Initial public release

tests/ai/test_model_ben2_onnx.py

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+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+import numpy as np
+from PIL import Image
+
+
+from server.models import onnx_ben2
+
+
+class FakeSession:
+    def get_providers(self):
+        return ["CPUExecutionProvider"]
+
+    def get_inputs(self):
+        return [SimpleNamespace(name="input")]
+
+    def get_outputs(self):
+        return [SimpleNamespace(name="alpha")]
+
+    def run(self, output_names, feeds):
+        self.output_names = output_names
+        self.feeds = feeds
+        gradient = np.linspace(0.0, 1.0, 1024, dtype=np.float32)
+        return [np.tile(gradient, (1024, 1))[None, None]]
+
+
+class Ben2OnnxTest(unittest.TestCase):
+    def test_infer_forwards_memory_release_boundary(self):
+        session = FakeSession()
+
+        with patch.object(
+            onnx_ben2,
+            "run_session",
+            side_effect=lambda model, names, feeds, **_options: model.run(
+                names, feeds
+            ),
+        ) as run:
+            result = onnx_ben2.infer_alpha(
+                session,
+                Image.new("RGB", (40, 20)),
+                release_memory=False,
+            )
+
+        self.assertEqual(result.shape, (20, 40))
+        self.assertFalse(run.call_args.kwargs["release_memory"])
+
+    def test_preprocess_matches_model_layout(self):
+        image = Image.new("RGB", (40, 20), (64, 128, 192))
+        result = onnx_ben2.preprocess(image)
+
+        self.assertEqual(result.shape, (1, 3, 1024, 1024))
+        self.assertEqual(result.dtype, np.float32)
+        self.assertTrue(result.flags.c_contiguous)
+
+    def test_infer_restores_original_size_and_alpha(self):
+        session = FakeSession()
+        image = Image.new("RGB", (40, 20), (10, 20, 30))
+
+        result = onnx_ben2.infer_alpha(session, image)
+
+        self.assertEqual(result.dtype, np.float32)
+        self.assertEqual(result.shape, (20, 40))
+        self.assertEqual(session.feeds["input"].shape, (1, 3, 1024, 1024))
+        self.assertLess(result[:, 0].max(), result[:, -1].min())
+        self.assertTrue(np.any(np.abs(result * 255 - np.rint(result * 255)) > 0.01))
+
+    def test_infer_rejects_nonfinite_mask(self):
+        with patch.object(onnx_ben2, "run_session", return_value=[np.full((1, 1, 2, 2), np.nan)]):
+            with self.assertRaisesRegex(RuntimeError, "invalid alpha"):
+                onnx_ben2.infer_alpha(FakeSession(), Image.new("RGB", (2, 2)))
+
+
+if __name__ == "__main__":
+    unittest.main()