| Name | onnx2torch2 JSON |
| Version |
2.0.0
JSON |
| download |
| home_page | None |
| Summary | ONNX to PyTorch converter |
| upload_time | 2024-04-04 15:24:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
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| keywords |
ai
onnx
torch
onnx2torch2
converters
|
| VCS |
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| requirements |
No requirements were recorded.
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<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/onnx2torch2">
<img src="https://badgen.net/pypi/v/onnx2torch2" />
</a>
<a href="https://anaconda.org/conda-forge/onnx2torch2">
<img src="https://img.shields.io/conda/vn/conda-forge/onnx2torch2" />
</a>
<a href="https://pypi.org/project/onnx2torch2">
<img src="https://badgen.net/pypi/license/onnx2torch2" />
</a>
<a href="https://github.com/psf/black">
<img src="https://img.shields.io/badge/code%20style-black-black" />
</a>
<a href="https://pypi.org/project/onnx2torch2">
<img src="https://img.shields.io/pypi/dm/onnx2torch2" />
</a>
<a href="https://github.com/untetherai/onnx2torch2/stargazers">
<img src="https://img.shields.io/github/stars/untetherai/onnx2torch2.svg?style=social&label=Star&maxAge=2592000" />
</a>
</p>
onnx2torch2 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/untetherai/onnx2torch2/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/untetherai/onnx2torch2/discussions).
## Installation
```bash
pip install .
```
## Usage
Below you can find some examples of use.
### Convert
```python
import onnx
import torch
from onnx2torch2 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 onnx2torch2 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](./onnx2torch2/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.
</details>
<details>
<summary>but has different behaviour</summary>
An example of such a module is [ScatterND](./onnx2torch2/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``.
</details>
## Opset version workaround
In case 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)
<details>
<summary>Example</summary>
```python
import onnx
from onnx import version_converter
import torch
from onnx2torch2 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 onnx2torch2 use `Cite this repository` button, or:
```
@misc{onnx2torch2,
title={onnx2torch2},
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.
Raw data
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"author_email": "untetherai <info@untether.ai>",
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"description": "<div align=\"center\">\n <img src=\"assets/logo/onnx2torch_light.png#gh-light-mode-only\">\n <img src=\"assets/logo/onnx2torch_dark.png#gh-dark-mode-only\">\n</div>\n\n<p align=\"center\">\n <a href=\"https://pypi.org/project/onnx2torch2\">\n <img src=\"https://badgen.net/pypi/v/onnx2torch2\" />\n </a>\n <a href=\"https://anaconda.org/conda-forge/onnx2torch2\">\n <img src=\"https://img.shields.io/conda/vn/conda-forge/onnx2torch2\" />\n </a>\n <a href=\"https://pypi.org/project/onnx2torch2\">\n <img src=\"https://badgen.net/pypi/license/onnx2torch2\" />\n </a>\n <a href=\"https://github.com/psf/black\">\n <img src=\"https://img.shields.io/badge/code%20style-black-black\" />\n </a>\n <a href=\"https://pypi.org/project/onnx2torch2\">\n <img src=\"https://img.shields.io/pypi/dm/onnx2torch2\" />\n </a>\n <a href=\"https://github.com/untetherai/onnx2torch2/stargazers\">\n <img src=\"https://img.shields.io/github/stars/untetherai/onnx2torch2.svg?style=social&label=Star&maxAge=2592000\" />\n </a>\n</p>\n\nonnx2torch2 is an ONNX to PyTorch converter.\nOur converter:\n* Is easy to use \u2013 Convert the ONNX model with the function call ``convert``;\n* Is easy to extend \u2013 Write your own custom layer in PyTorch and register it with ``@add_converter``;\n* Convert back to ONNX \u2013 You can convert the model back to ONNX using the ``torch.onnx.export`` function.\n\nIf you find an issue, please [let us know](https://github.com/untetherai/onnx2torch2/issues)! And feel free to create merge requests.\n\nPlease note that this converter covers only a limited number of PyTorch / ONNX models and operations.\nLet us know which models you use or want to convert from onnx to torch [here](https://github.com/untetherai/onnx2torch2/discussions).\n\n## Installation\n\n```bash\npip install .\n```\n\n## Usage\n\nBelow you can find some examples of use.\n\n### Convert\n\n```python\nimport onnx\nimport torch\nfrom onnx2torch2 import convert\n\n# Path to ONNX model\nonnx_model_path = '/some/path/mobile_net_v2.onnx'\n# You can pass the path to the onnx model to convert it or...\ntorch_model_1 = convert(onnx_model_path)\n\n# Or you can load a regular onnx model and pass it to the converter\nonnx_model = onnx.load(onnx_model_path)\ntorch_model_2 = convert(onnx_model)\n```\n\n### Execute\n\nWe can execute the returned ``PyTorch model`` in the same way as the original torch model.\n\n```python\nimport onnxruntime as ort\n# Create example data\nx = torch.ones((1, 2, 224, 224)).cuda()\n\nout_torch = torch_model_1(x)\n\nort_sess = ort.InferenceSession(onnx_model_path)\noutputs_ort = ort_sess.run(None, {'input': x.numpy()})\n\n# Check the Onnx output against PyTorch\nprint(torch.max(torch.abs(outputs_ort - out_torch.detach().numpy())))\nprint(np.allclose(outputs_ort, out_torch.detach().numpy(), atol=1.e-7))\n```\n\n## Models\n\nWe have tested the following models:\n\nSegmentation models:\n- [x] DeepLabv3plus\n- [x] DeepLabv3 resnet50 (torchvision)\n- [x] HRNet\n- [x] UNet (torchvision)\n- [x] FCN resnet50 (torchvision)\n- [x] lraspp mobilenetv3 (torchvision)\n\nDetection from MMdetection:\n- [x] [SSDLite with MobileNetV2 backbone](https://github.com/open-mmlab/mmdetection)\n- [x] [RetinaNet R50](https://github.com/open-mmlab/mmdetection)\n- [x] [SSD300 with VGG backbone](https://github.com/open-mmlab/mmdetection)\n- [x] [Yolov3_d53](https://github.com/open-mmlab/mmdetection)\n- [x] [Yolov5](https://github.com/ultralytics/yolov5)\n\nClassification from __torchvision__:\n- [x] Resnet18\n- [x] Resnet50\n- [x] MobileNet v2\n- [x] MobileNet v3 large\n- [x] EfficientNet_b{0, 1, 2, 3}\n- [x] WideResNet50\n- [x] ResNext50\n- [x] VGG16\n- [x] GoogleleNet\n- [x] MnasNet\n- [x] RegNet\n\nTransformers:\n- [x] Vit\n- [x] Swin\n- [x] GPT-J\n\n#### :page_facing_up: List of currently supported operations can be founded [here](operators.md).\n\n## How to add new operations to converter\n\nHere we show how to extend onnx2torch2 with new ONNX operation, that supported by both PyTorch and ONNX\n<details>\n<summary>and has the same behaviour</summary>\n\nAn example of such a module is [Relu](./onnx2torch2/node_converters/activations.py)\n\n```python\n@add_converter(operation_type='Relu', version=6)\n@add_converter(operation_type='Relu', version=13)\n@add_converter(operation_type='Relu', version=14)\ndef _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:\n return OperationConverterResult(\n torch_module=nn.ReLU(),\n onnx_mapping=onnx_mapping_from_node(node=node),\n )\n```\n\nHere we have registered an operation named ``Relu`` for opset versions 6, 13, 14.\nNote that the ``torch_module`` argument in ``OperationConverterResult`` must be a torch.nn.Module, not just a callable object!\nIf Operation's behaviour differs from one opset version to another, you should implement it separately.\n</details>\n\n<details>\n<summary>but has different behaviour</summary>\n\nAn example of such a module is [ScatterND](./onnx2torch2/node_converters/scatter_nd.py)\n\n```python\n# It is recommended to use Enum for string ONNX attributes.\nclass ReductionOnnxAttr(Enum):\n NONE = 'none'\n ADD = 'add'\n MUL = 'mul'\n\n\nclass OnnxScatterND(nn.Module, OnnxToTorchModuleWithCustomExport):\n def __init__(self, reduction: ReductionOnnxAttr):\n super().__init__()\n self._reduction = reduction\n\n # The following method should return ONNX attributes with their values as a dictionary.\n # The number of attributes, their names and values depend on opset version;\n # method should return correct set of attributes.\n # Note: add type-postfix for each key: reduction -> reduction_s, where s means \"string\".\n def _onnx_attrs(self, opset_version: int) -> Dict[str, Any]:\n onnx_attrs: Dict[str, Any] = {}\n\n # Here we handle opset versions < 16 where there is no \"reduction\" attribute.\n if opset_version < 16:\n if self._reduction != ReductionOnnxAttr.NONE:\n raise ValueError(\n 'ScatterND from opset < 16 does not support'\n f'reduction attribute != {ReductionOnnxAttr.NONE.value},'\n f'got {self._reduction.value}'\n )\n return onnx_attrs\n\n onnx_attrs['reduction_s'] = self._reduction.value\n return onnx_attrs\n\n def forward(\n self,\n data: torch.Tensor,\n indices: torch.Tensor,\n updates: torch.Tensor,\n ) -> torch.Tensor:\n def _forward():\n # ScatterND forward implementation...\n return output\n\n if torch.onnx.is_in_onnx_export():\n # Please follow our convention, args consists of:\n # forward function, operation type, operation inputs, operation attributes.\n onnx_attrs = self._onnx_attrs(opset_version=get_onnx_version())\n return DefaultExportToOnnx.export(_forward, 'ScatterND', data, indices, updates, onnx_attrs)\n\n return _forward()\n\n\n@add_converter(operation_type='ScatterND', version=11)\n@add_converter(operation_type='ScatterND', version=13)\n@add_converter(operation_type='ScatterND', version=16)\ndef _(node: OnnxNode, graph: OnnxGraph) -> OperationConverterResult:\n node_attributes = node.attributes\n reduction = ReductionOnnxAttr(node_attributes.get('reduction', 'none'))\n return OperationConverterResult(\n torch_module=OnnxScatterND(reduction=reduction),\n onnx_mapping=onnx_mapping_from_node(node=node),\n )\n```\nHere we have used a trick to convert the model from torch back to ONNX by defining the custom ``_ScatterNDExportToOnnx``.\n</details>\n\n## Opset version workaround\n\nIn case you are using a model with older opset, try the following workaround:\n\n[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)\n\n<details>\n<summary>Example</summary>\n\n```python\nimport onnx\nfrom onnx import version_converter\nimport torch\nfrom onnx2torch2 import convert\n\n# Load the ONNX model.\nmodel = onnx.load('model.onnx')\n# Convert the model to the target version.\ntarget_version = 13\nconverted_model = version_converter.convert_version(model, target_version)\n# Convert to torch.\ntorch_model = convert(converted_model)\ntorch.save(torch_model, 'model.pt')\n```\n\n</details>\n\nNote: use this only when the model does not convert to PyTorch using the existing opset version. Result might vary.\n\n## Citation\n\nTo cite onnx2torch2 use `Cite this repository` button, or:\n```\n@misc{onnx2torch2,\n title={onnx2torch2},\n author={ENOT developers and Kalgin, Igor and Yanchenko, Arseny and Ivanov, Pyoter and Goncharenko, Alexander},\n year={2021},\n howpublished={\\url{https://enot.ai/}},\n note={Version: x.y.z}\n}\n```\n\n## Acknowledgments\n\nThanks to Dmitry Chudakov [@cakeofwar42](https://github.com/cakeofwar42) for his contributions.\\\nSpecial thanks to Andrey Denisov [@denisovap2013](https://github.com/denisovap2013) for the logo design.\n",
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