将图形原型(pb/pbtxt)转换为SavedModel以在TensorFlow Serving或Cloud ML Engine中使用 [英] Convert a graph proto (pb/pbtxt) to a SavedModel for use in TensorFlow Serving or Cloud ML Engine

问题描述

我一直在关注 TensorFlow for Poets 2 我已经训练过的模型上的代码实验室，并用嵌入的权重创建了一个冻结的量化图.它被捕获在一个文件中-例如my_quant_graph.pb.

I've been following the TensorFlow for Poets 2 codelab on a model I've trained, and have created a frozen, quantized graph with embedded weights. It's captured in a single file - say my_quant_graph.pb.

因为我可以使用该图与 TensorFlow Android推理库进行推理很好，我以为我可以使用Cloud ML Engine做同样的事情，但似乎只适用于SavedModel模型.

Since I can use that graph for inference with the TensorFlow Android inference library just fine, I thought I could do the same with Cloud ML Engine, but it seems it only works on a SavedModel model.

如何简单地将冻结/量化的图形转换为单个PB文件以在ML引擎上使用?

How can I simply convert a frozen/quantized graph in a single pb file to use on ML engine?

推荐答案

事实证明，SavedModel提供了一些有关已保存图形的额外信息.假设冻结的图不需要资产，则只需要指定一个服务签名.

It turns out that a SavedModel provides some extra info around a saved graph. Assuming a frozen graph doesn't need assets, then it needs only a serving signature specified.

这是我运行的python代码，用于将图形转换为Cloud ML引擎接受的格式.注意，我只有一对输入/输出张量.

Here's the python code I ran to convert my graph to a format that Cloud ML engine accepted. Note I only have a single pair of input/output tensors.

import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants

export_dir = './saved'
graph_pb = 'my_quant_graph.pb'

builder = tf.saved_model.builder.SavedModelBuilder(export_dir)

with tf.gfile.GFile(graph_pb, "rb") as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())

sigs = {}

with tf.Session(graph=tf.Graph()) as sess:
    # name="" is important to ensure we don't get spurious prefixing
    tf.import_graph_def(graph_def, name="")
    g = tf.get_default_graph()
    inp = g.get_tensor_by_name("real_A_and_B_images:0")
    out = g.get_tensor_by_name("generator/Tanh:0")

    sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
        tf.saved_model.signature_def_utils.predict_signature_def(
            {"in": inp}, {"out": out})

    builder.add_meta_graph_and_variables(sess,
                                         [tag_constants.SERVING],
                                         signature_def_map=sigs)

builder.save()

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