如何在 Tensorflow Serving 中进行批处理？

部署 Tensorflow Serving 并运行 Inception-V3 测试。工作正常。

现在，想要为 Inception-V3 进行批处理。 例如。希望发送 10 张图像（而不是一张）进行预测。

怎么做？要更新哪些文件（inception_saved_model.py 或 inception_client.py）？这些更新是什么样的？图像如何传递到服务 - 是作为包含图像的文件夹传递还是如何传递？

感谢对此问题的一些见解。任何与此相关的代码片段都将非常有帮助。

===================================

更新了 inception_client.py

# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

#!/usr/bin/env python2.7

"""Send JPEG image to tensorflow_model_server loaded with inception model.
"""

from __future__ import print_function

"""Send JPEG image to tensorflow_model_server loaded with inception model.
"""

from __future__ import print_function

# This is a placeholder for a Google-internal import.

from grpc.beta import implementations
import tensorflow as tf
from tensorflow.python.platform import flags
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2


tf.app.flags.DEFINE_string('server', 'localhost:9000',
                            'PredictionService host:port')
tf.app.flags.DEFINE_string('image', '', 'path to image in JPEG format')
FLAGS = tf.app.flags.FLAGS


def main(_):
   host, port = FLAGS.server.split(':')
   channel = implementations.insecure_channel(host, int(port))
   stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
   # Send request
   #with open(FLAGS.image, 'rb') as f:
     # See prediction_service.proto for gRPC request/response details.
     #data = f.read()
     #request = predict_pb2.PredictRequest()
     #request.model_spec.name = 'inception'
     #request.model_spec.signature_name = 'predict_images'


 #    request.inputs['images'].CopyFrom(
 #        tf.contrib.util.make_tensor_proto(data, shape=[1]))
 #    result = stub.Predict(request, 10.0)  # 10 secs timeout
 #    print(result)


# Build a batch of images

    request = predict_pb2.PredictRequest()
 request.model_spec.name = 'inception'
 request.model_spec.signature_name = 'predict_images'
  
  image_data = []
  for image in FLAGS.image.split(','):
   with open(image, 'rb') as f:
     image_data.append(f.read())
  
  request.inputs['images'].CopyFrom(
      tf.contrib.util.make_tensor_proto(image_data, shape=[len(image_data)]))
  
  result = stub.Predict(request, 10.0)  # 10 secs timeout
  print(result)
 if __name__ == '__main__':
   tf.app.run()

您应该能够通过对请求构造代码进行少量更改来计算一批图像的预测inception_client.py。该文件中的以下几行创建一个带有包含单个图像的“批次”的请求（注意shape=[1]，这意味着“长度为 1 的向量”）：

with open(FLAGS.image, 'rb') as f:
  # See prediction_service.proto for gRPC request/response details.
  data = f.read()
  request = predict_pb2.PredictRequest()
  request.model_spec.name = 'inception'
  request.model_spec.signature_name = 'predict_images'
  request.inputs['images'].CopyFrom(
      tf.contrib.util.make_tensor_proto(data, shape=[1]))
  result = stub.Predict(request, 10.0)  # 10 secs timeout
  print(result)

您可以在同一向量中传递更多图像来对一批数据运行预测。例如，如果FLAGS.image是逗号分隔的文件名列表：

request = predict_pb2.PredictRequest()
request.model_spec.name = 'inception'
request.model_spec.signature_name = 'predict_images'

# Build a batch of images.
image_data = []
for image in FLAGS.image.split(','):
  with open(image, 'rb') as f:
    image_data.append(f.read())

request.inputs['images'].CopyFrom(
    tf.contrib.util.make_tensor_proto(image_data, shape=[len(image_data)]))

result = stub.Predict(request, 10.0)  # 10 secs timeout
print(result)

 if __name__ == '__main__':
   tf.app.run()