refactor(ml): model sessions (#10559)

machine-learning/ann/ann.py

@@ -52,8 +52,6 @@ class Ann(metaclass=_Singleton):
     def __init__(self, log_level: int = 3, tuning_level: int = 1, tuning_file: str | None = None) -> None:
         if not is_available:
             raise RuntimeError("libann is not available!")
-        if tuning_file and not exists(tuning_file):
-            raise ValueError("tuning_file must point to an existing (possibly empty) file!")
         if tuning_level == 0 and tuning_file is None:
             raise ValueError("tuning_level == 0 reads existing tuning information and requires a tuning_file")
         if tuning_level < 0 or tuning_level > 3:
@@ -67,6 +65,12 @@ class Ann(metaclass=_Singleton):
         self.input_shapes: dict[int, tuple[tuple[int], ...]] = {}
         self.ann: int | None = None
         self.new()
+        
+        if self.tuning_file is not None:
+            # make sure tuning file exists (without clearing contents)
+            # once filled, the tuning file reduces the cost/time of the first
+            # inference after model load by 10s of seconds
+            open(self.tuning_file, "a").close()
 
     def new(self) -> None:
         if self.ann is None:
@@ -95,17 +99,19 @@ class Ann(metaclass=_Singleton):
         model_path: str,
         fast_math: bool = True,
         fp16: bool = False,
-        save_cached_network: bool = False,
         cached_network_path: str | None = None,
     ) -> int:
         if not model_path.endswith((".armnn", ".tflite", ".onnx")):
             raise ValueError("model_path must be a file with extension .armnn, .tflite or .onnx")
         if not exists(model_path):
             raise ValueError("model_path must point to an existing file!")
+        
+        save_cached_network = False
         if cached_network_path is not None and not exists(cached_network_path):
-            raise ValueError("cached_network_path must point to an existing (possibly empty) file!")
-        if save_cached_network and cached_network_path is None:
-            raise ValueError("save_cached_network is True, cached_network_path must be specified!")
+            save_cached_network = True
+            # create empty model cache file
+            open(cached_network_path, "a").close()
+
         net_id: int = libann.load(
             self.ann,
             model_path.encode(),