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model.py
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model.py
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import os
import numpy as np
import tensorflow as tf
slim=tf.contrib.slim
from config import *
from tensorflow.python.ops import embedding_ops
from tensorflow.python.layers.core import Dense
from tensorflow.contrib import rnn
from tensorflow.python.framework import graph_util
from tensorflow.python.ops import math_ops
class Model(object):
def __init__(self,sess,param):
self.step = 0
self.__session = sess
self.is_training=True
self.__learn_rate = param["learn_rate"]
self.__learn_rate=param["learn_rate"]
self.__max_to_keep=param["max_to_keep"]
self.__checkPoint_dir = param["checkPoint_dir"]
self.__restore = param["b_restore"]
self.__mode= param["mode"]
self.is_training=True
self.__batch_size = param["batch_size"]
if self.__mode is "savaPb" :
self.__batch_size = 1
################ Building graph
with self.__session.as_default():
self.build_model()
###############参数初始化，或者读入参数
with self.__session.as_default():
self.init_op.run()
self.__saver = tf.train.Saver(tf.global_variables(), max_to_keep=self.__max_to_keep)
# Loading last save if needed
if self.__restore:
ckpt = tf.train.latest_checkpoint(self.__checkPoint_dir)
if ckpt:
self.step = int(ckpt.split('-')[1])
self.__saver.restore(self.__session, ckpt)
print('Restoring from epoch:{}'.format( self.step))
self.step+=1
def build_model(self):
def SegmentNet(input, scope, is_training, reuse=None):
with tf.variable_scope(scope, reuse=reuse):
with slim.arg_scope([slim.conv2d],
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm):
net = slim.conv2d(input, 32, [5, 5],scope='conv1')
net = slim.conv2d(net, 32, [5, 5], scope='conv2')
net=slim.max_pool2d(net,[2,2],[2,2],scope='pool1')
net = slim.conv2d(net, 64, [5, 5],scope='conv3')
net = slim.conv2d(net, 64, [5, 5], scope='conv4')
net = slim.conv2d(net, 64, [5, 5], scope='conv5')
net=slim.max_pool2d(net,[2,2],[2,2],scope='pool2')
net = slim.conv2d(net, 64, [5, 5],scope='conv6')
net = slim.conv2d(net, 64, [5, 5], scope='conv7')
net = slim.conv2d(net, 64, [5, 5],scope='conv8')
net = slim.conv2d(net, 64, [5, 5], scope='conv9')
net=slim.max_pool2d(net,[2,2],[2,2],scope='pool3')
net = slim.conv2d(net, 1024, [15, 15], scope='conv10')
features=net
net = slim.conv2d(net, 1, [1, 1],activation_fn=None, scope='conv11')
logits_pixel=net
net=tf.sigmoid(net, name=None)
mask=net
return features,logits_pixel,mask
def DecisionNet(feature,mask, scope, is_training,num_classes=2, reuse=None):
with tf.variable_scope(scope, reuse=reuse):
with slim.arg_scope([slim.conv2d],
padding='SAME',
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm):
net=tf.concat([feature,mask],axis=3)
net = slim.max_pool2d(net, [2, 2], [2, 2], scope='pool1')
net = slim.conv2d(net, 8, [5, 5], scope='conv1')
net = slim.max_pool2d(net, [2, 2], [2, 2], scope='pool2')
net = slim.conv2d(net, 16, [5, 5], scope='conv2')
net = slim.max_pool2d(net, [2, 2], [2, 2], scope='pool3')
net = slim.conv2d(net, 32, [5, 5], scope='conv3')
vector1=math_ops.reduce_mean(net,[1,2],name='pool4', keepdims=True)
vector2=math_ops.reduce_max(net,[1,2],name='pool5', keepdims=True)
vector3=math_ops.reduce_mean(mask,[1,2],name='pool6', keepdims=True)
vector4=math_ops.reduce_max(mask,[1,2],name='pool7', keepdims=True)
vector=tf.concat([vector1,vector2,vector3,vector4],axis=3)
vector=tf.squeeze(vector,axis=[1,2])
logits = slim.fully_connected(vector, num_classes,activation_fn=None)
output=tf.argmax(logits,axis=1)
return logits,output
Image = tf.placeholder(tf.float32, shape=(self.__batch_size, IMAGE_SIZE[0],IMAGE_SIZE[1], 1), name='Image')
PixelLabel=tf.placeholder(tf.float32, shape=(self.__batch_size, IMAGE_SIZE[0]/8,IMAGE_SIZE[1]/8, 1), name='PixelLabel')
Label = tf.placeholder(tf.int32, shape=(self.__batch_size), name='Label')
features, logits_pixel, mask=SegmentNet(Image,'segment',self.is_training)
logits_class,output_class=DecisionNet(features,mask, 'decision', self.is_training)
#损失函数
logits_pixel=tf.reshape(logits_pixel,[self.__batch_size,-1])
PixelLabel_reshape=tf.reshape(PixelLabel,[self.__batch_size,-1])
loss_pixel = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_pixel, labels=PixelLabel_reshape))
loss_class = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_class,labels=Label))
loss_total=loss_pixel+loss_class
optimizer = tf.train.GradientDescentOptimizer(self.__learn_rate)
train_var_list = [v for v in tf.trainable_variables() ]
train_segment_var_list = [v for v in tf.trainable_variables() if 'segment' in v.name ]
train_decision_var_list = [v for v in tf.trainable_variables() if 'decision' in v.name]
optimize_segment = optimizer.minimize(loss_pixel,var_list=train_segment_var_list)
optimize_decision = optimizer.minimize(loss_class, var_list=train_decision_var_list)
optimize_total = optimizer.minimize(loss_total, var_list=train_var_list)
init_op=tf.global_variables_initializer()
self.Image=Image
self.PixelLabel = PixelLabel
self.Label = Label
self.features = features
self.mask = mask
self.logits_class=logits_class
self.output_class=output_class
self.loss_pixel = loss_pixel
self.loss_class = loss_class
self.loss_total = loss_total
self.optimize_segment = optimize_segment
self.optimize_decision = optimize_decision
self.optimize_total = optimize_total
self.init_op=init_op
def save(self):
self.__saver.save(
self.__session,
os.path.join(self.__checkPoint_dir, 'ckp'),
global_step=self.step
)
# def save_PbModel(self):
# output_name=self.__decoded.op.name
# #output_name = self.__decoded.name.split(":")[0]
# input1_name=self.__inputs.name.split(":")[0]
# input2_name = self.__seq_len.name.split(":")[0]
# print("模型保存为pb格式，输入节点name：{}，{},输出节点name: {}".format(input1_name,input2_name,output_name))
# #constant_graph = graph_util.convert_variables_to_constants(self.__session, self.__session.graph_def, [output_name])
# constant_graph=graph_util.convert_variables_to_constants(self.__session,self.__session.graph_def,["SparseToDense"])
# with tf.gfile.GFile(self.__model_path+'Model.pb', mode='wb') as f:
# f.write(constant_graph.SerializeToString())
#def PbModel(self):
# with gfile.FastGFile('Model.pb', 'rb') as f:
# graph_def = tf.GraphDef()
# graph_def.ParseFromString(f.read())
# sess.graph.as_default()
# tf.import_graph_def(graph_def, name='') # 导入计算图
# for i, n in enumerate(graph_def.node):
# print("Name of the node - %s" % n.name)
#
# # 输入
# input_x = sess.graph.get_tensor_by_name('Placeholder:0')
# input_seq_len = sess.graph.get_tensor_by_name('seq_len:0')
# # 输出
# op = sess.graph.get_tensor_by_name('SparseToDense:0')