ssi4onnx
Simple Shape Inference tool for ONNX.
Decision gist · record as of 2026-08-14
Yes, if you work with ONNX models and need to repair or validate shape information. The package is lightweight (no runtime dependencies), permissively licensed, and solves a real problem in ONNX workflows. However, note that the project is dormant (last update 808 days ago), so expect no active maintenance or updates for recent ONNX features.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires ONNX to be installed separately; the package itself has no runtime dependencies but expects an ONNX model file as input.
- No runtime dependencies and a pure-Python wheel distribution make installation straightforward.
- The project is dormant (last update 808 days ago) but carries no known vulnerabilities.
License · maintenance · safety
MIT License (permissive) — MIT License permits unrestricted use, modification, and distribution with minimal restrictions, making it safe to integrate into proprietary or open-source projects.
last release 2024-05-28 (808 days) · last repo commit 2024-05-28 · 3 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,710 downloads/mo, #14,141 on PyPI
Alternatives
Verify before relying
pip install ssi4onnx
from ssi4onnx import shape_inference
estimated_graph = shape_inference(
input_onnx_file_path="model.onnx"
)- Whether shape inference handles all ONNX operator types or has known limitations on specific model architectures.
- Performance characteristics on large or complex ONNX graphs.
- Compatibility with recent ONNX opset versions beyond the package's last update.
What it is and what it does
ssi4onnx is a shape inference utility for ONNX models that automatically computes and fills in tensor dimension information that may be missing or incomplete. It works by analyzing the model graph structure and operator definitions to deduce output shapes from input specifications. The tool is useful when working with ONNX models that have been simplified, converted from other frameworks, or manually edited—situations where shape metadata may be incomplete or incorrect.
You can use it either as a command-line tool (passing ONNX files in and out) or as a Python library function that accepts an ONNX ModelProto object directly. The library has no external runtime dependencies beyond ONNX itself, making it lightweight to integrate into model processing pipelines. It's part of a broader ONNX processing toolkit maintained by PINTO0309.
Use it for
- Repair ONNX models with incomplete shape information before deploying to inference engines like TensorRT or ONNX Runtime.
- Debug shape mismatches in converted models (e.g., from PyTorch or TensorFlow) by inferring correct tensor dimensions.
- Preprocess ONNX graphs in a model optimization pipeline to ensure shape metadata is complete before further transformations.
- Validate ONNX model structure by checking whether shape inference succeeds without errors.
- Integrate shape inference into automated model conversion workflows for consistent shape annotation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with ONNX models and need to repair or validate shape information.
The package is lightweight (no runtime dependencies), permissively licensed, and solves a real problem in ONNX workflows. However, note that the project is dormant (last update 808 days ago), so expect no active maintenance or updates for recent ONNX features.
Install
ssi4onnx on PyPI
Before you install
No runtime dependencies and a pure-Python wheel distribution make installation straightforward. The project is dormant (last update 808 days ago) but carries no known vulnerabilities.
Requires ONNX to be installed separately; the package itself has no runtime dependencies but expects an ONNX model file as input.
License in practice
MIT License permits unrestricted use, modification, and distribution with minimal restrictions, making it safe to integrate into proprietary or open-source projects.
Quickstart
pip install ssi4onnx
from ssi4onnx import shape_inference
estimated_graph = shape_inference(
input_onnx_file_path="model.onnx"
)Verify before relying
- Whether shape inference handles all ONNX operator types or has known limitations on specific model architectures.
- Performance characteristics on large or complex ONNX graphs.
- Compatibility with recent ONNX opset versions beyond the package's last update.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 808 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,710 / month, #14,141 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: ssi4onnx-1.0.4-py3-none-any.whl
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See also sit4onnx · ssc4onnx · sde4onnx · snd4onnx · sio4onnx · scs4onnx · sbi4onnx · sne4onnx · json2onnx · onnxslim