[LEGACY] Converting a PyTorch Model#


The code described here has been deprecated! Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but you should not use it in contemporary applications.

This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the Converting a PyTorch Model article.

This page provides instructions on how to convert a model from the PyTorch format to the OpenVINO IR format.

The conversion is a required step to run inference using OpenVINO API. It is not required if you choose to work with OpenVINO under the PyTorch framework, using its torch.compile feature.

Converting a PyTorch model with PyTorch Frontend#

To convert a PyTorch model to the OpenVINO IR format, use the OVC API (superseding the previously used tool, MO). To do so, use the convert_model() method, like so:

import torchvision
import torch
from openvino.tools.mo import convert_model

model = torchvision.models.resnet50(weights='DEFAULT')
ov_model = convert_model(model)

Following PyTorch model formats are supported:

  • torch.nn.Module

  • torch.jit.ScriptModule

  • torch.jit.ScriptFunction

Converting certain PyTorch models may require model tracing, which needs the example_input parameter to be set, for example:

import torchvision
import torch
from openvino.tools.mo import convert_model

model = torchvision.models.resnet50(weights='DEFAULT')
ov_model = convert_model(model, example_input=torch.randn(1, 3, 100, 100))

example_input accepts the following formats:

  • openvino.runtime.Tensor

  • torch.Tensor

  • np.ndarray

  • list or tuple with tensors (openvino.runtime.Tensor / torch.Tensor / np.ndarray)

  • dictionary where key is the input name, value is the tensor (openvino.runtime.Tensor / torch.Tensor / np.ndarray)

Sometimes convert_model will produce inputs of the model with dynamic rank or dynamic type. Such model may not be supported by the hardware chosen for inference. To avoid this issue, use the input argument of convert_model. For more information, refer to Convert Models Represented as Python Objects.


The convert_model() method returns ov.Model that you can optimize, compile, or save to a file for subsequent use.

Exporting a PyTorch Model to ONNX Format#

It is also possible to export a PyTorch model to ONNX and then convert it to OpenVINO IR. To convert and deploy a PyTorch model this way, follow these steps:

  1. Export a PyTorch model to ONNX.

  2. Convert an ONNX model to produce an optimized Intermediate Representation of the model based on the trained network topology, weights, and biases values.

PyTorch models are defined in Python. To export them, use the torch.onnx.export() method. The code to evaluate or test the model is usually provided with its code and can be used for its initialization and export. The export to ONNX is crucial for this process, but it is covered by PyTorch framework, therefore, It will not be covered here in detail. For more information, refer to the Exporting PyTorch models to ONNX format guide.

To export a PyTorch model, you need to obtain the model as an instance of torch.nn.Module class and call the export function.

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.
# 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')

Additional Resources#

See the Model Conversion Tutorials page for a set of tutorials providing step-by-step instructions for converting specific PyTorch models. Here are some examples: