Introduction to ONNX Simplifier
ONNX Simplifier is a tool designed to streamline the ONNX (Open Neural Network Exchange) model conversion process. While ONNX is a robust tool for interoperability between different AI frameworks, it can sometimes create unnecessarily complex models. The ONNX Simplifier addresses this issue by optimizing these models, making them more efficient and easier to work with.
Background
The necessity for ONNX Simplifier arose when users like the project creator attempted to export simple operations to ONNX. As illustrated by a simple reshape operation using PyTorch, one would expect a straightforward output. Unfortunately, the conversion process sometimes results in an overly intricate model. This complexity not only complicates the understanding and management of the model but may also degrade performance.
Solution
ONNX Simplifier aims to tackle this complexity by optimizing ONNX models. It does so through a process called constant folding, where redundant operators are replaced with their constant outputs. This method streamlines the computation graph, significantly reducing its complexity.
Web Version
The ONNX Simplifier is available as a web-based tool at convertmodel.com. The online tool is incredibly user-friendly, requiring no installation. It operates locally in the browser, assuring users that their models remain secure.
Python Version
For those who prefer a Python environment, ONNX Simplifier can be installed via pip:
pip3 install -U pip && pip3 install onnxsim
Once installed, simplifying an ONNX model is as easy as running:
onnxsim input_onnx_model output_onnx_model
For more advanced options, users can refer to the help message via:
onnxsim -h
Demonstration
The benefit of the ONNX Simplifier is effectively highlighted through a comparison image between a complicated model and its simplified counterpart. This visualization clearly shows the reduction in complexity and the cleaner, more efficient model structure that results from using the simplifier.
In-Script Workflow
Integrating the ONNX Simplifier within a script is straightforward. By importing the necessary packages, users can load their ONNX model, apply the simplification process, and then use the cleaned model as a standard ONNX model object. Here is how it can be done:
import onnx
from onnxsim import simplify
# load your predefined ONNX model
model = onnx.load(filename)
# convert model
model_simp, check = simplify(model)
assert check, "Simplified ONNX model could not be validated"
# use model_simp as a standard ONNX model object
Projects Using ONNX Simplifier
The ONNX Simplifier has been adopted by several major projects including MXNet, MMDetection, YOLOv5, and ncnn. These projects benefit from the efficiency and simplicity provided by the tool.
Community and Support
For Chinese-speaking users, there is a QQ group available for discussions. English-speaking users are encouraged to join the ONNX Slack channel. In both forums, the creator, daquexian, is available for support and inquiries.
ONNX Simplifier stands out as a crucial utility for efficiently managing ONNX models by reducing their complexity, thus making them more feasible for various applications and research endeavors.