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onnx2torch

Convert ONNX models to PyTorch.

https://github.com/enot-autodl/onnx2torch

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Keywords

convert export onnx pytorch
Last synced: 6 months ago · JSON representation ·

Repository

Convert ONNX models to PyTorch.

Basic Info
  • Host: GitHub
  • Owner: ENOT-AutoDL
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 379 KB
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  • Stars: 698
  • Watchers: 10
  • Forks: 80
  • Open Issues: 43
  • Releases: 22
Topics
convert export onnx pytorch
Created about 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation Codeowners

README.md


onnx2torch is an ONNX to PyTorch converter. Our converter:

  • Is easy to use – Convert the ONNX model with the function call convert;
  • Is easy to extend – Write your own custom layer in PyTorch and register it with @add_converter;
  • Convert back to ONNX – You can convert the model back to ONNX using the torch.onnx.export function.

If you find an issue, please let us know! And feel free to create merge requests.

Please note that this converter covers only a limited number of PyTorch / ONNX models and operations. Let us know which models you use or want to convert from ONNX to PyTorch here.

Installation

bash pip install onnx2torch

or

bash conda install -c conda-forge onnx2torch

Usage

Below you can find some examples of use.

Convert

```python import onnx import torch from onnx2torch import convert

Path to ONNX model

onnxmodelpath = "/some/path/mobilenetv2.onnx"

You can pass the path to the onnx model to convert it or...

torchmodel1 = convert(onnxmodelpath)

Or you can load a regular onnx model and pass it to the converter

onnxmodel = onnx.load(onnxmodelpath) torchmodel2 = convert(onnxmodel) ```

Execute

We can execute the returned PyTorch model in the same way as the original torch model.

```python import onnxruntime as ort

Create example data

x = torch.ones((1, 2, 224, 224)).cuda()

outtorch = torchmodel_1(x)

ortsess = ort.InferenceSession(onnxmodelpath) outputsort = ort_sess.run(None, {"input": x.numpy()})

Check the Onnx output against PyTorch

print(torch.max(torch.abs(outputsort - outtorch.detach().numpy()))) print(np.allclose(outputsort, outtorch.detach().numpy(), atol=1.0e-7)) ```

Models

We have tested the following models:

Segmentation models:

  • [x] DeepLabV3+
  • [x] DeepLabV3 ResNet-50 (TorchVision)
  • [x] HRNet
  • [x] UNet (TorchVision)
  • [x] FCN ResNet-50 (TorchVision)
  • [x] LRASPP MobileNetV3 (TorchVision)

Detection from MMdetection:

Classification from TorchVision:

  • [x] ResNet-18
  • [x] ResNet-50
  • [x] MobileNetV2
  • [x] MobileNetV3 Large
  • [x] EfficientNet-B{0, 1, 2, 3}
  • [x] WideResNet-50
  • [x] ResNext-50
  • [x] VGG-16
  • [x] GoogLeNet
  • [x] MnasNet
  • [x] RegNet

Transformers:

  • [x] ViT
  • [x] Swin
  • [x] GPT-J

:pagefacingup: List of currently supported operations can be founded here.

How to add new operations to converter

Here we show how to extend onnx2torch with new ONNX operation, that supported by both PyTorch and ONNX

and has the same behaviour An example of such a module is [Relu](./onnx2torch/node_converters/activations.py) ```python @add_converter(operation_type="Relu", version=6) @add_converter(operation_type="Relu", version=13) @add_converter(operation_type="Relu", version=14) def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: return OperationConverterResult( torch_module=nn.ReLU(), onnx_mapping=onnx_mapping_from_node(node=node), ) ``` Here we have registered an operation named `Relu` for opset versions 6, 13, 14. Note that the `torch_module` argument in `OperationConverterResult` must be a torch.nn.Module, not just a callable object! If Operation's behaviour differs from one opset version to another, you should implement it separately.
but has different behaviour An example of such a module is [ScatterND](./onnx2torch/node_converters/scatter_nd.py) ```python # It is recommended to use Enum for string ONNX attributes. class ReductionOnnxAttr(Enum): NONE = "none" ADD = "add" MUL = "mul" class OnnxScatterND(nn.Module, OnnxToTorchModuleWithCustomExport): def __init__(self, reduction: ReductionOnnxAttr): super().__init__() self._reduction = reduction # The following method should return ONNX attributes with their values as a dictionary. # The number of attributes, their names and values depend on opset version; # method should return correct set of attributes. # Note: add type-postfix for each key: reduction -> reduction_s, where s means "string". def _onnx_attrs(self, opset_version: int) -> Dict[str, Any]: onnx_attrs: Dict[str, Any] = {} # Here we handle opset versions < 16 where there is no "reduction" attribute. if opset_version < 16: if self._reduction != ReductionOnnxAttr.NONE: raise ValueError( "ScatterND from opset < 16 does not support" f"reduction attribute != {ReductionOnnxAttr.NONE.value}," f"got {self._reduction.value}" ) return onnx_attrs onnx_attrs["reduction_s"] = self._reduction.value return onnx_attrs def forward( self, data: torch.Tensor, indices: torch.Tensor, updates: torch.Tensor, ) -> torch.Tensor: def _forward(): # ScatterND forward implementation... return output if torch.onnx.is_in_onnx_export(): # Please follow our convention, args consists of: # forward function, operation type, operation inputs, operation attributes. onnx_attrs = self._onnx_attrs(opset_version=get_onnx_version()) return DefaultExportToOnnx.export( _forward, "ScatterND", data, indices, updates, onnx_attrs ) return _forward() @add_converter(operation_type="ScatterND", version=11) @add_converter(operation_type="ScatterND", version=13) @add_converter(operation_type="ScatterND", version=16) def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult: node_attributes = node.attributes reduction = ReductionOnnxAttr(node_attributes.get("reduction", "none")) return OperationConverterResult( torch_module=OnnxScatterND(reduction=reduction), onnx_mapping=onnx_mapping_from_node(node=node), ) ``` Here we have used a trick to convert the model from torch back to ONNX by defining the custom `_ScatterNDExportToOnnx`.

Opset version workaround

Incase you are using a model with older opset, try the following workaround:

ONNX Version Conversion - Official Docs

Example ```python import onnx from onnx import version_converter import torch from onnx2torch import convert # Load the ONNX model. model = onnx.load("model.onnx") # Convert the model to the target version. target_version = 13 converted_model = version_converter.convert_version(model, target_version) # Convert to torch. torch_model = convert(converted_model) torch.save(torch_model, "model.pt") ```

Note: use this only when the model does not convert to PyTorch using the existing opset version. Result might vary.

Citation

To cite onnx2torch use Cite this repository button, or:

@misc{onnx2torch, title={onnx2torch}, author={ENOT developers and Kalgin, Igor and Yanchenko, Arseny and Ivanov, Pyoter and Goncharenko, Alexander}, year={2021}, howpublished={\url{https://enot.ai/}}, note={Version: x.y.z} }

Acknowledgments

Thanks to Dmitry Chudakov @cakeofwar42 for his contributions.\ Special thanks to Andrey Denisov @denisovap2013 for the logo design.

Owner

  • Name: ENOT
  • Login: ENOT-AutoDL
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
title: onnx2torch
message: "Please use this information to cite onnx2torch in research or other publications."
authors:
  - affiliation: ENOT LLC
    given-names: ENOT developers
  - family-names: Kalgin
    given-names: Igor
  - family-names: Yanchenko
    given-names: Arseny
  - family-names: Ivanov
    given-names: Pyoter
  - family-names: Goncharenko
    given-names: Alexander
date-released: 2021-12-14
url: "https://enot.ai"
repository-code: "https://github.com/ENOT-AutoDL/onnx2torch"
license: "Apache-2.0"
keywords:
  - onnx
  - pytorch
  - convert
  - deep learning
  - machine learning

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pypi.org: onnx2torch

ONNX to PyTorch converter

  • Documentation: https://onnx2torch.readthedocs.io/
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pypi.org: modlee-onnx2torch

ONNX to PyTorch converter

  • Documentation: https://modlee-onnx2torch.readthedocs.io/
  • License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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  • Latest release: 1.5.15
    published over 1 year ago
  • Versions: 3
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 52 Last month
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Stargazers count: 3.7%
Forks count: 7.3%
Dependent packages count: 9.6%
Average: 21.0%
Dependent repos count: 63.4%
Maintainers (4)
modleemichael brad-modlee mansiagr4 TarushS
Last synced: 6 months ago
pypi.org: onnx2torch-py313

ONNX to PyTorch converter

  • Documentation: https://onnx2torch-py313.readthedocs.io/
  • License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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  • Latest release: 1.6.0
    published 11 months ago
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 23,918 Last month
Rankings
Dependent packages count: 9.3%
Average: 30.9%
Dependent repos count: 52.5%
Maintainers (1)
beveradb
Last synced: 6 months ago
conda-forge.org: onnx2torch
  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Stargazers count: 22.6%
Dependent repos count: 34.0%
Forks count: 35.4%
Average: 35.8%
Dependent packages count: 51.2%
Last synced: 6 months ago

Dependencies

.github/workflows/lint.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • isort/isort-action master composite
  • psf/black stable composite
.github/workflows/stale.yml actions
  • actions/stale v5 composite
requirements.txt pypi
  • numpy >=1.16.4
  • onnx >=1.9.0
  • torch >=1.8.0
  • torchvision >=0.9.0
test-requirements.txt pypi
  • Pillow * test
  • googledrivedownloader * test
  • onnxruntime * test
  • pytest * test
  • requests * test