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/loggers/clearml/hpo.py

@@ -1,18 +1,21 @@
 from clearml import Task
+
 # Connecting ClearML with the current process,
 # from here on everything is logged automatically
 from clearml.automation import HyperParameterOptimizer, UniformParameterRange
 from clearml.automation.optuna import OptimizerOptuna
 
-task = Task.init(project_name='Hyper-Parameter Optimization',
-                 task_name='YOLOv5',
-                 task_type=Task.TaskTypes.optimizer,
-                 reuse_last_task_id=False)
+task = Task.init(
+    project_name="Hyper-Parameter Optimization",
+    task_name="YOLOv5",
+    task_type=Task.TaskTypes.optimizer,
+    reuse_last_task_id=False,
+)
 
 # Example use case:
 optimizer = HyperParameterOptimizer(
     # This is the experiment we want to optimize
-    base_task_id='<your_template_task_id>',
+    base_task_id="<your_template_task_id>",
     # here we define the hyper-parameters to optimize
     # Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
     # For Example, here we see in the base experiment a section Named: "General"
@@ -20,39 +23,40 @@ optimizer = HyperParameterOptimizer(
     # If you have `argparse` for example, then arguments will appear under the "Args" section,
     # and you should instead pass "Args/batch_size"
     hyper_parameters=[
-        UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1),
-        UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0),
-        UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98),
-        UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001),
-        UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0),
-        UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95),
-        UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2),
-        UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2),
-        UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0),
-        UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0),
-        UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0),
-        UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0),
-        UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7),
-        UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0),
-        UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0),
-        UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1),
-        UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9),
-        UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9),
-        UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0),
-        UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9),
-        UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9),
-        UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0),
-        UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001),
-        UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0),
-        UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0),
-        UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0),
-        UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0),
-        UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)],
+        UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1),
+        UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0),
+        UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98),
+        UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001),
+        UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0),
+        UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95),
+        UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2),
+        UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2),
+        UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0),
+        UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0),
+        UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0),
+        UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0),
+        UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7),
+        UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0),
+        UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0),
+        UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1),
+        UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0),
+        UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9),
+        UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0),
+        UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001),
+        UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0),
+        UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0),
+    ],
     # this is the objective metric we want to maximize/minimize
-    objective_metric_title='metrics',
-    objective_metric_series='mAP_0.5',
+    objective_metric_title="metrics",
+    objective_metric_series="mAP_0.5",
     # now we decide if we want to maximize it or minimize it (accuracy we maximize)
-    objective_metric_sign='max',
+    objective_metric_sign="max",
     # let us limit the number of concurrent experiments,
     # this in turn will make sure we do dont bombard the scheduler with experiments.
     # if we have an auto-scaler connected, this, by proxy, will limit the number of machine
@@ -81,4 +85,4 @@ optimizer.wait()
 # make sure background optimization stopped
 optimizer.stop()
 
-print('We are done, good bye')
+print("We are done, good bye")