[LEGACY] Converting a PaddlePaddle 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 PaddlePaddle Model article.

This page provides general instructions on how to convert a model from a PaddlePaddle format to the OpenVINO IR format using Model Optimizer. The instructions are different depending on PaddlePaddle model format.


PaddlePaddle models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the inference example for more details. Using convert_model is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.

Converting PaddlePaddle Model Inference Format#

PaddlePaddle inference model includes .pdmodel (storing model structure) and .pdiparams (storing model weight). For how to export PaddlePaddle inference model, please refer to the Exporting PaddlePaddle Inference Model Chinese guide.

To convert a PaddlePaddle model, use the mo script and specify the path to the input .pdmodel model file:

mo --input_model <INPUT_MODEL>.pdmodel

For example, this command converts a yolo v3 PaddlePaddle network to OpenVINO IR network:

mo --input_model=yolov3.pdmodel --input=image,im_shape,scale_factor --input_shape=[1,3,608,608],[1,2],[1,2] --reverse_input_channels --output=save_infer_model/scale_0.tmp_1,save_infer_model/scale_1.tmp_1

Converting PaddlePaddle Model From Memory Using Python API#

Model conversion API supports passing the following PaddlePaddle models directly from memory:

  • paddle.hapi.model.Model

  • paddle.fluid.dygraph.layers.Layer

  • paddle.fluid.executor.Executor

When you convert certain PaddlePaddle models, you may need to set the example_input or example_output parameters first. Below you will find examples that show how to convert aforementioned model formats using the parameters.

  • paddle.hapi.model.Model

    import paddle
    from openvino.tools.mo import convert_model
    # create a paddle.hapi.model.Model format model
    resnet50 = paddle.vision.models.resnet50()
    x = paddle.static.InputSpec([1,3,224,224], 'float32', 'x')
    y = paddle.static.InputSpec([1,1000], 'float32', 'y')
    model = paddle.Model(resnet50, x, y)
    # convert to OpenVINO IR format
    ov_model = convert_model(model)
    # optional: serialize OpenVINO IR to *.xml & *.bin
    from openvino.runtime import serialize
    serialize(ov_model, "ov_model.xml", "ov_model.bin")
  • paddle.fluid.dygraph.layers.Layer

    example_input is required while example_output is optional, and accept the following formats:

    list with tensor(paddle.Tensor) or InputSpec(paddle.static.input.InputSpec)

    import paddle
    from openvino.tools.mo import convert_model
    # create a paddle.fluid.dygraph.layers.Layer format model
    model = paddle.vision.models.resnet50()
    x = paddle.rand([1,3,224,224])
    # convert to OpenVINO IR format
    ov_model = convert_model(model, example_input=[x])
  • paddle.fluid.executor.Executor

    example_input and example_output are required, and accept the following formats:

    list or tuple with variable(paddle.static.data)

    import paddle
    from openvino.tools.mo import convert_model
    # create a paddle.fluid.executor.Executor format model
    x = paddle.static.data(name="x", shape=[1,3,224])
    y = paddle.static.data(name="y", shape=[1,3,224])
    relu = paddle.nn.ReLU()
    sigmoid = paddle.nn.Sigmoid()
    y = sigmoid(relu(x))
    exe = paddle.static.Executor(paddle.CPUPlace())
    # convert to OpenVINO IR format
    ov_model = convert_model(exe, example_input=[x], example_output=[y])


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

Supported PaddlePaddle Layers#

For the list of supported standard layers, refer to the Supported Operations page.

Frequently Asked Questions (FAQ)#

The model conversion API displays explanatory messages for typographical errors, incorrectly used options, or other issues. They describe the potential cause of the problem and give a link to the Model Optimizer FAQ, which provides instructions on how to resolve most issues. The FAQ also includes links to relevant sections in Convert a Model to help you understand what went wrong.

Additional Resources#

See the Model Conversion Tutorials page for a set of tutorials providing step-by-step instructions for converting specific PaddlePaddle models.