🐛 [Fix] bbox_nms function (#76)

* Use valid_con instead of valid_cls in batched_nms

* Update test_bbox_nms

* Improve comments

* Apply black

tests/test_utils/test_bounding_box_utils.py

@@ -146,23 +146,64 @@ def test_anc2box_autoanchor(inference_v7_cfg: Config):
 
 
 def test_bbox_nms():
-    cls_dist = tensor(
-        [[[0.1, 0.7, 0.2], [0.6, 0.3, 0.1]], [[0.4, 0.4, 0.2], [0.5, 0.4, 0.1]]]  # Example class distribution
+    cls_dist = torch.tensor(
+        [
+            [
+                [0.7, 0.1, 0.2],  # High confidence, class 0
+                [0.3, 0.6, 0.1],  # High confidence, class 1
+                [-3.0, -2.0, -1.0],  # low confidence, class 2
+                [0.6, 0.2, 0.2],  # Medium confidence, class 0
+            ],
+            [
+                [0.55, 0.25, 0.2],  # Medium confidence, class 0
+                [-4.0, -0.5, -2.0],  # low confidence, class 1
+                [0.15, 0.2, 0.65],  # Medium confidence, class 2
+                [0.8, 0.1, 0.1],  # High confidence, class 0
+            ],
+        ],
+        dtype=float32,
     )
-    bbox = tensor(
-        [[[50, 50, 100, 100], [60, 60, 110, 110]], [[40, 40, 90, 90], [70, 70, 120, 120]]],  # Example bounding boxes
+
+    bbox = torch.tensor(
+        [
+            [
+                [0, 0, 160, 120],  # Overlaps with box 4
+                [160, 120, 320, 240],
+                [0, 120, 160, 240],
+                [16, 12, 176, 132],
+            ],
+            [
+                [0, 0, 160, 120],  # Overlaps with box 4
+                [160, 120, 320, 240],
+                [0, 120, 160, 240],
+                [16, 12, 176, 132],
+            ],
+        ],
         dtype=float32,
     )
+
     nms_cfg = NMSConfig(min_confidence=0.5, min_iou=0.5)
 
-    expected_output = [
-        tensor(
+    # Batch 1:
+    #  - box 1 is kept with class 0 as it has a higher confidence than box 4 i.e. box 4 is filtered out
+    #  - box 2 is kept with class 1
+    #  - box 3 is rejected by the confidence filter
+    # Batch 2:
+    #  - box 4 is kept with class 0 as it has a higher confidence than box 1 i.e. box 1 is filtered out
+    #  - box 2 is rejected by the confidence filter
+    #  - box 3 is kept with class 2
+    expected_output = torch.tensor(
+        [
             [
-                [1.0000, 50.0000, 50.0000, 100.0000, 100.0000, 0.6682],
-                [0.0000, 60.0000, 60.0000, 110.0000, 110.0000, 0.6457],
-            ]
-        )
-    ]
+                [0.0, 0.0, 0.0, 160.0, 120.0, 0.6682],
+                [1.0, 160.0, 120.0, 320.0, 240.0, 0.6457],
+            ],
+            [
+                [0.0, 16.0, 12.0, 176.0, 132.0, 0.6900],
+                [2.0, 0.0, 120.0, 160.0, 240.0, 0.6570],
+            ],
+        ]
+    )
 
     output = bbox_nms(cls_dist, bbox, nms_cfg)
 

yolo/utils/bounding_box_utils.py

@@ -387,7 +387,7 @@ def bbox_nms(cls_dist: Tensor, bbox: Tensor, nms_cfg: NMSConfig, confidence: Opt
     valid_box = bbox[valid_mask.repeat(1, 1, 4)].view(-1, 4)
 
     batch_idx, *_ = torch.where(valid_mask)
-    nms_idx = batched_nms(valid_box, valid_cls, batch_idx, nms_cfg.min_iou)
+    nms_idx = batched_nms(valid_box, valid_con, batch_idx, nms_cfg.min_iou)
     predicts_nms = []
     for idx in range(cls_dist.size(0)):
         instance_idx = nms_idx[idx == batch_idx[nms_idx]]