feat: Implement unified memory management for ONNX model inference

- Introduced a new memory management policy for ONNX Runtime to release temporary GPU memory after inference completion.
- Established a shared inference entry point for all ONNX models, differentiating between long-lived sessions and temporary memory arenas.
- Updated CUDA sessions to utilize a shrinkable memory arena, reclaiming GPU memory post-inference.
- Ensured consistent memory release semantics across BEN2, DA3, MoGe-2, and Upscale models while reusing existing sessions.
- Added lifecycle tests to validate temporary memory release and session reuse.
- Modified model adapters to integrate the new memory management features, ensuring backward compatibility with existing inference behavior.
- Enhanced unit tests to cover new memory release scenarios and verify correct behavior across different execution providers.

tests/test_model_moge2_onnx.py

@@ -56,6 +56,9 @@ class FakeSession:
         self.calls.append((names, inputs))
         return [self.outputs[name] for name in names]
 
+    def get_providers(self):
+        return ["CPUExecutionProvider"]
+
     def get_outputs(self):
         return [FakeInput(name) for name in self.outputs]
 
@@ -145,6 +148,35 @@ class Moge2OnnxTest(unittest.TestCase):
         self.assertEqual(inputs["num_tokens"].dtype, np.int64)
         self.assertEqual(inputs["num_tokens"], 3600)
 
+    def test_infer_forwards_memory_release_boundary(self):
+        height, width = 8, 12
+        raw = {
+            "points": np.zeros((1, height, width, 3), dtype=np.float32),
+            "normal": np.zeros((1, height, width, 3), dtype=np.float32),
+            "mask": np.ones((1, height, width), dtype=np.float32),
+            "scale": np.ones((1,), dtype=np.float32),
+        }
+        session = FakeSession(raw)
+
+        with (
+            patch.object(self.moge, "postprocess", return_value={}),
+            patch.object(
+                self.moge,
+                "run_session",
+                side_effect=lambda model, names, feeds, **_options: model.run(
+                    names, feeds
+                ),
+            ) as run,
+        ):
+            self.moge.infer(
+                session,
+                np.zeros((height, width, 3), dtype=np.float32),
+                0,
+                release_memory=False,
+            )
+
+        self.assertFalse(run.call_args.kwargs["release_memory"])
+
     def test_postprocess_recovers_camera_projection_and_metric_scale(self):
         height, width = 64, 80
         focal = np.float32(0.9)