class Vgg16():
def __init__(self, cfgs, saveDir=None, initmodelfile=None, run='training'):
self.cfgs = cfgs
self.saveDir = saveDir
self.io = IO()
weights_file = initmodelfile
self.data_dict = np.load(weights_file, encoding='latin1').item()
self.io.print_info("Model weights loaded from {}".format(
self.cfgs['model_weights_path']))
self.images = tf.placeholder(tf.float32, [
None, self.cfgs[run]['image_height'],
self.cfgs[run]['image_width'], self.cfgs[run]['n_channels']
],
name="inputlayer")
self.edgemaps = tf.placeholder(tf.float32, [
None, self.cfgs[run]['image_height'],
self.cfgs[run]['image_width'], 1
])
self.define_model()
def define_model(self):
"""
Load VGG params from disk without FC layers A
Add branch layers (with deconv) after each CONV block
"""
start_time = time.time()
self.conv1_1 = self.conv_layer_vgg(self.images, "conv1_1")
self.conv1_2 = self.conv_layer_vgg(self.conv1_1, "conv1_2")
self.side_1 = self.side_layer(self.conv1_2, "side_1", 1)
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.io.print_info('Added CONV-BLOCK-1+SIDE-1')
self.conv2_1 = self.conv_layer_vgg(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer_vgg(self.conv2_1, "conv2_2")
self.side_2 = self.side_layer(self.conv2_2, "side_2", 2)
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.io.print_info('Added CONV-BLOCK-2+SIDE-2')
self.conv3_1 = self.conv_layer_vgg(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer_vgg(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer_vgg(self.conv3_2, "conv3_3")
self.side_3 = self.side_layer(self.conv3_3, "side_3", 4)
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
self.io.print_info('Added CONV-BLOCK-3+SIDE-3')
self.conv4_1 = self.conv_layer_vgg(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer_vgg(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer_vgg(self.conv4_2, "conv4_3")
self.side_4 = self.side_layer(self.conv4_3, "side_4", 8)
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
self.io.print_info('Added CONV-BLOCK-4+SIDE-4')
self.conv5_1 = self.conv_layer_vgg(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer_vgg(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer_vgg(self.conv5_2, "conv5_3")
self.side_5 = self.side_layer(self.conv5_3, "side_5", 16)
self.io.print_info('Added CONV-BLOCK-5+SIDE-5')
self.side_outputs = [
self.side_1, self.side_2, self.side_3, self.side_4, self.side_5
]
w_shape = [1, 1, len(self.side_outputs), 1]
self.fuse = self.conv_layer(tf.concat(self.side_outputs, axis=3),
w_shape,
name='fuse_1',
use_bias=False,
w_init=tf.constant_initializer(0.2))
self.io.print_info('Added FUSE layer')
# complete output maps from side layer and fuse layers
self.outputs = self.side_outputs + [self.fuse]
self.data_dict = None
self.io.print_info("Build model finished: {:.4f}s".format(time.time() -
start_time))
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name=name)
def conv_layer_vgg(self, bottom, name):
"""
Adding a conv layer + weight parameters from a dict
"""
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv_biases = self.get_bias(name)
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def conv_layer(self,
x,
W_shape,
b_shape=None,
name=None,
padding='SAME',
use_bias=True,
w_init=None,
b_init=None):
W = self.weight_variable(W_shape, w_init)
tf.summary.histogram('weights_{}'.format(name), W)
if use_bias:
b = self.bias_variable([b_shape], b_init)
tf.summary.histogram('biases_{}'.format(name), b)
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=padding)
return conv + b if use_bias else conv
def deconv_layer(self, x, upscale, name, padding='SAME', w_init=None):
x_shape = tf.shape(x)
in_shape = x.shape.as_list()
w_shape = [upscale * 2, upscale * 2, in_shape[-1], 1]
strides = [1, upscale, upscale, 1]
W = self.weight_variable(w_shape, w_init)
tf.summary.histogram('weights_{}'.format(name), W)
out_shape = tf.stack([x_shape[0], x_shape[1], x_shape[2], w_shape[2]
]) * tf.constant(strides, tf.int32)
deconv = tf.nn.conv2d_transpose(x,
W,
out_shape,
strides=strides,
padding=padding)
return deconv
def side_layer(self, inputs, name, upscale):
"""
https://github.com/s9xie/hed/blob/9e74dd710773d8d8a469ad905c76f4a7fa08f945/examples/hed/train_val.prototxt#L122
1x1 conv followed with Deconvoltion layer to upscale the size of input image sans color
"""
with tf.variable_scope(name):
in_shape = inputs.shape.as_list()
w_shape = [1, 1, in_shape[-1], 1]
classifier = self.conv_layer(inputs,
w_shape,
b_shape=1,
w_init=tf.constant_initializer(),
b_init=tf.constant_initializer(),
name=name + '_reduction')
classifier = self.deconv_layer(
classifier,
upscale=upscale,
name='{}_deconv_{}'.format(name, upscale),
w_init=tf.truncated_normal_initializer(stddev=0.1))
return classifier
def get_conv_filter(self, name):
return tf.constant(self.data_dict[name][0], name="filter")
def get_bias(self, name):
return tf.constant(self.data_dict[name][1], name="biases")
def weight_variable(self, shape, initial):
init = initial(shape)
return tf.Variable(init)
def bias_variable(self, shape, initial):
init = initial(shape)
return tf.Variable(init)
def setup_testing(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs for predictions
"""
self.predictions = []
for idx, b in enumerate(self.outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
self.predictions.append(output)
def setup_training(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs
Compute total loss := side_layer_loss + fuse_layer_loss
Compute predicted edge maps from fuse layer as pseudo performance metric to track
"""
self.predictions = []
self.loss = 0
self.io.print_warning('Deep supervision application set to {}'.format(
self.cfgs['deep_supervision']))
for idx, b in enumerate(self.side_outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
cost = sigmoid_cross_entropy_balanced(
b, self.edgemaps, name='cross_entropy{}'.format(idx))
self.predictions.append(output)
if self.cfgs['deep_supervision']:
self.loss += (self.cfgs['loss_weights'] * cost)
fuse_output = tf.nn.sigmoid(self.fuse, name='fuse')
tf.summary.image('fuseSigmoid', fuse_output)
fuse_cost = sigmoid_cross_entropy_balanced(self.fuse,
self.edgemaps,
name='cross_entropy_fuse')
tf.summary.image('cvSourceEdge', self.edgemaps)
self.predictions.append(fuse_output)
self.loss += (self.cfgs['loss_weights'] * fuse_cost)
pred = tf.cast(tf.greater(fuse_output, 0.5),
tf.int32,
name='predictions')
error = tf.cast(tf.not_equal(pred, tf.cast(self.edgemaps, tf.int32)),
tf.float32)
self.error = tf.reduce_mean(error, name='pixel_error')
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('error', self.error)
self.merged_summary = tf.summary.merge_all()
save_dir = self.saveDir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
self.train_writer = tf.summary.FileWriter(self.saveDir + '/train',
session.graph)
self.val_writer = tf.summary.FileWriter(self.saveDir + '/val')
class Vgg16():
def __init__(self, cfgs, saveDir=None, initmodelfile=None, run='training'):
self.cfgs = cfgs
self.saveDir = saveDir
self.io = IO()
weights_file = initmodelfile
self.data_dict = np.load(weights_file, encoding='latin1').item()
self.io.print_info("Model weights loaded from {}".format(
self.cfgs['model_weights_path']))
self.images = tf.placeholder(tf.float32, [
None, self.cfgs[run]['image_height'],
self.cfgs[run]['image_width'], self.cfgs[run]['n_channels']
],
name="inputlayer")
self.edgemaps = tf.placeholder(tf.float32, [
None, self.cfgs[run]['image_height'],
self.cfgs[run]['image_width'], 1
])
self.weights_regularizer = None
self.filter_initializer = tf.contrib.layers.xavier_initializer()
self.define_model()
def define_model(self):
"""
Load VGG params from disk without FC layers A
Add branch layers (with deconv) after each CONV block
"""
start_time = time.time()
self.conv1_1 = self.conv_layer_vgg(self.images, "conv1_1")
self.conv1_2 = self.conv_layer_vgg(self.conv1_1, "conv1_2")
with tf.variable_scope('dsn1'):
#self.side_1 = self.side_layer(self.conv1_2, "side_1", 1)
self.side_1 = self.dsn_1x1_conv2d(self.conv1_2, 1)
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.io.print_info('Added CONV-BLOCK-1+SIDE-1')
self.conv2_1 = self.conv_layer_vgg(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer_vgg(self.conv2_1, "conv2_2")
with tf.variable_scope('dsn2'):
#self.side_2 = self.side_layer(self.conv2_2, "side_2", 2)
self.side_2 = self.dsn_1x1_conv2d(self.conv2_2, 1)
self.side_2 = self.dsn_deconv2d_with_upsample_factor(
self.side_2, 1, upsample_factor=2)
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.io.print_info('Added CONV-BLOCK-2+SIDE-2')
self.conv3_1 = self.conv_layer_vgg(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer_vgg(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer_vgg(self.conv3_2, "conv3_3")
with tf.variable_scope('dsn3'):
#self.side_3 = self.side_layer(self.conv3_3, "side_3", 4)
self.side_3 = self.dsn_1x1_conv2d(self.conv3_3, 1)
self.side_3 = self.dsn_deconv2d_with_upsample_factor(
self.side_3, 1, upsample_factor=4)
self.pool3 = self.max_pool(self.conv3_3, 'pool3')
self.io.print_info('Added CONV-BLOCK-3+SIDE-3')
self.conv4_1 = self.conv_layer_vgg(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer_vgg(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer_vgg(self.conv4_2, "conv4_3")
with tf.variable_scope('dsn4'):
#self.side_4 = self.side_layer(self.conv4_3, "side_4", 8)
self.side_4 = self.dsn_1x1_conv2d(self.conv4_3, 1)
self.side_4 = self.dsn_deconv2d_with_upsample_factor(
self.side_4, 1, upsample_factor=8)
self.pool4 = self.max_pool(self.conv4_3, 'pool4')
self.io.print_info('Added CONV-BLOCK-4+SIDE-4')
self.conv5_1 = self.conv_layer_vgg(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer_vgg(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer_vgg(self.conv5_2, "conv5_3")
with tf.variable_scope('dsn5'):
#self.side_5 = self.side_layer(self.conv5_3, "side_5", 16)
self.side_5 = self.dsn_1x1_conv2d(self.conv5_3, 1)
self.side_5 = self.dsn_deconv2d_with_upsample_factor(
self.side_5, 1, upsample_factor=16)
self.io.print_info('Added CONV-BLOCK-5+SIDE-5')
self.side_outputs = [
self.side_1, self.side_2, self.side_3, self.side_4, self.side_5
]
w_shape = [1, 1, len(self.side_outputs), 1]
with tf.variable_scope('fuse'):
#self.fuse = self.conv_layer(tf.concat(self.side_outputs, axis=3),
# w_shape, name='fuse_1', use_bias=False,
# w_init=tf.constant_initializer(0.2))
self.fuse = tf.concat([
self.side_1, self.side_2, self.side_3, self.side_4, self.side_5
], 3)
self.fuse = self.output_1x1_conv2d(self.fuse, 1)
# complete output maps from side layer and fuse layers
self.outputs = self.side_outputs + [self.fuse]
self.data_dict = None
self.io.print_info("Build model finished: {:.4f}s".format(time.time() -
start_time))
def dsn_1x1_conv2d(self, 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=self.filter_initializer,
kernel_regularizer=self.weights_regularizer)
#if const.use_batch_norm:
# outputs = tf.layers.batch_normalization(outputs, training=is_training)
## no activation
return outputs
def output_1x1_conv2d(self, 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=self.filter_initializer,
kernel_regularizer=self.weights_regularizer)
## no batch normalization
## no activation
return outputs
def dsn_deconv2d_with_upsample_factor(self, 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=self.filter_initializer,
kernel_regularizer=self.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
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name=name)
def conv_layer_vgg(self, bottom, name):
"""
Adding a conv layer + weight parameters from a dict
"""
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv_biases = self.get_bias(name)
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def conv_layer(self,
x,
W_shape,
b_shape=None,
name=None,
padding='SAME',
use_bias=True,
w_init=None,
b_init=None):
W = self.weight_variable(W_shape, w_init)
tf.summary.histogram('weights_{}'.format(name), W)
if use_bias:
b = self.bias_variable([b_shape], b_init)
tf.summary.histogram('biases_{}'.format(name), b)
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=padding)
return conv + b if use_bias else conv
def deconv_layer(self, x, upscale, name, padding='SAME', w_init=None):
x_shape = tf.shape(x)
in_shape = x.shape.as_list()
w_shape = [upscale * 2, upscale * 2, in_shape[-1], 1]
strides = [1, upscale, upscale, 1]
W = self.weight_variable(w_shape, w_init)
tf.summary.histogram('weights_{}'.format(name), W)
out_shape = tf.stack([x_shape[0], x_shape[1], x_shape[2], w_shape[2]
]) * tf.constant(strides, tf.int32)
deconv = tf.nn.conv2d_transpose(x,
W,
out_shape,
strides=strides,
padding=padding)
return deconv
def side_layer(self, inputs, name, upscale):
"""
https://github.com/s9xie/hed/blob/9e74dd710773d8d8a469ad905c76f4a7fa08f945/examples/hed/train_val.prototxt#L122
1x1 conv followed with Deconvoltion layer to upscale the size of input image sans color
"""
with tf.variable_scope(name):
in_shape = inputs.shape.as_list()
w_shape = [1, 1, in_shape[-1], 1]
classifier = self.conv_layer(inputs,
w_shape,
b_shape=1,
w_init=tf.constant_initializer(),
b_init=tf.constant_initializer(),
name=name + '_reduction')
classifier = self.deconv_layer(
classifier,
upscale=upscale,
name='{}_deconv_{}'.format(name, upscale),
w_init=tf.truncated_normal_initializer(stddev=0.1))
return classifier
def get_conv_filter(self, name):
return tf.constant(self.data_dict[name][0], name="filter")
def get_bias(self, name):
return tf.constant(self.data_dict[name][1], name="biases")
def weight_variable(self, shape, initial):
init = initial(shape)
return tf.Variable(init)
def bias_variable(self, shape, initial):
init = initial(shape)
return tf.Variable(init)
def setup_testing(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs for predictions
"""
self.predictions = []
for idx, b in enumerate(self.outputs):
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
self.predictions.append(output)
def setup_training(self, session):
"""
Apply sigmoid non-linearity to side layer ouputs + fuse layer outputs
Compute total loss := side_layer_loss + fuse_layer_loss
Compute predicted edge maps from fuse layer as pseudo performance metric to track
"""
self.predictions = []
self.loss = 0
self.io.print_warning('Deep supervision application set to {}'.format(
self.cfgs['deep_supervision']))
for idx, b in enumerate(self.side_outputs):
#tf.summary.image('output_{}'.format(idx), b)
output = tf.nn.sigmoid(b, name='output_{}'.format(idx))
cost = sigmoid_cross_entropy_balanced(
b, self.edgemaps, name='cross_entropy{}'.format(idx))
self.predictions.append(output)
if self.cfgs['deep_supervision']:
self.loss += (self.cfgs['loss_weights'] * cost)
#tf.summary.image('fuse', self.fuse)
fuse_output = tf.nn.sigmoid(self.fuse, name='fuse')
tf.summary.image('fuseSigmoid', fuse_output)
fuse_cost = sigmoid_cross_entropy_balanced(self.fuse,
self.edgemaps,
name='cross_entropy_fuse')
#tf.summary.image('cvSource', self.images)
tf.summary.image('cvSourceEdge', self.edgemaps)
self.predictions.append(fuse_output)
self.loss += (self.cfgs['loss_weights'] * fuse_cost)
pred = tf.cast(tf.greater(fuse_output, 0.5),
tf.int32,
name='predictions')
error = tf.cast(tf.not_equal(pred, tf.cast(self.edgemaps, tf.int32)),
tf.float32)
self.error = tf.reduce_mean(error, name='pixel_error')
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('error', self.error)
self.merged_summary = tf.summary.merge_all()
save_dir = self.saveDir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
self.train_writer = tf.summary.FileWriter(save_dir + '/train',
session.graph)
self.val_writer = tf.summary.FileWriter(save_dir + '/val')
'''
for bn test
'''
def build(self, input_image, is_training):
with tf.name_scope('processing'):
#bgr cv2
b, g, r = tf.split(input_image, 3, axis=3)
image = tf.concat([b * 0.00390625, g * 0.00390625, r * 0.00390625],
axis=3)
# vgg16
# block 1
self.conv1_1 = self.conv_bn_f(image,
is_training=is_training,
name='conv1_1')
self.conv1_2 = self.conv_bn_f(self.conv1_1,
is_training=is_training,
name='conv1_2')
self.pool1 = self.max_pool(self.conv1_2, name='pool1')
# block 2
self.conv2_1 = self.conv_bn_f(self.pool1,
is_training=is_training,
name='conv2_1')
self.conv2_2 = self.conv_bn_f(self.conv2_1,
is_training=is_training,
name='conv2_2')
self.pool2 = self.max_pool(self.conv2_2, name='pool2')
# block 3
self.conv3_1 = self.conv_bn_f(self.pool2,
is_training=is_training,
name='conv3_1')
self.conv3_2 = self.conv_bn_f(self.conv3_1,
is_training=is_training,
name='conv3_2')
self.conv3_3 = self.conv_bn_f(self.conv3_2,
is_training=is_training,
name='conv3_3')
self.pool3 = self.max_pool(self.conv3_3, name='pool3')
# block 4
self.conv4_1 = self.conv_bn_f(self.pool3,
is_training=is_training,
name='conv4_1')
self.conv4_2 = self.conv_bn_f(self.conv4_1,
is_training=is_training,
name='conv4_2')
self.conv4_3 = self.conv_bn_f(self.conv4_2,
is_training=is_training,
name='conv4_3')
self.pool4 = self.max_pool(self.conv4_3, name='pool4')
# block 5
self.conv5_1 = self.conv_bn_f(self.pool4,
is_training=is_training,
name='conv5_1')
self.conv5_2 = self.conv_bn_f(self.conv5_1,
is_training=is_training,
name='conv5_2')
self.conv5_3 = self.conv_bn_f(self.conv5_2,
is_training=is_training,
name='conv5_3')
self.upscore_dsn1_1 = self.conv_bn(self.conv1_1,
ksize=[1, 1, 64, 1],
is_training=is_training,
name='upscore_dsn1_1')
self.upscore_dsn1_2 = self.conv_bn(self.conv1_2,
ksize=[1, 1, 64, 1],
is_training=is_training,
name='upscore_dsn1_2')
self.score_dsn2_1 = self.conv_bn(self.conv2_1,
ksize=[1, 1, 128, 1],
is_training=is_training,
name='score_dsn2_1')
self.upscore_dsn2_1 = self.upsampling(self.score_dsn2_1,
tf.shape(image)[1:3])
self.score_dsn2_2 = self.conv_bn(self.conv2_2,
ksize=[1, 1, 128, 1],
is_training=is_training,
name='score_dsn2_2')
self.upscore_dsn2_2 = self.upsampling(self.score_dsn2_2,
tf.shape(image)[1:3])
self.score_dsn3_1 = self.conv_bn(self.conv3_1,
ksize=[1, 1, 256, 1],
is_training=is_training,
name='score_dsn3_1')
self.upscore_dsn3_1 = self.upsampling(self.score_dsn3_1,
tf.shape(image)[1:3])
self.score_dsn3_2 = self.conv_bn(self.conv3_2,
ksize=[1, 1, 256, 1],
is_training=is_training,
name='score_dsn3_2')
self.upscore_dsn3_2 = self.upsampling(self.score_dsn3_2,
tf.shape(image)[1:3])
self.score_dsn3_3 = self.conv_bn(self.conv3_3,
ksize=[1, 1, 256, 1],
is_training=is_training,
name='score_dsn3_3')
self.upscore_dsn3_3 = self.upsampling(self.score_dsn3_3,
tf.shape(image)[1:3])
self.score_dsn4_1 = self.conv_bn(self.conv4_1,
ksize=[1, 1, 512, 1],
is_training=is_training,
name='score_dsn4_1')
self.upscore_dsn4_1 = self.upsampling(self.score_dsn4_1,
tf.shape(image)[1:3])
self.score_dsn4_2 = self.conv_bn(self.conv4_2,
ksize=[1, 1, 512, 1],
is_training=is_training,
name='score_dsn4_2')
self.upscore_dsn4_2 = self.upsampling(self.score_dsn4_2,
tf.shape(image)[1:3])
self.score_dsn4_3 = self.conv_bn(self.conv4_3,
ksize=[1, 1, 512, 1],
is_training=is_training,
name='score_dsn4_3')
self.upscore_dsn4_3 = self.upsampling(self.score_dsn4_3,
tf.shape(image)[1:3])
self.score_dsn5_1 = self.conv_bn(self.conv5_1,
ksize=[1, 1, 512, 1],
is_training=is_training,
name='score_dsn5_1')
self.upscore_dsn5_1 = self.upsampling(self.score_dsn5_1,
tf.shape(image)[1:3])
self.score_dsn5_2 = self.conv_bn(self.conv5_2,
ksize=[1, 1, 512, 1],
is_training=is_training,
name='score_dsn5_2')
self.upscore_dsn5_2 = self.upsampling(self.score_dsn5_2,
tf.shape(image)[1:3])
self.score_dsn5_3 = self.conv_bn(self.conv5_3,
ksize=[1, 1, 512, 1],
is_training=is_training,
name='score_dsn5_3')
self.upscore_dsn5_3 = self.upsampling(self.score_dsn5_3,
tf.shape(image)[1:3])
self.concat = tf.concat([
self.upscore_dsn1_1, self.upscore_dsn1_2, self.upscore_dsn2_1,
self.upscore_dsn2_2, self.upscore_dsn3_1, self.upscore_dsn3_2,
self.upscore_dsn3_3, self.upscore_dsn4_1, self.upscore_dsn4_2,
self.upscore_dsn4_3, self.upscore_dsn5_1, self.upscore_dsn5_2,
self.upscore_dsn5_3
],
axis=3)
self.score = self.conv_bn(self.concat,
ksize=[1, 1, 13, self.class_number],
is_training=is_training,
name='score')
self.softmax = tf.nn.softmax(self.score + tf.constant(1e-4))
self.pred = tf.argmax(self.softmax, axis=-1)
'''
tf.contrib.layers.batch_norm(inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None,
param_initializers=None, param_regularizers=None, updates_collections=tf.GraphKeys.UPDATE_OPS, is_training=True,
reuse=None, variables_collections=None, outputs_collections=None, trainable=True, batch_weights=None, fused=None,
data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None, renorm=False, renorm_clipping=None, renorm_decay=0.99, adjustment=None)
tf.contrib.layers.batch_norm 参数：
1 inputs： 输入
2 decay ：衰减系数。合适的衰减系数值接近1.0,特别是含多个9的值：0.999(默认值),0.99,0.9。如果训练集表现很好而验证/测试集表现得不好，选择小的系数（推荐使用0.9）。如果想要提高稳定性，zero_debias_moving_mean设为True
3 center：如果为True，有beta偏移量；如果为False，无beta偏移量
4 scale：如果为True，则乘以gamma。如果为False，gamma则不使用。当下一层是线性的时（例如nn.relu），由于缩放可以由下一层完成，所以可以禁用该层。
5 epsilon：避免被零除
6 activation_fn：用于激活，默认为线性激活函数
7 param_initializers ： beta, gamma, moving mean and moving variance的优化初始化
8 param_regularizers ： beta and gamma正则化优化
9 updates_collections ：Collections来收集计算的更新操作。updates_ops需要使用train_op来执行。如果为None，则会添加控件依赖项以确保更新已计算到位。
10 is_training:图层是否处于训练模式。在训练模式下，它将积累转入的统计量moving_mean并 moving_variance使用给定的指数移动平均值 decay。当它不是在训练模式，那么它将使用的数值moving_mean和moving_variance。
11 scope：可选范围variable_scope
注意：训练时，需要更新moving_mean和moving_variance。默认情况下，更新操作被放入tf.GraphKeys.UPDATE_OPS，所以需要添加它们作为依赖项train_op。例如：
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss)
可以将updates_collections = None设置为强制更新，但可能会导致速度损失，尤其是在分布式设置中。
'''
def conv_bn_f(self, bottom, is_training, name):
# finu-tune and batch_norm ; fine-tune not shape,shape had known
with tf.variable_scope(name):
weights = self.get_conv_filter_v2(name)
out = tf.nn.conv2d(bottom,
filter=weights,
strides=[1, 1, 1, 1],
padding='SAME')
biases = self.get_bias_v2(name)
out = tf.nn.bias_add(out, biases)
#bn before relu and train True test False
out = tf.contrib.layers.batch_norm(
out, center=True, scale=True,
is_training=is_training) # 这里的scale为真需要注意下，神马意思
out = tf.nn.relu(out)
return out
def conv_bn(self, bottom, ksize, is_training, name):
# initialize and batch_norm ; stride =[1,1,1,1]
with tf.variable_scope(name):
weights = tf.get_variable('weights',
ksize,
tf.float32,
initializer=xavier_initializer())
biases = tf.get_variable('biases', [ksize[-1]],
tf.float32,
initializer=tf.constant_initializer(0.0))
out = tf.nn.conv2d(bottom,
filter=weights,
strides=[1, 1, 1, 1],
padding='SAME')
out = tf.nn.bias_add(out, biases)
#bn
out = tf.contrib.layers.batch_norm(out,
center=True,
scale=True,
is_training=is_training)
out = tf.nn.relu(out)
return out
def get_conv_filter_v2(self, name):
init = tf.constant_initializer(self.vgg16_params[name]['weights'])
shape = self.vgg16_params[name]['weights'].shape
var = tf.get_variable('weights',
shape=shape,
dtype=tf.float32,
initializer=init)
return var
def get_bias_v2(self, name):
init = tf.constant_initializer(self.vgg16_params[name]['biases'])
shape = self.vgg16_params[name]['biases'].shape # tuple
bias = tf.get_variable('biases',
shape=shape,
dtype=tf.float32,
initializer=init)
return bias
def upsampling(self, bottom, feature_map_size):
# feature_map_size: int [h,w]
return tf.image.resize_bilinear(bottom, size=feature_map_size)
