[DOCS] Updating Supported Model Formats article (#18495)
* supported_model_formats * add-method * apply-commits-18214 Applying commits from: https://github.com/openvinotoolkit/openvino/pull/18214 * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * Update supported_model_formats.md * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * Update docs/MO_DG/prepare_model/convert_model/supported_model_formats.md * review-suggestions * Update supported_model_formats.md
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Sebastian Golebiewski
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@ -17,31 +17,533 @@
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openvino_docs_MO_DG_prepare_model_convert_model_tutorials
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.. meta::
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:description: In OpenVINO, ONNX, PaddlePaddle, TensorFlow and TensorFlow Lite
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models do not require any prior conversion, while MxNet, Caffe and Kaldi do.
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:description: Learn about supported model formats and the methods used to convert, read and compile them in OpenVINO™.
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**OpenVINO IR (Intermediate Representation)** - the proprietary format of OpenVINO™, benefiting from the full extent of its features.
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**OpenVINO IR (Intermediate Representation)** - the proprietary and default format of OpenVINO, benefiting from the full extent of its features. All other model formats presented below will ultimately be converted to :doc:`OpenVINO IR <openvino_ir>`.
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**ONNX, PaddlePaddle, TensorFlow, TensorFlow Lite** - formats supported directly, which means they can be used with
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OpenVINO Runtime without any prior conversion. For a guide on how to run inference on ONNX, PaddlePaddle, or TensorFlow,
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see how to :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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**PyTorch, TensorFlow, ONNX, and PaddlePaddle** may be used without any prior conversion and can be read by OpenVINO Runtime API by the use of ``read_model()`` or ``compile_model()``. Additional adjustment for the model can be performed using the ``convert_model()`` method, which allows you to set shapes, types or the layout of model inputs, cut parts of the model, freeze inputs etc. The detailed information of capabilities of ``convert_model()`` can be found in :doc:`this <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` article.
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**MXNet, Caffe, Kaldi** - legacy formats that need to be converted to OpenVINO IR before running inference.
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The model conversion in some cases may involve intermediate steps. OpenVINO is currently proceeding
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**to deprecate these formats** and **remove their support entirely in the future**.
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Below you will find code examples for each method, for all supported model formats.
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.. tab-set::
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.. tab-item:: PyTorch
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:sync: torch
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.. tab-set::
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.. tab-item:: Python
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:sync: py
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* The ``convert_model()`` method:
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This is the only method applicable to PyTorch models.
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.. dropdown:: List of supported formats:
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* **Python objects**:
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* ``torch.nn.Module``
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* ``torch.jit.ScriptModule``
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* ``torch.jit.ScriptFunction``
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.. code-block:: py
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:force:
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model = torchvision.models.resnet50(pretrained=True)
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ov_model = convert_model(model)
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compiled_model = core.compile_model(ov_model, "AUTO")
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For more details on conversion, refer to the
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:doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch>`
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and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
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on this topic.
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.. tab-item:: TensorFlow
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:sync: tf
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.. tab-set::
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.. tab-item:: Python
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:sync: py
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* The ``convert_model()`` method:
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When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
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.. dropdown:: List of supported formats:
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* **Files**:
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* SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
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* Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
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* MetaGraph - ``<INPUT_META_GRAPH>.meta``
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* **Python objects**:
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* ``tf.keras.Model``
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* ``tf.keras.layers.Layer``
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* ``tf.Module``
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* ``tf.compat.v1.Graph``
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* ``tf.compat.v1.GraphDef``
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* ``tf.function``
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* ``tf.compat.v1.session``
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* ``tf.train.checkpoint``
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.. code-block:: py
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:force:
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ov_model = convert_model("saved_model.pb")
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compiled_model = core.compile_model(ov_model, "AUTO")
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For more details on conversion, refer to the
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:doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
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and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/101-tensorflow-to-openvino-with-output.html>`__
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on this topic.
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* The ``read_model()`` and ``compile_model()`` methods:
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.. dropdown:: List of supported formats:
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* **Files**:
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* SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
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* Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
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* MetaGraph - ``<INPUT_META_GRAPH>.meta``
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.. code-block:: py
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:force:
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ov_model = read_model("saved_model.pb")
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compiled_model = core.compile_model(ov_model, "AUTO")
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For a guide on how to run inference, see how to
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:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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For TensorFlow format, see :doc:`TensorFlow Frontend Capabilities and Limitations <openvino_docs_MO_DG_TensorFlow_Frontend>`.
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.. tab-item:: C++
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:sync: cpp
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
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* Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
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* MetaGraph - ``<INPUT_META_GRAPH>.meta``
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.. code-block:: cpp
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ov::CompiledModel compiled_model = core.compile_model("saved_model.pb", "AUTO");
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For a guide on how to run inference, see how to
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:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: C
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:sync: c
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* SavedModel - ``<SAVED_MODEL_DIRECTORY>`` or ``<INPUT_MODEL>.pb``
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* Checkpoint - ``<INFERENCE_GRAPH>.pb`` or ``<INFERENCE_GRAPH>.pbtxt``
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* MetaGraph - ``<INPUT_META_GRAPH>.meta``
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.. code-block:: c
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ov_compiled_model_t* compiled_model = NULL;
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ov_core_compile_model_from_file(core, "saved_model.pb", "AUTO", 0, &compiled_model);
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For a guide on how to run inference, see how to
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:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: CLI
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:sync: cli
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You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
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.. code-block:: sh
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mo --input_model <INPUT_MODEL>.pb
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For details on the conversion, refer to the
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:doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`.
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.. tab-item:: TensorFlow Lite
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:sync: tflite
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.. tab-set::
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.. tab-item:: Python
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:sync: py
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* The ``convert_model()`` method:
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When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.tflite``
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.. code-block:: py
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:force:
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ov_model = convert_model("<INPUT_MODEL>.tflite")
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compiled_model = core.compile_model(ov_model, "AUTO")
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For more details on conversion, refer to the
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:doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
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and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/119-tflite-to-openvino-with-output.html>`__
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on this topic.
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* The ``read_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.tflite``
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.. code-block:: py
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:force:
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ov_model = read_model("<INPUT_MODEL>.tflite")
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compiled_model = core.compile_model(ov_model, "AUTO")
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.tflite``
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.. code-block:: py
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:force:
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compiled_model = core.compile_model("<INPUT_MODEL>.tflite", "AUTO")
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For a guide on how to run inference, see how to
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:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: C++
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:sync: cpp
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.tflite``
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.. code-block:: cpp
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ov::CompiledModel compiled_model = core.compile_model("<INPUT_MODEL>.tflite", "AUTO");
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For a guide on how to run inference, see how to
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:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: C
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:sync: c
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.tflite``
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.. code-block:: c
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ov_compiled_model_t* compiled_model = NULL;
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ov_core_compile_model_from_file(core, "<INPUT_MODEL>.tflite", "AUTO", 0, &compiled_model);
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For a guide on how to run inference, see how to
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:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: CLI
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:sync: cli
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* The ``convert_model()`` method:
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You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.tflite``
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.. code-block:: sh
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mo --input_model <INPUT_MODEL>.tflite
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For details on the conversion, refer to the
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:doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>`.
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.. tab-item:: ONNX
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:sync: onnx
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.. tab-set::
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.. tab-item:: Python
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:sync: py
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* The ``convert_model()`` method:
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When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.onnx``
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.. code-block:: py
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:force:
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ov_model = convert_model("<INPUT_MODEL>.onnx")
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compiled_model = core.compile_model(ov_model, "AUTO")
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For more details on conversion, refer to the
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:doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
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and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
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on this topic.
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* The ``read_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.onnx``
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.. code-block:: py
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:force:
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ov_model = read_model("<INPUT_MODEL>.onnx")
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compiled_model = core.compile_model(ov_model, "AUTO")
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.onnx``
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.. code-block:: py
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:force:
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compiled_model = core.compile_model("<INPUT_MODEL>.onnx", "AUTO")
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For a guide on how to run inference, see how to :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: C++
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:sync: cpp
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.onnx``
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.. code-block:: cpp
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ov::CompiledModel compiled_model = core.compile_model("<INPUT_MODEL>.onnx", "AUTO");
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For a guide on how to run inference, see how to :doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
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.. tab-item:: C
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:sync: c
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* The ``compile_model()`` method:
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.. dropdown:: List of supported formats:
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* **Files**:
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* ``<INPUT_MODEL>.onnx``
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.. code-block:: c
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ov_compiled_model_t* compiled_model = NULL;
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ov_core_compile_model_from_file(core, "<INPUT_MODEL>.onnx", "AUTO", 0, &compiled_model);
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For details on the conversion, refer to the :doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
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.. tab-item:: CLI
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:sync: cli
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* The ``convert_model()`` method:
|
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|
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You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
|
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|
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.. dropdown:: List of supported formats:
|
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|
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* **Files**:
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* ``<INPUT_MODEL>.onnx``
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.. code-block:: sh
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mo --input_model <INPUT_MODEL>.onnx
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For details on the conversion, refer to the
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:doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
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.. tab-item:: PaddlePaddle
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:sync: pdpd
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.. tab-set::
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.. tab-item:: Python
|
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:sync: py
|
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|
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* The ``convert_model()`` method:
|
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|
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When you use the ``convert_model()`` method, you have more control and you can specify additional adjustments for ``ov.Model``. The ``read_model()`` and ``compile_model()`` methods are easier to use, however, they do not have such capabilities. With ``ov.Model`` you can choose to optimize, compile and run inference on it or serialize it into a file for subsequent use.
|
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|
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.. dropdown:: List of supported formats:
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|
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* **Files**:
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* ``<INPUT_MODEL>.pdmodel``
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* **Python objects**:
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* ``paddle.hapi.model.Model``
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* ``paddle.fluid.dygraph.layers.Layer``
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* ``paddle.fluid.executor.Executor``
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.. code-block:: py
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:force:
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ov_model = convert_model("<INPUT_MODEL>.pdmodel")
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compiled_model = core.compile_model(ov_model, "AUTO")
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For more details on conversion, refer to the
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:doc:`guide <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>`
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and an example `tutorial <https://docs.openvino.ai/nightly/notebooks/103-paddle-to-openvino-classification-with-output.html>`__
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on this topic.
|
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|
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* The ``read_model()`` method:
|
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|
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.. dropdown:: List of supported formats:
|
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|
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* **Files**:
|
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|
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* ``<INPUT_MODEL>.pdmodel``
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.. code-block:: py
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:force:
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ov_model = read_model("<INPUT_MODEL>.pdmodel")
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compiled_model = core.compile_model(ov_model, "AUTO")
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* The ``compile_model()`` method:
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|
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.. dropdown:: List of supported formats:
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|
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* **Files**:
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* ``<INPUT_MODEL>.pdmodel``
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.. code-block:: py
|
||||
:force:
|
||||
|
||||
compiled_model = core.compile_model("<INPUT_MODEL>.pdmodel", "AUTO")
|
||||
|
||||
For a guide on how to run inference, see how to
|
||||
:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
|
||||
|
||||
.. tab-item:: C++
|
||||
:sync: cpp
|
||||
|
||||
* The ``compile_model()`` method:
|
||||
|
||||
.. dropdown:: List of supported formats:
|
||||
|
||||
* **Files**:
|
||||
|
||||
* ``<INPUT_MODEL>.pdmodel``
|
||||
|
||||
.. code-block:: cpp
|
||||
|
||||
ov::CompiledModel compiled_model = core.compile_model("<INPUT_MODEL>.pdmodel", "AUTO");
|
||||
|
||||
For a guide on how to run inference, see how to
|
||||
:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
|
||||
|
||||
.. tab-item:: C
|
||||
:sync: c
|
||||
|
||||
* The ``compile_model()`` method:
|
||||
|
||||
.. dropdown:: List of supported formats:
|
||||
|
||||
* **Files**:
|
||||
|
||||
* ``<INPUT_MODEL>.pdmodel``
|
||||
|
||||
.. code-block:: c
|
||||
|
||||
ov_compiled_model_t* compiled_model = NULL;
|
||||
ov_core_compile_model_from_file(core, "<INPUT_MODEL>.pdmodel", "AUTO", 0, &compiled_model);
|
||||
|
||||
For a guide on how to run inference, see how to
|
||||
:doc:`Integrate OpenVINO™ with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`.
|
||||
|
||||
.. tab-item:: CLI
|
||||
:sync: cli
|
||||
|
||||
* The ``convert_model()`` method:
|
||||
|
||||
You can use ``mo`` command-line tool to convert a model to IR. The obtained IR can then be read by ``read_model()`` and inferred.
|
||||
|
||||
.. dropdown:: List of supported formats:
|
||||
|
||||
* **Files**:
|
||||
|
||||
* ``<INPUT_MODEL>.pdmodel``
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
mo --input_model <INPUT_MODEL>.pdmodel
|
||||
|
||||
For details on the conversion, refer to the
|
||||
:doc:`article <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>`.
|
||||
|
||||
|
||||
**MXNet, Caffe, and Kaldi** are legacy formats that need to be converted to OpenVINO IR before running inference. The model conversion in some cases may involve intermediate steps. For more details, refer to the :doc:`MXNet <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet>`, :doc:`Caffe <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>`, :doc:`Kaldi <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi>` conversion guides.
|
||||
|
||||
OpenVINO is currently proceeding **to deprecate these formats** and **remove their support entirely in the future**. Converting these formats to ONNX or using an LTS version might be a viable solution for inference in OpenVINO Toolkit.
|
||||
|
||||
.. note::
|
||||
|
||||
To convert models, :doc:`install OpenVINO™ Development Tools <openvino_docs_install_guides_install_dev_tools>`,
|
||||
which include model conversion API.
|
||||
|
||||
|
||||
Refer to the following articles for details on conversion for different formats and models:
|
||||
|
||||
* :doc:`How to convert ONNX <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>`
|
||||
* :doc:`How to convert PaddlePaddle <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>`
|
||||
* :doc:`How to convert TensorFlow <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>`
|
||||
* :doc:`How to convert TensorFlow Lite <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>`
|
||||
* :doc:`How to convert MXNet <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet>`
|
||||
* :doc:`How to convert Caffe <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>`
|
||||
* :doc:`How to convert Kaldi <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi>`
|
||||
|
||||
* :doc:`Conversion examples for specific models <openvino_docs_MO_DG_prepare_model_convert_model_tutorials>`
|
||||
* :doc:`Model preparation methods <openvino_docs_model_processing_introduction>`
|
||||
|
||||
@endsphinxdirective
|
||||
|
||||
Loading…
Reference in New Issue
Block a user