Tutorial 5: Exporting a model to ONNX

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves.

Supported Models
Usage
- Prerequisite

Supported Models

So far, our codebase supports onnx exporting from pytorch models trained with MMPose. The supported models include:

ResNet
HRNet
HigherHRNet

Usage

For simple exporting, you can use the script here. Note that the package onnx and onnxruntime are required for verification after exporting.

Prerequisite

First, install onnx.

pip install onnx onnxruntime

We provide a python script to export the pytorch model trained by MMPose to ONNX.

python tools/deployment/pytorch2onnx.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--shape ${SHAPE}] \
    [--verify] [--show] [--output-file ${OUTPUT_FILE}] [--opset-version ${VERSION}]

Optional arguments:

--shape: The shape of input tensor to the model. If not specified, it will be set to 1 3 256 192.
--verify: Determines whether to verify the exported model, runnably and numerically. If not specified, it will be set to False.
--show: Determines whether to print the architecture of the exported model. If not specified, it will be set to False.
--output-file: The output onnx model name. If not specified, it will be set to tmp.onnx.
--opset-version: Determines the operation set version of onnx, we recommend you to use a higher version such as 11 for compatibility. If not specified, it will be set to 11.

Please fire an issue if you discover any checkpoints that are not perfectly exported or suffer some loss in accuracy.

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