Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

IrError(OutputNodeNotFound("/Cast_output_0")) #210

Open
BinaryWarlock opened this issue Apr 9, 2024 · 2 comments
Open

IrError(OutputNodeNotFound("/Cast_output_0")) #210

BinaryWarlock opened this issue Apr 9, 2024 · 2 comments

Comments

@BinaryWarlock
Copy link

Describe the bug
IrError(OutputNodeNotFound("/Cast_output_0"))

To Reproduce
Steps to reproduce the behavior:
Using onnx-clip model which you can grab at https://lakera-clip.s3.eu-west-1.amazonaws.com/clip_image_model_vitb32.onnx (about 336 MiB)

  1. python -m onnxruntime.tools.make_dynamic_shape_fixed --dim_param image_batch_size --dim_value 1 clip_image_model_vitb32.onnx clip_image_model_vitb32_batchsize1.onnx
  2. nnx prepare clip_image_model_vitb32_batchsize1.onnx clip_image_model_vitb32_batchsize1.prepared.onnx
  3. from wonnx import Session; Session.from_path('clip_image_model_vitb32_batchsize1.prepared.onnx')
  4. Receive IrError(OutputNodeNotFound("/Cast_output_0"))

The node does seem to exist when examined in https://netron.app/ so I don't know what's happening here.

@cchance27
Copy link

Running into a similar issue with a faceparser onnx... i end up with.... output node for output /cp/resnet/conv1/Conv_output_0 not found

but checking in netron... its available

@phhusson
Copy link

phhusson commented Jun 14, 2024

Using onnx-simplifier on my model seemed to bypass that error. I tried to understand why but I failed.

Edit: Nevermind the reason was that I had to do nnx prepare on the mode, (which actually doesn't work on my model but that's another story). The error OutputNodeFound("/Constant_output_0") here meant that it couldn't find the shape of /Constant_output_0 because I didn't launch shape inference first. onnx-simplify alongst the simplification does compute the shapes.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants