---
id: svs4onnx
version: "1.0.0"
license: MIT License
license_treatment: permissive
maintenance: abandoned
---

# svs4onnx — Simple model output OP additional tools.

License: permissive · Maintenance: abandoned · Downloads: 82.5K/mo

## What it is and what it does

svs4onnx is a lightweight utility for modifying ONNX model graphs by redirecting variable connections. It takes an ONNX model, identifies an output variable from one node, and rewires it to feed into a specified input variable of another node, then outputs the modified graph. This is useful when you need to restructure model topology—for example, bypassing intermediate operations or connecting different stages of a pipeline.

The tool offers both a command-line interface and a Python API. You can load an ONNX file or pass a graph object directly, specify the source output variable and target input variable, and get back a modified ModelProto. It supports Python 3.6 and later.

Use it for:

- Redirect a model's intermediate output to skip or replace downstream operations during inference.
- Rewire multi-stage pipelines to connect outputs from one model directly to inputs of another.
- Modify model topology for debugging or testing by changing which nodes feed into which.
- Adapt pre-trained models to new input/output configurations by swapping variable connections.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Swaps connections between output and input variables in ONNX model graphs, allowing you to redirect one node's output to feed into a different node's input.

No, unless you have a specific legacy use case. The package is abandoned—last release 2022-09-17 with no commits since then—and has not been tested against current ONNX versions. If you need ONNX graph manipulation, consider maintained alternatives.

## Install

pip install svs4onnx
uv add svs4onnx
poetry add svs4onnx

## Installing svs4onnx

Before you install: Low install friction; pure Python wheel with no runtime dependencies. However, the package is abandoned—last release was 2022-09-17 and no commits since then.

License in practice: MIT License permits commercial and private use, modification, and distribution with minimal restrictions.

Quickstart:

pip install svs4onnx
from svs4onnx import variable_switch
onnx_graph = variable_switch(
    from_output_variable_name="cast1_output",
    to_input_variable_name="StatefulPartitionedCall/strided_slice_21",
    input_onnx_file_path="model.onnx",
    output_onnx_file_path="model_switched.onnx"
)

Requires onnx to be installed separately; not declared as a runtime dependency in the package metadata.

Verify before relying:

- Whether onnx is a required peer dependency or optional; setup docs show it installed separately but package lists zero runtime dependencies.
- Compatibility with recent ONNX versions and whether the tool works with current model architectures.

## Package facts

- License: MIT License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: abandoned
- Downloads: 82.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

onnx graph variable rewiring, onnx node connection modification, onnx output to input redirect, onnx model graph surgery, onnx variable connection swap, onnx-tools, model-surgery

[View on SkillFed](https://skillfed.io/packages/svs4onnx) · [View on PyPI](https://pypi.org/project/svs4onnx/)