simple_http_string_infer_client.py

#!/usr/bin/env python
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import argparse
import numpy as np
import sys

import tritonclient.http as httpclient

# unicode() doesn't exist on python3, for how we use it the
# corresponding function is bytes()
if sys.version_info.major == 3:
    unicode = bytes


def simple_string_inference(triton_client):
    model_name = 'simple_string'

    inputs = []
    outputs = []
    inputs.append(httpclient.InferInput('INPUT0', [1, 16], "BYTES"))
    inputs.append(httpclient.InferInput('INPUT1', [1, 16], "BYTES"))

    # Create the data for the two input tensors. Initialize the first
    # to unique integers and the second to all ones.
    in0 = np.arange(start=0, stop=16, dtype=np.int32)
    in0 = np.expand_dims(in0, axis=0)
    in1 = np.ones(shape=(1, 16), dtype=np.int32)
    expected_sum = np.add(in0, in1)
    expected_diff = np.subtract(in0, in1)

    # The 'simple_string' model expects 2 BYTES tensors where each
    # element in those tensors is the utf-8 string representation of
    # an integer. The BYTES tensors must be represented by a numpy
    # array with dtype=np.object_.
    in0n = np.array([str(x).encode('utf-8') for x in in0.reshape(in0.size)],
                    dtype=np.object_)
    input0_data = in0n.reshape(in0.shape)
    in1n = np.array([str(x).encode('utf-8') for x in in1.reshape(in1.size)],
                    dtype=np.object_)
    input1_data = in1n.reshape(in1.shape)

    # Initialize the data
    inputs[0].set_data_from_numpy(input0_data, binary_data=True)
    inputs[1].set_data_from_numpy(input1_data, binary_data=False)

    outputs.append(httpclient.InferRequestedOutput('OUTPUT0', binary_data=True))
    outputs.append(httpclient.InferRequestedOutput('OUTPUT1',
                                                   binary_data=False))

    results = triton_client.infer(model_name=model_name,
                                  inputs=inputs,
                                  outputs=outputs)

    # Get the output arrays from the results
    output0_data = results.as_numpy('OUTPUT0')
    output1_data = results.as_numpy('OUTPUT1')

    for i in range(16):
        print(
            str(input0_data[0][i]) + " + " + str(input1_data[0][i]) + " = " +
            str(output0_data[0][i]))
        print(
            str(input0_data[0][i]) + " - " + str(input1_data[0][i]) + " = " +
            str(output1_data[0][i]))

        # Convert result from string to int to check result
        r0 = int(output0_data[0][i])
        r1 = int(output1_data[0][i])
        if expected_sum[0][i] != r0:
            print("error: incorrect sum")
            sys.exit(1)
        if expected_diff[0][i] != r1:
            print("error: incorrect difference")
            sys.exit(1)


def identity_inference(triton_client, np_array, binary_data):
    model_name = "simple_identity"
    inputs = []
    outputs = []

    inputs.append(httpclient.InferInput('INPUT0', np_array.shape, "BYTES"))
    inputs[0].set_data_from_numpy(np_array, binary_data=binary_data)

    outputs.append(
        httpclient.InferRequestedOutput('OUTPUT0', binary_data=binary_data))

    results = triton_client.infer(model_name=model_name,
                                  inputs=inputs,
                                  outputs=outputs)
    if (np_array.dtype == np.object_):
        print(results.as_numpy('OUTPUT0'))
        if binary_data:
            if not np.array_equal(np_array, results.as_numpy('OUTPUT0')):
                print(results.as_numpy('OUTPUT0'))
                print("error: incorrect output")
                sys.exit(1)
        else:
            expected_array = np.array([
                unicode(str(x), encoding='utf-8')
                for x in results.as_numpy('OUTPUT0').flatten()
            ],
                                      dtype=np.object_)
            expected_array = expected_array.reshape([1, 16])
            if not np.array_equal(np_array, expected_array):
                print(results.as_numpy('OUTPUT0'))
                print("error: incorrect output")
                sys.exit(1)
    else:
        encoded_results = np.char.encode(
            results.as_numpy('OUTPUT0').astype(str))
        if not np.array_equal(np_array, encoded_results):
            print(encoded_results)
            print("error: incorrect output")
            sys.exit(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. Default is localhost:8000.')

    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()

    # Example using BYTES input tensors with utf-8 encoded string
    # elements
    simple_string_inference(triton_client)

    # Example using BYTES input tensor with utf-8 encoded string that
    # has an embedded null character.
    null_chars_array = np.array(
        ["he\x00llo".encode('utf-8') for i in range(16)], dtype=np.object_)
    null_char_data = null_chars_array.reshape([1, 16])
    identity_inference(triton_client, null_char_data, True)  # Using binary data
    identity_inference(triton_client, null_char_data, False)  # Using JSON data

    # Example using BYTES input tensor with 16 elements, where each
    # element is a 4-byte binary blob with value 0x00010203. Can use
    # dtype=np.bytes_ in this case.
    bytes_data = [b'\x00\x01\x02\x03' for i in range(16)]
    np_bytes_data = np.array(bytes_data, dtype=np.bytes_)
    np_bytes_data = np_bytes_data.reshape([1, 16])
    identity_inference(triton_client, np_bytes_data, True)  # Using binary data
    identity_inference(triton_client, np_bytes_data, False)  # Using JSON data

    print('PASS: string')