Fix h-label loss normalization issue w/ exclusive label group of singe label (#2604)

* Fix h-label loss normalization issue w/ exclusive label group with signle label

* Fix non-linear version

---------
Signed-off-by: Songki Choi <[email protected]>

Author: Songki Choi

CHANGELOG.md

@@ -13,6 +13,7 @@ All notable changes to this project will be documented in this file.
 
 - Fix IBLoss enablement with DeiT-Tiny when class incremental training (<https://github.com/openvinotoolkit/training_extensions/pull/2595>)
 - Fix mmcls bug not wrapping model in DataParallel on CPUs (<https://github.com/openvinotoolkit/training_extensions/pull/2601>)
+- Fix h-label loss normalization issue w/ exclusive label group of singe label (<https://github.com/openvinotoolkit/training_extensions/pull/2604>)
 
 ## \[v1.4.3\]
 

src/otx/algorithms/classification/adapters/mmcls/models/heads/custom_hierarchical_linear_cls_head.py

@@ -105,7 +105,7 @@ def forward_train(self, cls_score, gt_label, **kwargs):
                     losses["loss"] += multiclass_loss
                     num_effective_heads_in_batch += 1
 
-        if self.hierarchical_info["num_multiclass_heads"] > 1:
+        if num_effective_heads_in_batch > 0:
             losses["loss"] /= num_effective_heads_in_batch
 
         if self.compute_multilabel_loss:

src/otx/algorithms/classification/adapters/mmcls/models/heads/custom_hierarchical_non_linear_cls_head.py

@@ -135,7 +135,7 @@ def forward_train(self, cls_score, gt_label, **kwargs):
                     losses["loss"] += multiclass_loss
                     num_effective_heads_in_batch += 1
 
-        if self.hierarchical_info["num_multiclass_heads"] > 1:
+        if num_effective_heads_in_batch > 0:
             losses["loss"] /= num_effective_heads_in_batch
 
         if self.compute_multilabel_loss: