Converting a PaddlePaddle Model

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.

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

MO Python API supports passing PaddlePaddle models directly from memory.

Following PaddlePaddle model formats are supported:

• paddle.hapi.model.Model

• paddle.fluid.dygraph.layers.Layer

• paddle.fluid.executor.Executor

Converting certain PaddlePaddle models may require setting example_input or example_output. Below examples show how to execute such the conversion.

• Example of converting paddle.hapi.model.Model format 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")
  

• Example of converting paddle.fluid.dygraph.layers.Layer format model:

  example_input is required while example_output is optional, which 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])
  

• Example of converting paddle.fluid.executor.Executor format model:

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

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

  import paddle
  from openvino.tools.mo import convert_model
  
  paddle.enable_static()
  
  # 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())
  exe.run(paddle.static.default_startup_program())
  
  # convert to OpenVINO IR format
  ov_model = convert_model(exe, example_input=[x], example_output=[y])
  

Supported PaddlePaddle Layers

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

Officially Supported PaddlePaddle Models

The following PaddlePaddle models have been officially validated and confirmed to work (as of OpenVINO 2022.1):

Model Name	Model Type	Description
ppocr-det	optical character recognition	Models are exported from PaddleOCR. Refer to READ.md.
ppocr-rec	optical character recognition	Models are exported from PaddleOCR. Refer to READ.md.
ResNet-50	classification	Models are exported from PaddleClas. Refer to getting_started_en.md.
MobileNet v2	classification	Models are exported from PaddleClas. Refer to getting_started_en.md.
MobileNet v3	classification	Models are exported from PaddleClas. Refer to getting_started_en.md.
BiSeNet v2	semantic segmentation	Models are exported from PaddleSeg. Refer to model_export.md.
DeepLab v3 plus	semantic segmentation	Models are exported from PaddleSeg. Refer to model_export.md.
Fast-SCNN	semantic segmentation	Models are exported from PaddleSeg. Refer to model_export.md.
OCRNET	semantic segmentation	Models are exported from PaddleSeg. Refer to model_export.md.
Yolo v3	detection	Models are exported from PaddleDetection. Refer to EXPORT_MODEL.md.
ppyolo	detection	Models are exported from PaddleDetection. Refer to EXPORT_MODEL.md.
MobileNetv3-SSD	detection	Models are exported from PaddleDetection. Refer to EXPORT_MODEL.md.
U-Net	semantic segmentation	Models are exported from PaddleSeg. Refer to model_export.md.
BERT	language representation	Models are exported from PaddleNLP. Refer to README.md.

Frequently Asked Questions (FAQ)

When Model Optimizer is unable to run to completion due to typographical errors, incorrectly used options, or other issues, it provides explanatory messages. 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 the Model Optimizer Developer Guide 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.