ultralytics 8.1.38 fix deprecated Ray Tune .is_session_enabled() (#9432)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.37"
+__version__ = "8.1.38"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/data/base.py

@@ -81,6 +81,8 @@ class BaseDataset(Dataset):
         if self.rect:
             assert self.batch_size is not None
             self.set_rectangle()
+        if isinstance(cache, str):
+            cache = cache.lower()
 
         # Buffer thread for mosaic images
         self.buffer = []  # buffer size = batch size

ultralytics/data/dataset.py

@@ -261,8 +261,8 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
         if augment and args.fraction < 1.0:  # reduce training fraction
             self.samples = self.samples[: round(len(self.samples) * args.fraction)]
         self.prefix = colorstr(f"{prefix}: ") if prefix else ""
-        self.cache_ram = args.cache is True or args.cache == "ram"  # cache images into RAM
-        self.cache_disk = args.cache == "disk"  # cache images on hard drive as uncompressed *.npy files
+        self.cache_ram = args.cache is True or str(args.cache).lower() == "ram"  # cache images into RAM
+        self.cache_disk = str(args.cache).lower() == "disk"  # cache images on hard drive as uncompressed *.npy files
         self.samples = self.verify_images()  # filter out bad images
         self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples]  # file, index, npy, im
         scale = (1.0 - args.scale, 1.0)  # (0.08, 1.0)
@@ -285,8 +285,9 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
     def __getitem__(self, i):
         """Returns subset of data and targets corresponding to given indices."""
         f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
-        if self.cache_ram and im is None:
-            im = self.samples[i][3] = cv2.imread(f)
+        if self.cache_ram:
+            if im is None:  # Warning: two separate if statements required here, do not combine this with previous line
+                im = self.samples[i][3] = cv2.imread(f)
         elif self.cache_disk:
             if not fn.exists():  # load npy
                 np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)

ultralytics/utils/callbacks/raytune.py

@@ -14,7 +14,7 @@ except (ImportError, AssertionError):
 
 def on_fit_epoch_end(trainer):
     """Sends training metrics to Ray Tune at end of each epoch."""
-    if ray.tune.is_session_enabled():
+    if ray.train._internal.session._get_session():  # replacement for deprecated ray.tune.is_session_enabled()
         metrics = trainer.metrics
         metrics["epoch"] = trainer.epoch
         session.report(metrics)

ultralytics/utils/tuner.py

@@ -40,7 +40,7 @@ def run_ray_tune(
         train_args = {}
 
     try:
-        subprocess.run("pip install ray[tune]<=2.9.3".split(), check=True)  # do not add single quotes here
+        subprocess.run("pip install ray[tune]".split(), check=True)  # do not add single quotes here
 
         import ray
         from ray import tune
@@ -48,7 +48,7 @@ def run_ray_tune(
         from ray.air.integrations.wandb import WandbLoggerCallback
         from ray.tune.schedulers import ASHAScheduler
     except ImportError:
-        raise ModuleNotFoundError('Ray Tune required but not found. To install run: pip install "ray[tune]<=2.9.3"')
+        raise ModuleNotFoundError('Ray Tune required but not found. To install run: pip install "ray[tune]"')
 
     try:
         import wandb