quantize_training.py

# 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.
# ==============================================================================
"""Quantize training for TensorFlow."""
from tensorflow.core.framework import graph_pb2
from tensorflow.python._pywrap_quantize_training import DoQuantizeTrainingOnGraphDefHelper
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export


# Migrated this python code from deprecated quantize_training.i
@deprecation.deprecated(
    None,
    "GraphDef quantized training rewriter is deprecated in the long term.")
@tf_export(v1=["train.do_quantize_training_on_graphdef"])
def do_quantize_training_on_graphdef(input_graph, num_bits):
  """A general quantization scheme is being developed in `tf.contrib.quantize`.

  Consider using that instead, though since it is in the tf.contrib namespace,
  it is not subject to backward compatibility guarantees.

  Args:
    input_graph: A `GraphDef`.
    num_bits: The number of bits for quantize training.

  Returns:
    The graph with quantize training done.
  """

  graph = graph_pb2.GraphDef()
  result_graph_string = DoQuantizeTrainingOnGraphDefHelper(
      input_graph.SerializeToString(), num_bits)

  graph.ParseFromString(result_graph_string)
  return graph