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docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
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* [Converting Your ONNX* Model](prepare_model/convert_model/Convert_Model_From_ONNX.md)
* [Converting Faster-RCNN ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_Faster_RCNN.md)
* [Converting Mask-RCNN ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md)
* [Converting DLRM ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_DLRM.md)
* [Converting GPT2 ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_GPT2.md)
* [Converting Your PyTorch* Model](prepare_model/convert_model/Convert_Model_From_PyTorch.md)
* [Converting F3Net PyTorch* Model](prepare_model/convert_model/pytorch_specific/Convert_F3Net.md)
* [Converting QuartzNet PyTorch* Model](prepare_model/convert_model/pytorch_specific/Convert_QuartzNet.md)
* [Converting YOLACT PyTorch* Model](prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md)
* [Model Optimizations Techniques](prepare_model/Model_Optimization_Techniques.md)
* [Cutting parts of the model](prepare_model/convert_model/Cutting_Model.md)
* [Sub-graph Replacement in Model Optimizer](prepare_model/customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md)

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Listed models are built with the operation set version 8 except the GPT-2 model. Models that are upgraded to higher operation set versions may not be supported.
## Supported Pytorch* Models via ONNX Conversion
Starting from the 2019R4 release, the OpenVINO™ toolkit officially supports public Pytorch* models (from `torchvision` 0.2.1 and `pretrainedmodels` 0.7.4 packages) via ONNX conversion.
The list of supported topologies is presented below:
|Package Name|Supported Models|
|:----|:----|
| [Torchvision Models](https://pytorch.org/docs/stable/torchvision/index.html) | alexnet, densenet121, densenet161, densenet169, densenet201, resnet101, resnet152, resnet18, resnet34, resnet50, vgg11, vgg13, vgg16, vgg19 |
| [Pretrained Models](https://github.com/Cadene/pretrained-models.pytorch) | alexnet, fbresnet152, resnet101, resnet152, resnet18, resnet34, resnet152, resnet18, resnet34, resnet50, resnext101_32x4d, resnext101_64x4d, vgg11 |
| [ESPNet Models](https://github.com/sacmehta/ESPNet/tree/master/pretrained) | |
| [MobileNetV3](https://github.com/d-li14/mobilenetv3.pytorch) | |
## Supported PaddlePaddle* Models via ONNX Conversion
Starting from the R5 release, the OpenVINO™ toolkit officially supports public PaddlePaddle* models via ONNX conversion.
The list of supported topologies downloadable from PaddleHub is presented below:

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# Converting a PyTorch* Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
PyTorch* framework is supported through export to ONNX\* format. A summary of the steps for optimizing and deploying a model that was trained with the PyTorch\* framework:
1. [Export PyTorch model to ONNX\*](#export-to-onnx).
2. [Configure the Model Optimizer](../Config_Model_Optimizer.md) for ONNX\*.
3. [Convert an ONNX\* model](Convert_Model_From_ONNX.md) to produce an optimized [Intermediate Representation (IR)](../../IR_and_opsets.md) of the model based on the trained network topology, weights, and biases values.
4. Test the model in the Intermediate Representation format using the [Inference Engine](../../../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) in the target environment via provided [sample applications](../../../IE_DG/Samples_Overview.md).
5. [Integrate](../../../IE_DG/Samples_Overview.md) the Inference Engine in your application to deploy the model in the target environment.
## Supported Topologies
Here is the list of models that were tested and are guaranteed to be supported.
It is not a full list of models that can be converted to ONNX\* and to IR.
|Package Name|Supported Models|
|:----|:----|
| [Torchvision Models](https://pytorch.org/docs/stable/torchvision/index.html) | alexnet, densenet121, densenet161, densenet169, densenet201, resnet101, resnet152, resnet18, resnet34, resnet50, vgg11, vgg13, vgg16, vgg19 |
| [Pretrained Models](https://github.com/Cadene/pretrained-models.pytorch) | alexnet, fbresnet152, resnet101, resnet152, resnet18, resnet34, resnet152, resnet18, resnet34, resnet50, resnext101_32x4d, resnext101_64x4d, vgg11 |
**Other supported topologies**
* [ESPNet Models](https://github.com/sacmehta/ESPNet/tree/master/pretrained)
* [MobileNetV3](https://github.com/d-li14/mobilenetv3.pytorch)
* F3Net topology can be converted using [Convert PyTorch\* F3Net to the IR](pytorch_specific/Convert_F3Net.md) instruction.
* QuartzNet topologies from [NeMo project](https://github.com/NVIDIA/NeMo) can be converted using [Convert PyTorch\* QuartzNet to the IR](pytorch_specific/Convert_QuartzNet.md) instruction.
* YOLACT topology can be converted using [Convert PyTorch\* YOLACT to the IR](pytorch_specific/Convert_YOLACT.md) instruction.
## Export PyTorch\* Model to ONNX\* Format <a name="export-to-onnx"></a>
PyTorch models are defined in a Python\* code, to export such models use `torch.onnx.export()` method.
Only the basics will be covered here, the step to export to ONNX\* is crucial but it is covered by PyTorch\* framework.
For more information, please refer to [PyTorch\* documentation](https://pytorch.org/docs/stable/onnx.html).
To export a PyTorch\* model you need to obtain the model as an instance of `torch.nn.Module` class and call the `export` function.
```python
import torch
# Instantiate your model. This is just a regular PyTorch model that will be exported in the following steps.
model = SomeModel()
# Evaluate the model to switch some operations from training mode to inference.
model.eval()
# Create dummy input for the model. It will be used to run the model inside export function.
dummy_input = torch.randn(1, 3, 224, 224)
# Call the export function
torch.onnx.export(model, (dummy_input, ), 'model.onnx')
```
## Known Issues
* Not all PyTorch\* operations can be exported to ONNX\* opset 9 which is used by default, as of version 1.8.1.
It is recommended to export models to opset 11 or higher when export to default opset 9 is not working. In that case, use `opset_version`
option of the `torch.onnx.export`. For more information about ONNX* opset, refer to the [Operator Schemas](https://github.com/onnx/onnx/blob/master/docs/Operators.md).

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<tab type="usergroup" title="Converting Your ONNX* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX">
<tab type="user" title="Convert ONNX* Faster R-CNN Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Faster_RCNN"/>
<tab type="user" title="Convert ONNX* Mask R-CNN Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_Mask_RCNN"/>
<tab type="user" title="Converting DLRM ONNX* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM"/>
<tab type="user" title="Convert PyTorch* QuartzNet Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_QuartzNet"/>
<tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_YOLACT"/>
<tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_F3Net"/>
<tab type="user" title="Convert ONNX* GPT-2 Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_GPT2"/>
<tab type="user" title="Convert DLRM ONNX* Model to the Intermediate Representation" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_onnx_specific_Convert_DLRM"/>
<tab type="usergroup" title="Converting Your PyTorch* Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch">
<tab type="user" title="Convert PyTorch* QuartzNet Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_QuartzNet"/>
<tab type="user" title="Convert PyTorch* YOLACT Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT"/>
<tab type="user" title="Convert PyTorch* F3Net Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_F3Net"/>
</tab>
<tab type="user" title="Model Optimizations Techniques" url="@ref openvino_docs_MO_DG_prepare_model_Model_Optimization_Techniques"/>
<tab type="user" title="Cutting off Parts of a Model" url="@ref openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model"/>