Permanently Inject Constant into Tensorflow Graph for Inference

Question

I train a model with a placeholder for is_training:

is_training_ph = tf.placeholder(tf.bool)

however once training and validation are done, I would like to permanently inject a constant of false in for this value and then "re-optimize" the graph (ie using optimize_for_inference). Is there something along the lines of freeze_graph that will do this?

Answer 1

One possibility is to use the tf.import_graph_def() function and its input_map argument to rewrite the value of that tensor in the graph. For example, you could structure your program as follows:

with tf.Graph().as_default() as training_graph:
  # Build model.
  is_training_ph = tf.placeholder(tf.bool, name="is_training")
  # ...
training_graph_def = training_graph.as_graph_def()

with tf.Graph().as_default() as temp_graph:
  tf.import_graph_def(training_graph_def,
                      input_map={is_training_ph.name: tf.constant(False)})
temp_graph_def = temp_graph.as_graph_def()

After building temp_graph_def, you can use it as the input to freeze_graph.

---

An alternative, which might be more compatible with the freeze_graph and optimize_for_inference scripts (which make assumptions about variable names and checkpoint keys) would be to modify TensorFlow's graph_util.convert_variables_to_constants() function so that it converts placeholders instead:

def convert_placeholders_to_constants(input_graph_def,
                                      placeholder_to_value_map):
  """Replaces placeholders in the given tf.GraphDef with constant values.

  Args:
    input_graph_def: GraphDef object holding the network.
    placeholder_to_value_map: A map from the names of placeholder tensors in
      `input_graph_def` to constant values.

  Returns:
    GraphDef containing a simplified version of the original.
  """

  output_graph_def = tf.GraphDef()

  for node in input_graph_def.node:
    output_node = tf.NodeDef()
    if node.op == "Placeholder" and node.name in placeholder_to_value_map:
      output_node.op = "Const"
      output_node.name = node.name
      dtype = node.attr["dtype"].type
      data = np.asarray(placeholder_to_value_map[node.name],
                        dtype=tf.as_dtype(dtype).as_numpy_dtype)
      output_node.attr["dtype"].type = dtype
      output_node.attr["value"].CopyFrom(tf.AttrValue(
          tensor=tf.contrib.util.make_tensor_proto(data,
                                                   dtype=dtype,
                                                   shape=data.shape)))
    else:
      output_node.CopyFrom(node)

    output_graph_def.node.extend([output_node])

  return output_graph_def

...then you could build training_graph_def as above, and write:

temp_graph_def = convert_placeholders_to_constants(training_graph_def,
                                                   {is_training_ph.op.name: False})