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_da3_onnx.py

@@ -3,6 +3,7 @@ import tempfile
 import unittest
 from pathlib import Path
 from types import SimpleNamespace
+from unittest.mock import patch
 
 import numpy as np
 from PIL import Image
@@ -21,6 +22,9 @@ import onnx_da3
 
 
 class FakeSession:
+    def get_providers(self):
+        return ["CPUExecutionProvider"]
+
     def get_inputs(self):
         return [SimpleNamespace(name="images"), SimpleNamespace(name="strategy")]
 
@@ -36,6 +40,27 @@ class FakeSession:
 
 
 class Da3OnnxTest(unittest.TestCase):
+    def test_infer_forwards_memory_release_boundary(self):
+        with tempfile.TemporaryDirectory() as directory:
+            image_path = Path(directory) / "input.png"
+            Image.new("RGB", (80, 60)).save(image_path)
+            session = FakeSession()
+            with patch.object(
+                onnx_da3,
+                "run_session",
+                side_effect=lambda model, names, feeds, **_options: model.run(
+                    names, feeds
+                ),
+            ) as run:
+                onnx_da3.infer(
+                    session,
+                    [image_path],
+                    process_res=56,
+                    release_memory=False,
+                )
+
+        self.assertFalse(run.call_args.kwargs["release_memory"])
+
     def test_target_size_uses_patch_multiple(self):
         self.assertEqual(
             onnx_da3.target_size((800, 600), 504, "upper_bound_resize"),