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mobilenet.py
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#!/usr/bin/python
#coding=utf-8
'''
#############################################
这份 mobilenet.py 位于 unet-tutorial 目录里，
目前这个是最新版本，添加了 v2 version，
其他的工程可以直接使用这份代码或参考这里的实现。
#############################################
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import const
import tensorflow as tf
'''
alpha 对应论文的参数 Width Multiplier: Thinner Models
论文中还有另外一个超参数 ==> Resolution Multiplier: Reduced Representation
这个其实就是影响 input image 的 size
The second hyper-parameter to reduce the computational
cost of a neural network is a resolution multiplier ρ.
We apply this to the input image and the internal representation of
every layer is subsequently reduced by the same multiplier.
In practice we implicitly set ρ by setting the input resolution
'''
def mobilenet_v1(inputs, alpha, is_training):
'''
https://arxiv.org/pdf/1704.04861v1.pdf
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
reference code
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py
https://github.com/keras-team/keras/blob/master/keras/applications/mobilenet.py
https://github.com/tensorflow/tensorflow/blob/r1.4/tensorflow/python/keras/_impl/keras/applications/mobilenet.py
'''
assert const.use_batch_norm == True
if alpha not in [0.25, 0.50, 0.75, 1.0]:
raise ValueError('alpha can be one of'
'`0.25`, `0.50`, `0.75` or `1.0` only.')
filter_initializer = tf.contrib.layers.xavier_initializer()
def _conv2d(inputs, filters, kernel_size, stride, scope=''):
with tf.variable_scope(scope):
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
strides=(stride, stride),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
'''
https://github.com/udacity/deep-learning/blob/master/batch-norm/Batch_Normalization_Solutions.ipynb
https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization
'''
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
def _depthwise_conv2d(inputs,
pointwise_conv_filters,
depthwise_conv_kernel_size,
stride,
scope=''):
with tf.variable_scope(scope):
with tf.variable_scope('depthwise_conv'):
'''
tf.layers Module 里面有一个 tf.layers.separable_conv2d 函数，
但是它的内部调用流程是 depthwise convolution --> pointwise convolution --> activation func，
而 MobileNet V1 风格的卷积层的内部调用流程应该是
depthwise conv --> batch norm --> relu --> pointwise conv --> batch norm --> relu，
所以需要用其他的手段组装出想要的调用流程，
一种办法是使用 tf.nn.depthwise_conv2d，但是这个 API 比较底层，代码写起来很笨重。
后来找到了另外一种可行的办法，借助 tf.contrib.layers.separable_conv2d 函数，
tf.contrib.layers.separable_conv2d 的第二个参数 num_outputs 如果设置为 None，
则只会调用内部的 depthwise conv2d 部分，而不执行 pointwise conv2d 部分。
这样就可以组装出 MobileNet V1 需要的 layer 结构了。
TensorFlow 提供了四种 API，都命名为 separable_conv2d，但是又存在各种细微的差别，
有兴趣的读者可以自行阅读相关文档
tf.contrib.layers.separable_conv2d [Aliases tf.contrib.layers.separable_convolution2d]
VS
tf.keras.backend.separable_conv2d
VS
tf.layers.separable_conv2d
VS
tf.nn.separable_conv2d
'''
outputs = tf.contrib.layers.separable_conv2d(
inputs,
None, # https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py
depthwise_conv_kernel_size,
depth_multiplier=1,
stride=(stride, stride), # stride is just for tf.layers.separable_conv2d
padding='SAME',
activation_fn=None,
weights_initializer=filter_initializer,
biases_initializer=None)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
with tf.variable_scope('pointwise_conv'):
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
outputs = tf.layers.conv2d(outputs,
pointwise_conv_filters,
(1, 1),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
def _avg_pool2d(inputs, scope=''):
inputs_shape = inputs.get_shape().as_list()
assert len(inputs_shape) == 4
pool_height = inputs_shape[1]
pool_width = inputs_shape[2]
with tf.variable_scope(scope):
outputs = tf.layers.average_pooling2d(inputs,
[pool_height, pool_width],
strides=(1, 1),
padding='valid')
return outputs
'''
执行分类任务的网络结构，通常还可以作为实现其他任务的网络结构的 base architecture，
为了方便代码复用，这里只需要实现出卷积层构成的主体部分，
外部调用者根据各自的需求使用这里返回的 output 和 end_points。
比如对于分类任务，按照如下方式使用这个函数
image_height = 224
image_width = 224
image_channels = 3
x = tf.placeholder(tf.float32, [None, image_height, image_width, image_channels])
is_training = tf.placeholder(tf.bool, name='is_training')
output, net = mobilenet_v1(x, 1.0, is_training)
print('output shape is: %r' % (output.get_shape().as_list()))
output = tf.layers.flatten(output)
output = tf.layers.dense(output,
units=1024, # 1024 class
activation=None,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer())
print('output shape is: %r' % (output.get_shape().as_list()))
'''
with tf.variable_scope('mobilenet_v1', 'mobilenet_v1', [inputs]):
end_points = {}
net = inputs
net = _conv2d(net, 32, [3, 3], stride=2, scope='block0')
# print('\r ++++ block0 shape: %s' % (net.get_shape().as_list()))
end_points['block0'] = net
net = _depthwise_conv2d(net, 64, [3, 3], stride=1, scope='block1')
end_points['block1'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=2, scope='block2')
end_points['block2'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block3')
end_points['block3'] = net
net = _depthwise_conv2d(net, 256, [3, 3], stride=2, scope='block4')
end_points['block4'] = net
net = _depthwise_conv2d(net, 256, [3, 3], stride=1, scope='block5')
end_points['block5'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=2, scope='block6')
end_points['block6'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block7')
end_points['block7'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block8')
end_points['block8'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block9')
end_points['block9'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block10')
end_points['block10'] = net
net = _depthwise_conv2d(net, 512, [3, 3], stride=1, scope='block11')
end_points['block11'] = net
net = _depthwise_conv2d(net, 1024, [3, 3], stride=2, scope='block12')
end_points['block12'] = net
net = _depthwise_conv2d(net, 1024, [3, 3], stride=1, scope='block13')
end_points['block13'] = net
output = _avg_pool2d(net, scope='output')
return output, end_points
def mobilenet_v2_func_blocks(is_training):
assert const.use_batch_norm == True
filter_initializer = tf.contrib.layers.xavier_initializer()
activation_func = tf.nn.relu6
def conv2d(inputs, filters, kernel_size, stride, scope=''):
with tf.variable_scope(scope):
with tf.variable_scope('conv2d'):
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
strides=(stride, stride),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
'''
https://github.com/udacity/deep-learning/blob/master/batch-norm/Batch_Normalization_Solutions.ipynb
https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization
'''
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
def _1x1_conv2d(inputs, filters, stride):
kernel_size = [1, 1]
with tf.variable_scope('1x1_conv2d'):
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
strides=(stride, stride),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
# no activation_func
return outputs
def expansion_conv2d(inputs, expansion, stride):
input_shape = inputs.get_shape().as_list()
assert len(input_shape) == 4
filters = input_shape[3] * expansion
kernel_size = [1, 1]
with tf.variable_scope('expansion_1x1_conv2d'):
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
strides=(stride, stride),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = activation_func(outputs)
return outputs
def projection_conv2d(inputs, filters, stride):
kernel_size = [1, 1]
with tf.variable_scope('projection_1x1_conv2d'):
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
strides=(stride, stride),
padding='same',
activation=None,
use_bias=False,
kernel_initializer=filter_initializer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
# no activation_func
return outputs
def depthwise_conv2d(inputs,
depthwise_conv_kernel_size,
stride):
with tf.variable_scope('depthwise_conv2d'):
outputs = tf.contrib.layers.separable_conv2d(
inputs,
None, # https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py
depthwise_conv_kernel_size,
depth_multiplier=1,
stride=(stride, stride),
padding='SAME',
activation_fn=None,
weights_initializer=filter_initializer,
biases_initializer=None)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = activation_func(outputs)
return outputs
def avg_pool2d(inputs, scope=''):
inputs_shape = inputs.get_shape().as_list()
assert len(inputs_shape) == 4
pool_height = inputs_shape[1]
pool_width = inputs_shape[2]
with tf.variable_scope(scope):
outputs = tf.layers.average_pooling2d(inputs,
[pool_height, pool_width],
strides=(1, 1),
padding='valid')
return outputs
def inverted_residual_block(inputs,
filters,
stride,
expansion=6,
scope=''):
assert stride == 1 or stride == 2
depthwise_conv_kernel_size = [3, 3]
pointwise_conv_filters = filters
with tf.variable_scope(scope):
net = inputs
net = expansion_conv2d(net, expansion, stride=1)
net = depthwise_conv2d(net, depthwise_conv_kernel_size, stride=stride)
net = projection_conv2d(net, pointwise_conv_filters, stride=1)
if stride == 1:
# print('----------------- test, net.get_shape().as_list()[3] = %r' % net.get_shape().as_list()[3])
# print('----------------- test, inputs.get_shape().as_list()[3] = %r' % inputs.get_shape().as_list()[3])
# 如果 net.get_shape().as_list()[3] != inputs.get_shape().as_list()[3]
# 借助一个 1x1 的卷积让他们的 channels 相等，然后再相加
if net.get_shape().as_list()[3] != inputs.get_shape().as_list()[3]:
inputs = _1x1_conv2d(inputs, net.get_shape().as_list()[3], stride=1)
net = net + inputs
return net
else:
# stride == 2
return net
func_blocks = {}
func_blocks['conv2d'] = conv2d
func_blocks['inverted_residual_block'] = inverted_residual_block
func_blocks['avg_pool2d'] = avg_pool2d
func_blocks['filter_initializer'] = filter_initializer
func_blocks['activation_func'] = activation_func
return func_blocks
def mobilenet_v2(inputs, is_training):
assert const.use_batch_norm == True
func_blocks = mobilenet_v2_func_blocks(is_training)
_conv2d = func_blocks['conv2d']
_inverted_residual_block = func_blocks['inverted_residual_block']
_avg_pool2d = func_blocks['avg_pool2d']
with tf.variable_scope('mobilenet_v2', 'mobilenet_v2', [inputs]):
end_points = {}
net = inputs
net = _conv2d(net, 32, [3, 3], stride=2, scope='block0_0') # size/2
end_points['block0'] = net
print('!! debug block0, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 16, stride=1, expansion=1, scope='block1_0')
end_points['block1'] = net
print('!! debug block1, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 24, stride=2, scope='block2_0') # size/4
net = _inverted_residual_block(net, 24, stride=1, scope='block2_1')
end_points['block2'] = net
print('!! debug block2, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 32, stride=2, scope='block3_0') # size/8
net = _inverted_residual_block(net, 32, stride=1, scope='block3_1')
net = _inverted_residual_block(net, 32, stride=1, scope='block3_2')
end_points['block3'] = net
print('!! debug block3, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 64, stride=2, scope='block4_0') # size/16
net = _inverted_residual_block(net, 64, stride=1, scope='block4_1')
net = _inverted_residual_block(net, 64, stride=1, scope='block4_2')
net = _inverted_residual_block(net, 64, stride=1, scope='block4_3')
end_points['block4'] = net
print('!! debug block4, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 96, stride=1, scope='block5_0')
net = _inverted_residual_block(net, 96, stride=1, scope='block5_1')
net = _inverted_residual_block(net, 96, stride=1, scope='block5_2')
end_points['block5'] = net
print('!! debug block5, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 160, stride=2, scope='block6_0') # size/32
net = _inverted_residual_block(net, 160, stride=1, scope='block6_1')
net = _inverted_residual_block(net, 160, stride=1, scope='block6_2')
end_points['block6'] = net
print('!! debug block6, net shape is: {}'.format(net.get_shape()))
net = _inverted_residual_block(net, 320, stride=1, scope='block7_0')
end_points['block7'] = net
print('!! debug block7, net shape is: {}'.format(net.get_shape()))
net = _conv2d(net, 1280, [1, 1], stride=1, scope='block8_0')
end_points['block8'] = net
print('!! debug block8, net shape is: {}'.format(net.get_shape()))
output = _avg_pool2d(net, scope='output')
print('!! debug after avg_pool, net shape is: {}'.format(output.get_shape()))
return output, end_points