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wengbenjue/optimized_tf_serving_client.py

Forked from masroorhasan/optimized_tf_serving_client.py
Created July 7, 2019 03:29
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  1. @masroorhasan masroorhasan renamed this gist Feb 24, 2019. 1 changed file with 0 additions and 0 deletions.
    0 optimized_tf_client.py → optimized_tf_serving_client.py
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    File renamed without changes.
  2. @masroorhasan masroorhasan created this gist Feb 24, 2019.
    48 changes: 48 additions & 0 deletions optimized_tf_client.py
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    Original file line number Diff line number Diff line change
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    #!/usr/bin/env python
    from __future__ import print_function
    import argparse
    import numpy as np
    import time
    tt = time.time()

    import cv2
    from grpc.beta import implementations

    from protos.tensorflow.core.framework import tensor_pb2
    from protos.tensorflow.core.framework import tensor_shape_pb2
    from protos.tensorflow.core.framework import types_pb2
    from protos.tensorflow_serving.apis import predict_pb2
    from protos.tensorflow_serving.apis import prediction_service_pb2

    parser = argparse.ArgumentParser(description='incetion grpc client flags.')
    parser.add_argument('--host', default='0.0.0.0', help='inception serving host')
    parser.add_argument('--port', default='9000', help='inception serving port')
    parser.add_argument('--image', default='', help='path to JPEG image file')
    FLAGS = parser.parse_args()

    def main():
    # create prediction service client stub
    channel = implementations.insecure_channel(FLAGS.host, int(FLAGS.port))
    stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)

    # create request
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'resnet'
    request.model_spec.signature_name = 'serving_default'

    # read image into numpy array
    img = cv2.imread(FLAGS.image).astype(np.float32)

    # convert to tensor proto and make request
    # shape is in NHWC (num_samples x height x width x channels) format
    dims = [tensor_shape_pb2.TensorShapeProto.Dim(size=dim) for dim in [1]+list(img.shape)]
    tensor = tensor_pb2.TensorProto(
    dtype=types_pb2.DT_FLOAT,
    tensor_shape=tensor_shape_pb2.TensorShapeProto(dim=dims),
    float_val=list(img.reshape(-1)))
    request.inputs['input'].CopyFrom(tensor)
    resp = stub.Predict(request, 30.0)
    print('total time: {}s'.format(time.time() - tt))

    if __name__ == '__main__':
    main()