🐛 [Fix] PIL read image, manually close image

yolo/tools/data_loader.py

@@ -133,7 +133,8 @@ class YoloDataset(Dataset):
 
     def get_data(self, idx):
         img_path, bboxes = self.data[idx]
-        img = Image.open(img_path).convert("RGB")
+        with Image.open(img_path) as img:
+            img = img.convert("RGB")
         return img, bboxes, img_path
 
     def get_more_data(self, num: int = 1):

yolo/tools/loss_functions.py

@@ -129,9 +129,9 @@ class DualLoss:
             self.dfl_rate * (aux_dfl * self.aux_rate + main_dfl),
             self.cls_rate * (aux_cls * self.aux_rate + main_cls),
         ]
-        loss_dict = dict()
-        for name, value in zip(["Box", "DFL", "BCE"], total_loss):
-            loss_dict[f"Loss/{name}Loss"] = value.detach()
+        loss_dict = {
+            f"Loss/{name}Loss": value.detach().item() for name, value in zip(["Box", "DFL", "BCE"], total_loss)
+        }
         return sum(total_loss), loss_dict
 
 

yolo/utils/bounding_box_utils.py

@@ -69,7 +69,8 @@ def calculate_iou(bbox1, bbox2, metrics="iou") -> Tensor:
         (bbox2[..., 2] - bbox2[..., 0]) / (bbox2[..., 3] - bbox2[..., 1] + EPS)
     )
     v = (4 / (math.pi**2)) * (arctan**2)
-    alpha = v / (v - iou + 1 + EPS)
+    with torch.no_grad():
+        alpha = v / (v - iou + 1 + EPS)
     # Compute CIoU
     ciou = diou - alpha * v
     return ciou.to(dtype)

yolo/utils/logger.py

@@ -3,7 +3,7 @@ import logging
 from rich.console import Console
 from rich.logging import RichHandler
 
-logger = logging.getLogger(__name__)
+logger = logging.getLogger("yolo")
 logger.setLevel(logging.DEBUG)
 logger.propagate = False
 if not logger.hasHandlers():

yolo/utils/logging_utils.py

@@ -154,9 +154,6 @@ class YOLORichProgressBar(RichProgressBar):
 
 
 class YOLORichModelSummary(RichModelSummary):
-
-    from typing_extensions import override
-
     @staticmethod
     @override
     def summarize(