feat: export classifier with YOLO OpenVINO interface

tests/test_classifier_adapter.py

@@ -0,0 +1,48 @@
+import pytest
+import torch
+from torch import nn
+
+from scripts.export_classifier_openvino import YoloClassifierAdapter, class_order, preprocessing
+
+
+class FixedClassifier(nn.Module):
+    def forward(self, x):
+        return torch.tensor([1.0, 3.0, -1.0]).expand(x.shape[0], -1)
+
+
+def test_yolo_contract_and_class_reordering():
+    adapter = YoloClassifierAdapter(FixedClassifier(), 128, 336, [2, 0, 1])
+    result = adapter(torch.rand(1, 3, 128, 128))
+    assert result.shape == (1, 7, 336)
+    torch.testing.assert_close(result[0, :4, 0], torch.tensor([64., 64., 128., 128.]))
+    expected = torch.tensor([1., 3., -1.]).softmax(0)[[2, 0, 1]]
+    torch.testing.assert_close(result[0, 4:, 0], expected)
+    assert result[0, 4:, 0].argmax().item() == 2
+    assert torch.count_nonzero(result[:, :, 1:]) == 0
+
+
+def test_normalization_matches_training_for_each_image():
+    adapter = YoloClassifierAdapter(FixedClassifier(), 128, 1, [0, 1, 2])
+    images = torch.stack((torch.rand(3, 128, 128), torch.ones(3, 128, 128)))
+    expected = torch.stack([(x - x.mean()) / (x.std() + 1e-5) for x in images])
+    torch.testing.assert_close(adapter.normalize(images), expected)
+    assert torch.isfinite(adapter(images)).all()
+
+
+def test_class_sets_must_match():
+    assert class_order(["normal", "mosquito"], ["mosquito", "normal"]) == [1, 0]
+    with pytest.raises(ValueError, match="Class sets differ"):
+        class_order(["normal", "mosquito"], ["normal", "blackdot"])
+    with pytest.raises(ValueError, match="Duplicate"):
+        class_order(["normal", "normal"], ["normal"])
+
+
+def test_refuses_resampling_and_unknown_preprocessing():
+    config = {"data": {"val_transforms": [
+        {"name": "Resize", "size": [160, 160]}, {"name": "ToTensor"},
+        {"name": "PerImageStandardize"}]}}
+    with pytest.raises(ValueError, match="Training size differs"):
+        preprocessing(config, 128)
+    config["data"]["val_transforms"] = [{"name": "Normalize"}]
+    with pytest.raises(ValueError, match="Unsupported"):
+        preprocessing(config, 128)