mirror of
https://github.com/immich-app/immich.git
synced 2025-11-04 03:27:09 -05:00
77 lines
2.7 KiB
Python
77 lines
2.7 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from typing import Any, NamedTuple
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import numpy as np
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from numpy.typing import NDArray
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from immich_ml.config import log, settings
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from immich_ml.schemas import SessionNode
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from .rknnpool import RknnPoolExecutor, is_available, soc_name
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is_available = is_available and settings.rknn
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model_prefix = Path("rknpu") / soc_name if is_available and soc_name is not None else None
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def run_inference(rknn_lite: Any, input: list[NDArray[np.float32]]) -> list[NDArray[np.float32]]:
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outputs: list[NDArray[np.float32]] = rknn_lite.inference(inputs=input, data_format="nchw")
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return outputs
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input_output_mapping: dict[str, dict[str, Any]] = {
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"detection": {
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"input": {"norm_tensor:0": (1, 3, 640, 640)},
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"output": {
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"norm_tensor:1": (12800, 1),
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"norm_tensor:2": (3200, 1),
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"norm_tensor:3": (800, 1),
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"norm_tensor:4": (12800, 4),
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"norm_tensor:5": (3200, 4),
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"norm_tensor:6": (800, 4),
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"norm_tensor:7": (12800, 10),
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"norm_tensor:8": (3200, 10),
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"norm_tensor:9": (800, 10),
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},
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},
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"recognition": {"input": {"norm_tensor:0": (1, 3, 112, 112)}, "output": {"norm_tensor:1": (1, 512)}},
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}
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class RknnSession:
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def __init__(self, model_path: Path) -> None:
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self.model_type = "detection" if "detection" in model_path.parts else "recognition"
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self.tpe = settings.rknn_threads
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log.info(f"Loading RKNN model from {model_path} with {self.tpe} threads.")
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self.rknnpool = RknnPoolExecutor(model_path=model_path.as_posix(), tpes=self.tpe, func=run_inference)
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log.info(f"Loaded RKNN model from {model_path} with {self.tpe} threads.")
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def get_inputs(self) -> list[SessionNode]:
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return [RknnNode(name=k, shape=v) for k, v in input_output_mapping[self.model_type]["input"].items()]
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def get_outputs(self) -> list[SessionNode]:
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return [RknnNode(name=k, shape=v) for k, v in input_output_mapping[self.model_type]["output"].items()]
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def run(
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self,
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output_names: list[str] | None,
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input_feed: dict[str, NDArray[np.float32]] | dict[str, NDArray[np.int32]],
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run_options: Any = None,
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) -> list[NDArray[np.float32]]:
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input_data: list[NDArray[np.float32]] = [np.ascontiguousarray(v) for v in input_feed.values()]
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self.rknnpool.put(input_data)
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res = self.rknnpool.get()
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if res is None:
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raise RuntimeError("RKNN inference failed!")
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return res
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class RknnNode(NamedTuple):
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name: str | None
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shape: tuple[int, ...]
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__all__ = ["RknnSession", "RknnNode", "is_available", "soc_name", "model_prefix"]
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