# Copyright 2015 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.
# ==============================================================================
r"""Converts checkpoint variables into Const ops in a standalone GraphDef file.

This script is designed to take a GraphDef proto, a SaverDef proto, and a set of
variable values stored in a checkpoint file, and output a GraphDef with all of
the variable ops converted into const ops containing the values of the
variables.

It's useful to do this when we need to load a single file in C++, especially in
environments like mobile or embedded where we may not have access to the
RestoreTensor ops and file loading calls that they rely on.

An example of command-line usage is:
bazel build tensorflow/python/tools:freeze_graph && \
bazel-bin/tensorflow/python/tools/freeze_graph \
--input_graph=some_graph_def.pb \
--input_checkpoint=model.ckpt-8361242 \
--output_graph=/tmp/frozen_graph.pb --output_node_names=softmax

You can also look at freeze_graph_test.py for an example of how to use it.

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from google.protobuf import text_format

from tensorflow.core.framework import graph_pb2
from tensorflow.core.protobuf import saver_pb2
from tensorflow.core.protobuf.meta_graph_pb2 import MetaGraphDef
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.client import session
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import importer
from tensorflow.python.platform import app
from tensorflow.python.platform import gfile
from tensorflow.python.saved_model import loader
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.tools import saved_model_utils
from tensorflow.python.training import saver as saver_lib

FLAGS = None


def freeze_graph_with_def_protos(input_graph_def,
                                 input_saver_def,
                                 input_checkpoint,
                                 output_node_names,
                                 restore_op_name,
                                 filename_tensor_name,
                                 output_graph,
                                 clear_devices,
                                 initializer_nodes,
                                 variable_names_blacklist="",
                                 input_meta_graph_def=None,
                                 input_saved_model_dir=None,
                                 saved_model_tags=None):
  """Converts all variables in a graph and checkpoint into constants."""
  del restore_op_name, filename_tensor_name  # Unused by updated loading code.

  # 'input_checkpoint' may be a prefix if we're using Saver V2 format
  if (not input_saved_model_dir and
      not saver_lib.checkpoint_exists(input_checkpoint)):
    print("Input checkpoint '" + input_checkpoint + "' doesn't exist!")
    return -1

  if not output_node_names:
    print("You need to supply the name of a node to --output_node_names.")
    return -1

  # Remove all the explicit device specifications for this node. This helps to
  # make the graph more portable.
  if clear_devices:
    if input_meta_graph_def:
      for node in input_meta_graph_def.graph_def.node:
        node.device = ""
    elif input_graph_def:
      for node in input_graph_def.node:
        node.device = ""

  if input_graph_def:
    _ = importer.import_graph_def(input_graph_def, name="")
  with session.Session() as sess:
    if input_saver_def:
      saver = saver_lib.Saver(saver_def=input_saver_def)
      saver.restore(sess, input_checkpoint)
    elif input_meta_graph_def:
      restorer = saver_lib.import_meta_graph(
          input_meta_graph_def, clear_devices=True)
      restorer.restore(sess, input_checkpoint)
      if initializer_nodes:
        sess.run(initializer_nodes.split(","))
    elif input_saved_model_dir:
      if saved_model_tags is None:
        saved_model_tags = []
      loader.load(sess, saved_model_tags, input_saved_model_dir)
    else:
      var_list = {}
      reader = pywrap_tensorflow.NewCheckpointReader(input_checkpoint)
      var_to_shape_map = reader.get_variable_to_shape_map()
      for key in var_to_shape_map:
        try:
          tensor = sess.graph.get_tensor_by_name(key + ":0")
        except KeyError:
          # This tensor doesn't exist in the graph (for example it's
          # 'global_step' or a similar housekeeping element) so skip it.
          continue
        var_list[key] = tensor
      saver = saver_lib.Saver(var_list=var_list)
      saver.restore(sess, input_checkpoint)
      if initializer_nodes:
        sess.run(initializer_nodes.split(","))

    variable_names_blacklist = (variable_names_blacklist.split(",")
                                if variable_names_blacklist else None)

    if input_meta_graph_def:
      output_graph_def = graph_util.convert_variables_to_constants(
          sess,
          input_meta_graph_def.graph_def,
          output_node_names.split(","),
          variable_names_blacklist=variable_names_blacklist)
    else:
      output_graph_def = graph_util.convert_variables_to_constants(
          sess,
          input_graph_def,
          output_node_names.split(","),
          variable_names_blacklist=variable_names_blacklist)

  # Write GraphDef to file if output path has been given.
  if output_graph:
    with gfile.GFile(output_graph, "wb") as f:
      f.write(output_graph_def.SerializeToString())

  return output_graph_def


def _parse_input_graph_proto(input_graph, input_binary):
  """Parser input tensorflow graph into GraphDef proto."""
  if not gfile.Exists(input_graph):
    print("Input graph file '" + input_graph + "' does not exist!")
    return -1
  input_graph_def = graph_pb2.GraphDef()
  mode = "rb" if input_binary else "r"
  with gfile.FastGFile(input_graph, mode) as f:
    if input_binary:
      input_graph_def.ParseFromString(f.read())
    else:
      text_format.Merge(f.read(), input_graph_def)
  return input_graph_def


def _parse_input_meta_graph_proto(input_graph, input_binary):
  """Parser input tensorflow graph into MetaGraphDef proto."""
  if not gfile.Exists(input_graph):
    print("Input meta graph file '" + input_graph + "' does not exist!")