tensorflow从.ckpt文件中读取任意变量的实现方法

import tensorflow as tf
import numpy as np
from tensorflow.python import pywrap_tensorflow
 
file_name = '/home/dl/projectBo/tf-faster-rcnn/data/imagenet_weights/vgg16.ckpt' #.ckpt的路径
name_variable_to_restore = 'vgg_16/fc7/weights' #要读取权重的变量名
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
var_to_shape_map = reader.get_variable_to_shape_map()
print('shape', var_to_shape_map[name_variable_to_restore]) #输出这个变量的尺寸
fc7_conv = tf.get_variable("fc7", var_to_shape_map[name_variable_to_restore], trainable=False) # 定义接收权重的变量名
restorer_fc = tf.train.Saver({name_variable_to_restore: fc7_conv }) #定义恢复变量的对象
sess = tf.Session()
sess.run(tf.variables_initializer([fc7_conv], name='init')) #必须初始化
restorer_fc.restore(sess, file_name) #恢复变量
print(sess.run(fc7_conv)) #输出结果

def fix_variables(self, sess, pretrained_model):
 print('Fix VGG16 layers..')
 with tf.variable_scope('Fix_VGG16') as scope:
  with tf.device("/cpu:0"):
   # fix the vgg16 issue from conv weights to fc weights
   # fix RGB to BGR
   fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False)      
   fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False)
   conv1_rgb = tf.get_variable("conv1_rgb", [3, 3, 3, 64], trainable=False)   #定义接收权重的变量，不可被训练
   restorer_fc = tf.train.Saver({self._scope + "/fc6/weights": fc6_conv, 
                  self._scope + "/fc7/weights": fc7_conv,
                  self._scope + "/conv1/conv1_1/weights": conv1_rgb}) #定义恢复变量的对象
   restorer_fc.restore(sess, pretrained_model) #恢复这些变量
 
   sess.run(tf.assign(self._variables_to_fix[self._scope + '/fc6/weights:0'], tf.reshape(fc6_conv, 
             self._variables_to_fix[self._scope + '/fc6/weights:0'].get_shape())))
   sess.run(tf.assign(self._variables_to_fix[self._scope + '/fc7/weights:0'], tf.reshape(fc7_conv, 
             self._variables_to_fix[self._scope + '/fc7/weights:0'].get_shape())))
   sess.run(tf.assign(self._variables_to_fix[self._scope + '/conv1/conv1_1/weights:0'], 
             tf.reverse(conv1_rgb, [2])))         #将vgg16中的fc6、fc7中的权重reshape赋给faster-rcnn中的fc6、fc7

from tensorflow.python import pywrap_tensorflow
 
model=VGG16()#此处构建vgg16模型
variables = tf.global_variables()#获取模型中所有变量
 
file_name='vgg16.ckpt'#vgg16网络模型
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
var_to_shape_map = reader.get_variable_to_shape_map()#获取ckpt模型中的变量名
print(var_to_shape_map)
 
sess=tf.Session()
 
my_scope='my/'#外加的空间名
variables_to_restore={}#构建字典：需要的变量和对应的模型变量的映射
for v in variables:
  if my_scope in v.name and v.name.split(':')[0].split(my_scope)[1] in var_to_shape_map:
    print('Variables restored: %s' % v.name)
    variables_to_restore[v.name.split(':0')[0][len(my_scope):]]=v
  elif v.name.split(':')[0] in var_to_shape_map:
    print('Variables restored: %s' % v.name)
    variables_to_restore[v.name]=v
 
restorer=tf.train.Saver(variables_to_restore)#将需要加载的变量作为参数输入
restorer.restore(sess, file_name)

fc6_conv = tf.get_variable("fc6_conv", [7, 7, 512, 4096], trainable=False)
fc7_conv = tf.get_variable("fc7_conv", [1, 1, 4096, 4096], trainable=False)
        
restorer_fc = tf.train.Saver({"vgg_16/fc6/weights": fc6_conv,
               "vgg_16/fc7/weights": fc7_conv,
               })
restorer_fc.restore(sess, pretrained_model)
sess.run(tf.assign(self._variables_to_fix['my/vgg_16/fc6/weights:0'], tf.reshape(fc6_conv,self._variables_to_fix['my/vgg_16/fc6/weights:0'].get_shape())))  
sess.run(tf.assign(self._variables_to_fix['my/vgg_16/fc7/weights:0'], tf.reshape(fc7_conv,self._variables_to_fix['my/vgg_16/fc7/weights:0'].get_shape())))