Skip to content
Simplify your onnx model
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github/ISSUE_TEMPLATE Update issue templates Sep 18, 2019
imgs Move images in to imgs/ Apr 2, 2019
onnxsim Add some comments for last commit Aug 6, 2019
.gitignore Update .gitignore Apr 2, 2019
.travis.yml Set version in in CI Jul 29, 2019
LICENSE Create LICENSE Apr 2, 2019 Set version in in CI Jul 29, 2019

ONNX Simplifier

PyPI version PyPI pyversions PyPI license PRs Welcome

ONNX is great, but sometimes too complicated.


One day I wanted to export the following simple reshape operation to ONNX:

import torch

class JustReshape(torch.nn.Module):
    def __init__(self):
        super(JustReshape, self).__init__()

    def forward(self, x):
        return x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2]))

net = JustReshape()
model_name = 'just_reshape.onnx'
dummy_input = torch.randn(2, 3, 4, 5)
torch.onnx.export(net, dummy_input, model_name, input_names=['input'], output_names=['output'])

Input shape in ONNX is static, so what I expected is


However, I got the following complicated model even after polishing:


Moreover, there are also some operations performed on weights (like this), which can all be eliminated by offline computation.

Our solution

ONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph and then replaces the redundant operators with their constant outputs.

Install it via pip (Python >= 3.5)

pip3 install onnx-simplifier


python3 -m onnxsim input_model output_model


An overall comparison between a complicated model and its simplified version:

Comparison between old model and new model

You can’t perform that action at this time.