2025-01-19 12:02:08 +08:00

50 lines
1.6 KiB
Python

from __future__ import annotations
from pathlib import Path
from typing import Any
import numpy as np
from numpy.typing import NDArray
from app.schemas import SessionNode
from rknn.rknnpool import RknnPoolExecutor, soc_name
from ..config import log, settings
def runInference(rknn_lite: Any, input: list[NDArray[np.float32]]) -> list[NDArray[np.float32]]:
outputs: list[NDArray[np.float32]] = rknn_lite.inference(inputs=input, data_format="nchw")
return outputs
class RknnSession:
def __init__(self, model_path: Path | str):
self.model_path = Path(str(model_path).replace("model", soc_name))
self.tpe = settings.rknn_threads
log.info(f"Loading RKNN model from {self.model_path} with {self.tpe} threads.")
self.rknnpool = RknnPoolExecutor(rknnModel=self.model_path.as_posix(), tpes=self.tpe, func=runInference)
log.info(f"Loaded RKNN model from {self.model_path} with {self.tpe} threads.")
def __del__(self) -> None:
self.rknnpool.release()
def get_inputs(self) -> list[SessionNode]:
raise NotImplementedError
def get_outputs(self) -> list[SessionNode]:
raise NotImplementedError
def run(
self,
output_names: list[str] | None,
input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
run_options: Any = None,
) -> list[NDArray[np.float32]]:
input_data: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()]
self.rknnpool.put(input_data)
outputs: list[NDArray[np.float32]] = self.rknnpool.get()
return outputs