ultralytics 8.0.81 single-line docstring updates (#2061)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/utils/loss.py

@@ -12,9 +12,11 @@ class VarifocalLoss(nn.Module):
     """Varifocal loss by Zhang et al. https://arxiv.org/abs/2008.13367."""
 
     def __init__(self):
+        """Initialize the VarifocalLoss class."""
         super().__init__()
 
     def forward(self, pred_score, gt_score, label, alpha=0.75, gamma=2.0):
+        """Computes varfocal loss."""
         weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
         with torch.cuda.amp.autocast(enabled=False):
             loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction='none') *
@@ -25,6 +27,7 @@ class VarifocalLoss(nn.Module):
 class BboxLoss(nn.Module):
 
     def __init__(self, reg_max, use_dfl=False):
+        """Initialize the BboxLoss module with regularization maximum and DFL settings."""
         super().__init__()
         self.reg_max = reg_max
         self.use_dfl = use_dfl
@@ -64,6 +67,7 @@ class KeypointLoss(nn.Module):
         self.sigmas = sigmas
 
     def forward(self, pred_kpts, gt_kpts, kpt_mask, area):
+        """Calculates keypoint loss factor and Euclidean distance loss for predicted and actual keypoints."""
         d = (pred_kpts[..., 0] - gt_kpts[..., 0]) ** 2 + (pred_kpts[..., 1] - gt_kpts[..., 1]) ** 2
         kpt_loss_factor = (torch.sum(kpt_mask != 0) + torch.sum(kpt_mask == 0)) / (torch.sum(kpt_mask != 0) + 1e-9)
         # e = d / (2 * (area * self.sigmas) ** 2 + 1e-9)  # from formula