本文讲述如下问题：

1.如何定义list类型的placeholder？

2.如何将普通python函数包装成TensorFlow算子，加入到NN网络中？

具体见代码：

import tensorflow as tf
import numpy as npdef gen_tfrecords():with tf.python_io.TFRecordWriter(r"D:\my.tfrecords") as tf_writer:features = {}features['scale'] = tf.train.Feature(int64_list=tf.train.Int64List(value=[15]))xmin = []xmax = []ymin = []ymax = []for i in range(2):xmin.append(float(i))xmax.append(float(i+500))ymin.append(float(i))ymax.append(float(i+500))# 变长数据以list形式存储features['xmin'] = tf.train.Feature(float_list=tf.train.FloatList(value=xmin))features['xmax'] = tf.train.Feature(float_list=tf.train.FloatList(value=xmax))features['ymin'] = tf.train.Feature(float_list=tf.train.FloatList(value=ymin))features['ymax'] = tf.train.Feature(float_list=tf.train.FloatList(value=ymax))tf_features = tf.train.Features(feature=features)tf_example = tf.train.Example(features=tf_features)tf_serialized = tf_example.SerializeToString()tf_writer.write(tf_serialized)
gen_tfrecords()
def parse_tf(example_proto):dics = {}#定长数据解析dics['scale'] = tf.FixedLenFeature(shape=[], dtype=tf.int64)#列表数据解析dics['xmin'] = tf.VarLenFeature(tf.float32)dics['xmax'] = tf.VarLenFeature(tf.float32)dics['ymin'] = tf.VarLenFeature(tf.float32)dics['ymax'] = tf.VarLenFeature(tf.float32)parse_example = tf.parse_single_example(serialized=example_proto,features=dics)xmin = parse_example['xmin']xmax = parse_example['xmax']ymin = parse_example['ymin']ymax = parse_example['ymax']scale = parse_example['scale']return scale,xmin,xmax,ymin,ymaxdef scale_image(scale):w = 10h = 10w = w*scaleh = h*scalereturn w,h
def scale_image2(scale):w = 10h = 10w = w*scaleh = h*scalelst = [w,h]#如果想要返回一个list，需要将其封装为一个ndarrayreturn np.array(lst)
def image_s(scale):w = 10h = 10w = w*scaleh = h*scalereturn w*hdef calc_image_s(xmin,xmax,ymin,ymax):ss = []for i in range(len(xmin)):s = (xmax[i]-xmin[i])*(ymax[i]-ymin[i])ss.append(s)return np.array(ss)scale_p = tf.placeholder(dtype=tf.int64)
#如果placeholder的shape不写，则可表示各种类型的数据，这里可用于表示list类型的数据
x_min_p = tf.placeholder(dtype=tf.float32)
x_max_p = tf.placeholder(dtype=tf.float32)
y_min_p = tf.placeholder(dtype=tf.float32)
y_max_p = tf.placeholder(dtype=tf.float32)#tf.py_func用来将不同python函数包裹成TensorFlow算子，返回值是tensor，Tout表示函数返回值的类型，单个返回值不用[]，多个返回值，要用[]
nw,nh = tf.py_func(scale_image,inp=[scale_p],Tout=[tf.int64,tf.int64])
nw_nh = tf.py_func(scale_image2,inp=[scale_p],Tout=tf.int64)
s = tf.py_func(image_s,inp=[scale_p],Tout=tf.int64)
ss= tf.py_func(calc_image_s,inp=[x_min_p,x_max_p,y_min_p,y_max_p],Tout=tf.float32)two = tf.constant(value=2,dtype=tf.float32)
s2 = tf.multiply(ss,two)dataset = tf.data.TFRecordDataset(r"D:\my.tfrecords")
dataset = dataset.map(parse_tf).batch(1).repeat(1)iterator = dataset.make_one_shot_iterator()next_element = iterator.get_next()
with tf.Session() as session:scale, xmin, xmax, ymin, ymax = session.run(fetches=next_element)w,h = session.run(fetches=[nw,nh],feed_dict={scale_p:scale})print(w,h)w_h = session.run(fetches=[nw_nh], feed_dict={scale_p: scale})print(w_h)s1 = session.run(fetches=[s], feed_dict={scale_p: scale})print(s1)s1 = session.run(fetches=[ss], feed_dict={x_min_p:xmin.values,x_max_p:xmax.values,y_min_p:ymin.values,y_max_p:ymax.values})print(s1)s22 = session.run(fetches=[s2], feed_dict={x_min_p: xmin.values, x_max_p: xmax.values, y_min_p: ymin.values, y_max_p: ymax.values})print(s22)

结果如下：

[150] [150]
[array([[150],[150]], dtype=int64)]
[array([22500], dtype=int64)]
[array([250000., 250000.], dtype=float32)]
[array([500000., 500000.], dtype=float32)]

定义新的op时会用到该方法，据官网介绍，这种做法，目前不支持分布式与模型保存。但是对于辅助op，基本上够用了，例如faster r-cnn中anchor的生成与RPN训练时label的生成。

tf.py_func 要求包裹的函数，输入输出均为ndarray