Fix Learning Rate and Loss Handling in Tile Classifier MaskRCNN EfficientNet (#3873)

* fixes

* add test case

Author: eugene123tw

src/otx/algorithms/detection/adapters/mmdet/models/detectors/custom_maskrcnn_tile_optimized.py

@@ -149,6 +149,8 @@ def forward_train(
             img, img_metas, gt_bboxes, gt_labels, gt_bboxes_ignore, gt_masks, proposals, **kwargs
         )
         losses.update(rcnn_loss)
+        if "acc" in losses:
+            losses.pop("acc")
         return losses
 
     @staticmethod

src/otx/algorithms/detection/adapters/mmdet/utils/config_utils.py

@@ -122,11 +122,8 @@ def patch_tiling(config, hparams, dataset=None):
 
             if config.model.backbone.type == "efficientnet_b2b":
                 learning_rate = 0.002
-                logger.info(
-                    f"Patched {config.model.backbone.type} LR: "
-                    f"{hparams.learning_parameters.learning_rate} -> {learning_rate}"
-                )
-                hparams.learning_parameters.learning_rate = learning_rate
+                logger.info(f"Patched {config.model.backbone.type} LR: " f"{config.optimizer.lr} -> {learning_rate}")
+                config.optimizer.lr = learning_rate
 
             config.data.train.filter_empty_gt = False
 

tests/unit/algorithms/detection/tiling/test_tiling_tile_classifier.py

@@ -211,3 +211,4 @@ def test_patch_tiling_func(self):
         hyper_parameters.tiling_parameters.enable_tiling = True
         hyper_parameters.tiling_parameters.enable_tile_classifier = True
         patch_tiling(cfg, hyper_parameters, self.dataset)
+        assert cfg.optimizer.lr == 0.002, "Learning rate should be 0.002 when MRCNN EfficientNet is used"