onnx2torch 1.5.4

Nice Onnx to Pytorch converter

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 torch here.

Installation

pip install onnx2torch

or

conda install -c conda-forge onnx2torch

Usage

Below you can find some examples of use.

Convert

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.e-7))

Models

We have tested the following models:

Segmentation models:

• DeepLabv3plus
• DeepLabv3 resnet50 (torchvision)
• HRNet
• UNet (torchvision)
• FCN resnet50 (torchvision)
• lraspp mobilenetv3 (torchvision)

Detection from MMdetection:

• SSDLite with MobileNetV2 backbone
• RetinaNet R50
• SSD300 with VGG backbone
• Yolov3_d53
• Yolov5

Classification from torchvision:

• Resnet18
• Resnet50
• MobileNet v2
• MobileNet v3 large
• EfficientNet_b{0, 1, 2, 3}
• WideResNet50
• ResNext50
• VGG16
• GoogleleNet
• 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 add the module:

1. Supported by both PyTorch and ONNX and has the same behaviour.

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.

2. Operations supported by PyTorch and ONNX BUT have different behaviour

class OnnxExpand(nn.Module, OnnxToTorchModuleWithCustomExport):

    def forward(self, input_tensor: torch.Tensor, shape: torch.Tensor) -> torch.Tensor:
        # wrap all forward function logic to local function without arguments
        def _forward():
            return input_tensor * torch.ones(torch.Size(shape), dtype=input_tensor.dtype, device=input_tensor.device)

        if torch.onnx.is_in_onnx_export():
            # we need to send forward function wrapper as the first argument, all other
            # arguments will be send to symbolic method on the next step
            return _ExpandExportToOnnx.set_forward_and_apply(_forward, input_tensor, shape)

        # execute forward
        return _forward()


class _ExpandExportToOnnx(CustomExportToOnnx):

    @staticmethod
    def symbolic(graph: torch_C.Graph, input_tensor: torch_C.Value, shape: torch_C.Value, *args) -> torch_C.Value:
        return graph.op('Expand', input_tensor, shape, outputs=1)


@add_converter(operation_type='Expand', version=8)
@add_converter(operation_type='Expand', version=13)
def _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:  # pylint: disable=unused-argument
    return OperationConverterResult(
        torch_module=OnnxExpand(),
        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 _ExpandExportToOnnx.

Acknowledgments

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