Update Actions with Lychee and GitHub Token (#12592)

* Add Ultralytics Actions

* Auto-format by Ultralytics actions

* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/triton.py

@@ -1,6 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-""" Utils to interact with the Triton Inference Server
-"""
+"""Utils to interact with the Triton Inference Server."""
 
 import typing
 from urllib.parse import urlparse
@@ -9,9 +8,11 @@ import torch
 
 
 class TritonRemoteModel:
-    """ A wrapper over a model served by the Triton Inference Server. It can
-    be configured to communicate over GRPC or HTTP. It accepts Torch Tensors
-    as input and returns them as outputs.
+    """
+    A wrapper over a model served by the Triton Inference Server.
+
+    It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as
+    outputs.
     """
 
     def __init__(self, url: str):
@@ -21,7 +22,7 @@ class TritonRemoteModel:
         """
 
         parsed_url = urlparse(url)
-        if parsed_url.scheme == 'grpc':
+        if parsed_url.scheme == "grpc":
             from tritonclient.grpc import InferenceServerClient, InferInput
 
             self.client = InferenceServerClient(parsed_url.netloc)  # Triton GRPC client
@@ -31,51 +32,55 @@ class TritonRemoteModel:
 
             def create_input_placeholders() -> typing.List[InferInput]:
                 return [
-                    InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']]
+                    InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"]
+                ]
 
         else:
             from tritonclient.http import InferenceServerClient, InferInput
 
             self.client = InferenceServerClient(parsed_url.netloc)  # Triton HTTP client
             model_repository = self.client.get_model_repository_index()
-            self.model_name = model_repository[0]['name']
+            self.model_name = model_repository[0]["name"]
             self.metadata = self.client.get_model_metadata(self.model_name)
 
             def create_input_placeholders() -> typing.List[InferInput]:
                 return [
-                    InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']]
+                    InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"]
+                ]
 
         self._create_input_placeholders_fn = create_input_placeholders
 
     @property
     def runtime(self):
-        """Returns the model runtime"""
-        return self.metadata.get('backend', self.metadata.get('platform'))
+        """Returns the model runtime."""
+        return self.metadata.get("backend", self.metadata.get("platform"))
 
     def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]:
-        """ Invokes the model. Parameters can be provided via args or kwargs.
-        args, if provided, are assumed to match the order of inputs of the model.
-        kwargs are matched with the model input names.
+        """
+        Invokes the model.
+
+        Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of
+        the model. kwargs are matched with the model input names.
         """
         inputs = self._create_inputs(*args, **kwargs)
         response = self.client.infer(model_name=self.model_name, inputs=inputs)
         result = []
-        for output in self.metadata['outputs']:
-            tensor = torch.as_tensor(response.as_numpy(output['name']))
+        for output in self.metadata["outputs"]:
+            tensor = torch.as_tensor(response.as_numpy(output["name"]))
             result.append(tensor)
         return result[0] if len(result) == 1 else result
 
     def _create_inputs(self, *args, **kwargs):
         args_len, kwargs_len = len(args), len(kwargs)
         if not args_len and not kwargs_len:
-            raise RuntimeError('No inputs provided.')
+            raise RuntimeError("No inputs provided.")
         if args_len and kwargs_len:
-            raise RuntimeError('Cannot specify args and kwargs at the same time')
+            raise RuntimeError("Cannot specify args and kwargs at the same time")
 
         placeholders = self._create_input_placeholders_fn()
         if args_len:
             if args_len != len(placeholders):
-                raise RuntimeError(f'Expected {len(placeholders)} inputs, got {args_len}.')
+                raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.")
             for input, value in zip(placeholders, args):
                 input.set_data_from_numpy(value.cpu().numpy())
         else: