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model.py
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model.py
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import tensorflow as tf
import tensorflow.contrib as tc
import tensorflow.contrib.layers as tcl
from rbox_functions import *
class VGG16(object):
def __init__(self, prior_num, para_num, cls_num, is_fpn, use_third_layer, BATCH_SIZE):
self.name = 'VGG16'
self.eps = 1e-10
self.scale = 20.0
self.para_num = para_num
self.cls_num = cls_num
self.prior_num = prior_num
self.cls_out = cls_num + 1
self.loc_output_num = prior_num[-1] * para_num
self.conf_output_num = prior_num[-1] * (cls_num + 1)
self.fpn = is_fpn
self.use_third_layer = use_third_layer
self.BATCH_SIZE = BATCH_SIZE
def __call__(self, inputs, reuse=False):
with tf.variable_scope(self.name) as scope:
if reuse:
scope.reuse_variables()
c1_1 = tcl.conv2d(inputs, num_outputs=64, kernel_size=3, stride=1, padding='SAME', scope='conv1_1')
c1_2 = tcl.conv2d(c1_1, num_outputs=64, kernel_size=3, stride=1, padding='SAME', scope='conv1_2')
p1 = tcl.max_pool2d(inputs=c1_2, kernel_size=2, stride=2, padding='SAME')
c2_1 = tcl.conv2d(p1, num_outputs=128, kernel_size=3, stride=1, padding='SAME', scope='conv2_1')
c2_2 = tcl.conv2d(c2_1, num_outputs=128, kernel_size=3, stride=1, padding='SAME', scope='conv2_2')
p2 = tcl.max_pool2d(inputs=c2_2, kernel_size=2, stride=2, padding='SAME')
c3_1 = tcl.conv2d(p2, num_outputs=256, kernel_size=3, stride=1, padding='SAME', scope='conv3_1')
c3_2 = tcl.conv2d(c3_1, num_outputs=256, kernel_size=3, stride=1, padding='SAME', scope='conv3_2')
c3_3 = tcl.conv2d(c3_2, num_outputs=256, kernel_size=3, stride=1, padding='SAME', scope='conv3_3')
p3 = tcl.max_pool2d(inputs=c3_3, kernel_size=2, stride=2, padding='SAME')
c4_1 = tcl.conv2d(p3, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv4_1')
c4_2 = tcl.conv2d(c4_1, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv4_2')
c4_3 = tcl.conv2d(c4_2, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv4_3')
if self.fpn:
p4 = tcl.max_pool2d(inputs=c4_3, kernel_size=2, stride=2, padding='SAME')
c5_1 = tcl.conv2d(p4, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv5_1')
c5_2 = tcl.conv2d(c5_1, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv5_2')
c5_3 = tcl.conv2d(c5_2, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv5_3')
c4_3_conv = tcl.conv2d(c4_3, num_outputs=256, kernel_size=1, stride=1, padding='SAME',weights_initializer=tf.random_normal_initializer(0, 0.1),biases_initializer=tf.constant_initializer(0.1), scope='conv43_conv')
c5_3_conv = tcl.conv2d(c5_3, num_outputs=256, kernel_size=1, stride=1, padding='SAME',weights_initializer=tf.random_normal_initializer(0, 0.1), biases_initializer=tf.constant_initializer(0.1), scope='conv53_conv')
# c5_3_up = tcl.conv2d_transpose(c5_3, num_outputs=512, kernel_size=3, stride=2, padding='SAME', weights_initializer = tf.random_normal_initializer(0,0.1), biases_initializer=tf.constant_initializer(0.1), scope='conv53_up')
c4_3_shape = tf.shape(c4_3_conv)
c5_3_up = tf.image.resize_images(c5_3_conv, c4_3_shape[1:3], method=0)
c4_3_new = tf.add(c4_3_conv, c5_3_up)
o4 = self.normalize_channel(c4_3_new)
loc4 = tcl.conv2d(o4, num_outputs=self.prior_num[1] * self.para_num, kernel_size=3, stride=1,padding='SAME', activation_fn=None, scope='loc4')
conf4 = tcl.conv2d(o4, num_outputs=self.prior_num[1] * self.cls_out, kernel_size=3, stride=1,padding='SAME', activation_fn=None, scope='conf4')
if self.use_third_layer:
c3_3_conv = tcl.conv2d(c3_3, num_outputs=256, kernel_size=1, stride=1, padding='SAME',weights_initializer=tf.random_normal_initializer(0, 0.1),biases_initializer=tf.constant_initializer(0.1), scope='conv33_conv')
c3_3_shape = tf.shape(c3_3_conv)
c4_3_up = tf.image.resize_images(c4_3_new, c3_3_shape[1:3], method=0)
c3_3_new = tf.add(c3_3_conv, c4_3_up)
o3 = self.normalize_channel(c3_3_new)
loc3 = tcl.conv2d(o3, num_outputs=self.prior_num[0] * self.para_num, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='loc3')
conf3 = tcl.conv2d(o3, num_outputs=self.prior_num[0] * self.cls_out, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='conf3')
for idx in range(self.BATCH_SIZE):
loc3_i = tf.slice(loc3, [idx, 0, 0, 0], [1, -1, -1, -1])
loc3_i_reshape = tf.reshape(loc3_i, [-1, self.para_num])
loc4_i = tf.slice(loc4, [idx, 0, 0, 0], [1, -1, -1, -1])
loc4_i_reshape = tf.reshape(loc4_i, [-1, self.para_num])
if idx == 0:
loc_reshape = tf.concat([loc3_i_reshape, loc4_i_reshape], axis=0)
else:
loc34_i_reshape = tf.concat([loc3_i_reshape, loc4_i_reshape], axis=0)
loc_reshape = tf.concat([loc_reshape, loc34_i_reshape], axis=0)
conf3_i = tf.slice(conf3, [idx, 0, 0, 0], [1, -1, -1, -1])
conf3_i_reshape = tf.reshape(conf3_i, [-1, self.cls_out])
conf4_i = tf.slice(conf4, [idx, 0, 0, 0], [1, -1, -1, -1])
conf4_i_reshape = tf.reshape(conf4_i, [-1, self.cls_out])
if idx == 0:
conf_reshape = tf.concat([conf3_i_reshape, conf4_i_reshape], axis=0)
else:
conf34_i_reshape = tf.concat([conf3_i_reshape, conf4_i_reshape], axis=0)
conf_reshape = tf.concat([conf_reshape, conf34_i_reshape], axis=0)
else:
loc_reshape = tf.reshape(loc4, [-1, self.para_num])
conf_reshape = tf.reshape(conf4, [-1, self.cls_out])
else:
if self.use_third_layer:
o3 = self.normalize_channel(c3_3)
loc3 = tcl.conv2d(o3, num_outputs=self.prior_num[0] * self.para_num, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='loc3')
conf3 = tcl.conv2d(o3, num_outputs=self.prior_num[0] * self.cls_out, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='conf3')
o4 = self.normalize_channel(c4_3)
loc4 = tcl.conv2d(o4, num_outputs=self.loc_output_num, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='loc4')
conf4 = tcl.conv2d(o4, num_outputs=self.conf_output_num, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='conf4')
for idx in range(self.BATCH_SIZE):
loc3_i = tf.slice(loc3, [idx, 0, 0, 0], [1, -1, -1, -1])
loc3_i_reshape = tf.reshape(loc3_i, [-1, self.para_num])
loc4_i = tf.slice(loc4, [idx, 0, 0, 0], [1, -1, -1, -1])
loc4_i_reshape = tf.reshape(loc4_i, [-1, self.para_num])
if idx == 0:
loc_reshape = tf.concat([loc3_i_reshape, loc4_i_reshape], axis=0)
else:
loc34_i_reshape = tf.concat([loc3_i_reshape, loc4_i_reshape], axis=0)
loc_reshape = tf.concat([loc_reshape, loc34_i_reshape], axis=0)
conf3_i = tf.slice(conf3, [idx, 0, 0, 0], [1, -1, -1, -1])
conf3_i_reshape = tf.reshape(conf3_i, [-1, self.cls_out])
conf4_i = tf.slice(conf4, [idx, 0, 0, 0], [1, -1, -1, -1])
conf4_i_reshape = tf.reshape(conf4_i, [-1, self.cls_out])
if idx == 0:
conf_reshape = tf.concat([conf3_i_reshape, conf4_i_reshape], axis=0)
else:
conf34_i_reshape = tf.concat([conf3_i_reshape, conf4_i_reshape], axis=0)
conf_reshape = tf.concat([conf_reshape, conf34_i_reshape], axis=0)
else:
o4 = self.normalize_channel(c4_3)
# p4 = tcl.max_pool2d(inputs=c4_3, kernel_size=2, stride=2, padding='SAME')
# c5_1 = tcl.conv2d(p4, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv5_1')
# c5_2 = tcl.conv2d(c5_1, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv5_2')
# c5_3 = tcl.conv2d(c5_2, num_outputs=512, kernel_size=3, stride=1, padding='SAME', scope='conv5_3')
# p5 = tcl.max_pool2d(inputs=c5_3, kernel_size=2, stride=2, padding='SAME')
# fc = tcl.flatten(p5)
# outputs = tcl.fully_connected(fc, num_outputs=21, activation_fn=None, scope='fc')
loc = tcl.conv2d(o4, num_outputs=self.loc_output_num, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='loc')
conf = tcl.conv2d(o4, num_outputs=self.conf_output_num, kernel_size=3, stride=1, padding='SAME', activation_fn=None, scope='conf')
loc_reshape = tf.reshape(loc, [-1, self.para_num])
conf_reshape = tf.reshape(conf, [-1, self.cls_num + 1])
return loc_reshape, conf_reshape
def normalize_channel(self, inputs):
norm_data = tf.square(inputs)
norm_data = tf.reduce_sum(norm_data, axis=3)
norm_data = tf.sqrt(norm_data + self.eps)
norm_data = tf.expand_dims(norm_data, axis=3)
outputs = self.scale * tf.divide(inputs, norm_data)
return outputs
def regular_loss(self, lamda):
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
loss = []
for var in var_list:
loss += [tcl.l2_regularizer(lamda)(var)]
return loss
@property
def vars(self):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
@property
def vars_train(self):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='trains')