`ultralytics 8.2.32` Apple MPS device Autobatch handling (#13568)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.31"
+__version__ = "8.2.32"
 
 import os
 

ultralytics/utils/__init__.py

@@ -395,7 +395,7 @@ def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
         (None)
     """
     yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
-    dump = yaml.dump(yaml_dict, sort_keys=False, allow_unicode=True)
+    dump = yaml.dump(yaml_dict, sort_keys=False, allow_unicode=True, width=float("inf"))
     LOGGER.info(f"Printing '{colorstr('bold', 'black', yaml_file)}'\n\n{dump}")
 
 

ultralytics/utils/autobatch.py

@@ -45,8 +45,8 @@ def autobatch(model, imgsz=640, fraction=0.60, batch_size=DEFAULT_CFG.batch):
     prefix = colorstr("AutoBatch: ")
     LOGGER.info(f"{prefix}Computing optimal batch size for imgsz={imgsz} at {fraction * 100}% CUDA memory utilization.")
     device = next(model.parameters()).device  # get model device
-    if device.type == "cpu":
-        LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}")
+    if device.type in {"cpu", "mps"}:
+        LOGGER.info(f"{prefix} ⚠️ intended for CUDA devices, using default batch-size {batch_size}")
         return batch_size
     if torch.backends.cudnn.benchmark:
         LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}")