remove cls_weight

ultralytics/cfg/default.yaml

@@ -116,7 +116,6 @@ mosaic: 1.0 # (float) image mosaic (probability)
 mixup: 0.0 # (float) image mixup (probability)
 copy_paste: 0.0 # (float) segment copy-paste (probability)
 copy_paste_mode: "flip"  # (str) the method to do copy_paste augmentation (flip, mixup)
-cls_weight: False  # (bool) Whether to sample images with cls weights when doing mosaic augmentation
 auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
 erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
 crop_fraction: 1.0 # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.

ultralytics/data/augment.py

@@ -188,7 +188,7 @@ class Mosaic(BaseMixTransform):
         n (int, optional): The grid size, either 4 (for 2x2) or 9 (for 3x3).
     """
 
-    def __init__(self, dataset, imgsz=640, p=1.0, n=4, use_cls_weight=False):
+    def __init__(self, dataset, imgsz=640, p=1.0, n=4):
         """Initializes the object with a dataset, image size, probability, and border."""
         assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."
         assert n in {4, 9}, "grid must be equal to 4 or 9."
@@ -197,13 +197,10 @@ class Mosaic(BaseMixTransform):
         self.imgsz = imgsz
         self.border = (-imgsz // 2, -imgsz // 2)  # width, height
         self.n = n
-        self.use_cls_weight = use_cls_weight
 
-    def get_indexes(self, buffer=False):
+    def get_indexes(self, buffer=True):
         """Return a list of random indexes from the dataset."""
-        if self.use_cls_weight and self.dataset.cls_weights is not None:
-            return np.random.choice(len(self.dataset), self.n - 1, replace=False, p=self.dataset.cls_weights)
-        elif buffer:  # select images from buffer
+        if buffer:  # select images from buffer
             return random.choices(list(self.dataset.buffer), k=self.n - 1)
         else:  # select any images
             return [random.randint(0, len(self.dataset) - 1) for _ in range(self.n - 1)]
@@ -379,10 +376,7 @@ class MixUp(BaseMixTransform):
 
     def get_indexes(self):
         """Get a random index from the dataset."""
-        if self.dataset.cls_weights is not None:
-            return np.random.choice(len(self.dataset), 1, replace=False, p=self.dataset.cls_weights)
-        else:
-            return random.randint(0, len(self.dataset) - 1)
+        return random.randint(0, len(self.dataset) - 1)
 
     def _mix_transform(self, labels):
         """Applies MixUp augmentation as per https://arxiv.org/pdf/1710.09412.pdf."""
@@ -840,10 +834,7 @@ class CopyPaste(BaseMixTransform):
             >>> print(index)
             42
         """
-        if self.dataset.cls_weights is not None:
-            return np.random.choice(len(self.dataset), 1, replace=False, p=self.dataset.cls_weights)
-        else:
-            return random.randint(0, len(self.dataset) - 1)
+        return random.randint(0, len(self.dataset) - 1)
 
     def _mix_transform(self, labels):
         """Applies CopyPaste augmentation."""
@@ -1204,7 +1195,7 @@ class RandomLoadText:
 
 def v8_transforms(dataset, imgsz, hyp, stretch=False):
     """Convert images to a size suitable for YOLOv8 training."""
-    mosaic = Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic, use_cls_weight=False)
+    mosaic = Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic)
     affine = RandomPerspective(
         degrees=hyp.degrees,
         translate=hyp.translate,
@@ -1221,7 +1212,7 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
         pre_transform.append(
             CopyPaste(
                 dataset,
-                pre_transform=Compose([Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic, use_cls_weight=False), affine]),
+                pre_transform=Compose([Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic), affine]),
                 p=hyp.copy_paste,
             )
         )

ultralytics/data/base.py

@@ -93,37 +93,9 @@ class BaseDataset(Dataset):
         if (self.cache == "ram" and self.check_cache_ram()) or self.cache == "disk":
             self.cache_images()
 
-        self.cls_weights = self.calculate_cls_weights() if hyp.cls_weight else None
         # Transforms
         self.transforms = self.build_transforms(hyp=hyp)
 
-    def calculate_cls_weights(self):
-        cls = np.concatenate([l["cls"].reshape(-1) for l in self.labels])
-        counts = np.bincount(cls.astype(int), minlength=len(self.data["names"]))
-        class_weights = counts.sum() / counts
-        # weights = np.zeros(len(self.labels))
-        im_weights = np.ones(len(self.labels))
-        for i, label in enumerate(self.labels):
-            cls = label["cls"].reshape(-1).astype(np.int32)
-            if len(cls) == 0:
-                continue
-            im_weights[i] = np.sum(class_weights[cls])
-
-        # import matplotlib.pyplot as plt
-        # plt.switch_backend("Agg")
-        # _, ax = plt.subplots(2, 1, figsize=(21, 6), tight_layout=True)
-        # ax = ax.ravel()
-        # ax[0].plot(im_weights / im_weights.sum())
-        # plt.savefig("cls.png")
-        # exit()
-        return im_weights / im_weights.sum()
-
-        # weights[i] = np.mean(counts[cls])
-        # set mean value of weights for background images
-        # weights = np.where(weights == 0, weights.mean(), weights)
-        # weights = weights.max() - weights + 1
-        # return weights / weights.sum()
-
     def get_img_files(self, img_path):
         """Read image files."""
         try: