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#!/usr/bin/env python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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) |