more linting

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

@@ -1,9 +1,10 @@
 # This code is from leafqycc/rknn-multi-threaded
 # Following Apache License 2.0
 
-from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import Future, ThreadPoolExecutor
 from pathlib import Path
 from queue import Queue
+from typing import Callable, TypeVar
 
 import numpy as np
 from numpy.typing import NDArray
@@ -21,7 +22,7 @@ def get_soc(device_tree_path: Path | str) -> str | None:
                     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)
+        log.warning("Could not read /proc/device-tree/compatible. Reason: %s", e)
     return None
 
 
@@ -56,19 +57,24 @@ def init_rknn(model_path: str) -> "RKNNLite":
 
 
 class RknnPoolExecutor:
-    def __init__(self, model_path: str, tpes: int, func):
+    def __init__(
+        self,
+        model_path: str,
+        tpes: int,
+        func: Callable[["RKNNLite", list[NDArray[np.float32]]], list[NDArray[np.float32]]],
+    ) -> None:
         self.tpes = tpes
-        self.queue = Queue()
+        self.queue: Queue[Future[list[NDArray[np.float32]]]] = 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:
+    def put(self, inputs: list[NDArray[np.float32]]) -> 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]:
+    def get(self) -> list[NDArray[np.float32]] | None:
         if self.queue.empty():
             return None
         fut = self.queue.get()