I have Oak D pro PoE, and I want to convert the ONNX TResnet weights to RVC2 weights to run on device. I tried the online approach by using the API key, but I got this error:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/usr/local/lib/python3.10/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/app/modelconverter/packages/rvc2/exporter.py", line 335, in _superblob_compile_step
subprocess_run(["compile_tool", *args], silent=True)
File "/app/modelconverter/utils/subprocess.py", line 67, in subprocess_run
raise SubprocessException(info_string)
modelconverter.utils.exceptions.SubprocessException: Command `compile_tool` finished in 0.95 seconds with return code 1.
[ STDERR ]:
[ GENERAL_ERROR ]
/mnt/docker/openvino/src/plugins/intel_myriad/graph_transformer/src/middleend/passes/adjust_data_location.cpp:227 [Internal Error]: Memory allocation failed: unable to satisfy requirements for stage ReduceMean with type ReduceMean
[ STDOUT ]:
Is this model too complex for RVC2? For Oak D pro PoE I can only use RVC2, right? Is there any other way to make this work? Will custom OPEN CL kernel work for this model?
