堆栈与线程的使用

if not USE_QUEUE_LOADING:
	print ("not use queue loading, just sequential loading...")
        train_input  = tf.placeholder(tf.float32, shape=(BATCH_SIZE, IMG_SIZE[0], IMG_SIZE[1], 1))
        train_gt = tf.placeholder(tf.float32, shape=(BATCH_SIZE, IMG_SIZE[0], IMG_SIZE[1], 1))
else:
	print ("use queue loading")
        train_input_single  = tf.placeholder(tf.float32, shape=(IMG_SIZE[0], IMG_SIZE[1], 1))#设置变量类型
	train_gt_single  = tf.placeholder(tf.float32, shape=(IMG_SIZE[0], IMG_SIZE[1], 1))
        #定义队列的容量，待传入对象的类型/尺寸
	q = tf.FIFOQueue(10000, [tf.float32, tf.float32], [[IMG_SIZE[0], IMG_SIZE[1], 1], [IMG_SIZE[0], IMG_SIZE[1], 1]])#先入先出队列
	enqueue_op = q.enqueue([train_input_single, train_gt_single])#一次入10000个块
        train_input, train_gt = q.dequeue_many(BATCH_SIZE)#每次出一个批的块
     

#一定要打乱train_list的顺序

if USE_QUEUE_LOADING:
     # create threads
	num_thread=20
	coord = tf.train.Coordinator()   #创建一个线程管理器（协调器）对象。
                for i in range(num_thread):
		length = len(train_list)//num_thread#每一个线程处理length长度的多个块
		t = threading.Thread(target=load_and_enqueue, args=(coord, train_list[i*length:(i+1)*length],enqueue_op, train_input_single, train_gt_single,  i, num_thread))
		threads.append(t)
		t.start()

#读取每一个线程内部的信息

#file_list=train_list[i*length:(i+1)*length]
def load_and_enqueue(coord,file_list,enqueue_op,train_input_single,train_gt_single,i,num_thread=1):
           length=len(file_list)
           count=0
           try:
                 while not coord.should_stop():
                         i = count%length
                         input_image=scipy.io.loadmat(file_list[i][1])('patch').reshape([img_size[0],img_size[1],1])
                         get_image =  scipy.io.loadmat(file_list[i][0])('patch').reshape([img_size[0],img_size[1],1])
                         sess.run(equeue_op,feed_dict={train_input_single:input_image,train_gt_single:gt_image})
                         count+=1
           expect Exception as e:
               print ("stopping...", idx, e)