Converting a PyTorch Model

This page provides instructions on how to convert a model from the PyTorch format to the OpenVINO IR format using Model Optimizer. Model Optimizer Python API allows the conversion of PyTorch models using the convert_model() method.

(Experimental) Converting a PyTorch model with PyTorch Frontend

Example of PyTorch model conversion:

import torchvision
import torch
from import convert_model

model = torchvision.models.resnet50(pretrained=True)
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 input_shape or example_input parameters to be set.

  • example_input is used as example input for model tracing.

  • input_shape is used for constructing a float zero-filled torch.Tensor for model tracing.

Example of using example_input:

import torchvision
import torch
from import convert_model

model = torchvision.models.resnet50(pretrained=True)
ov_model = convert_model(model, example_input=torch.zeros(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)


The convert_model() method returns ov.model that you can optimize, compile, or serialize into a file for subsequent use.

Exporting a PyTorch Model to ONNX Format

Currently, the most robust method of converting PyTorch models is exporting a PyTorch model to ONNX and then converting it to IR. To convert and deploy a PyTorch model, follow these steps:

  1. Export a PyTorch model to ONNX.

  2. Convert the 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')

Known Issues

As of version 1.8.1, not all PyTorch operations can be exported to ONNX opset 9 which is used by default. 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 page.

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: