typing be happy.

machine-learning/app/sessions/rknn.py

@@ -13,8 +13,8 @@ from rknn.rknnpool import rknnPoolExecutor, soc_name
 from ..config import log, settings
 
 
-def runInfrence(rknn_lite, input):
+def runInfrence(rknn_lite: Any, input: NDArray[np.float32]) -> list[NDArray[np.float32]]:
-    outputs = rknn_lite.inference(inputs=[input], data_format="nchw")
+    outputs: list[NDArray[np.float32]] = rknn_lite.inference(inputs=[input], data_format="nchw")
 
     return outputs
 
@@ -23,8 +23,6 @@ class RknnSession:
     def __init__(self, model_path: Path | str):
         self.model_path = Path(str(model_path).replace("model", soc_name))
         self.ort_model_path = Path(str(self.model_path).replace(f"{soc_name}.rknn", "model.onnx"))
-        self.inputs = None
-        self.outputs = None
 
         if "textual" in str(self.model_path):
             self.tpe = settings.rknn_textual_threads
@@ -36,36 +34,38 @@ class RknnSession:
         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=runInfrence)
 
-    def __del__(self):
+    def __del__(self) -> None:
         self.rknnpool.release()
 
+    def _load_ort_session(self) -> None:
+        self.ort_session = ort.InferenceSession(
+            self.ort_model_path.as_posix(),
+        )
+        self.inputs: list[SessionNode] = self.ort_session.get_inputs()
+        self.outputs: list[SessionNode] = self.ort_session.get_outputs()
+        del self.ort_session
+
     def get_inputs(self) -> list[SessionNode]:
-        if not self.inputs:
+        try:
-            self.ort_session = ort.InferenceSession(
+            return self.inputs
-                self.ort_model_path.as_posix(),
+        except AttributeError:
-            )
+            self._load_ort_session()
-            self.inputs = self.ort_session.get_inputs()
+            return self.inputs
-            self.outputs = self.ort_session.get_outputs()
-            del self.ort_session
-        return self.inputs
 
     def get_outputs(self) -> list[SessionNode]:
-        if not self.outputs:
+        try:
-            self.ort_session = ort.InferenceSession(
+            return self.outputs
-                self.ort_model_path.as_posix(),
+        except AttributeError:
-            )
+            self._load_ort_session()
-            self.inputs = self.ort_session.get_inputs()
+            return self.outputs
-            self.outputs = self.ort_session.get_outputs()
-            del self.ort_session
-        return self.outputs
 
     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 = [np.ascontiguousarray(v) for v in input_feed.values()][0]
+        input_data: NDArray[np.float32] = np.ascontiguousarray(list(input_feed.values())[0], dtype=np.float32)
         self.rknnpool.put(input_data)
-        outputs = self.rknnpool.get()[0]
+        outputs: list[NDArray[np.float32]] = self.rknnpool.get()[0]
         return outputs

machine-learning/rknn/rknnpool.py

@@ -4,6 +4,8 @@
 import os
 from concurrent.futures import ThreadPoolExecutor
 from queue import Queue
+import numpy as np
+from numpy.typing import NDArray
 
 supported_socs = ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
 coremask_supported_socs = ["rk3576","rk3588"]
@@ -63,7 +65,7 @@ def initRKNNs(rknnModel="./rknnModel/yolov5s.rknn", TPEs=1):
 
 
 class rknnPoolExecutor:
-    def __init__(self, rknnModel, TPEs, func):
+    def __init__(self, rknnModel: str, TPEs: int, func):
         self.TPEs = TPEs
         self.queue = Queue()
         self.rknnPool = initRKNNs(rknnModel, TPEs)
@@ -71,17 +73,17 @@ class rknnPoolExecutor:
         self.func = func
         self.num = 0
 
-    def put(self, frame):
+    def put(self, frame) -> None:
         self.queue.put(self.pool.submit(self.func, self.rknnPool[self.num % self.TPEs], frame))
         self.num += 1
 
-    def get(self):
+    def get(self) -> list[list[NDArray[np.float32]], bool]:
         if self.queue.empty():
             return None, False
         fut = self.queue.get()
         return fut.result(), True
 
-    def release(self):
+    def release(self) -> None:
         self.pool.shutdown()
         for rknn_lite in self.rknnPool:
             rknn_lite.release()