将常量永久注入 Tensorflow 图以进行推理 [英] Permanently Inject Constant into Tensorflow Graph for Inference

问题描述

我训练一个带有 is_training 占位符的模型:

I train a model with a placeholder for is_training:

is_training_ph = tf.placeholder(tf.bool)

但是，一旦完成训练和验证，我想为此值永久注入 false 常量，然后重新优化"图形(即使用 optimize_for_inference>code.有没有类似于 freeze>freeze/code> 会这样做吗?

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?

推荐答案

一种可能性是使用 tf.import_graph_def() 函数及其 input_map 参数以重写图中该张量的值.例如，您可以按如下方式构建您的程序:

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()

构建temp_graph_def后，您可以将其用作freeze_graph的输入.

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

另一种可能与 freeze_graph 和 optimize_for_inference 脚本(对变量名称和检查点键进行假设)更兼容的替代方法是修改 TensorFlow 的 graph_util.convert_variables_to_constants() 函数，以便它转换占位符:

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

...然后你可以像上面那样构建 training_graph_def，然后写:

...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})

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