Add ONNX model inference implementations for MoGe-2, BEN2, and DA3

- Introduced `onnx_moge2.py` for MoGe-2 ONNX inference with NumPy post-processing.
- Added `onnx_ben2.py` for BEN2 ONNX inference with Pillow and NumPy preprocessing.
- Created `onnx_da3.py` for Depth Anything 3 inference with ONNX Runtime and NumPy.
- Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`.
- Developed unit tests for server functionalities in `test_server.py`, including model caching and job management.
- Enhanced job server to handle frame extraction without requiring models.

tests/test_ben2_onnx.py

@@ -11,11 +11,11 @@ SOURCE_DIR = (
     Path(__file__).resolve().parents[1]
     / "src"
     / "snapform"
-    / "ai_service"
+    / "server"
 )
 sys.path.insert(0, str(SOURCE_DIR))
 
-import ben2_onnx
+import onnx_ben2
 
 
 class FakeSession:
@@ -35,7 +35,7 @@ class FakeSession:
 class Ben2OnnxTest(unittest.TestCase):
     def test_preprocess_matches_model_layout(self):
         image = Image.new("RGB", (40, 20), (64, 128, 192))
-        result = ben2_onnx.preprocess(image)
+        result = onnx_ben2.preprocess(image)
 
         self.assertEqual(result.shape, (1, 3, 1024, 1024))
         self.assertEqual(result.dtype, np.float32)
@@ -45,7 +45,7 @@ class Ben2OnnxTest(unittest.TestCase):
         session = FakeSession()
         image = Image.new("RGB", (40, 20), (10, 20, 30))
 
-        result = ben2_onnx.infer(session, image)
+        result = onnx_ben2.infer(session, image)
 
         self.assertEqual(result.mode, "RGBA")
         self.assertEqual(result.size, image.size)