removed old test for forward wrapper, fixed defaults

src/super_gradients/training/models/detection_models/customizable_detector.py

@@ -202,7 +202,7 @@ def get_dataset_processing_params(self):
             conf=self._default_nms_iou,
             nms_top_k=self._default_nms_top_k,
             max_predictions=self._default_max_predictions,
-            multi_label_per_box=self._multi_label_per_box,
+            multi_label_per_box=self._default_multi_label_per_box,
             class_agnostic_nms=self._default_class_agnostic_nms,
         )
 

src/super_gradients/training/models/detection_models/sliding_window_detection_forward_wrapper.py

@@ -62,6 +62,15 @@ def __init__(
         self.tile_step = tile_step
         self.min_tile_threshold = min_tile_threshold
 
+        self._class_names: Optional[List[str]] = None
+        self._image_processor: Optional[Processing] = None
+        self._default_nms_iou: float = 0.7
+        self._default_nms_conf: float = 0.5
+        self._default_nms_top_k: int = 1024
+        self._default_max_predictions = 300
+        self._default_multi_label_per_box = True
+        self._default_class_agnostic_nms = False
+
         # Processing params
         self.model = model
         self.set_dataset_processing_params(**self.model.get_dataset_processing_params())

tests/unit_tests/detection_sliding_window_wrapper_test.py

@@ -6,36 +6,12 @@
 from super_gradients.training import Trainer
 from super_gradients.training.models.detection_models.sliding_window_detection_forward_wrapper import SlidingWindowInferenceDetectionWrapper
 from super_gradients.training.metrics import DetectionMetrics
-from super_gradients.training import training_hyperparams
 
 
 class SlidingWindowWrapperTest(unittest.TestCase):
     def setUp(self):
         self.mini_coco_data_dir = str(Path(__file__).parent.parent / "data" / "tinycoco")
 
-    def test_train_with_sliding_window_wrapper_validation(self):
-        train_params = training_hyperparams.get("coco2017_yolo_nas_s")
-
-        train_params["valid_metrics_list"] = [
-            DetectionMetrics(
-                normalize_targets=True,
-                post_prediction_callback=None,
-                num_cls=80,
-            )
-        ]
-        train_params["max_epochs"] = 2
-        train_params["lr_warmup_epochs"] = 0
-        train_params["lr_cooldown_epochs"] = 0
-        train_params["average_best_models"] = False
-        train_params["mixed_precision"] = False
-        train_params["validation_forward_wrapper"] = SlidingWindowInferenceDetectionWrapper(tile_size=320, tile_step=160, tile_nms_iou=0.65, tile_nms_conf=0.03)
-
-        dl = coco2017_val_yolo_nas(dataset_params=dict(data_dir=self.mini_coco_data_dir))
-
-        trainer = Trainer("test_yolo_nas_s_coco_with_sliding_window")
-        model = models.get("yolo_nas_s", num_classes=80, pretrained_weights="coco")
-        trainer.train(model=model, training_params=train_params, train_loader=dl, valid_loader=dl)
-
     def test_yolo_nas_s_coco_with_sliding_window(self):
         trainer = Trainer("test_yolo_nas_s_coco_with_sliding_window")
         model = models.get("yolo_nas_s", num_classes=80, pretrained_weights="coco")