TensorFlow从CKPT和PB文件读取变量值的方法

from tensorflow.python import pywrap_tensorflow 
import tensorflow as tf
with tf.Graph().as_default(): 
 with tf.Session() as sess: 
 ckpt = tf.train.get_checkpoint_state('./model_ckpt') #保存ckpt文件的文件夹
 if ckpt and ckpt.model_checkpoint_path: 
 reader = pywrap_tensorflow.NewCheckpointReader('./model_ckpt/model.ckpt-999') #自己保存的ckpt文件名
 all_variables = reader.get_variable_to_shape_map() 
 w1 = reader.get_tensor("Variable_1") 
 print(w1.shape) 
 print(w1) 
 else: print('No checkpoint file found')

import tensorflow as tf
from tensorflow.python.framework import graph_util
from tensorflow.python.platform import gfile
import numpy as np
sess = tf.Session()
with gfile.FastGFile('Yourpb.pb', 'rb') as f: #自己保存的pb文件
 graph_def = tf.GraphDef()
 graph_def.ParseFromString(f.read())
 sess.graph.as_default()
 tf.import_graph_def(graph_def, name='') 
 print(sess.run('Variable_1:0'))

def load_reader(path):
 assert os.path.exists(path), "Provided incorrect path to the file. {} doesn't exist".format(path)
 return tf.train.NewCheckpointReader(path)

all_var_descriptions = reader.debug_string().split()
var_names, var_shapes = all_var[::3], all_var[2::3]
print(var_names[:4])
print(var_shapes[:4])

saved_shapes = reader.get_variable_to_shape_map()
print('fire9/squeeze1x1/kernels:', saved_shapes['fire9/squeeze1x1/kernels'])
fire9/squeeze1x1/kernels: [1, 1, 512, 64]
reader.has_tensor(var_name)

names_that_exit = {var_name: reader.has_tensor(var_name) for var_name in var_names[:10]}
for key in names_that_exit:
 print(key.decode()+':', names_that_exit[key])

fire8/squeeze1x1/kernels/Momentum: True
fire9/expand3x3/kernels: True
iou: True
fire9/expand3x3/biases: True
fire9/expand1x1/kernels: True
fire9/expand3x3/kernels/Momentum: True
fire9/expand1x1/biases/Momentum: True
fire9/squeeze1x1/biases: True
fire9/expand1x1/kernels/Momentum: True
fire9/squeeze1x1/kernels: True
reader.get_tensor(tensor_name)

def recover_var(reader, var_name):
 recovered_var = 'var to be recovered'
 try:
  recovered_var = reader.get_tensor(var_name)
 except:
  assert reader.has_tensor(var_name),\
  "{} variable doesn't exist in the check point. Please check the variable name".format(var_name)
 return recovered_var

checkpoint_var = recover_var(reader, 'conv1/kernels')
print ("Recovered variable has the following shape: \n", checkpoint_var.shape)
new_var = tf.Variable(initial_value=checkpoint_var, name="new_conv1")
print ("New variable will be initialized with recovered values and the following shape: \n", new_var.get_shape())

Recovered variable has the following shape: 
(3, 3, 3, 64)
New variable will be initialized with recovered values and the following shape: 
(3, 3, 3, 64)