predict: formatting

plugins/onnx/src/ort/__init__.py

@@ -40,15 +40,20 @@
     "scrypted_yolov8n_320",
 ]
 
+
 def parse_labels(names):
     j = ast.literal_eval(names)
     ret = {}
     for k, v in j.items():
         ret[int(k)] = v
     return ret
 
+
 class ONNXPlugin(
-    PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.Settings, scrypted_sdk.DeviceProvider
+    PredictPlugin,
+    scrypted_sdk.BufferConverter,
+    scrypted_sdk.Settings,
+    scrypted_sdk.DeviceProvider,
 ):
     def __init__(self, nativeId: str | None = None, forked: bool = False):
         super().__init__(nativeId=nativeId, forked=forked)
@@ -67,7 +72,11 @@ def __init__(self, nativeId: str | None = None, forked: bool = False):
 
         print(f"model {model}")
 
-        onnxmodel = model if self.scrypted_yolo_nas else "best" if self.scrypted_model else model
+        onnxmodel = (
+            model
+            if self.scrypted_yolo_nas
+            else "best" if self.scrypted_model else model
+        )
 
         model_version = "v3"
         onnxfile = self.downloadFile(
@@ -92,22 +101,28 @@ def __init__(self, nativeId: str | None = None, forked: bool = False):
                 sess_options = onnxruntime.SessionOptions()
 
                 providers: list[str] = []
-                if sys.platform == 'darwin':
+                if sys.platform == "darwin":
                     providers.append("CoreMLExecutionProvider")
 
-                if ('linux' in sys.platform or 'win' in sys.platform) and (platform.machine() == 'x86_64' or platform.machine() == 'AMD64'):
+                if ("linux" in sys.platform or "win" in sys.platform) and (
+                    platform.machine() == "x86_64" or platform.machine() == "AMD64"
+                ):
                     deviceId = int(deviceId)
-                    providers.append(("CUDAExecutionProvider", { "device_id": deviceId }))
+                    providers.append(("CUDAExecutionProvider", {"device_id": deviceId}))
 
-                providers.append('CPUExecutionProvider')
+                providers.append("CPUExecutionProvider")
 
-                compiled_model = onnxruntime.InferenceSession(onnxfile, sess_options=sess_options, providers=providers)
+                compiled_model = onnxruntime.InferenceSession(
+                    onnxfile, sess_options=sess_options, providers=providers
+                )
                 compiled_models.append(compiled_model)
 
                 input = compiled_model.get_inputs()[0]
                 self.model_dim = input.shape[2]
                 self.input_name = input.name
-                self.labels = parse_labels(compiled_model.get_modelmeta().custom_metadata_map['names'])
+                self.labels = parse_labels(
+                    compiled_model.get_modelmeta().custom_metadata_map["names"]
+                )
 
         except:
             import traceback
@@ -130,7 +145,7 @@ def executor_initializer():
                 providers.remove("CPUExecutionProvider")
             # join the remaining providers string
             self.provider = ", ".join(providers)
-            print('Runtime initialized on thread {}'.format(thread_name))
+            print("Runtime initialized on thread {}".format(thread_name))
 
         self.executor = concurrent.futures.ThreadPoolExecutor(
             initializer=executor_initializer,
@@ -222,11 +237,11 @@ async def getSettings(self) -> list[Setting]:
                 "title": "Execution Device",
                 "readonly": True,
                 "value": self.provider,
-            }
+            },
         ]
 
     async def putSetting(self, key: str, value: SettingValue):
-        if (key == 'deviceIds'):
+        if key == "deviceIds":
             value = json.dumps(value)
         self.storage.setItem(key, value)
         await self.onDeviceEvent(scrypted_sdk.ScryptedInterface.Settings.value, None)
@@ -240,7 +255,7 @@ def get_input_size(self) -> Tuple[int, int]:
         return [self.model_dim, self.model_dim]
 
     async def detect_once(self, input: Image.Image, settings: Any, src_size, cvss):
-        def prepare(): 
+        def prepare():
             im = np.array(input)
             im = np.expand_dims(input, axis=0)
             im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
@@ -250,7 +265,7 @@ def prepare():
 
         def predict(input_tensor):
             compiled_model = self.compiled_models[threading.current_thread().name]
-            output_tensors = compiled_model.run(None, { self.input_name: input_tensor })
+            output_tensors = compiled_model.run(None, {self.input_name: input_tensor})
             if self.scrypted_yolov10:
                 return yolo.parse_yolov10(output_tensors[0][0])
             if self.scrypted_yolo_nas:

plugins/openvino/src/ov/__init__.py

@@ -173,7 +173,11 @@ def __init__(self, nativeId: str | None = None, forked: bool = False):
         self.sigmoid = model == "yolo-v4-tiny-tf"
         self.modelName = model
 
-        ovmodel = "best-converted" if self.scrypted_yolov9 else "best" if self.scrypted_model else model
+        ovmodel = (
+            "best-converted"
+            if self.scrypted_yolov9
+            else "best" if self.scrypted_model else model
+        )
 
         model_version = "v7"
         xmlFile = self.downloadFile(