Add docstrings to YOLOv5 functions (#12760)

* Add docstrings to top level files * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Update activations.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
2024-02-25 14:04:01 +01:00
1 parent ca00c34c2a
commit 41603da162
@@ -12,6 +12,9 @@ from .general import crop_mask
 class ComputeLoss:
    # Compute losses
    def __init__(self, model, autobalance=False, overlap=False):
+        """Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap
+        handling.
+        """
        self.sort_obj_iou = False
        self.overlap = overlap
        device = next(model.parameters()).device  # get model device
@@ -109,13 +112,15 @@ class ComputeLoss:
        return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()

    def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
-        # Mask loss for one image
+        """Calculates and normalizes single mask loss for YOLOv5 between predicted and ground truth masks."""
        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")
        return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()

    def build_targets(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+        """Prepares YOLOv5 targets for loss computation; inputs targets (image, class, x, y, w, h), output target
+        classes/boxes.
+        """
        na, nt = self.na, targets.shape[0]  # number of anchors, targets
        tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
        gain = torch.ones(8, device=self.device)  # normalized to gridspace gain