try_demo.py

#!/usr/bin/env python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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# modification, are permitted provided that the following conditions
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import argparse
import queue
import sys
import uuid
from functools import partial

import numpy as np
import tritonclient.grpc as grpcclient
from tritonclient.utils import InferenceServerException

##
import time
import threading
###

FLAGS = None


class UserData:
    def __init__(self):
        self._completed_requests = queue.Queue()


# Define the callback function. Note the last two parameters should be
# result and error. InferenceServerClient would povide the results of an
# inference as grpcclient.InferResult in result. For successful
# inference, error will be None, otherwise it will be an object of
# tritonclientutils.InferenceServerException holding the error details
def callback(user_data, result, error):
    if error:
        user_data._completed_requests.put(error)
    else:
        user_data._completed_requests.put(result)


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 and it gRPC port. Default is localhost:8001.",
    # )
    parser.add_argument(
        "-u",
        "--url",
        type=str,
        required=False,
        default="10.199.14.151:8001",
        help="Inference server URL and it gRPC port. Default is localhost:8001.",
    )
    parser.add_argument(
        "-t",
        "--stream-timeout",
        type=float,
        required=False,
        default=None,
        help="Stream timeout in seconds. Default is None.",
    )
    # parser.add_argument(
    #     "-d",
    #     "--dyna",
    #     action="store_true",
    #     required=False,
    #     default=False,
    #     help="Assume dynamic sequence model",
    # )
    # parser.add_argument(
    #     "-o",
    #     "--offset",
    #     type=int,
    #     required=False,
    #     default=0,
    #     help="Add offset to sequence ID used",
    # )

    FLAGS = parser.parse_args()

    # # We use custom "sequence" models which take 1 input
    # # value. The output is the accumulated value of the inputs. See
    # # src/custom/sequence.
    # int_sequence_model_name = (
    #     "simple_dyna_sequence" if FLAGS.dyna else "simple_sequence"
    # )
    # string_sequence_model_name = (
    #     "simple_string_dyna_sequence" if FLAGS.dyna else "simple_sequence"
    # )
    model_name = 'ensemble_mllm'
    model_version = ""
    batch_size = 1
    # img_url = f"https://s3plus.sankuai.com/automl-pkgs/0000.jpeg"
    img_url = "/workdir/yanghandi/gradio_demo/static/0000.jpeg"
    # img_url = f"https://s3plus.sankuai.com/automl-pkgs/0003.jpeg"
    text = f"详细描述一下这张图片"
    sequence_id = 100
    int_sequence_id0 = sequence_id

    result_list = []
    user_data = UserData()

    # It is advisable to use client object within with..as clause
    # when sending streaming requests. This ensures the client
    # is closed when the block inside with exits.
    with grpcclient.InferenceServerClient(
        url=FLAGS.url, verbose=FLAGS.verbose
    ) as triton_client:
        try:
            # Establish stream
            triton_client.start_stream(
                callback=partial(callback, user_data),
                stream_timeout=FLAGS.stream_timeout,
            )

            # Create the tensor for INPUT
            inputs = []
            img_url_bytes = img_url.encode("utf-8")
            img_url_bytes = np.array(img_url_bytes, dtype=bytes)
            img_url_bytes = img_url_bytes.reshape([1, -1])

            inputs.append(grpcclient.InferInput('IMAGE_URL', img_url_bytes.shape, "BYTES"))
            inputs[0].set_data_from_numpy(img_url_bytes)

            text_bytes = text.encode("utf-8")
            text_bytes = np.array(text_bytes, dtype=bytes)
            text_bytes = text_bytes.reshape([1, -1])
            # text_input = np.expand_dims(text_bytes, axis=0)
            text_input = text_bytes

            inputs.append(grpcclient.InferInput('TEXT', text_input.shape, "BYTES"))
            inputs[1].set_data_from_numpy(text_input)

            outputs = []
            outputs.append(grpcclient.InferRequestedOutput("OUTPUT"))
            # Issue the asynchronous sequence inference.
            triton_client.async_stream_infer(
                model_name=model_name,
                inputs=inputs,
                outputs=outputs,
                request_id="{}".format(sequence_id),
                sequence_id=sequence_id,
                sequence_start=True,
                sequence_end=True,
            )

        except InferenceServerException as error:
            print(error)
            sys.exit(1)

        # Retrieve results...
        recv_count = 0

        #####

        ####

        while True:
             
            # if len(result_list) == 80:
            #     print("1")
            data_item = user_data._completed_requests.get()
            # try:
            #     data_item = user_data._completed_requests.get(timeout=5)
            # except Exception as e:
            #     print("queue wrong")
            #     break
            if type(data_item) == InferenceServerException:
                print('InferenceServerException: ', data_item)
                sys.exit(1)
            this_id = data_item.get_response().id.split("_")[0]
            if int(this_id) != int_sequence_id0:
                print("unexpected sequence id returned by the server: {}".format(this_id))
                sys.exit(1)

            result = data_item.as_numpy("OUTPUT")
            if len(result[0][0])==0:
                break

            result_list.append(data_item.as_numpy("OUTPUT"))

            recv_count = recv_count + 1
            result_str = ''.join([item[0][0].decode('utf-8') for item in result_list])
            print(f"{len(result_list)}: {result_str}")


        print("hd",result_str)
    print("PASS: Sequence")
    print("hd",result_str)