __init__(
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self, n_filter=32, filter_size=(1, 1), strides=(1, 1), act=None, padding="VALID", data_format="channels_last",
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dilation_rate=(1, 1), depth_multiplier=1, depthwise_init=tl.initializers.truncated_normal(stddev=0.02),
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pointwise_init=tl.initializers.truncated_normal(stddev=0.02), b_init=tl.initializers.constant(value=0.0),
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in_channels=None, name=None
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):
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super(SeparableConv2d, self).__init__(name, act=act)
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self.n_filter = n_filter
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self.filter_size = filter_size
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self._strides = self.strides = strides
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self.padding = padding
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self.data_format = data_format
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self._dilation_rate = self.dilation_rate = dilation_rate
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self.depth_multiplier = depth_multiplier
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self.depthwise_init = depthwise_init
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self.pointwise_init = pointwise_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels:
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self.build(None)
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self._built = True
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logging.info(
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"SeparableConv2d %s: n_filter: %d filter_size: %s strides: %s depth_multiplier: %d act: %s" % (
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self.name, n_filter, str(filter_size), str(strides), depth_multiplier,
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self.act.__class__.__name__ if self.act is not None else 'No Activation'
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)
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)
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def __repr__(self):
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actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
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s = (
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'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
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', stride={strides }, padding={padding}'
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)
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if self.dilation_rate != (1, ) * len(self.dilation_rate):
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s += ', dilation={dilation_rate}'
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if self.b_init is None:
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s += ', bias=False'
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s += (', ' + actstr)
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if self.data_format == 'channels_last':
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self.data_format = 'NHWC'
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if self.in_channels is None:
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self.in_channels = inputs_shape[-1]
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self._strides = [1, self._strides[0], self._strides[1], 1]
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self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1]
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elif self.data_format == 'channels_first':
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self.data_format = 'NCHW'
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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self._strides = [1, 1, self._strides[0], self._strides[1]]
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self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]]
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else:
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raise Exception("data_format should be either channels_last or channels_first")
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if BACKEND == 'tensorflow':
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self.depthwise_filter_shape = (
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self.filter_size[0], self.filter_size[1], self.in_channels, self.depth_multiplier
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)
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self.pointwise_filter_shape = (1, 1, self.depth_multiplier * self.in_channels, self.n_filter)
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elif BACKEND == 'mindspore':
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self.depthwise_filter_shape = (
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self.filter_size[0], self.filter_size[1], 1, self.depth_multiplier * self.in_channels
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)
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self.pointwise_filter_shape = (1, 1, self.depth_multiplier * self.in_channels, self.n_filter)
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self.depthwise_W = self._get_weights(
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'depthwise_filters', shape=self.depthwise_filter_shape, init=self.depthwise_init
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)
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self.pointwise_W = self._get_weights(
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'pointwise_filters', shape=self.pointwise_filter_shape, init=self.pointwise_init
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)
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self.b_init_flag = False
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if self.b_init:
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self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
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self.bias_add = tl.ops.BiasAdd(self.data_format)
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self.b_init_flag = True
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self.act_init_flag = False
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if self.act:
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self.act_init_flag = True
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self.separable_conv2d = tl.ops.SeparableConv2D(
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strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate,
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out_channel=self.n_filter, k_size=self.filter_size, in_channel=self.in_channels,
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depth_multiplier=self.depth_multiplier
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)
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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outputs = self.separable_conv2d(inputs, self.depthwise_W, self.pointwise_W)
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if self.b_init_flag:
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outputs = self.bias_add(outputs, self.b)
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if self.act_init_flag:
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outputs = self.act(outputs)
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return outputs
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