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/autobatch.py

@@ -1,7 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Auto-batch utils
-"""
+"""Auto-batch utils."""
 
 from copy import deepcopy
 
@@ -27,14 +25,14 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
     #     print(autobatch(model))
 
     # Check device
-    prefix = colorstr('AutoBatch: ')
-    LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
+    prefix = colorstr("AutoBatch: ")
+    LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}")
     device = next(model.parameters()).device  # get model device
-    if device.type == 'cpu':
-        LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
+    if device.type == "cpu":
+        LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}")
         return batch_size
     if torch.backends.cudnn.benchmark:
-        LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
+        LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}")
         return batch_size
 
     # Inspect CUDA memory
@@ -45,7 +43,7 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
     r = torch.cuda.memory_reserved(device) / gb  # GiB reserved
     a = torch.cuda.memory_allocated(device) / gb  # GiB allocated
     f = t - (r + a)  # GiB free
-    LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
+    LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free")
 
     # Profile batch sizes
     batch_sizes = [1, 2, 4, 8, 16]
@@ -53,11 +51,11 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
         img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
         results = profile(img, model, n=3, device=device)
     except Exception as e:
-        LOGGER.warning(f'{prefix}{e}')
+        LOGGER.warning(f"{prefix}{e}")
 
     # Fit a solution
     y = [x[2] for x in results if x]  # memory [2]
-    p = np.polyfit(batch_sizes[:len(y)], y, deg=1)  # first degree polynomial fit
+    p = np.polyfit(batch_sizes[: len(y)], y, deg=1)  # first degree polynomial fit
     b = int((f * fraction - p[1]) / p[0])  # y intercept (optimal batch size)
     if None in results:  # some sizes failed
         i = results.index(None)  # first fail index
@@ -65,8 +63,8 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
             b = batch_sizes[max(i - 1, 0)]  # select prior safe point
     if b < 1 or b > 1024:  # b outside of safe range
         b = batch_size
-        LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
+        LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.")
 
     fraction = (np.polyval(p, b) + r + a) / t  # actual fraction predicted
-    LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
+    LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅")
     return b