client.py

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from tritonclient.utils import *
import tritonclient.grpc as grpcclient
import tritonclient.http as httpclient

import numpy as np

# 调用入口为集成模型

model_name = "model_pipeline"

with httpclient.InferenceServerClient("localhost:8000") as client:
    input0_data = np.random.rand(3, 640, 640).astype(np.float32)
    inputs = [
        httpclient.InferInput("IMAGE", input0_data.shape, np_to_triton_dtype(input0_data.dtype))
    ]

    inputs[0].set_data_from_numpy(input0_data)

    outputs = [
        httpclient.InferRequestedOutput("CLASSIFICATION"),
        httpclient.InferRequestedOutput("BBOXES")
    ]

    response = client.infer(model_name,
                            inputs,
                            request_id=str(1),
                            outputs=outputs)
    result = response.get_response()
    print("OUTPUT0 ({})".format(response.as_numpy("CLASSIFICATION")))
    print("OUTPUT0 ({})".format(response.as_numpy("BBOXES")))