grpc_explicit_int_content_client.py

#!/usr/bin/env python
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import argparse

import grpc
import numpy as np
from tritonclient.grpc import service_pb2, service_pb2_grpc

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-v",
        "--verbose",
        action="store_true",
        required=False,
        default=False,
        help="Enable verbose output",
    )
    parser.add_argument(
        "-u",
        "--url",
        type=str,
        required=False,
        default="localhost:8001",
        help="Inference server URL. Default is localhost:8001.",
    )

    FLAGS = parser.parse_args()

    # We use a simple model that takes 2 input tensors of 16 integers
    # each and returns 2 output tensors of 16 integers each. One
    # output tensor is the element-wise sum of the inputs and one
    # output is the element-wise difference.
    model_name = "simple"
    model_version = ""
    batch_size = 1

    # Create gRPC stub for communicating with the server
    channel = grpc.insecure_channel(FLAGS.url)
    grpc_stub = service_pb2_grpc.GRPCInferenceServiceStub(channel)

    # Generate the request
    request = service_pb2.ModelInferRequest()
    request.model_name = model_name
    request.model_version = model_version

    # Input data
    input0_data = [i for i in range(16)]
    input1_data = [1 for i in range(16)]

    # Populate the inputs in inference request
    input0 = service_pb2.ModelInferRequest().InferInputTensor()
    input0.name = "INPUT0"
    input0.datatype = "INT32"
    input0.shape.extend([1, 16])
    input0.contents.int_contents[:] = input0_data

    input1 = service_pb2.ModelInferRequest().InferInputTensor()
    input1.name = "INPUT1"
    input1.datatype = "INT32"
    input1.shape.extend([1, 16])
    input1.contents.int_contents[:] = input1_data
    request.inputs.extend([input0, input1])

    # Populate the outputs in the inference request
    output0 = service_pb2.ModelInferRequest().InferRequestedOutputTensor()
    output0.name = "OUTPUT0"

    output1 = service_pb2.ModelInferRequest().InferRequestedOutputTensor()
    output1.name = "OUTPUT1"
    request.outputs.extend([output0, output1])

    response = grpc_stub.ModelInfer(request)

    output_results = []
    index = 0
    for output in response.outputs:
        shape = []
        for value in output.shape:
            shape.append(value)
        output_results.append(
            np.frombuffer(response.raw_output_contents[index], dtype=np.int32)
        )
        output_results[-1] = np.resize(output_results[-1], shape)
        index += 1

    if len(output_results) != 2:
        print("expected two output results")
        sys.exit(1)

    for i in range(16):
        print(
            str(input0_data[i])
            + " + "
            + str(input1_data[i])
            + " = "
            + str(output_results[0][0][i])
        )
        print(
            str(input0_data[i])
            + " - "
            + str(input1_data[i])
            + " = "
            + str(output_results[1][0][i])
        )
        if (input0_data[i] + input1_data[i]) != output_results[0][0][i]:
            print("sync infer error: incorrect sum")
            sys.exit(1)
        if (input0_data[i] - input1_data[i]) != output_results[1][0][i]:
            print("sync infer error: incorrect difference")
            sys.exit(1)

    # Populating additional content field should generate an error
    request.raw_input_contents.extend([np.array(input0_data[0:8]).tobytes()])
    request.inputs[0].contents.int_contents[:] = input0_data[8:]

    try:
        response = grpc_stub.ModelInfer(request)
    except Exception as e:
        if (
            "contents field must not be specified when using "
            "raw_input_contents for 'INPUT0' for model 'simple'" in e.__str__()
        ):
            print("PASS: explicit int")