forked from openvinotoolkit/openvino
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add PyTorch section to the documentation (openvinotoolkit#4972)
* Add PyTorch section to the documentation * Apply review feedback * Remove section about loop * Apply review feedback * Apply review feedback * Apply review feedback
- Loading branch information
Showing
7 changed files
with
64 additions
and
16 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
53 changes: 53 additions & 0 deletions
53
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| # 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). |
File renamed without changes.
File renamed without changes.
File renamed without changes.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters