From 1321c38b0639197e6adc755592497e810c5e095e Mon Sep 17 00:00:00 2001
From: Oleksandr Bezdieniezhnykh <oleksandr.bezdieniezhnykh@pwc.com>
Date: Sat, 28 Mar 2026 17:32:39 +0200
Subject: [PATCH] [AZ-171] Add export tests for dynamic batch ONNX and CoreML

Made-with: Cursor
---
 tests/test_export.py | 113 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 tests/test_export.py

diff --git a/tests/test_export.py b/tests/test_export.py
new file mode 100644
index 0000000..866fd00
--- /dev/null
+++ b/tests/test_export.py
@@ -0,0 +1,113 @@
+import shutil
+from pathlib import Path
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+import pytest
+from ultralytics import YOLO
+
+import constants as c
+import exports as exports_mod
+
+_TESTS_DIR = Path(__file__).resolve().parent
+_CONFIG_TEST = _TESTS_DIR.parent / "config.test.yaml"
+_DATASET_IMAGES = _TESTS_DIR / "root" / "data" / "images"
+
+
+@pytest.fixture(scope="module")
+def exported_models(tmp_path_factory):
+    tmp = tmp_path_factory.mktemp("export")
+    model_dir = tmp / "models"
+    model_dir.mkdir()
+
+    # Arrange
+    pt_path = str(model_dir / "test.pt")
+    model = YOLO("yolo11n.pt")
+    model.save(pt_path)
+
+    old_config = c.config
+    c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(tmp))
+
+    onnx_out = model_dir / "test.onnx"
+    coreml_out = model_dir / "test.mlpackage"
+
+    # Act
+    exports_mod.export_onnx(pt_path)
+    exports_mod.export_coreml(pt_path)
+
+    yield {
+        "pt": pt_path,
+        "onnx": str(onnx_out) if onnx_out.exists() else str(next(model_dir.glob("*.onnx"))),
+        "coreml": str(coreml_out) if coreml_out.exists() else str(next(model_dir.glob("*.mlpackage"), "")),
+        "model_dir": model_dir,
+    }
+
+    c.config = old_config
+
+
+class TestOnnxExport:
+    def test_onnx_file_created(self, exported_models):
+        # Assert
+        p = Path(exported_models["onnx"])
+        assert p.exists()
+        assert p.stat().st_size > 0
+
+    def test_onnx_batch_dimension_is_dynamic(self, exported_models):
+        # Arrange
+        session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
+        batch_dim = session.get_inputs()[0].shape[0]
+
+        # Assert
+        assert isinstance(batch_dim, str) or batch_dim == -1
+
+    def test_onnx_inference_batch_1(self, exported_models):
+        # Arrange
+        session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
+        meta = session.get_inputs()[0]
+        imgsz = c.config.export.onnx_imgsz
+        imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
+        if not imgs:
+            pytest.skip("no test images")
+        blob = cv2.dnn.blobFromImage(
+            cv2.imread(str(imgs[0])), 1.0 / 255.0, (imgsz, imgsz), (0, 0, 0), swapRB=True, crop=False,
+        )
+
+        # Act
+        out = session.run(None, {meta.name: blob})
+
+        # Assert
+        assert out[0].shape[0] == 1
+
+    def test_onnx_inference_batch_multiple(self, exported_models):
+        # Arrange
+        session = ort.InferenceSession(exported_models["onnx"], providers=["CPUExecutionProvider"])
+        meta = session.get_inputs()[0]
+        imgsz = c.config.export.onnx_imgsz
+        imgs = sorted(_DATASET_IMAGES.glob("*.jpg"))
+        if not imgs:
+            pytest.skip("no test images")
+        single = cv2.dnn.blobFromImage(
+            cv2.imread(str(imgs[0])), 1.0 / 255.0, (imgsz, imgsz), (0, 0, 0), swapRB=True, crop=False,
+        )
+        batch = np.concatenate([single] * 4, axis=0)
+
+        # Act
+        out = session.run(None, {meta.name: batch})
+
+        # Assert
+        assert out[0].shape[0] == 4
+
+
+class TestCoremlExport:
+    def test_coreml_package_created(self, exported_models):
+        # Assert
+        pkgs = list(exported_models["model_dir"].glob("*.mlpackage"))
+        assert len(pkgs) >= 1
+
+    def test_coreml_package_has_model(self, exported_models):
+        # Assert
+        pkgs = list(exported_models["model_dir"].glob("*.mlpackage"))
+        assert len(pkgs) >= 1
+        model_file = pkgs[0] / "Data" / "com.apple.CoreML" / "model.mlmodel"
+        assert model_file.exists()