ee4ccec190
* First glance doc changes * Apply suggestions from code review Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> * Update docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow_Lite.md Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> --------- Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
112 lines
4.2 KiB
Markdown
112 lines
4.2 KiB
Markdown
# Use Case - Integrate and Save Preprocessing Steps Into IR {#openvino_docs_OV_UG_Preprocess_Usecase_save}
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@sphinxdirective
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Previous sections covered the topic of the :doc:`preprocessing steps <openvino_docs_OV_UG_Preprocessing_Details>`
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and the overview of :doc:`Layout <openvino_docs_OV_UG_Layout_Overview>` API.
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For many applications, it is also important to minimize read/load time of a model.
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Therefore, performing integration of preprocessing steps every time on application
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startup, after ``ov::runtime::Core::read_model``, may seem inconvenient. In such cases,
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once pre and postprocessing steps have been added, it can be useful to store new execution
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model to OpenVINO Intermediate Representation (OpenVINO IR, `.xml` format).
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Most available preprocessing steps can also be performed via command-line options,
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using Model Optimizer. For details on such command-line options, refer to the
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:doc:`Optimizing Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>`.
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Code example - Saving Model with Preprocessing to OpenVINO IR
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#############################################################
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When some preprocessing steps cannot be integrated into the execution graph using
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Model Optimizer command-line options (for example, ``YUV``->``RGB`` color space conversion,
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``Resize``, etc.), it is possible to write a simple code which:
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* Reads the original model (OpenVINO IR, TensorFlow, TensorFlow Lite, ONNX, PaddlePaddle).
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* Adds the preprocessing/postprocessing steps.
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* Saves resulting model as IR (``.xml`` and ``.bin``).
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Consider the example, where an original ONNX model takes one ``float32`` input with the
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``{1, 3, 224, 224}`` shape, the ``RGB`` channel order, and mean/scale values applied.
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In contrast, the application provides ``BGR`` image buffer with a non-fixed size and
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input images as batches of two. Below is the model conversion code that can be applied
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in the model preparation script for such a case.
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* Includes / Imports
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.. tab-set::
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.. tab-item:: C++
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:sync: cpp
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.. doxygensnippet:: docs/snippets/ov_preprocessing.cpp
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:language: cpp
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:fragment: ov:preprocess:save_headers
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.. tab-item:: Python
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:sync: py
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.. doxygensnippet:: docs/snippets/ov_preprocessing.py
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:language: Python
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:fragment: ov:preprocess:save_headers
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* Preprocessing & Saving to the OpenVINO IR code.
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.. tab-set::
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.. tab-item:: C++
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:sync: cpp
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.. doxygensnippet:: docs/snippets/ov_preprocessing.cpp
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:language: cpp
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:fragment: ov:preprocess:save
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.. tab-item:: Python
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:sync: py
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.. doxygensnippet:: docs/snippets/ov_preprocessing.py
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:language: Python
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:fragment: ov:preprocess:save
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Application Code - Load Model to Target Device
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##############################################
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After this, the application code can load a saved file and stop preprocessing. In this case, enable
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:doc:`model caching <openvino_docs_OV_UG_Model_caching_overview>` to minimize load
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time when the cached model is available.
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.. tab-set::
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.. tab-item:: C++
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:sync: cpp
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.. doxygensnippet:: docs/snippets/ov_preprocessing.cpp
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:language: cpp
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:fragment: ov:preprocess:save_load
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.. tab-item:: Python
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:sync: py
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.. doxygensnippet:: docs/snippets/ov_preprocessing.py
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:language: Python
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:fragment: ov:preprocess:save_load
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Additional Resources
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####################
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* :doc:`Preprocessing Details <openvino_docs_OV_UG_Preprocessing_Details>`
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* :doc:`Layout API overview <openvino_docs_OV_UG_Layout_Overview>`
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* :doc:`Model Optimizer - Optimize Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>`
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* :doc:`Model Caching Overview<openvino_docs_OV_UG_Model_caching_overview>`
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* The `ov::preprocess::PrePostProcessor <classov_1_1preprocess_1_1PrePostProcessor.html#doxid-classov-1-1preprocess-1-1-pre-post-processor>` C++ class documentation
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* The `ov::pass::Serialize <classov_1_1pass_1_1Serialize.html#doxid-classov-1-1pass-1-1-serialize>` - pass to serialize model to XML/BIN
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* The `ov::set_batch <namespaceov.html#doxid-namespaceov-1a3314e2ff91fcc9ffec05b1a77c37862b>` - update batch dimension for a given model
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@endsphinxdirective
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