Closed stealthinu closed 1 year ago
This ONNX refers to an axis that I am not assuming.
The error is correct. Because I did not implement it assuming axis=3
.
https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Flatten-13
The fix is already done and undergoing regression testing by CI, but I do not recommend using RundomNormalLike
. If you want to generate random numbers by normal fraction, it is better to pass the random number part that is assumed to be generated by RundomNormalLike
from the Android or Java logic as a variable in the input OP.
Thank you for your great advice! As you suggested, I will try giving randomly generated Tensors as initial values.
I tried it with version 1.8.1 and confirmed that it can be converted to TF completely. Thank you very much!
I'm glad it worked out. I will close this issue once.
Incidentally, my suggestion that it might be better to replace RandomNormalLike
with an external input variable was because I thought it might be possible to keep the length
of the model input OP dynamic.
Incidentally, my suggestion that it might be better to replace
RandomNormalLike
with an external input variable was because I thought it might be possible to keep thelength
of the model input OP dynamic.
Thank you for your response. In fact, I changed the model to have a fixed length in version 1.5 because it wouldn't work in ONNX without a fixed length.
Issue Type
Others
onnx2tf version number
1.7.33
onnx version number
1.13.1
tensorflow version number
2.12.0rc1
Download URL for ONNX
https://github.com/stealthinu/MMVC_Trainer/releases/download/v1.5onnx-tf/G_200000.onnx
Parameter Replacement JSON
Description
Purpose Product development
What I was trying to convert the currently-in-development alpha version of the MMVC-1.5 ONNX model, which has a fixed-length input. When attempting the conversion as shown below, I encountered an error.
It seems that "Flatten" here might not be necessary, but I couldn't figure out how to remove it.
I thought this error might be caused by the large size of the Tensor after flattening, so I reduced the size in the time dimension and changed the shape after flattening to [6144, 1]. However, the same error occurred.
Why I would like to make these modifications because I want to run the model in TFLite.
Resources None