如何从已存在的检查点文件（cpkt文件）种解析出里面变量——无需重新创建原始计算图

import tensorflow as tf
import os

CheckpointReader

tf.train.NewCheckpointReader是一个创建检查点读取器（CheckpointReader）对象的完美手段。 CheckpointReader中有几个非常有用的方法：

get_variable_to_shape_map() - 提供具有变量名称和形状的字典
debug_string() - 提供由检查点文件中所有变量组成的字符串
has_tensor(var_name) - 允许检查变量是否存在于检查点中
get_tensor(var_name) - 返回变量名称的张量

为了便于说明，我将定义一个函数来检查路径的有效性，并为您加载检查点读取器。

In [3]:

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)

In [34]:

your_path = 'logs/squeezeDet1024x1024/train/model.ckpt-0'
reader = load_reader(your_path)

reader.debug_string()

用于返回包含以下内容的一个字符串：

variable name（变量名）
data type（数据类型）
tensor shape（张量类型）

它返回字符串的各元素间均用空格符' '分隔，你可以使用debug_string来创建一个变量名列表，如下所示：

In [53]:

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])

输出：

['iou', 'fire9/squeeze1x1/kernels', 'fire9/squeeze1x1/biases', 'fire9/expand3x3/kernels/Momentum']
['[10,36864]', '[1,1,512,64]', '[64]', '[3,3,64,256]']

但是，对于完成同样的任务，更好的方法是使用reader.get_variable_to_shape_map（）

reader.get_variable_to_shape_map()

用于返回包含所有变量及其形状名称的字典，变量作为字典的Key，形状作为Value。

In [66]:

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)

返回bool值

这是一种方便的方法，允许您检查ckeckpoint中是否存在相关的变量。

In [51]:

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+':', 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)

返回包含检查点的张量值的NumPy数组

正常使用方法是先恢复一个张量，然后用恢复的张量初始化你自己的变量：

In [60]:

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

In [67]:

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)