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@ -691,6 +691,10 @@ def load_inference_model(dirname,
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parameters were saved in a single binary
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file. If parameters were saved in separate
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files, set it as 'None'.
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pserver_endpoints(list|None): This only need by distributed inference.
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When use distributed look up table in training,
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We also need it in inference.The parameter is
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a list of pserver endpoints.
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Returns:
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tuple: The return of this function is a tuple with three elements:
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@ -709,12 +713,16 @@ def load_inference_model(dirname,
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exe = fluid.Executor(fluid.CPUPlace())
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path = "./infer_model"
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endpoints = ["127.0.0.1:2023","127.0.0.1:2024"]
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[inference_program, feed_target_names, fetch_targets] =
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fluid.io.load_inference_model(dirname=path, executor=exe)
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results = exe.run(inference_program,
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feed={feed_target_names[0]: tensor_img},
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fetch_list=fetch_targets)
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# if we need lookup table, we will use:
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fluid.io.load_inference_model(dirname=path, executor=exe, pserver_endpoints=endpoints)
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# In this exsample, the inference program was saved in the
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# "./infer_model/__model__" and parameters were saved in
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# separate files in ""./infer_model".
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