Add: test code for CoreMLExecutionProvider on a trial basis

Currently ONNXRuntime's CoreML support is not very good, and on my environment, I get errors like “coreml_execution_provider.cc:192 operator() Input (/sdp/flows.3/GatherND_2_output_0) has a dynamic shape ({-1,-1}) but the runtime shape ({0,10}) has zero elements. This is not supported by the CoreML EP.” and inference fails.
I hope this will be fixed in a future version of ONNXRuntime, and leave the test code as it is.

.gitignore

@@ -5,6 +5,8 @@ dist/
 .coverage*
 .ipynb_checkpoints/
 .ruff_cache/
+.DS_Store
+._*
 
 /*.yml
 !/default_config.yml

pyproject.toml

@@ -109,6 +109,8 @@ test = "pytest -s tests/test_main.py::test_synthesize_onnx_cpu"
 test-cuda = "pytest -s tests/test_main.py::test_synthesize_onnx_cuda"
 # Usage: `hatch run test-onnx:test-directml`
 test-directml = "pytest -s tests/test_main.py::test_synthesize_onnx_directml"
+# Usage: `hatch run test-onnx:test-coreml`
+test-coreml = "pytest -s tests/test_main.py::test_synthesize_onnx_coreml"
 # Usage: `hatch run test-onnx:coverage`
 test-cov = "coverage run -m pytest -s tests/test_main.py::test_synthesize_onnx_cpu"
 # Usage: `hatch run test-onnx:cov-report`

style_bert_vits2/utils/__init__.py

@@ -5,10 +5,12 @@ def torch_device_to_onnx_providers(
     device: str,
 ) -> Sequence[Union[str, tuple[str, dict[str, Any]]]]:
     if device.startswith("cuda"):
+        return [
             # cudnn_conv_algo_search を DEFAULT にすると推論速度が大幅に向上する
             # ref: https://medium.com/neuml/debug-onnx-gpu-performance-c9290fe07459
-        return [
             ("CUDAExecutionProvider", {"cudnn_conv_algo_search": "DEFAULT"}),
+            # CUDA が利用できない場合、可能であれば DirectML を利用する
+            ("DmlExecutionProvider", {"device_id": 0}),
             ("CPUExecutionProvider", {}),
         ]
     else:

tests/test_main.py

@@ -125,3 +125,12 @@ def test_synthesize_onnx_directml():
             ("DmlExecutionProvider", {"device_id": 0}),
         ],
     )
+
+
+def test_synthesize_onnx_coreml():
+    synthesize(
+        inference_type="onnx",
+        onnx_providers=[
+            ("CoreMLExecutionProvider", {}),
+        ],
+    )