Source code for tensorlayer.layers.convolution.quan_conv

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

import tensorflow as tf

from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig

from tensorlayer.layers.utils import quantize_active_overflow
from tensorlayer.layers.utils import quantize_weight_overflow

from tensorlayer import logging

from tensorlayer.decorators import deprecated_alias

__all__ = ['QuanConv2d']

[docs]class QuanConv2d(Layer): """The :class:`QuanConv2dWithBN` class is a quantized convolutional layer with BN, which weights are 'bitW' bits and the output of the previous layer are 'bitA' bits while inferencing. Note that, the bias vector would not be binarized. Parameters ---------- prev_layer : :class:`Layer` Previous layer. bitW : int The bits of this layer's parameter bitA : int The bits of the output of previous layer 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. padding : str The padding algorithm type: "SAME" or "VALID". act : activation function The activation function of this layer. bitW : int The bits of this layer's parameter bitA : int The bits of the output of previous layer use_gemm : boolean If True, use gemm instead of ``tf.matmul`` for inferencing. (TODO). 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. W_init_args : dictionary The arguments for the weight matrix initializer. b_init_args : dictionary The arguments for the bias vector initializer. use_cudnn_on_gpu : bool Default is False. data_format : str "NHWC" or "NCHW", default is "NHWC". name : str A unique layer name. Examples --------- >>> import tensorflow as tf >>> import tensorlayer as tl >>> x = tf.placeholder(tf.float32, [None, 256, 256, 3]) >>> net = tl.layers.InputLayer(x, name='input') >>> net = tl.layers.QuanConv2d(net, 32, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name='qcnn1') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn1') ... >>> net = tl.layers.QuanConv2d(net, 64, (5, 5), (1, 1), padding='SAME', act=tf.nn.relu, name='qcnn2') >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2') >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=True, name='bn2') """ @deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release def __init__( self, prev_layer, n_filter=32, filter_size=(3, 3), strides=(1, 1), padding='SAME', act=None, bitW=8, bitA=8, use_gemm=False, W_init=tf.truncated_normal_initializer(stddev=0.02), b_init=tf.constant_initializer(value=0.0), W_init_args=None, b_init_args=None, use_cudnn_on_gpu=None, data_format=None, name='quan_cnn2d', ): super(QuanConv2d, self ).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name) "QuanConv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (, n_filter, str(filter_size), str(strides), padding, self.act.__name__ if self.act is not None else 'No Activation' ) ) self.inputs = quantize_active_overflow(self.inputs, bitA) # Do not remove if use_gemm: raise Exception("TODO. The current version use tf.matmul for inferencing.") if len(strides) != 2: raise ValueError("len(strides) should be 2.") try: pre_channel = int(prev_layer.outputs.get_shape()[-1]) except Exception: # if pre_channel is ?, it happens when using Spatial Transformer Net pre_channel = 1 logging.warning("[warnings] unknow input channels, set to 1") shape = (filter_size[0], filter_size[1], pre_channel, n_filter) strides = (1, strides[0], strides[1], 1) with tf.variable_scope(name): W = tf.get_variable( name='W_conv2d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args ) W = quantize_weight_overflow(W, bitW) self.outputs = tf.nn.conv2d( self.inputs, W, strides=strides, padding=padding, use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format ) if b_init: b = tf.get_variable( name='b_conv2d', shape=(shape[-1]), initializer=b_init, dtype=LayersConfig.tf_dtype, **self.b_init_args ) self.outputs = tf.nn.bias_add(self.outputs, b, name='bias_add') self.outputs = self._apply_activation(self.outputs) self._add_layers(self.outputs) if b_init: self._add_params([W, b]) else: self._add_params(W)