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/segment/general.py

@@ -6,8 +6,7 @@ import torch.nn.functional as F
 
 def crop_mask(masks, boxes):
     """
-    "Crop" predicted masks by zeroing out everything not in the predicted bbox.
-    Vectorized by Chong (thanks Chong).
+    "Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong).
 
     Args:
         - masks should be a size [n, h, w] tensor of masks
@@ -35,7 +34,7 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
 
     c, mh, mw = protos.shape  # CHW
     masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
-    masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
+    masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
     masks = crop_mask(masks, bboxes)  # CHW
     return masks.gt_(0.5)
 
@@ -63,7 +62,7 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
 
     masks = crop_mask(masks, downsampled_bboxes)  # CHW
     if upsample:
-        masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
+        masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
     return masks.gt_(0.5)
 
 
@@ -85,7 +84,7 @@ def process_mask_native(protos, masks_in, bboxes, shape):
     bottom, right = int(mh - pad[1]), int(mw - pad[0])
     masks = masks[:, top:bottom, left:right]
 
-    masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0]  # CHW
+    masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
     masks = crop_mask(masks, bboxes)  # CHW
     return masks.gt_(0.5)
 
@@ -144,17 +143,17 @@ def masks_iou(mask1, mask2, eps=1e-7):
     return intersection / (union + eps)
 
 
-def masks2segments(masks, strategy='largest'):
+def masks2segments(masks, strategy="largest"):
     # Convert masks(n,160,160) into segments(n,xy)
     segments = []
-    for x in masks.int().cpu().numpy().astype('uint8'):
+    for x in masks.int().cpu().numpy().astype("uint8"):
         c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
         if c:
-            if strategy == 'concat':  # concatenate all segments
+            if strategy == "concat":  # concatenate all segments
                 c = np.concatenate([x.reshape(-1, 2) for x in c])
-            elif strategy == 'largest':  # select largest segment
+            elif strategy == "largest":  # select largest segment
                 c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
         else:
             c = np.zeros((0, 2))  # no segments found
-        segments.append(c.astype('float32'))
+        segments.append(c.astype("float32"))
     return segments