How to reduce reformat layer in QAT???
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Description
Description
I want to finetune a quantized yolo model, and export to TRT.
I carefully read the QDQ document and some existed issues to place and remove unused QDQ nodes, the model have 92% int8 precision layers but still have 70 reformat layers.
Environment
TensorRT Version: 8.5.0.2
NVIDIA GPU: agx orin 16GB
NVIDIA Driver Version:
CUDA Version: 11.4
CUDNN Version:
Operating System: Ubuntu 20.04
Python Version (if applicable): 3.8.10
Tensorflow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if so, version):
Relevant Files
Model link:
model.zip
Use normal trtexec command to convert onnx to engine.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the attached model.zip and reproduce the engine using the normal trtexec command described in the issue. Review the QDQ documentation and the TREX visualization to investigate the reported 70 reformat layers. Done means identifying the cause and documenting a verified way to reduce them.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100