RVC-Project / RVC-Project/Retrieval-based-Voice-Conversion-WebUI
:onnxruntime:, session_state.cc:1169 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution
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Description
while running inference coming across this error .
2024-09-11 11:17:28 | INFO | infer.modules.vc.modules | Get sid: guanguanV1.pth
2024-09-11 11:17:28 | INFO | infer.modules.vc.modules | Loading: assets/weights/guanguanV1.pth
2024-09-11 11:17:28 | INFO | infer.modules.vc.modules | Select index: logs\guanguanV1.index
2024-09-11 11:17:55 | INFO | infer.modules.vc.pipeline | Loading rmvpe model,assets/rmvpe/rmvpe.pt
2024-09-11 11:17:56.0223850 [W:onnxruntime:, session_state.cc:1169 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.
2024-09-11 11:17:56.0261045 [W:onnxruntime:, session_state.cc:1171 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments.
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start by reproducing inference with assets/weights/guanguanV1.pth and assets/rmvpe/rmvpe.pt, then inspect infer.modules.vc.modules and infer.modules.vc.pipeline around model loading. Determine whether the ONNX Runtime warning causes incorrect results or only affects execution-provider placement; done means documenting the cause and resolving it or confirming expected behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100