主要前几天看到集成ｏｐｅｎｃｖ,发现前面的方式比较麻烦，现在就找资料研究，发现这个方式非常不错。
Tensorflow模型的graph结构可以保存为.pb文件或者.pbtxt文件，或者.meta文件，其中只有.pbtxt文件是可读的。
网上大牛们训练好的网络，将模型保存为一个统一的.pb文件，这个文件中不止保存着模型网络的结构和变量名，还保存了所有变量的值，如果我们想利用别人训练好的模型对自己的数据进行测试，往往要对这个模型做一些修改，这时我们经常需要知道原有模型里面的一些张量名称，但是.pb文件和.meta文件都是不可读的，所有有必要对这两种文件进行格式转换。
①.meta文件
这种情况下，通常还需要其他几个checkpoint文件，checkpoint ，model.cpkt.index，model.cpkt.data 等，可以使用tensofrflow安装目录下的 /home/zhaixingzhe/tensorflow/tensorflow/python/tools/inspect_checkpoint.py 文件打印输出模型中所有张量(tensor)和操作(op)的名称，下面是inspect_checkpoint.py的全部代码：
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A simple script for inspect checkpoint files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import numpy as np
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.platform import app
from tensorflow.python.platform import flags
FLAGS = None
def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors):
"""Prints tensors in a checkpoint file.
If no `tensor_name` is provided, prints the tensor names and shapes
in the checkpoint file.
If `tensor_name` is provided, prints the content of the tensor.
Args:
file_name: Name of the checkpoint file.
tensor_name: Name of the tensor in the checkpoint file to print.
all_tensors: Boolean indicating whether to print all tensors.
"""
try:
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
if all_tensors:
var_to_shape_map = reader.get_variable_to_shape_map()
for key in sorted(var_to_shape_map):
print("tensor_name: ", key)
print(reader.get_tensor(key))
elif not tensor_name:
print(reader.debug_string().decode("utf-8"))
else:
print("tensor_name: ", tensor_name)
print(reader.get_tensor(tensor_name))
except Exception as e: # pylint: disable=broad-except
print(str(e))
if "corrupted compressed block contents" in str(e):
print("It's likely that your checkpoint file has been compressed "
"with SNAPPY.")
if ("Data loss" in str(e) and
(any([e in file_name for e in [".index", ".meta", ".data"]]))):
proposed_file = ".".join(file_name.split(".")[0:-1])
v2_file_error_template = """
It's likely that this is a V2 checkpoint and you need to provide the filename
*prefix*. Try removing the '.' and extension. Try:
inspect checkpoint --file_name = {}"""
print(v2_file_error_template.format(proposed_file))
def parse_numpy_printoption(kv_str):
"""Sets a single numpy printoption from a string of the form 'x=y'.
See documentation on numpy.set_printoptions() for details about what values
x and y can take. x can be any option listed there other than 'formatter'.
Args:
kv_str: A string of the form 'x=y', such as 'threshold=100000'
Raises:
argparse.ArgumentTypeError: If the string couldn't be used to set any
nump printoption.
"""
k_v_str = kv_str.split("=", 1)
if len(k_v_str) != 2 or not k_v_str[0]:
raise argparse.ArgumentTypeError("'%s' is not in the form k=v." % kv_str)
k, v_str = k_v_str
printoptions = np.get_printoptions()
if k not in printoptions:
raise argparse.ArgumentTypeError("'%s' is not a valid printoption." % k)
v_type = type(printoptions[k])
if v_type is type(None):
raise argparse.ArgumentTypeError(
"Setting '%s' from the command line is not supported." % k)
try:
v = (v_type(v_str) if v_type is not bool
else flags.BooleanParser().parse(v_str))
except ValueError as e:
raise argparse.ArgumentTypeError(e.message)
np.set_printoptions(**{k: v})
def main(unused_argv):
if not FLAGS.file_name:
print("Usage: inspect_checkpoint --file_name=checkpoint_file_name "
"[--tensor_name=tensor_to_print]")
sys.exit(1)
else:
print_tensors_in_checkpoint_file(FLAGS.file_name, FLAGS.tensor_name,
FLAGS.all_tensors)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--file_name", type=str, default="", help="Checkpoint filename. "
"Note, if using Checkpoint V2 format, file_name is the "
"shared prefix between all files in the checkpoint.")
parser.add_argument(
"--tensor_name",
type=str,
default="",
help="Name of the tensor to inspect")
parser.add_argument(
"--all_tensors",
nargs="?",
const=True,
type="bool",
default=False,
help="If True, print the values of all the tensors.")
parser.add_argument(
"--printoptions",
nargs="*",
type=parse_numpy_printoption,
help="Argument for numpy.set_printoptions(), in the form 'k=v'.")
FLAGS, unparsed = parser.parse_known_args()
app.run(main=main, argv=[sys.argv[0]] + unparsed)
②.pb文件
下面的代码定义了两个函数，可以实现.pb文件和.pbtxt文件之间的转换。
import tensorflow as tf
from tensorflow.python.platform import gfile
from google.protobuf import text_format
def convert_pb_to_pbtxt(filename):
with gfile.FastGFile(filename,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
tf.train.write_graph(graph_def, './', 'protobuf.pbtxt', as_text=True)
return
def convert_pbtxt_to_pb(filename):
"""Returns a `tf.GraphDef` proto representing the data in the given pbtxt file.
Args:
filename: The name of a file containing a GraphDef pbtxt (text-formatted
`tf.GraphDef` protocol buffer data).
"""
with tf.gfile.FastGFile(filename, 'r') as f:
graph_def = tf.GraphDef()
file_content = f.read()
# Merges the human-readable string in `file_content` into `graph_def`.
text_format.Merge(file_content, graph_def)
tf.train.write_graph( graph_def , './' , 'protobuf.pb' , as_text = False )
return
