fix: suppress normal pseudo boxes in classifier exports

tests/test_classifier_adapter.py

@@ -37,6 +37,41 @@ def test_class_sets_must_match():
         class_order(["normal", "normal"], ["normal"])
 
 
+def test_background_suppression_uses_reordered_output_index():
+    # Source class 1 wins, but it is output class 2 after reordering.
+    adapter = YoloClassifierAdapter(FixedClassifier(), 128, 336, [2, 0, 1], background_index=2)
+    result = adapter(torch.rand(1, 3, 128, 128))
+    assert result.shape == (1, 7, 336)
+    assert torch.count_nonzero(result[:, 4:, :]) == 0
+    # The existing C++ decoder returns zero candidates at any positive threshold.
+    assert not (result[0, 4:, :].max(dim=0).values >= 0.25).any()
+
+
+def test_foreground_class_indices_and_scores_are_preserved():
+    foreground = YoloClassifierAdapter(FixedClassifier(), 128, 336, [0, 1, 2],
+                                      background_index=0)
+    legacy = YoloClassifierAdapter(FixedClassifier(), 128, 336, [0, 1, 2])
+    x = torch.rand(1, 3, 128, 128)
+    torch.testing.assert_close(foreground(x), legacy(x))
+    assert foreground(x)[0, 4:, 0].argmax() == 1
+
+
+def test_background_gate_is_per_image_in_batch():
+    class BatchClassifier(nn.Module):
+        def forward(self, x):
+            return x[:, :, 0, 0]
+
+    x = torch.zeros(2, 3, 4, 4)
+    x[0, 0, 0, 0] = 3  # normal
+    x[1, 1, 0, 0] = 3  # blackdot
+    adapter = YoloClassifierAdapter(BatchClassifier(), 4, 10, [0, 1, 2],
+                                    eps=None, background_index=0)
+    result = adapter(x)
+    assert not torch.count_nonzero(result[0, 4:, :])
+    assert result[1, 4:, 0].argmax() == 1
+    assert result[1, 5, 0] > 0.9
+
+
 def test_refuses_resampling_and_unknown_preprocessing():
     config = {"data": {"val_transforms": [
         {"name": "Resize", "size": [160, 160]}, {"name": "ToTensor"},