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.exportfunction.
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.
pip install onnx2torchBelow you can find some examples of use.
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)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))We have tested the following models:
Segmentation models:
- DeepLabv3plus
- DeepLabv3 resnet50 (torchvision)
- HRNet
- UNet (torchvision)
- FCN resnet50 (torchvision)
- lraspp mobilenetv3 (torchvision)
Detection from MMdetection:
Classification from torchvision:
- Resnet18
- Resnet50
- MobileNet v2
- MobileNet v3 large
- EfficientNet_b{0, 1, 2, 3}
- WideResNet50
- ResNext50
- VGG16
- GoogleleNet
- MnasNet
- RegNet
Here we show how to add the module:
- 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.
- Operations supported by PyTorch and ONNX BUT have different behaviour
class OnnxExpand(nn.Module):
def forward(self, input_tensor: torch.Tensor, shape: torch.Tensor) -> torch.Tensor:
output = input_tensor * torch.ones(torch.Size(shape), dtype=input_tensor.dtype, device=input_tensor.device)
if torch.onnx.is_in_onnx_export():
return _ExpandExportToOnnx.set_output_and_apply(output, input_tensor, shape)
return output
class _ExpandExportToOnnx(CustomExportToOnnx):
@staticmethod
def symbolic(graph: torch_C.Graph, *args) -> torch_C.Value:
return graph.op('Expand', *args, 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.