Question

我已经查看了stackoverflow上的几个帖子，并且已经使用了几天了，但是唉，我无法通过tensorflow服务正确地提供对象检测模型。

我访问了以下链接： How to properly serve an object detection model from Tensorflow Object Detection API?

和

https://github.com/tensorflow/tensorflow/issues/11863

这就是我所做的。

我已下载ssd_mobilenet_v1_coco_11_06_2017.tar.gz，其中包含以下文件：

frozen_inference_graph.pb
graph.pbtxt
model.ckpt.data-00000-of-00001
model.ckpt.index
model.ckpt.meta

使用以下脚本，我能够成功地将frozen_inference_graph.pb转换为SavedModel（在目录ssd_mobilenet_v1_coco_11_06_2017 /已保存）

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

# Specify version 1
export_dir = './saved/1'
graph_pb = 'frozen_inference_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()
    ipdb.set_trace()
    inp = g.get_tensor_by_name("image_tensor:0")
    outputs = {}
    outputs["detection_boxes"] = g.get_tensor_by_name('detection_boxes:0')
    outputs["detection_scores"] = g.get_tensor_by_name('detection_scores:0')
    outputs["detection_classes"] = g.get_tensor_by_name('detection_classes:0')
    outputs["num_detections"] = g.get_tensor_by_name('num_detections:0')

    output_tensor = tf.concat([tf.expand_dims(t, 0) for t in outputs], 0)
    # or use tf.gather??

    # 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": output_tensor} )

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

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

builder.save()

我收到以下错误：

bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server
--port=9000 --model_base_path=/serving/ssd_mobilenet_v1_coco_11_06_2017/saved

2017-09-17 22:33:21.325087: W tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:268] No versions of servable default found under base path /serving/ssd_mobilenet_v1_coco_11_06_2017/saved/1

我知道我需要一个客户端连接到服务器来进行预测。但是，我甚至无法正确地为模型服务。

Answer 1

您需要稍微更改原始帖子所做的导出签名。此脚本为您进行必要的更改：

 $OBJECT_DETECTION_CONFIG=object_detection/samples/configs/ssd_mobilenet_v1_pets.config 

$ python object_detection/export_inference_graph.py \ --input_type encoded_image_string_tensor \ --pipeline_config_path ${OBJECT_DETECTION_CONFIG} \ --trained_checkpoint_prefix ${YOUR_LOCAL_CHK_DIR}/model.ckpt-${CHECKPOINT_NUMBER} \ --output_directory ${YOUR_LOCAL_EXPORT_DIR}

有关该计划正在做什么的更多详细信息，请参阅：

https://cloud.google.com/blog/big-data/2017/09/performing-prediction-with-tensorflow-object-detection-models-on-google-cloud-machine-learning-engine

Tensorflow服务已保存模型ssd_mobilenet_v1_coco

1 个答案: