如何将 tf.example 发送到 TensorFlow Serving gRPC 预测请求中

Question

我有 tf.example 形式的数据，并试图以预测形式（使用 gRPC）向保存的模型发出请求。我无法确定实现此目的的方法调用。

我从众所周知的汽车定价 DNN 回归模型（https://github.com/tensorflow/models/blob/master/samples/cookbook/regression/dnn_regression.py）开始，我已经通过 TF Serving 导出并安装了该模型码头集装箱

import grpc
import numpy as np
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc

stub = prediction_service_pb2_grpc.PredictionServiceStub(grpc.insecure_channel("localhost:8500"))

tf_ex = tf.train.Example(
    features=tf.train.Features(
        feature={
            'curb-weight': tf.train.Feature(float_list=tf.train.FloatList(value=[5.1])),
            'highway-mpg': tf.train.Feature(float_list=tf.train.FloatList(value=[3.3])),
            'body-style': tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"wagon"])),
            'make': tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"Honda"])),
        }
    )
)

request = predict_pb2.PredictRequest()
request.model_spec.name = "regressor_test"

# Tried this:
request.inputs['inputs'].CopyFrom(tf_ex)

# Also tried this:
request.inputs['inputs'].CopyFrom(tf.contrib.util.make_tensor_proto(tf_ex))

# This doesn't work either:
request.input.example_list.examples.extend(tf_ex)

# If it did work, I would like to inference on it like this:
result = self.stub.Predict(request, 10.0)

感谢您的任何建议

Answer 1

我假设您的 saveModel 有一个serving_input_receiver_fn作为string输入并解析为tf.Example. 将 SavedModel 与估算器结合使用

def serving_example_input_receiver_fn():
    serialized_tf_example = tf.placeholder(dtype=tf.string)
    receiver_tensors = {'inputs': serialized_tf_example}   
    features = tf.parse_example(serialized_tf_example, YOUR_EXAMPLE_SCHEMA)
    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

所以，serving_input_receiver_fn接受一个字符串，所以你必须SerializeToString你的tf.Example(). 此外，serving_input_receiver_fn其工作方式类似于input_fn训练，以批量方式将数据转储到模型中。

代码可能会更改为：

request = predict_pb2.PredictRequest()
request.model_spec.name = "regressor_test"
request.model_spec.signature_name = 'your method signature, check use saved_model_cli'
request.inputs['inputs'].CopyFrom(tf.make_tensor_proto([tf_ex.SerializeToString()], dtype=types_pb2.DT_STRING))