Converting a PaddlePaddle Model

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

Note

PaddlePaddle model serialized in a file can be loaded by openvino.Core.read_model or openvino.Core.compile_model methods by OpenVINO runtime API without preparing OpenVINO IR first. Refer to the inference example for more details. Using openvino.convert_model is still recommended if model load latency matters for the inference application.

Converting PaddlePaddle Model Files

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

To convert a PaddlePaddle model, use the ovc or openvino.convert_model and specify the path to the input .pdmodel model file:

Python

import openvino as ov
ov.convert_model('your_model_file.pdmodel')

CLI

ovc your_model_file.pdmodel

For example, this command converts a YOLOv3 PaddlePaddle model to OpenVINO IR model:

Python

import openvino as ov
ov.convert_model('yolov3.pdmodel')

CLI

ovc yolov3.pdmodel

Converting PaddlePaddle Python Model

Model conversion API supports passing PaddlePaddle models directly in Python without saving them to files in the user code.

Following PaddlePaddle model object types are supported:

• paddle.hapi.model.Model

• paddle.fluid.dygraph.layers.Layer

• paddle.fluid.executor.Executor

Some PaddlePaddle models may require setting example_input or output for conversion as shown in the examples below:

• Example of converting paddle.hapi.model.Model format model:

  import paddle
  import openvino as ov
  
  # 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 = ov.convert_model(model)
  
  ov.save_model(ov_model, "resnet50.xml")
  

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

  example_input is required while output is optional. example_input accepts the following formats:

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

  import paddle
  import openvino as ov
  
  # 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 = ov.convert_model(model, example_input=[x])
  

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

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

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

  import paddle
  import openvino as ov
  
  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 = ov.convert_model(exe, example_input=[x], output=[y])
  

Supported PaddlePaddle Layers

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

Additional Resources

Check out more examples of model conversion in interactive Python tutorials.