Source code for tensorlayer.layers.convolution.ternary_conv

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

import tensorflow as tf

import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import compute_alpha, ternary_operation

__all__ = ['TernaryConv2d']


[docs]class TernaryConv2d(Layer): """ The :class:`TernaryConv2d` class is a 2D ternary CNN layer, which weights are either -1 or 1 or 0 while inference. Note that, the bias vector would not be tenarized. Parameters ---------- n_filter : int The number of filters. filter_size : tuple of int The filter size (height, width). strides : tuple of int The sliding window strides of corresponding input dimensions. It must be in the same order as the ``shape`` parameter. act : activation function The activation function of this layer. padding : str The padding algorithm type: "SAME" or "VALID". use_gemm : boolean If True, use gemm instead of ``tf.matmul`` for inference. TODO: support gemm data_format : str "channels_last" (NHWC, default) or "channels_first" (NCHW). dilation_rate : tuple of int Specifying the dilation rate to use for dilated convolution. W_init : initializer The initializer for the the weight matrix. b_init : initializer or None The initializer for the the bias vector. If None, skip biases. in_channels : int The number of in channels. name : None or str A unique layer name. Examples --------- With TensorLayer >>> net = tl.layers.Input([8, 12, 12, 32], name='input') >>> ternaryconv2d = tl.layers.QuanConv2d( ... n_filter=64, filter_size=(5, 5), strides=(1, 1), act=tf.nn.relu, padding='SAME', name='ternaryconv2d' ... )(net) >>> print(ternaryconv2d) >>> output shape : (8, 12, 12, 64) """ def __init__( self, n_filter=32, filter_size=(3, 3), strides=(1, 1), act=None, padding='SAME', use_gemm=False, data_format="channels_last", dilation_rate=(1, 1), W_init=tl.initializers.truncated_normal(stddev=0.02), b_init=tl.initializers.constant(value=0.0), in_channels=None, name=None # 'ternary_cnn2d', ): super().__init__(name, act=act) self.n_filter = n_filter self.filter_size = filter_size self.strides = self._strides = strides self.padding = padding self.use_gemm = use_gemm self.data_format = data_format self.dilation_rate = self._dilation_rate = dilation_rate self.W_init = W_init self.b_init = b_init self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "TernaryConv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % ( self.name, n_filter, str(filter_size), str(strides), padding, self.act.__name__ if self.act is not None else 'No Activation' ) ) if use_gemm: raise Exception("TODO. The current version use tf.matmul for inferencing.") if len(self.strides) != 2: raise ValueError("len(strides) should be 2.") def __repr__(self): actstr = self.act.__name__ if self.act is not None else 'No Activation' s = ( '{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}' ', strides={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") self.filter_shape = (self.filter_size[0], self.filter_size[1], self.in_channels, self.n_filter) self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init) if self.b_init: self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init) def forward(self, inputs): alpha = compute_alpha(self.W) W_ = ternary_operation(self.W) W_ = tf.multiply(alpha, W_) outputs = tf.nn.conv2d( input=inputs, filters=W_, strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate, name=self.name ) if self.b_init: outputs = tf.nn.bias_add(outputs, self.b, data_format=self.data_format, name='bias_add') if self.act: outputs = self.act(outputs) return outputs