Closed fschvart closed 1 year ago
We haven't support pytorch2.0 yet, please use lower version of pytorch.
We haven't support pytorch2.0 yet, please use lower version of pytorch.
Why is it in the mmdeploy 1.3 docker image though? I can't deploy segformer model because of this.
We haven't support pytorch2.0 yet, please use lower version of pytorch.
Why is it in the mmdeploy 1.3 docker image though? I can't deploy segformer model because of this.
Hi which docker file is used in your case? is prebuild docker?
is prebuild docker?
Yes, it is the docker image in docker hub.
Run a container with the image:
docker run -it openmmlab/mmdeploy:ubuntu20.04-cuda11.8-mmdeploy1.3.0 /bin/bash
Show torch info:
pip3 show torch
Name: torch
Version: 2.0.0+cu118
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Home-page: https://pytorch.org/
Author: PyTorch Team
Author-email: packages@pytorch.org
License: BSD-3
Location: /usr/local/lib/python3.8/dist-packages
Requires: filelock, jinja2, networkx, sympy, triton, typing-extensions
Required-by: torchvision, triton
Checklist
Describe the bug
I'm using mmdeploy 1.0.0, on a segformer model that was trained using mmsegmentation 1.0.0, and it won't let me convert. It seems like an operation was used that isn't supported yet by ONNX (aten::unflatten)
Segformer does appear in the list of supported models for mmsegmentation, and if I recall correctly, I was able to convert it to ONNX in the past.
I trained the model on the latest Nvidia PyTorch docker (CUDA 12.0, PyTorch 2.0.0)
I'll really appreciate your help!
Reproduction
python deploy.py ../configs/mmseg/segmentation_oonxruntime_dynamic.py .....
Environment
Error traceback