Hello I want to convert my pytorch model to be able to use with khadas NPU. I’m getting this error:
RuntimeError: version_number <= kMaxSupportedFileFormatVersion INTERNAL ASSERT FAILED at /pytorch/caffe2/serialize/inline_container.cc:131, please report a bug to PyTorch. Attempted to read a PyTorch file with version 3, but the maximum supported version for reading is 1. Your PyTorch installation may be too old. (init at /pytorch/caffe2/serialize/inline_container.cc:131)
Apparently the error comes because the library torch used to load the model is too old compared to the one used to create the model. The library torch to load model is:
frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x33 (0x7fc8aa130273 in /home/glema/aml_npu_sdk/acuity-toolkit/bin/acuitylib/libc10.so)
frame #1: caffe2::serialize::PyTorchStreamReader::init() + 0x1e9a (0x7fc8ad0bf00a in /home/glema/aml_npu_sdk/acuity-toolkit/bin/acuitylib/libtorch.so)
Is it possible to upgrade the library inside the repo to support newer versions of pytorch?
What version of Pytorch should I use to create the model, to be able to use your library when converting it?
Thanks for your answer. After I convert it to onnx, I was able to run the convert script with no issues. However, I don’t see any outputs in the folder I’m running the command.
@Gabriel_Lema The obtained data is the original data and needs to be processed again. KSNN has a demo of SSD, but it is different from your model, maybe that can help you
Yes I meant before the NMS part. But now it is working correctly
I converted it with the mean and std expected in the preprocessing, and then in the nn inference I don’t preprocess it, and just do