Convert a Model

@sphinxdirective

.. _deep learning model optimizer:

.. toctree:: :maxdepth: 1 :hidden:

openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model openvino_docs_MO_DG_Additional_Optimization_Use_Cases openvino_docs_MO_DG_FP16_Compression openvino_docs_MO_DG_Python_API openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ

.. meta:: :description: Model conversion (MO) furthers the transition between training and deployment environments, it adjusts deep learning models for optimal execution on target devices.

To convert a model to OpenVINO model format (ov.Model), you can use the following command:

.. tab-set::

.. tab-item:: Python
   :sync: py

   .. code-block:: py
      :force:

      from openvino.tools.mo import convert_model
      ov_model = convert_model(INPUT_MODEL)

.. tab-item:: CLI
   :sync: cli

   .. code-block:: sh

      mo --input_model INPUT_MODEL

If the out-of-the-box conversion (only the input_model parameter is specified) is not successful, use the parameters mentioned below to override input shapes and cut the model:

model conversion API provides two parameters to override original input shapes for model conversion: input and input_shape. For more information about these parameters, refer to the :doc:Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model> guide.
To cut off unwanted parts of a model (such as unsupported operations and training sub-graphs), use the input and output parameters to define new inputs and outputs of the converted model. For a more detailed description, refer to the :doc:Cutting Off Parts of a Model <openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model> guide.

You can also insert additional input pre-processing sub-graphs into the converted model by using the mean_values, scales_values, layout, and other parameters described in the :doc:Embedding Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases> article.

The compress_to_fp16 compression parameter in mo command-line tool allows generating IR with constants (for example, weights for convolutions and matrix multiplications) compressed to FP16 data type. For more details, refer to the :doc:Compression of a Model to FP16 <openvino_docs_MO_DG_FP16_Compression> guide.

To get the full list of conversion parameters, run the following command:

.. tab-set::

.. tab-item:: Python
   :sync: py

   .. code-block:: py
      :force:

      from openvino.tools.mo import convert_model
      ov_model = convert_model(help=True)

.. tab-item:: CLI
   :sync: cli

   .. code-block:: sh

      mo --help

Examples of model conversion parameters #######################################

Below is a list of separate examples for different frameworks and model conversion parameters:

Launch model conversion for a TensorFlow MobileNet model in the binary protobuf format:

.. tab-set::

.. tab-item:: Python
   :sync: py

   .. code-block:: py
      :force:

      from openvino.tools.mo import convert_model
      ov_model = convert_model("MobileNet.pb")

.. tab-item:: CLI
   :sync: cli

   .. code-block:: sh

      mo --input_model MobileNet.pb

Launch model conversion for a TensorFlow BERT model in the SavedModel format with three inputs. Specify input shapes explicitly where the batch size and the sequence length equal 2 and 30 respectively:

.. tab-set::

.. tab-item:: Python
   :sync: py

   .. code-block:: py
      :force:

      from openvino.tools.mo import convert_model
      ov_model = convert_model("BERT", input_shape=[[2,30],[2,30],[2,30]])

.. tab-item:: CLI
   :sync: cli

   .. code-block:: sh

      mo --saved_model_dir BERT --input_shape [2,30],[2,30],[2,30]

For more information, refer to the :doc:Converting a TensorFlow Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow> guide.

Launch model conversion for an ONNX OCR model and specify new output explicitly:

.. tab-set::
```
.. tab-item:: Python
   :sync: py

   .. code-block:: py
      :force:

      from openvino.tools.mo import convert_model
      ov_model = convert_model("ocr.onnx", output="probabilities")

.. tab-item:: CLI
   :sync: cli

   .. code-block:: sh

      mo --input_model ocr.onnx --output probabilities
```
For more information, refer to the :doc:Converting an ONNX Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX> guide.

.. note::

PyTorch models must be exported to the ONNX format before conversion into IR. More information can be found in :doc:Converting a PyTorch Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch>.

Launch model conversion for a PaddlePaddle UNet model and apply mean-scale normalization to the input:

.. tab-set::

.. tab-item:: Python
   :sync: py

   .. code-block:: py
      :force:

      from openvino.tools.mo import convert_model
      ov_model = convert_model("unet.pdmodel", mean_values=[123,117,104], scale=255)

.. tab-item:: CLI
   :sync: cli

   .. code-block:: sh

      mo --input_model unet.pdmodel --mean_values [123,117,104] --scale 255

For more information, refer to the :doc:Converting a PaddlePaddle Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle> guide.

To get conversion recipes for specific TensorFlow, ONNX, and PyTorch models, refer to the :doc:Model Conversion Tutorials <openvino_docs_MO_DG_prepare_model_convert_model_tutorials>.
For more information about IR, see :doc:Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™ <openvino_docs_MO_DG_IR_and_opsets>.
For more information about support of neural network models trained with various frameworks, see :doc:OpenVINO Extensibility Mechanism <openvino_docs_Extensibility_UG_Intro>

@endsphinxdirective

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Convert a Model

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