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/loss.py

@@ -1,7 +1,5 @@
 # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Loss functions
-"""
+"""Loss functions."""
 
 import torch
 import torch.nn as nn
@@ -19,7 +17,7 @@ class BCEBlurWithLogitsLoss(nn.Module):
     # BCEwithLogitLoss() with reduced missing label effects.
     def __init__(self, alpha=0.05):
         super().__init__()
-        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
+        self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none")  # must be nn.BCEWithLogitsLoss()
         self.alpha = alpha
 
     def forward(self, pred, true):
@@ -40,7 +38,7 @@ class FocalLoss(nn.Module):
         self.gamma = gamma
         self.alpha = alpha
         self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+        self.loss_fcn.reduction = "none"  # required to apply FL to each element
 
     def forward(self, pred, true):
         loss = self.loss_fcn(pred, true)
@@ -54,9 +52,9 @@ class FocalLoss(nn.Module):
         modulating_factor = (1.0 - p_t) ** self.gamma
         loss *= alpha_factor * modulating_factor
 
-        if self.reduction == 'mean':
+        if self.reduction == "mean":
             return loss.mean()
-        elif self.reduction == 'sum':
+        elif self.reduction == "sum":
             return loss.sum()
         else:  # 'none'
             return loss
@@ -70,7 +68,7 @@ class QFocalLoss(nn.Module):
         self.gamma = gamma
         self.alpha = alpha
         self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+        self.loss_fcn.reduction = "none"  # required to apply FL to each element
 
     def forward(self, pred, true):
         loss = self.loss_fcn(pred, true)
@@ -80,9 +78,9 @@ class QFocalLoss(nn.Module):
         modulating_factor = torch.abs(true - pred_prob) ** self.gamma
         loss *= alpha_factor * modulating_factor
 
-        if self.reduction == 'mean':
+        if self.reduction == "mean":
             return loss.mean()
-        elif self.reduction == 'sum':
+        elif self.reduction == "sum":
             return loss.sum()
         else:  # 'none'
             return loss
@@ -97,14 +95,14 @@ 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)
 
@@ -167,9 +165,9 @@ 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']
+        lbox *= self.hyp["box"]
+        lobj *= self.hyp["obj"]
+        lcls *= self.hyp["cls"]
         bs = tobj.shape[0]  # batch size
 
         return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
@@ -183,16 +181,20 @@ class ComputeLoss:
         targets = torch.cat((targets.repeat(na, 1, 1), ai[..., 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
@@ -203,7 +205,7 @@ 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