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/loss.py

@@ -18,14 +18,14 @@ class ComputeLoss:
         h = model.hyp  # hyperparameters
 
         # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device))
+        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device))
 
         # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
+        self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0))  # positive, negative BCE targets
 
         # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
+        g = h["fl_gamma"]  # focal loss gamma
         if g > 0:
             BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
@@ -82,7 +82,7 @@ class ComputeLoss:
 
                 # Mask regression
                 if tuple(masks.shape[-2:]) != (mask_h, mask_w):  # downsample
-                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
+                    masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
                 marea = xywhn[i][:, 2:].prod(1)  # mask width, height normalized
                 mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
                 for bi in b.unique():
@@ -100,10 +100,10 @@ class ComputeLoss:
 
         if self.autobalance:
             self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        lseg *= self.hyp['box'] / bs
+        lbox *= self.hyp["box"]
+        lobj *= self.hyp["obj"]
+        lcls *= self.hyp["cls"]
+        lseg *= self.hyp["box"] / bs
 
         loss = lbox + lobj + lcls + lseg
         return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
@@ -111,7 +111,7 @@ class ComputeLoss:
     def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
         # Mask loss for one image
         pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])  # (n,32) @ (32,80,80) -> (n,80,80)
-        loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
+        loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
 
     def build_targets(self, p, targets):
@@ -132,16 +132,20 @@ class ComputeLoss:
         targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2)  # append anchor indices
 
         g = 0.5  # bias
-        off = torch.tensor(
-            [
-                [0, 0],
-                [1, 0],
-                [0, 1],
-                [-1, 0],
-                [0, -1],  # j,k,l,m
-                # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-            ],
-            device=self.device).float() * g  # offsets
+        off = (
+            torch.tensor(
+                [
+                    [0, 0],
+                    [1, 0],
+                    [0, 1],
+                    [-1, 0],
+                    [0, -1],  # j,k,l,m
+                    # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
+                ],
+                device=self.device,
+            ).float()
+            * g
+        )  # offsets
 
         for i in range(self.nl):
             anchors, shape = self.anchors[i], p[i].shape
@@ -152,7 +156,7 @@ class ComputeLoss:
             if nt:
                 # Matches
                 r = t[..., 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
+                j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"]  # compare
                 # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                 t = t[j]  # filter