mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 17:41:52 +08:00
294607fc4a
* Update keypointdetection result docs * Update im.copy() to im in examples * Update new Api, fastdeploy::vision::Visualize to fastdeploy::vision * Update SwapBackgroundSegmentation && SwapBackgroundMatting to SwapBackground * Update README_CN.md * Update README_CN.md * Update preprocessor.h * PaddleSeg supports triton serving * Add PaddleSeg simple serving example * Add PaddleSeg triton serving client code * Update triton serving runtime config.pbtxt * Update paddleseg grpc client * Add paddle serving README
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
import logging
|
|
import numpy as np
|
|
import time
|
|
from typing import Optional
|
|
import cv2
|
|
import json
|
|
|
|
from tritonclient import utils as client_utils
|
|
from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput, service_pb2_grpc, service_pb2
|
|
|
|
LOGGER = logging.getLogger("run_inference_on_triton")
|
|
|
|
|
|
class SyncGRPCTritonRunner:
|
|
DEFAULT_MAX_RESP_WAIT_S = 120
|
|
|
|
def __init__(
|
|
self,
|
|
server_url: str,
|
|
model_name: str,
|
|
model_version: str,
|
|
*,
|
|
verbose=False,
|
|
resp_wait_s: Optional[float]=None, ):
|
|
self._server_url = server_url
|
|
self._model_name = model_name
|
|
self._model_version = model_version
|
|
self._verbose = verbose
|
|
self._response_wait_t = self.DEFAULT_MAX_RESP_WAIT_S if resp_wait_s is None else resp_wait_s
|
|
|
|
self._client = InferenceServerClient(
|
|
self._server_url, verbose=self._verbose)
|
|
error = self._verify_triton_state(self._client)
|
|
if error:
|
|
raise RuntimeError(
|
|
f"Could not communicate to Triton Server: {error}")
|
|
|
|
LOGGER.debug(
|
|
f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} "
|
|
f"are up and ready!")
|
|
|
|
model_config = self._client.get_model_config(self._model_name,
|
|
self._model_version)
|
|
model_metadata = self._client.get_model_metadata(self._model_name,
|
|
self._model_version)
|
|
LOGGER.info(f"Model config {model_config}")
|
|
LOGGER.info(f"Model metadata {model_metadata}")
|
|
|
|
for tm in model_metadata.inputs:
|
|
print("tm:", tm)
|
|
self._inputs = {tm.name: tm for tm in model_metadata.inputs}
|
|
self._input_names = list(self._inputs)
|
|
self._outputs = {tm.name: tm for tm in model_metadata.outputs}
|
|
self._output_names = list(self._outputs)
|
|
self._outputs_req = [
|
|
InferRequestedOutput(name) for name in self._outputs
|
|
]
|
|
|
|
def Run(self, inputs):
|
|
"""
|
|
Args:
|
|
inputs: list, Each value corresponds to an input name of self._input_names
|
|
Returns:
|
|
results: dict, {name : numpy.array}
|
|
"""
|
|
infer_inputs = []
|
|
for idx, data in enumerate(inputs):
|
|
infer_input = InferInput(self._input_names[idx], data.shape,
|
|
"UINT8")
|
|
infer_input.set_data_from_numpy(data)
|
|
infer_inputs.append(infer_input)
|
|
|
|
results = self._client.infer(
|
|
model_name=self._model_name,
|
|
model_version=self._model_version,
|
|
inputs=infer_inputs,
|
|
outputs=self._outputs_req,
|
|
client_timeout=self._response_wait_t, )
|
|
results = {name: results.as_numpy(name) for name in self._output_names}
|
|
return results
|
|
|
|
def _verify_triton_state(self, triton_client):
|
|
if not triton_client.is_server_live():
|
|
return f"Triton server {self._server_url} is not live"
|
|
elif not triton_client.is_server_ready():
|
|
return f"Triton server {self._server_url} is not ready"
|
|
elif not triton_client.is_model_ready(self._model_name,
|
|
self._model_version):
|
|
return f"Model {self._model_name}:{self._model_version} is not ready"
|
|
return None
|
|
|
|
|
|
if __name__ == "__main__":
|
|
model_name = "paddleseg"
|
|
model_version = "1"
|
|
url = "localhost:8001"
|
|
runner = SyncGRPCTritonRunner(url, model_name, model_version)
|
|
im = cv2.imread("cityscapes_demo.png")
|
|
im = np.array([im, ])
|
|
# batch input
|
|
# im = np.array([im, im, im])
|
|
for i in range(1):
|
|
result = runner.Run([im, ])
|
|
for name, values in result.items():
|
|
print("output_name:", name)
|
|
# values is batch
|
|
for value in values:
|
|
value = json.loads(value)
|
|
print(
|
|
"Only print the first 20 labels in label_map of SEG_RESULT")
|
|
value["label_map"] = value["label_map"][:20]
|
|
print(value)
|