simple_http_sequence_sync_infer_client.py

#!/usr/bin/env python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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import argparse
import queue
import sys
import uuid

import numpy as np
import tritonclient.http as httpclient
from tritonclient.utils import InferenceServerException

FLAGS = None


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


def sync_send(
    triton_client,
    result_list,
    values,
    batch_size,
    sequence_id,
    model_name,
    model_version,
):
    count = 1
    for value in values:
        # Create the tensor for INPUT
        value_data = np.full(shape=[batch_size, 1], fill_value=value, dtype=np.int32)
        inputs = []
        inputs.append(httpclient.InferInput("INPUT", value_data.shape, "INT32"))
        # Initialize the data
        inputs[0].set_data_from_numpy(value_data)
        outputs = []
        outputs.append(httpclient.InferRequestedOutput("OUTPUT"))
        # Issue the synchronous sequence inference.
        result = triton_client.infer(
            model_name=model_name,
            inputs=inputs,
            outputs=outputs,
            sequence_id=sequence_id,
            sequence_start=(count == 1),
            sequence_end=(count == len(values)),
        )
        result_list.append(result.as_numpy("OUTPUT"))
        count = count + 1


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:8000",
        help="Inference server URL and it HTTP port. Default is localhost:8000.",
    )
    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()

    try:
        triton_client = httpclient.InferenceServerClient(
            url=FLAGS.url, verbose=FLAGS.verbose
        )
    except Exception as e:
        print("context creation failed: " + str(e))
        sys.exit()

    # 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_version = ""
    batch_size = 1

    values = [11, 7, 5, 3, 2, 0, 1]

    # Will use two sequences and send them synchronously. Note the
    # sequence IDs should be non-zero because zero is reserved for
    # non-sequence requests.
    int_sequence_id0 = 1000 + FLAGS.offset * 2
    int_sequence_id1 = 1001 + FLAGS.offset * 2

    # For string sequence IDs, the dyna backend requires that the
    # sequence id be decodable into an integer, otherwise we'll use
    # a UUID4 sequence id and a model that doesn't require corrid
    # control.
    string_sequence_id0 = str(1002) if FLAGS.dyna else str(uuid.uuid4())

    int_result0_list = []
    int_result1_list = []

    string_result0_list = []

    user_data = UserData()

    try:
        sync_send(
            triton_client,
            int_result0_list,
            [0] + values,
            batch_size,
            int_sequence_id0,
            int_sequence_model_name,
            model_version,
        )
        sync_send(
            triton_client,
            int_result1_list,
            [100] + [-1 * val for val in values],
            batch_size,
            int_sequence_id1,
            int_sequence_model_name,
            model_version,
        )
        sync_send(
            triton_client,
            string_result0_list,
            [20] + [-1 * val for val in values],
            batch_size,
            string_sequence_id0,
            string_sequence_model_name,
            model_version,
        )
    except InferenceServerException as error:
        print(error)
        sys.exit(1)

    for i in range(len(int_result0_list)):
        int_seq0_expected = 1 if (i == 0) else values[i - 1]
        int_seq1_expected = 101 if (i == 0) else values[i - 1] * -1

        # For string sequence ID we are testing two different backends
        if i == 0 and FLAGS.dyna:
            string_seq0_expected = 20
        elif i == 0 and not FLAGS.dyna:
            string_seq0_expected = 21
        elif i != 0 and FLAGS.dyna:
            string_seq0_expected = values[i - 1] * -1 + int(
                string_result0_list[i - 1][0][0]
            )
        else:
            string_seq0_expected = values[i - 1] * -1

        # The dyna_sequence custom backend adds the correlation ID
        # to the last request in a sequence.
        if FLAGS.dyna and (i != 0) and (values[i - 1] == 1):
            int_seq0_expected += int_sequence_id0
            int_seq1_expected += int_sequence_id1
            string_seq0_expected += int(string_sequence_id0)

        print(
            "["
            + str(i)
            + "] "
            + str(int_result0_list[i][0][0])
            + " : "
            + str(int_result1_list[i][0][0])
            + " : "
            + str(string_result0_list[i][0][0])
        )

        if (
            (int_seq0_expected != int_result0_list[i][0][0])
            or (int_seq1_expected != int_result1_list[i][0][0])
            or (string_seq0_expected != string_result0_list[i][0][0])
        ):
            print(
                "[ expected ] "
                + str(int_seq0_expected)
                + " : "
                + str(int_seq1_expected)
                + " : "
                + str(string_seq0_expected)
            )
            sys.exit(1)

    print("PASS: Sequence")