#! /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
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = ['BinaryConv2d']
[docs]class BinaryConv2d(Layer):
"""
The :class:`BinaryConv2d` class is a 2D binary CNN layer, which weights are either -1 or 1 while inference.
Note that, the bias vector would not be binarized.
Parameters
----------
prev_layer : :class:`Layer`
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.
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).
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.BinaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', name='bcnn1')
>>> 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.SignLayer(net)
>>> net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', name='bcnn2')
>>> 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),
act=None,
padding='SAME',
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,
# act=None,
# shape=(5, 5, 1, 100),
# strides=(1, 1, 1, 1),
# padding='SAME',
# 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='binary_cnn2d',
):
super(BinaryConv2d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"BinaryConv2d %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(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("unknown 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(W)
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)