Tests improvements (#4616)

Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

ultralytics/utils/loss.py

@@ -19,7 +19,8 @@ class VarifocalLoss(nn.Module):
         """Initialize the VarifocalLoss class."""
         super().__init__()
 
-    def forward(self, pred_score, gt_score, label, alpha=0.75, gamma=2.0):
+    @staticmethod
+    def forward(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):
@@ -28,14 +29,14 @@ class VarifocalLoss(nn.Module):
         return loss
 
 
-# Losses
 class FocalLoss(nn.Module):
     """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
 
     def __init__(self, ):
         super().__init__()
 
-    def forward(self, pred, label, gamma=1.5, alpha=0.25):
+    @staticmethod
+    def forward(pred, label, gamma=1.5, alpha=0.25):
         """Calculates and updates confusion matrix for object detection/classification tasks."""
         loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
         # p_t = torch.exp(-loss)
@@ -89,6 +90,7 @@ class BboxLoss(nn.Module):
 
 
 class KeypointLoss(nn.Module):
+    """Criterion class for computing training losses."""
 
     def __init__(self, sigmas) -> None:
         super().__init__()
@@ -103,8 +105,8 @@ class KeypointLoss(nn.Module):
         return kpt_loss_factor * ((1 - torch.exp(-e)) * kpt_mask).mean()
 
 
-# Criterion class for computing Detection training losses
 class v8DetectionLoss:
+    """Criterion class for computing training losses."""
 
     def __init__(self, model):  # model must be de-paralleled
 
@@ -199,8 +201,8 @@ class v8DetectionLoss:
         return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
 
 
-# Criterion class for computing training losses
 class v8SegmentationLoss(v8DetectionLoss):
+    """Criterion class for computing training losses."""
 
     def __init__(self, model):  # model must be de-paralleled
         super().__init__(model)
@@ -294,8 +296,8 @@ class v8SegmentationLoss(v8DetectionLoss):
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
 
 
-# Criterion class for computing training losses
 class v8PoseLoss(v8DetectionLoss):
+    """Criterion class for computing training losses."""
 
     def __init__(self, model):  # model must be de-paralleled
         super().__init__(model)
@@ -374,7 +376,8 @@ class v8PoseLoss(v8DetectionLoss):
 
         return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
 
-    def kpts_decode(self, anchor_points, pred_kpts):
+    @staticmethod
+    def kpts_decode(anchor_points, pred_kpts):
         """Decodes predicted keypoints to image coordinates."""
         y = pred_kpts.clone()
         y[..., :2] *= 2.0
@@ -384,6 +387,7 @@ class v8PoseLoss(v8DetectionLoss):
 
 
 class v8ClassificationLoss:
+    """Criterion class for computing training losses."""
 
     def __call__(self, preds, batch):
         """Compute the classification loss between predictions and true labels."""