onnx2torch
ONNX to PyTorch converter
Decision gist · record as of 2026-08-14
Yes, if you need to convert ONNX models to PyTorch and your model uses supported operations. The package is stable and permissively licensed, with low install friction. However, verify that your specific ONNX model's operations are supported before committing to it—the converter does not support all ONNX operations, and maintenance has slowed. If your model is unsupported, you may need to implement custom converters or consider alternatives.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch and torchvision installed; conversion success depends on whether the ONNX model's operations are in the supported set.
- Low install friction with a pure Python wheel.
- Maintenance is aging—last release was over a year ago, though the repository remains active and the project is marked Production/Stable.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive; you may use, modify, and distribute this package freely in commercial and private projects provided you include the license and attribute the original work.
last release 2024-08-07 (737 days) · last repo commit 2025-10-14 · 739 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 225,660 downloads/mo, #9,216 on PyPI
Alternatives
Verify before relying
pip install onnx2torch
import torch
from onnx2torch import convert
torch_model = convert("/path/to/model.onnx")
output = torch_model(torch.randn(1, 3, 224, 224))- Whether all operations in your specific ONNX model are currently supported by this version.
- Performance characteristics of converted models compared to native PyTorch implementations.
- Compatibility with ONNX opset versions beyond those documented in the description.
What it is and what it does
onnx2torch is a converter that transforms ONNX model files into native PyTorch modules. It accepts either a file path or a loaded ONNX model object and returns a PyTorch nn.Module that can be used directly for inference or training. The converter is designed to be extensible—you can register custom PyTorch layers for unsupported ONNX operations using a decorator pattern, and converted models can be exported back to ONNX using torch.onnx.export.
The package supports a curated set of models including popular segmentation architectures (DeepLabV3+, UNet, HRNet), detection models (YOLOv3, YOLOv5, RetinaNet), classification networks (ResNet, MobileNet, EfficientNet, ViT), and transformers (Swin, GPT-J). However, it covers only a limited number of ONNX operations, so not all ONNX models will convert successfully. The maintainers explicitly encourage users to report unsupported models and operations.
Use it for
- Convert a pre-trained ONNX model to PyTorch for fine-tuning or inference in a PyTorch-native pipeline.
- Integrate ONNX-exported models from other frameworks into a PyTorch codebase.
- Extend the converter with custom layers to support domain-specific ONNX operations not yet in the core library.
- Validate ONNX model behavior by running inference in PyTorch and comparing outputs against ONNX Runtime.
- Deploy ONNX models trained elsewhere as native PyTorch modules without maintaining separate inference code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to convert ONNX models to PyTorch and your model uses supported operations.
The package is stable and permissively licensed, with low install friction. However, verify that your specific ONNX model's operations are supported before committing to it—the converter does not support all ONNX operations, and maintenance has slowed. If your model is unsupported, you may need to implement custom converters or consider alternatives.
Install
onnx2torch on PyPI
Before you install
Low install friction with a pure Python wheel. Maintenance is aging—last release was over a year ago, though the repository remains active and the project is marked Production/Stable.
Requires torch and torchvision installed; conversion success depends on whether the ONNX model's operations are in the supported set.
License in practice
Apache License 2.0 is permissive; you may use, modify, and distribute this package freely in commercial and private projects provided you include the license and attribute the original work.
Quickstart
pip install onnx2torch
import torch
from onnx2torch import convert
torch_model = convert("/path/to/model.onnx")
output = torch_model(torch.randn(1, 3, 224, 224))Verify before relying
- Whether all operations in your specific ONNX model are currently supported by this version.
- Performance characteristics of converted models compared to native PyTorch implementations.
- Compatibility with ONNX opset versions beyond those documented in the description.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpyonnxtorchtorchvision |
| Maintenance | Aging 737 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 225,660 / month, #9,216 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: PythonProgramming Language :: Python :: 3 :: Only |
Evidence: onnx2torch-1.5.15-py3-none-any.whl
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See also pnnx · tf2onnx · onnxmltools · onnx2tf · onnxsim · onnxconverter-common · onnx-graphsurgeon · litert-torch · torchprofile · hoptorch