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[DOCS] OVC docs adjustments (#19918) (#19924) 

authored-by: Tatiana Savina <tatiana.savina@intel.com>
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Karol Blaszczak 2023-09-19 08:56:45 +02:00 committed by GitHub
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docs/Documentation/model_introduction.md
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@ -221,7 +221,7 @@ The figure below illustrates the typical workflow for deploying a trained deep-l
Convert a Model in CLI: ``ovc``
###############################
Another option for model conversion is to use ``ovc`` command-line tool, which stands for OpenVINO Model Converter. The tool combines both ``openvino.convert_model`` and ``openvino.save_model`` functionalities. It is convenient to use when the original model is ready for inference and is in one of the supported file formats: ONNX, TensorFlow, TensorFlow Lite, or PaddlePaddle. As a result, ``ovc`` produces an OpenVINO IR, consisting of ``.xml`` and ``.bin`` files, which needs to be read with the ``ov.read_model()`` method. You can compile and infer the ``ov.Model`` later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
Another option for model conversion is to use ``ovc`` command-line tool, which stands for OpenVINO Model Converter. The tool combines both ``openvino.convert_model`` and ``openvino.save_model`` functionalities. It is convenient to use when the original model is ready for inference and is in one of the supported file formats: ONNX, TensorFlow, TensorFlow Lite, or PaddlePaddle. As a result, ``ovc`` produces an OpenVINO IR, consisting of ``.xml`` and ``.bin`` files, which needs to be read with the ``openvino.Core.read_model`` method. You can compile and infer the ``ov.Model`` later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`
.. note::
PyTorch models cannot be converted with ``ovc``, use ``openvino.convert_model`` instead.

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docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_Paddle.md
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@ -89,7 +89,7 @@ Some PaddlePaddle models may require setting ``example_input`` or ``output`` for
* Example of converting ``paddle.fluid.dygraph.layers.Layer`` format model:
``example_input`` is required while ``output`` is optional, which accept the following formats:
``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``)

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docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_PyTorch.md
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@ -40,8 +40,8 @@ The value for the ``example_input`` parameter can be easily derived from knowing
import torch
import openvino as ov
model = torchvision.models.resnet50(pretrained=True)
ov_model = ov.convert_model(model, example_input=example_input=torch.rand(1, 3, 224, 224))
model = torchvision.models.resnet50(weights='DEFAULT')
ov_model = ov.convert_model(model, example_input=torch.rand(1, 3, 224, 224))
In practice, the code to evaluate or test the PyTorch model is usually provided with the model itself and can be used to generate a proper ``example_input`` value. A modified example of using ``resnet50`` model from ``torchvision`` is presented below. It demonstrates how to switch inference in the existing PyTorch application to OpenVINO and how to get value for ``example_input``: