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hed_net.py
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#!/usr/bin/python
#coding=utf8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import os
import numpy as np
import const
from mobilenet import *
def class_balanced_sigmoid_cross_entropy(logits, label):
## ref - https://github.com/ppwwyyxx/tensorpack/blob/master/examples/HED/hed.py
"""
The class-balanced cross entropy loss,
as in `Holistically-Nested Edge Detection
<http://arxiv.org/abs/1504.06375>`_.
Args:
logits: of shape (b, ...).
label: of the same shape. the ground truth in {0,1}.
Returns:
class-balanced cross entropy loss.
"""
with tf.name_scope('class_balanced_sigmoid_cross_entropy'):
count_neg = tf.reduce_sum(1.0 - label) # 样本中0的数量
count_pos = tf.reduce_sum(label) # 样本中1的数量(远小于count_neg)
# print('debug, ==========================, count_pos is: {}'.format(count_pos))
beta = count_neg / (count_neg + count_pos) ## e.g. 60000 / (60000 + 800) = 0.9868
pos_weight = beta / (1.0 - beta) ## 0.9868 / (1.0 - 0.9868) = 0.9868 / 0.0132 = 74.75
cost = tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=label, pos_weight=pos_weight)
cost = tf.reduce_mean(cost * (1 - beta))
# 如果样本中1的数量等于0，那就直接让 cost 为 0，因为 beta == 1 时， 除法 pos_weight = beta / (1.0 - beta) 的结果是无穷大
zero = tf.equal(count_pos, 0.0)
final_cost = tf.where(zero, 0.0, cost)
return final_cost
def mobilenet_v2_style_hed(inputs, batch_size, is_training):
assert const.use_batch_norm == True
assert const.use_kernel_regularizer == False
if const.use_kernel_regularizer:
weights_regularizer = tf.contrib.layers.l2_regularizer(scale=0.0001)
else:
weights_regularizer = None
####################################################
func_blocks = mobilenet_v2_func_blocks(is_training)
# print('============ func_blocks are: %r' % func_blocks)
_conv2d = func_blocks['conv2d']
_inverted_residual_block = func_blocks['inverted_residual_block']
_avg_pool2d = func_blocks['avg_pool2d']
filter_initializer = func_blocks['filter_initializer']
activation_func = func_blocks['activation_func']
####################################################
def _dsn_1x1_conv2d(inputs, filters):
kernel_size = [1, 1]
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## no activation
use_bias=False,
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
## no activation
return outputs
def _output_1x1_conv2d(inputs, filters):
kernel_size = [1, 1]
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## no activation
use_bias=True, ## use bias
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
## no batch normalization
## no activation
return outputs
def _dsn_deconv2d_with_upsample_factor(inputs, filters, upsample_factor):
## https://github.com/s9xie/hed/blob/master/examples/hed/train_val.prototxt
## 从这个原版代码里看，是这样计算 kernel_size 的
kernel_size = [2 * upsample_factor, 2 * upsample_factor]
outputs = tf.layers.conv2d_transpose(inputs,
filters,
kernel_size,
strides=(upsample_factor, upsample_factor),
padding='same',
activation=None, ## no activation
use_bias=True, ## use bias
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
## 概念上来说，deconv2d 已经是最后的输出 layer 了，只不过最后还有一步 1x1 的 conv2d 把 5 个 deconv2d 的输出再融合到一起
## 所以不需要再使用 batch normalization 了
return outputs
with tf.variable_scope('hed', 'hed', [inputs]):
end_points = {}
net = inputs
## mobilenet v2 as base net
with tf.variable_scope('mobilenet_v2'):
# 标准的 mobilenet v2 里面并没有这两层，
# 这里是为了得到和 input image 相同 size 的 feature map 而增加的层
net = _conv2d(net, 3, [3, 3], stride=1, scope='block0_0')
net = _conv2d(net, 6, [3, 3], stride=1, scope='block0_1')
dsn1 = net
net = _conv2d(net, 12, [3, 3], stride=2, scope='block0_2') # size/2
net = _inverted_residual_block(net, 6, stride=1, expansion=1, scope='block1_0')
dsn2 = net
net = _inverted_residual_block(net, 12, stride=2, scope='block2_0') # size/4
net = _inverted_residual_block(net, 12, stride=1, scope='block2_1')
dsn3 = net
net = _inverted_residual_block(net, 24, stride=2, scope='block3_0') # size/8
net = _inverted_residual_block(net, 24, stride=1, scope='block3_1')
net = _inverted_residual_block(net, 24, stride=1, scope='block3_2')
dsn4 = net
net = _inverted_residual_block(net, 48, stride=2, scope='block4_0') # size/16
net = _inverted_residual_block(net, 48, stride=1, scope='block4_1')
net = _inverted_residual_block(net, 48, stride=1, scope='block4_2')
net = _inverted_residual_block(net, 48, stride=1, scope='block4_3')
net = _inverted_residual_block(net, 64, stride=1, scope='block5_0')
net = _inverted_residual_block(net, 64, stride=1, scope='block5_1')
net = _inverted_residual_block(net, 64, stride=1, scope='block5_2')
dsn5 = net
## dsn layers
with tf.variable_scope('dsn1'):
dsn1 = _dsn_1x1_conv2d(dsn1, 1)
# print('!! debug, dsn1 shape is: {}'.format(dsn1.get_shape()))
## no need deconv2d
with tf.variable_scope('dsn2'):
dsn2 = _dsn_1x1_conv2d(dsn2, 1)
# print('!! debug, dsn2 shape is: {}'.format(dsn2.get_shape()))
dsn2 = _dsn_deconv2d_with_upsample_factor(dsn2, 1, upsample_factor = 2)
# print('!! debug, dsn2 shape is: {}'.format(dsn2.get_shape()))
with tf.variable_scope('dsn3'):
dsn3 = _dsn_1x1_conv2d(dsn3, 1)
# print('!! debug, dsn3 shape is: {}'.format(dsn3.get_shape()))
dsn3 = _dsn_deconv2d_with_upsample_factor(dsn3, 1, upsample_factor = 4)
# print('!! debug, dsn3 shape is: {}'.format(dsn3.get_shape()))
with tf.variable_scope('dsn4'):
dsn4 = _dsn_1x1_conv2d(dsn4, 1)
# print('!! debug, dsn4 shape is: {}'.format(dsn4.get_shape()))
dsn4 = _dsn_deconv2d_with_upsample_factor(dsn4, 1, upsample_factor = 8)
# print('!! debug, dsn4 shape is: {}'.format(dsn4.get_shape()))
with tf.variable_scope('dsn5'):
dsn5 = _dsn_1x1_conv2d(dsn5, 1)
# print('!! debug, dsn5 shape is: {}'.format(dsn5.get_shape()))
dsn5 = _dsn_deconv2d_with_upsample_factor(dsn5, 1, upsample_factor = 16)
# print('!! debug, dsn5 shape is: {}'.format(dsn5.get_shape()))
# dsn fuse
with tf.variable_scope('dsn_fuse'):
dsn_fuse = tf.concat([dsn1, dsn2, dsn3, dsn4, dsn5], 3)
# print('debug, dsn_fuse shape is: {}'.format(dsn_fuse.get_shape()))
dsn_fuse = _output_1x1_conv2d(dsn_fuse, 1)
# print('debug, dsn_fuse shape is: {}'.format(dsn_fuse.get_shape()))
return dsn_fuse, dsn1, dsn2, dsn3, dsn4, dsn5
def mobilenet_v1_style_hed(inputs, batch_size, is_training):
assert const.use_batch_norm == True
assert const.use_kernel_regularizer == False
alpha = 1.0
filter_initializer = tf.contrib.layers.xavier_initializer()
if const.use_kernel_regularizer:
weights_regularizer = tf.contrib.layers.l2_regularizer(scale=0.001)
else:
weights_regularizer = None
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
'''stride is just for tf.layers.separable_conv2d, means depthwise_conv_stride'''
def _depthwise_conv2d(inputs,
pointwise_conv_filters,
depthwise_conv_kernel_size,
stride,
scope=''):
with tf.variable_scope(scope):
with tf.variable_scope('depthwise_conv'):
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)
'''
!!!important!!! tf.contrib.layers.separable_conv2d already has a depthwise convolution and a pointwise convolution,
but By passing num_outputs=None, separable_conv2d produces only a depthwise convolution layer
ref -- https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py
'''
with tf.variable_scope('pointwise_conv'):
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
outputs = tf.layers.conv2d(outputs,
pointwise_conv_filters, ##!! here, pointwise_conv_filters * alpha
(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 _dsn_1x1_conv2d(inputs, filters):
kernel_size = [1, 1]
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## no activation
use_bias=False,
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
outputs = tf.layers.batch_normalization(outputs, training=is_training)
## no activation
return outputs
def _output_1x1_conv2d(inputs, filters):
kernel_size = [1, 1]
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## no activation
use_bias=True, ## use bias
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
## no batch normalization
## no activation
return outputs
def _dsn_deconv2d_with_upsample_factor(inputs, filters, upsample_factor):
## https://github.com/s9xie/hed/blob/master/examples/hed/train_val.prototxt
## 从这个原版代码里看，是这样计算 kernel_size 的
kernel_size = [2 * upsample_factor, 2 * upsample_factor]
outputs = tf.layers.conv2d_transpose(inputs,
filters,
kernel_size,
strides=(upsample_factor, upsample_factor),
padding='same',
activation=None, ## no activation
use_bias=True, ## use bias
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
## 概念上来说，deconv2d 已经是最后的输出 layer 了，只不过最后还有一步 1x1 的 conv2d 把 5 个 deconv2d 的输出再融合到一起
## 所以不需要再使用 batch normalization 了
return outputs
with tf.variable_scope('hed', 'hed', [inputs]):
end_points = {}
net = inputs
## mobilenet v1 as base net
with tf.variable_scope('mobilenet_v1'):
# 标准的 mobilenet v1 里面并没有这两层，
# 这里是为了得到和 input image 相同 size 的 feature map 而增加的层
net = _conv2d(net, 6, [3, 3], stride=1, scope='extra_block0')
net = _conv2d(net, 6, [3, 3], stride=1, scope='extra_block1')
dsn1 = net
net = _conv2d(net, 8, [3, 3], stride=2, scope='block0')
# print('\r ++++ block0 shape: %s' % (net.get_shape().as_list()))
end_points['block0'] = net
net = _depthwise_conv2d(net, 16, [3, 3], stride=1, scope='block1')
end_points['block1'] = net
dsn2 = net
net = _depthwise_conv2d(net, 32, [3, 3], stride=2, scope='block2')
end_points['block2'] = net
net = _depthwise_conv2d(net, 32, [3, 3], stride=1, scope='block3')
end_points['block3'] = net
dsn3 = net
net = _depthwise_conv2d(net, 64, [3, 3], stride=2, scope='block4')
end_points['block4'] = net
net = _depthwise_conv2d(net, 64, [3, 3], stride=1, scope='block5')
end_points['block5'] = net
dsn4 = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=2, scope='block6')
end_points['block6'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block7')
end_points['block7'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block8')
end_points['block8'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block9')
end_points['block9'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block10')
end_points['block10'] = net
net = _depthwise_conv2d(net, 128, [3, 3], stride=1, scope='block11')
end_points['block11'] = net
dsn5 = net
## dsn layers
with tf.variable_scope('dsn1'):
dsn1 = _dsn_1x1_conv2d(dsn1, 1)
print('!! debug, dsn1 shape is: {}'.format(dsn1.get_shape()))
## no need deconv2d
with tf.variable_scope('dsn2'):
dsn2 = _dsn_1x1_conv2d(dsn2, 1)
print('!! debug, dsn2 shape is: {}'.format(dsn2.get_shape()))
dsn2 = _dsn_deconv2d_with_upsample_factor(dsn2, 1, upsample_factor = 2)
print('!! debug, dsn2 shape is: {}'.format(dsn2.get_shape()))
with tf.variable_scope('dsn3'):
dsn3 = _dsn_1x1_conv2d(dsn3, 1)
print('!! debug, dsn3 shape is: {}'.format(dsn3.get_shape()))
dsn3 = _dsn_deconv2d_with_upsample_factor(dsn3, 1, upsample_factor = 4)
print('!! debug, dsn3 shape is: {}'.format(dsn3.get_shape()))
with tf.variable_scope('dsn4'):
dsn4 = _dsn_1x1_conv2d(dsn4, 1)
print('!! debug, dsn4 shape is: {}'.format(dsn4.get_shape()))
dsn4 = _dsn_deconv2d_with_upsample_factor(dsn4, 1, upsample_factor = 8)
print('!! debug, dsn4 shape is: {}'.format(dsn4.get_shape()))
with tf.variable_scope('dsn5'):
dsn5 = _dsn_1x1_conv2d(dsn5, 1)
print('!! debug, dsn5 shape is: {}'.format(dsn5.get_shape()))
dsn5 = _dsn_deconv2d_with_upsample_factor(dsn5, 1, upsample_factor = 16)
print('!! debug, dsn5 shape is: {}'.format(dsn5.get_shape()))
# dsn fuse
with tf.variable_scope('dsn_fuse'):
dsn_fuse = tf.concat([dsn1, dsn2, dsn3, dsn4, dsn5], 3)
print('debug, dsn_fuse shape is: {}'.format(dsn_fuse.get_shape()))
dsn_fuse = _output_1x1_conv2d(dsn_fuse, 1)
print('debug, dsn_fuse shape is: {}'.format(dsn_fuse.get_shape()))
return dsn_fuse, dsn1, dsn2, dsn3, dsn4, dsn5
def vgg_style_hed(inputs, batch_size, is_training):
filter_initializer = tf.contrib.layers.xavier_initializer()
if const.use_kernel_regularizer:
weights_regularizer = tf.contrib.layers.l2_regularizer(scale=0.0005)
else:
weights_regularizer = None
def _vgg_conv2d(inputs, filters, kernel_size):
use_bias = True
if const.use_batch_norm:
use_bias = False
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## call relu after batch normalization
use_bias=use_bias,
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
if const.use_batch_norm:
outputs = tf.layers.batch_normalization(outputs, training=is_training)
outputs = tf.nn.relu(outputs)
return outputs
def _max_pool2d(inputs):
outputs = tf.layers.max_pooling2d(inputs,
[2, 2],
strides=(2, 2),
padding='same')
return outputs
def _dsn_1x1_conv2d(inputs, filters):
use_bias = True
if const.use_batch_norm:
use_bias = False
kernel_size = [1, 1]
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## no activation
use_bias=use_bias,
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
if const.use_batch_norm:
outputs = tf.layers.batch_normalization(outputs, training=is_training)
## no activation
return outputs
def _output_1x1_conv2d(inputs, filters):
kernel_size = [1, 1]
outputs = tf.layers.conv2d(inputs,
filters,
kernel_size,
padding='same',
activation=None, ## no activation
use_bias=True, ## use bias
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
## no batch normalization
## no activation
return outputs
def _dsn_deconv2d_with_upsample_factor(inputs, filters, upsample_factor):
## https://github.com/s9xie/hed/blob/master/examples/hed/train_val.prototxt
## 从这个原版代码里看，是这样计算 kernel_size 的
kernel_size = [2 * upsample_factor, 2 * upsample_factor]
outputs = tf.layers.conv2d_transpose(inputs,
filters,
kernel_size,
strides=(upsample_factor, upsample_factor),
padding='same',
activation=None, ## no activation
use_bias=True, ## use bias
kernel_initializer=filter_initializer,
kernel_regularizer=weights_regularizer)
## 概念上来说，deconv2d 已经是最后的输出 layer 了，只不过最后还有一步 1x1 的 conv2d 把 5 个 deconv2d 的输出再融合到一起
## 所以不需要再使用 batch normalization 了
return outputs
# ref https://github.com/s9xie/hed/blob/master/examples/hed/train_val.prototxt
with tf.variable_scope('hed', 'hed', [inputs]):
end_points = {}
net = inputs
with tf.variable_scope('conv1'):
net = _vgg_conv2d(net, 12, [3, 3])
net = _vgg_conv2d(net, 12, [3, 3])
dsn1 = net
net = _max_pool2d(net)
with tf.variable_scope('conv2'):
net = _vgg_conv2d(net, 24, [3, 3])
net = _vgg_conv2d(net, 24, [3, 3])
dsn2 = net
net = _max_pool2d(net)
with tf.variable_scope('conv3'):
net = _vgg_conv2d(net, 48, [3, 3])
net = _vgg_conv2d(net, 48, [3, 3])
net = _vgg_conv2d(net, 48, [3, 3])
dsn3 = net
net = _max_pool2d(net)
with tf.variable_scope('conv4'):
net = _vgg_conv2d(net, 96, [3, 3])
net = _vgg_conv2d(net, 96, [3, 3])
net = _vgg_conv2d(net, 96, [3, 3])
dsn4 = net
net = _max_pool2d(net)
with tf.variable_scope('conv5'):
net = _vgg_conv2d(net, 192, [3, 3])
net = _vgg_conv2d(net, 192, [3, 3])
net = _vgg_conv2d(net, 192, [3, 3])
dsn5 = net
# net = _max_pool2d(net) # no need this pool layer
## dsn layers
with tf.variable_scope('dsn1'):
dsn1 = _dsn_1x1_conv2d(dsn1, 1)
print('!! debug, dsn1 shape is: {}'.format(dsn1.get_shape()))
## no need deconv2d
with tf.variable_scope('dsn2'):
dsn2 = _dsn_1x1_conv2d(dsn2, 1)
print('!! debug, dsn2 shape is: {}'.format(dsn2.get_shape()))
dsn2 = _dsn_deconv2d_with_upsample_factor(dsn2, 1, upsample_factor = 2)
print('!! debug, dsn2 shape is: {}'.format(dsn2.get_shape()))
with tf.variable_scope('dsn3'):
dsn3 = _dsn_1x1_conv2d(dsn3, 1)
print('!! debug, dsn3 shape is: {}'.format(dsn3.get_shape()))
dsn3 = _dsn_deconv2d_with_upsample_factor(dsn3, 1, upsample_factor = 4)
print('!! debug, dsn3 shape is: {}'.format(dsn3.get_shape()))
with tf.variable_scope('dsn4'):
dsn4 = _dsn_1x1_conv2d(dsn4, 1)
print('!! debug, dsn4 shape is: {}'.format(dsn4.get_shape()))
dsn4 = _dsn_deconv2d_with_upsample_factor(dsn4, 1, upsample_factor = 8)
print('!! debug, dsn4 shape is: {}'.format(dsn4.get_shape()))
with tf.variable_scope('dsn5'):
dsn5 = _dsn_1x1_conv2d(dsn5, 1)
print('!! debug, dsn5 shape is: {}'.format(dsn5.get_shape()))
dsn5 = _dsn_deconv2d_with_upsample_factor(dsn5, 1, upsample_factor = 16)
print('!! debug, dsn5 shape is: {}'.format(dsn5.get_shape()))
##dsn fuse
with tf.variable_scope('dsn_fuse'):
dsn_fuse = tf.concat([dsn1, dsn2, dsn3, dsn4, dsn5], 3)
print('debug, dsn_fuse shape is: {}'.format(dsn_fuse.get_shape()))
dsn_fuse = _output_1x1_conv2d(dsn_fuse, 1)
print('debug, dsn_fuse shape is: {}'.format(dsn_fuse.get_shape()))
return dsn_fuse, dsn1, dsn2, dsn3, dsn4, dsn5