refactor

machine-learning/app/sessions/rknn/__init__.py

@@ -0,0 +1,71 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, NamedTuple
+
+import numpy as np
+from numpy.typing import NDArray
+
+from app.config import log, settings
+from app.schemas import SessionNode
+
+from .rknnpool import RknnPoolExecutor, is_available, soc_name
+
+is_available = is_available and settings.rknn
+model_prefix = Path("rknpu") / soc_name if is_available else None
+
+
+def run_inference(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
+
+
+input_output_mapping: dict[str, dict[str, Any]] = {
+    "detection": {
+        "input": {"norm_tensor:0": (1, 3, 640, 640)},
+        "output": {
+            "norm_tensor:1": (12800, 1),
+            "norm_tensor:2": (3200, 1),
+            "norm_tensor:3": (800, 1),
+            "norm_tensor:4": (12800, 4),
+            "norm_tensor:5": (3200, 4),
+            "norm_tensor:6": (800, 4),
+            "norm_tensor:7": (12800, 10),
+            "norm_tensor:8": (3200, 10),
+            "norm_tensor:9": (800, 10),
+        },
+    },
+    "recognition": {"input": {"norm_tensor:0": (1, 3, 112, 112)}, "output": {"norm_tensor:1": (1, 512)}},
+}
+
+
+class RknnSession:
+    def __init__(self, model_path: Path) -> None:
+        self.model_type = "detection" if "detection" in model_path.parts else "recognition"
+        self.tpe = settings.rknn_threads
+
+        log.info(f"Loading RKNN model from {model_path} with {self.tpe} threads.")
+        self.rknnpool = RknnPoolExecutor(model_path=model_path.as_posix(), tpes=self.tpe, func=run_inference)
+        log.info(f"Loaded RKNN model from {model_path} with {self.tpe} threads.")
+
+    def get_inputs(self) -> list[SessionNode]:
+        return [RknnNode(name=k, shape=v) for k, v in input_output_mapping[self.model_type]["input"].items()]
+
+    def get_outputs(self) -> list[SessionNode]:
+        return [RknnNode(name=k, shape=v) for k, v in input_output_mapping[self.model_type]["output"].items()]
+
+    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
+
+
+class RknnNode(NamedTuple):
+    name: str | None
+    shape: tuple[int, ...]

machine-learning/app/sessions/rknn/rknnpool.py

@@ -0,0 +1,85 @@
+# This code is from leafqycc/rknn-multi-threaded
+# Following Apache License 2.0
+
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from queue import Queue
+
+import numpy as np
+from numpy.typing import NDArray
+
+from app.config import log
+
+supported_socs = ["rk3566", "rk3588"]
+coremask_supported_socs = ["rk3576", "rk3588"]
+
+
+def get_soc(device_tree_path: Path | str) -> str | None:
+    try:
+        with Path(device_tree_path).open() as f:
+            device_compatible_str = f.read()
+            for soc in supported_socs:
+                if soc in device_compatible_str:
+                    return soc
+            log.warning("Device is not supported for RKNN")
+    except OSError as e:
+        log.warning("Could not read /proc/device-tree/compatible. Reason: %s", e.msg)
+    return None
+
+
+soc_name = None
+is_available = False
+try:
+    from rknnlite.api import RKNNLite
+
+    soc_name = get_soc("/proc/device-tree/compatible")
+    is_available = soc_name is not None
+except ImportError:
+    log.debug("RKNN is not available")
+
+
+def init_rknn(model_path: str) -> RKNNLite:
+    if not is_available:
+        raise RuntimeError("rknn is not available!")
+    rknn_lite = RKNNLite()
+    ret = rknn_lite.load_rknn(model_path)
+    if ret != 0:
+        raise RuntimeError("Load RKNN rknnModel failed")
+
+    if soc_name in coremask_supported_socs:
+        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_AUTO)
+    else:
+        ret = rknn_lite.init_runtime()  # Please do not set this parameter on other platforms.
+
+    if ret != 0:
+        raise RuntimeError("Init runtime environment failed")
+
+    return rknn_lite
+
+
+class RknnPoolExecutor:
+    def __init__(self, model_path: str, tpes: int, func):
+        self.tpes = tpes
+        self.queue = Queue()
+        self.rknn_pool = [init_rknn(model_path) for _ in range(tpes)]
+        self.pool = ThreadPoolExecutor(max_workers=tpes)
+        self.func = func
+        self.num = 0
+
+    def put(self, inputs) -> None:
+        self.queue.put(self.pool.submit(self.func, self.rknn_pool[self.num % self.tpes], inputs))
+        self.num += 1
+
+    def get(self) -> list[list[NDArray[np.float32]], bool]:
+        if self.queue.empty():
+            return None
+        fut = self.queue.get()
+        return fut.result()
+
+    def release(self) -> None:
+        self.pool.shutdown()
+        for rknn_lite in self.rknn_pool:
+            rknn_lite.release()
+
+    def __del__(self) -> None:
+        self.release()