# onnx2torch
**Repository Path**: RapidAI/onnx2torch
## Basic Information
- **Project Name**: onnx2torch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-01-25
- **Last Updated**: 2024-01-25
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
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 torch [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 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.e-7))
```
## Models
We have tested the following models:
Segmentation models:
- [x] DeepLabv3plus
- [x] DeepLabv3 resnet50 (torchvision)
- [x] HRNet
- [x] UNet (torchvision)
- [x] FCN resnet50 (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] Resnet18
- [x] Resnet50
- [x] MobileNet v2
- [x] MobileNet v3 large
- [x] EfficientNet_b{0, 1, 2, 3}
- [x] WideResNet50
- [x] ResNext50
- [x] VGG16
- [x] GoogleleNet
- [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.
## 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.
