openvino/docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

Convert a Model

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.. _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:

• input and input_shape - the model conversion API parameters used to override original input shapes for model conversion,

  For more information about the parameters, refer to the :doc:Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model> guide.

• input and output - the model conversion API parameters used to define new inputs and outputs of the converted model to cut off unwanted parts (such as unsupported operations and training sub-graphs),

  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.

• mean_values, scales_values, layout - the parameters used to insert additional input pre-processing sub-graphs into the converted model,

  For more details, see the :doc:Embedding Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases> article.

• compress_to_fp16 - a compression parameter in mo command-line tool, which 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:

1. 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.

2. 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>.

3. 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>

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