`ultralytics 8.0.109` HUB training fix (#2818)

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

tests/test_cli.py

@@ -29,7 +29,7 @@ def test_special_modes():
 
 @pytest.mark.parametrize('task,model,data', TASK_ARGS)
 def test_train(task, model, data):
-    run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1')
+    run(f'yolo train {task} model={model}.yaml data={data} imgsz=32 epochs=1 cache=disk')
 
 
 @pytest.mark.parametrize('task,model,data', TASK_ARGS)

tests/test_python.py

@@ -108,13 +108,13 @@ def test_amp():
 
 def test_train_scratch():
     model = YOLO(CFG)
-    model.train(data='coco8.yaml', epochs=1, imgsz=32)
+    model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='disk')  # test disk caching
     model(SOURCE)
 
 
 def test_train_pretrained():
     model = YOLO(MODEL)
-    model.train(data='coco8.yaml', epochs=1, imgsz=32)
+    model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='ram')  # test RAM caching
     model(SOURCE)
 
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.108'
+__version__ = '8.0.109'
 
 from ultralytics.hub import start
 from ultralytics.vit.rtdetr import RTDETR

ultralytics/yolo/data/base.py

@@ -54,7 +54,7 @@ class BaseDataset(Dataset):
                  hyp=DEFAULT_CFG,
                  prefix='',
                  rect=False,
-                 batch_size=None,
+                 batch_size=16,
                  stride=32,
                  pad=0.5,
                  single_cls=False,
@@ -77,6 +77,10 @@ class BaseDataset(Dataset):
             assert self.batch_size is not None
             self.set_rectangle()
 
+        # Buffer thread for mosaic images
+        self.buffer = []  # buffer size = batch size
+        self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0
+
         # Cache stuff
         if cache == 'ram' and not self.check_cache_ram():
             cache = False
@@ -88,10 +92,6 @@ class BaseDataset(Dataset):
         # Transforms
         self.transforms = self.build_transforms(hyp=hyp)
 
-        # Buffer thread for mosaic images
-        self.buffer = []  # buffer size = batch size
-        self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0
-
     def get_img_files(self, img_path):
         """Read image files."""
         try: