Ensure that checkpoint_num_classes is propagated from YAML to model

src/super_gradients/recipes/checkpoint_params/default_checkpoint_params.yaml

@@ -7,3 +7,9 @@ strict_load: # key matching strictness for loading checkpoint's weights
   _target_: super_gradients.training.sg_trainer.StrictLoad
   value: no_key_matching
 pretrained_weights: # a string describing the dataset of the pretrained weights (for example "imagenent").
+
+# num_classes of checkpoint_path/ pretrained_weights, when checkpoint_path is not None.
+# Used when num_classes != checkpoint_num_class.
+# In this case, the module will be initialized with checkpoint_num_class, then weights will be loaded.
+# Finally model.replace_head(new_num_classes=num_classes) is called to replace the head with new_num_classes.
+checkpoint_num_classes: # number of classes in the checkpoint

src/super_gradients/training/kd_trainer/kd_trainer.py

@@ -76,6 +76,7 @@ def train_from_config(cls, cfg: Union[DictConfig, dict]) -> None:
             pretrained_weights=cfg.student_checkpoint_params.pretrained_weights,
             checkpoint_path=cfg.student_checkpoint_params.checkpoint_path,
             load_backbone=cfg.student_checkpoint_params.load_backbone,
+            checkpoint_num_classes=cfg.student_checkpoint_params.checkpoint_num_classes,
         )
 
         teacher = models.get(
@@ -85,6 +86,7 @@ def train_from_config(cls, cfg: Union[DictConfig, dict]) -> None:
             pretrained_weights=cfg.teacher_checkpoint_params.pretrained_weights,
             checkpoint_path=cfg.teacher_checkpoint_params.checkpoint_path,
             load_backbone=cfg.teacher_checkpoint_params.load_backbone,
+            checkpoint_num_classes=cfg.teacher_checkpoint_params.checkpoint_num_classes,
         )
 
         recipe_logged_cfg = {"recipe_config": OmegaConf.to_container(cfg, resolve=True)}

src/super_gradients/training/sg_trainer/sg_trainer.py

@@ -380,6 +380,7 @@ def evaluate_from_recipe(cls, cfg: DictConfig) -> Tuple[nn.Module, Tuple]:
             pretrained_weights=cfg.checkpoint_params.pretrained_weights,
             checkpoint_path=cfg.checkpoint_params.checkpoint_path,
             load_backbone=cfg.checkpoint_params.load_backbone,
+            checkpoint_num_classes=cfg.checkpoint_params.checkpoint_num_classes,
         )
 
         # TEST
@@ -2340,6 +2341,7 @@ def quantize_from_config(cls, cfg: Union[DictConfig, dict]) -> Tuple[nn.Module,
             pretrained_weights=cfg.checkpoint_params.pretrained_weights,
             checkpoint_path=cfg.checkpoint_params.checkpoint_path,
             load_backbone=False,
+            checkpoint_num_classes=cfg.checkpoint_params.checkpoint_num_classes,
         )
 
         recipe_logged_cfg = {"recipe_config": OmegaConf.to_container(cfg, resolve=True)}