Awesome
<div align="center"> <img src="assets/logo/onnx2torch_light.png#gh-light-mode-only"> <img src="assets/logo/onnx2torch_dark.png#gh-dark-mode-only"> </div> <p align="center"> <a href="https://pypi.org/project/onnx2torch"> <img src="https://badgen.net/pypi/v/onnx2torch" /> </a> <a href="https://anaconda.org/conda-forge/onnx2torch"> <img src="https://img.shields.io/conda/vn/conda-forge/onnx2torch" /> </a> <a href="https://pypi.org/project/onnx2torch"> <img src="https://img.shields.io/github/license/ENOT-AutoDL/onnx2torch?color=blue" /> </a> <a href="https://pypi.org/project/onnx2torch"> <img src="https://img.shields.io/pypi/dm/onnx2torch?color=blue" /> </a> <a href="https://github.com/ENOT-AutoDL/onnx2torch/stargazers"> <img src="https://img.shields.io/github/stars/ENOT-AutoDL/onnx2torch.svg?style=social&label=Star&maxAge=2592000" /> </a> <br> <a href="https://github.com/psf/black"> <img src="https://img.shields.io/badge/code%20style-black-black?color=blue" /> </a> <a href="https://github.com/pre-commit/pre-commit"> <img src="https://img.shields.io/badge/pre--commit-enabled-blue?logo=pre-commit" /> </a> <a href="https://conventionalcommits.org"> <img src="https://img.shields.io/badge/Conventional%20Commits-1.0.0-%23FE5196?logo=conventionalcommits&logoColor=white&color=blue" /> </a> </p>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
pip install onnx2torch
or
conda install -c conda-forge onnx2torch
Usage
Below you can find some examples of use.
Convert
import onnx
import torch
from onnx2torch import convert
# Path to ONNX model
onnx_model_path = "/some/path/mobile_net_v2.onnx"
# You can pass the path to the onnx model to convert it or...
torch_model_1 = convert(onnx_model_path)
# Or you can load a regular onnx model and pass it to the converter
onnx_model = onnx.load(onnx_model_path)
torch_model_2 = convert(onnx_model)
Execute
We can execute the returned PyTorch model
in the same way as the original torch model.
import onnxruntime as ort
# Create example data
x = torch.ones((1, 2, 224, 224)).cuda()
out_torch = torch_model_1(x)
ort_sess = ort.InferenceSession(onnx_model_path)
outputs_ort = ort_sess.run(None, {"input": x.numpy()})
# Check the Onnx output against PyTorch
print(torch.max(torch.abs(outputs_ort - out_torch.detach().numpy())))
print(np.allclose(outputs_ort, out_torch.detach().numpy(), atol=1.0e-7))
Models
We have tested the following models:
Segmentation models:
- DeepLabV3+
- DeepLabV3 ResNet-50 (TorchVision)
- HRNet
- UNet (TorchVision)
- FCN ResNet-50 (TorchVision)
- LRASPP MobileNetV3 (TorchVision)
Detection from MMdetection:
Classification from TorchVision:
- ResNet-18
- ResNet-50
- MobileNetV2
- MobileNetV3 Large
- EfficientNet-B{0, 1, 2, 3}
- WideResNet-50
- ResNext-50
- VGG-16
- GoogLeNet
- MnasNet
- RegNet
Transformers:
- ViT
- Swin
- GPT-J
:page_facing_up: 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
<details> <summary>and has the same behaviour</summary>An example of such a module is Relu
@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.
An example of such a module is ScatterND
# 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
<details> <summary>Example</summary>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")
</details>
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.