Merge branch 'refs/heads/master' into feature/SG-1448-OBB

# Conflicts:
#	src/super_gradients/training/utils/distributed_training_utils.py

src/super_gradients/training/sg_trainer/sg_trainer.py

@@ -1428,6 +1428,14 @@ def get_finetune_lr_dict(self, lr: float) -> Dict[str, float]:
 
         self.ckpt_best_name = self.training_params.ckpt_best_name
 
+        if self.training_params.average_best_models and not self.training_params.save_model:
+            logger.warning(
+                "'training_params.average_best_models'  is enabled, but 'training_params.save_model' is disabled. \n"
+                "Model averaging requires saving snapshot checkpoints to function properly. As a result, "
+                "'training_params.average_best_models' will be disabled. "
+            )
+            self.training_params.average_best_models = False
+
         self.max_train_batches = self.training_params.max_train_batches
         self.max_valid_batches = self.training_params.max_valid_batches
 

src/super_gradients/training/utils/distributed_training_utils.py

@@ -10,7 +10,6 @@
 from torch import distributed as dist
 from torch.cuda.amp import autocast
 from torch.distributed import get_rank, all_gather_object
-from torch.distributed.elastic.multiprocessing import Std
 from torch.distributed.elastic.multiprocessing.errors import record
 from torch.distributed.launcher.api import LaunchConfig, elastic_launch
 
@@ -345,8 +344,6 @@ def restart_script_with_ddp(num_gpus: int = None):
         max_restarts=0,
         monitor_interval=5,
         start_method="spawn",
-        redirects=Std.NONE,
-        tee=Std.NONE,
         metrics_cfg={},
     )
 

src/super_gradients/training/utils/predict/prediction_pose_estimation_results.py

@@ -119,7 +119,14 @@ def save(
         :param show_confidence: Whether to show confidence scores on the image.
         :param box_thickness:   (Optional) Thickness of bounding boxes. If None, will adapt to the box size.
         """
-        image = self.draw(box_thickness=box_thickness, show_confidence=show_confidence)
+        image = self.draw(
+            edge_colors=edge_colors,
+            joint_thickness=joint_thickness,
+            keypoint_colors=keypoint_colors,
+            keypoint_radius=keypoint_radius,
+            box_thickness=box_thickness,
+            show_confidence=show_confidence,
+        )
         save_image(image=image, path=output_path)