#! /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)
logging.info(
"QuanConv2d %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'
)
)
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)