New train profile argument for loggers (#2862)

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

ultralytics/yolo/cfg/__init__.py

@@ -1,4 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+
 import contextlib
 import re
 import shutil
@@ -72,7 +73,7 @@ CFG_INT_KEYS = ('epochs', 'patience', 'batch', 'workers', 'seed', 'close_mosaic'
 CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', 'rect', 'cos_lr', 'overlap_mask', 'val',
                  'save_json', 'save_hybrid', 'half', 'dnn', 'plots', 'show', 'save_txt', 'save_conf', 'save_crop',
                  'show_labels', 'show_conf', 'visualize', 'augment', 'agnostic_nms', 'retina_masks', 'boxes', 'keras',
-                 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
+                 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader', 'profile')
 
 
 def cfg2dict(cfg):

ultralytics/yolo/cfg/default.yaml

@@ -31,6 +31,7 @@ close_mosaic: 0  # (int) disable mosaic augmentation for final epochs
 resume: False  # resume training from last checkpoint
 amp: True  # Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
 fraction: 1.0  # dataset fraction to train on (default is 1.0, all images in train set)
+profile: False  # profile ONNX and TensorRT speeds during training for loggers
 # Segmentation
 overlap_mask: True  # masks should overlap during training (segment train only)
 mask_ratio: 4  # mask downsample ratio (segment train only)