fixed cyclic lr state dict

src/super_gradients/training/sg_trainer/sg_trainer.py

@@ -85,6 +85,7 @@
     read_ckpt_state_dict,
     load_checkpoint_to_model,
     load_pretrained_weights,
+    get_scheduler_state,
 )
 from super_gradients.training.datasets.datasets_utils import DatasetStatisticsTensorboardLogger
 from super_gradients.training.utils.callbacks import (
@@ -666,7 +667,7 @@ def _save_checkpoint(
             state["processing_params"] = processing_params
 
         if self._torch_lr_scheduler is not None:
-            state["torch_scheduler_state_dict"] = self._torch_lr_scheduler.state_dict()
+            state["torch_scheduler_state_dict"] = get_scheduler_state(self._torch_lr_scheduler)
 
         # SAVES CURRENT MODEL AS ckpt_latest
         self.sg_logger.add_checkpoint(tag="ckpt_latest.pth", state_dict=state, global_step=epoch)

src/super_gradients/training/utils/checkpoint_utils.py

@@ -1,7 +1,7 @@
 import collections
 import os
 import tempfile
-from typing import Union, Mapping
+from typing import Union, Mapping, Dict
 
 import pkg_resources
 import torch
@@ -1629,3 +1629,19 @@ def _maybe_load_preprocessing_params(model: Union[nn.Module, HasPredict], checkp
                 "predict make sure to call set_dataset_processing_params."
             )
     return False
+
+
+def get_scheduler_state(scheduler) -> Dict[str, Tensor]:
+    """
+    Wrapper for getting a torch lr scheduler state dict, resolving some issues with CyclicLR
+    (see https://github.com/pytorch/pytorch/pull/91400)
+    :param scheduler: torch.optim.lr_scheduler._LRScheduler, the scheduler
+    :return:          the scheduler's state_dict
+    """
+    from super_gradients.training.utils import torch_version_is_greater_or_equal
+    from torch.optim.lr_scheduler import CyclicLR
+
+    state = scheduler.state_dict()
+    if isinstance(scheduler, CyclicLR) and not torch_version_is_greater_or_equal(2, 0):
+        del state["_scale_fn_ref"]
+    return state