mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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131 lines
4.3 KiB
Python
131 lines
4.3 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import queue
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import sys
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from functools import partial
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import numpy as np
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import tritonclient.grpc as grpcclient
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from tritonclient.utils import *
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FLAGS = None
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class UserData:
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def __init__(self):
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self._completed_requests = queue.Queue()
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# Define the callback function. Note the last two parameters should be
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# result and error. InferenceServerClient would povide the results of an
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# inference as grpcclient.InferResult in result. For successful
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# inference, error will be None, otherwise it will be an object of
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# tritonclientutils.InferenceServerException holding the error details
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def callback(user_data, result, error):
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if error:
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user_data._completed_requests.put(error)
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else:
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user_data._completed_requests.put(result)
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def async_stream_send(triton_client, values, request_id, model_name):
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infer_inputs = []
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outputs = []
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for idx, data in enumerate(values):
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data = np.array([data.encode('utf-8')], dtype=np.object_)
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infer_input = grpcclient.InferInput('INPUT_0', [len(data)], "BYTES")
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infer_input.set_data_from_numpy(data)
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infer_inputs.append(infer_input)
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outputs.append(grpcclient.InferRequestedOutput('OUTPUT_0'))
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# Issue the asynchronous sequence inference.
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triton_client.async_stream_infer(
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model_name=model_name,
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inputs=infer_inputs,
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outputs=outputs,
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request_id=request_id)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'-v',
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'--verbose',
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action="store_true",
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required=False,
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default=False,
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help='Enable verbose output')
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parser.add_argument(
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'-u',
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'--url',
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type=str,
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required=False,
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default='localhost:8001',
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help='Inference server URL and it gRPC port. Default is localhost:8001.')
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FLAGS = parser.parse_args()
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# We use custom "sequence" models which take 1 input
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# value. The output is the accumulated value of the inputs. See
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# src/custom/sequence.
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model_name = "streaming_pp_tts"
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values = ["哈哈哈哈"]
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request_id = "0"
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string_result0_list = []
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user_data = UserData()
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# It is advisable to use client object within with..as clause
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# when sending streaming requests. This ensures the client
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# is closed when the block inside with exits.
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with grpcclient.InferenceServerClient(
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url=FLAGS.url, verbose=FLAGS.verbose) as triton_client:
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try:
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# Establish stream
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triton_client.start_stream(callback=partial(callback, user_data))
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# Now send the inference sequences...
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async_stream_send(triton_client, values, request_id, model_name)
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except InferenceServerException as error:
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print(error)
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sys.exit(1)
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# Retrieve results...
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recv_count = 0
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result_dict = {}
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status = True
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while True:
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data_item = user_data._completed_requests.get()
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if type(data_item) == InferenceServerException:
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raise data_item
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else:
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this_id = data_item.get_response().id
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if this_id not in result_dict.keys():
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result_dict[this_id] = []
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result_dict[this_id].append((recv_count, data_item))
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sub_wav = data_item.as_numpy('OUTPUT_0')
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status = data_item.as_numpy('status')
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print('sub_wav = ', sub_wav, "subwav.shape = ", sub_wav.shape)
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print('status = ', status)
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if status[0] == 1:
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break
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recv_count += 1
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print("PASS: stream_client")
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