#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer.layers.core import LayersConfig
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
__all__ = [
'DepthwiseConv2d',
]
[docs]class DepthwiseConv2d(Layer):
"""Separable/Depthwise Convolutional 2D layer, see `tf.nn.depthwise_conv2d <https://www.tensorflow.org/versions/master/api_docs/python/tf/nn/depthwise_conv2d>`__.
Input:
4-D Tensor (batch, height, width, in_channels).
Output:
4-D Tensor (batch, new height, new width, in_channels * depth_multiplier).
Parameters
------------
prev_layer : :class:`Layer`
Previous layer.
filter_size : tuple of int
The filter size (height, width).
stride : tuple of int
The stride step (height, width).
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
dilation_rate: tuple of 2 int
The dilation rate in which we sample input values across the height and width dimensions in atrous convolution. If it is greater than 1, then all values of strides must be 1.
depth_multiplier : int
The number of channels to expand to.
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip bias.
W_init_args : dictionary
The arguments for the weight matrix initializer.
b_init_args : dictionary
The arguments for the bias vector initializer.
name : str
A unique layer name.
Examples
---------
>>> net = InputLayer(x, name='input')
>>> net = Conv2d(net, 32, (3, 3), (2, 2), b_init=None, name='cin')
>>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bnin')
...
>>> net = DepthwiseConv2d(net, (3, 3), (1, 1), b_init=None, name='cdw1')
>>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn11')
>>> net = Conv2d(net, 64, (1, 1), (1, 1), b_init=None, name='c1')
>>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn12')
...
>>> net = DepthwiseConv2d(net, (3, 3), (2, 2), b_init=None, name='cdw2')
>>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn21')
>>> net = Conv2d(net, 128, (1, 1), (1, 1), b_init=None, name='c2')
>>> net = BatchNormLayer(net, act=tf.nn.relu, is_train=is_train, name='bn22')
References
-----------
- tflearn's `grouped_conv_2d <https://github.com/tflearn/tflearn/blob/3e0c3298ff508394f3ef191bcd7d732eb8860b2e/tflearn/layers/conv.py>`__
- keras's `separableconv2d <https://keras.io/layers/convolutional/#separableconv2d>`__
"""
# https://zhuanlan.zhihu.com/p/31551004 https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/MobileNet.py
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
shape=(3, 3),
strides=(1, 1),
act=None,
padding='SAME',
dilation_rate=(1, 1),
depth_multiplier=1,
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,
name='depthwise_conv2d',
):
super(DepthwiseConv2d, self
).__init__(prev_layer=prev_layer, act=act, W_init_args=W_init_args, b_init_args=b_init_args, name=name)
logging.info(
"DepthwiseConv2d %s: shape: %s strides: %s pad: %s act: %s" % (
self.name, str(shape), str(strides), padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
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.info("[warnings] unknown input channels, set to 1")
shape = [shape[0], shape[1], pre_channel, depth_multiplier]
if len(strides) == 2:
strides = [1, strides[0], strides[1], 1]
if len(strides) != 4:
raise AssertionError("len(strides) should be 4.")
with tf.variable_scope(name):
W = tf.get_variable(
name='W_depthwise2d', shape=shape, initializer=W_init, dtype=LayersConfig.tf_dtype, **self.W_init_args
) # [filter_height, filter_width, in_channels, depth_multiplier]
self.outputs = tf.nn.depthwise_conv2d(self.inputs, W, strides=strides, padding=padding, rate=dilation_rate)
if b_init:
b = tf.get_variable(
name='b_depthwise2d', shape=(pre_channel * depth_multiplier), 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)