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

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

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

__all__ = ['TernaryConv2d']


[docs]class TernaryConv2d(Module): """ 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 ---------- out_channels : int The number of filters. kernel_size : tuple or int The filter size (height, width). stride : tuple or int The sliding window stride 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 or int Specifying the dilation rate to use for dilated convolution. W_init : initializer or str The initializer for the the weight matrix. b_init : initializer or None or str 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 = tlx.nn.Input([8, 12, 12, 32], name='input') >>> ternaryconv2d = tlx.nn.TernaryConv2d( ... out_channels=64, kernel_size=(5, 5), stride=(1, 1), act=tlx.ReLU, padding='SAME', name='ternaryconv2d' ... )(net) >>> print(ternaryconv2d) >>> output shape : (8, 12, 12, 64) """ def __init__( self, out_channels=32, kernel_size=(3, 3), stride=(1, 1), act=None, padding='SAME', use_gemm=False, data_format="channels_last", dilation=(1, 1), W_init='truncated_normal', b_init='constant', in_channels=None, name=None # 'ternary_cnn2d', ): super().__init__(name, act=act) self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size) self.stride = self._strides = self.check_param(stride) self.padding = padding self.use_gemm = use_gemm self.data_format = data_format self.dilation_rate = self._dilation_rate = self.check_param(dilation) self.W_init = self.str_to_init(W_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( "TernaryConv2d %s: out_channels: %d kernel_size: %s stride: %s pad: %s act: %s" % ( self.name, out_channels, str(kernel_size), str(stride), padding, self.act.__class__.__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.stride) != 2: raise ValueError("len(stride) 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={out_channels}, kernel_size={kernel_size}' ', stride={stride}, 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.kernel_size[0], self.kernel_size[1], self.in_channels, self.out_channels) 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.out_channels, ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(data_format=self.data_format) self.ternary_conv = tlx.ops.TernaryConv( weights=self.W, strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate ) 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.ternary_conv(inputs) if self.b_init: outputs = self.bias_add(outputs, self.b) if self.act: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs