mmcv/docs/onnx.md

Introduction of onnx module in MMCV (Experimental)

register_extra_symbolics

Some extra symbolic functions need to be registered before exporting PyTorch model to ONNX.

Example

import mmcv
from mmcv.onnx import register_extra_symbolics

opset_version = 11
register_extra_symbolics(opset_version)

ONNX simplify

Intention

mmcv.onnx.simplify is based on onnx-simplifier, which is a useful tool to make exported ONNX models slimmer by performing a series of optimization. However, for Pytorch models with custom op from mmcv, it would break down. Thus, custom ops for ONNX Runtime should be registered.

Prerequisite

mmcv.onnx.simplify has three dependencies: onnx, onnxoptimizer, onnxruntime. After installation of mmcv, you have to install them manually using pip.

pip install onnx onnxoptimizer onnxruntime

Usage

import onnx
import numpy as np

import mmcv
from mmcv.onnx.simplify import simplify

dummy_input = np.random.randn(1, 3, 224, 224).astype(np.float32)
input = {'input':dummy_input}
input_file = 'sample.onnx'
output_file = 'slim.onnx'
model = simplify(input_file, [input], output_file)

FAQs

• None