Tensorflow 模型文件格式转换

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，往往会利用我上篇博客讲的方法，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，

参见我的下一篇博客《Tensorflow之迁移学习》，

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_importfrom __future__ import divisionfrom __future__ import print_functionimport argparseimport sysimport numpy as npfrom tensorflow.python import pywrap_tensorflowfrom tensorflow.python.platform import appfrom tensorflow.python.platform import flagsFLAGS = Nonedef 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 tffrom tensorflow.python.platform import gfilefrom google.protobuf import text_formatdef 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)  returndef 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