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

Convert ONNX models to PyTorch.
https://github.com/enot-autodl/onnx2torch

convert export onnx pytorch

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Convert ONNX models to PyTorch.

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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](https://github.com/ENOT-AutoDL/onnx2torch/issues)!

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](https://github.com/ENOT-AutoDL/onnx2torch/discussions).



## 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

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.



```python

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:



- [x] DeepLabV3+

- [x] DeepLabV3 ResNet-50 (TorchVision)

- [x] HRNet

- [x] UNet (TorchVision)

- [x] FCN ResNet-50 (TorchVision)

- [x] LRASPP MobileNetV3 (TorchVision)



Detection from MMdetection:



- [x] [SSDLite with MobileNetV2 backbone](https://github.com/open-mmlab/mmdetection)

- [x] [RetinaNet R50](https://github.com/open-mmlab/mmdetection)

- [x] [SSD300 with VGG backbone](https://github.com/open-mmlab/mmdetection)

- [x] [YOLOv3 d53](https://github.com/open-mmlab/mmdetection)

- [x] [YOLOv5](https://github.com/ultralytics/yolov5)



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



#### :page_facing_up: List of currently supported operations can be founded [here](operators.md).



## 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](https://github.com/onnx/onnx/blob/main/docs/PythonAPIOverview.md#converting-version-of-an-onnx-model-within-default-domain-aionnx)



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](https://github.com/cakeofwar42) for his contributions.\

Special thanks to Andrey Denisov [@denisovap2013](https://github.com/denisovap2013) for the logo design.