add legacy note to mo section (#21432)
This commit is contained in:
Tatiana Savina
GitHub
parent
525c031959
commit
f37cf22db8
@ -8,13 +8,13 @@ Legacy Conversion API
|
||||
: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
|
||||
Supported_Model_Formats_MO_DG
|
||||
Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>
|
||||
Cutting Off Parts of a Model <openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model>
|
||||
Embedding Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>
|
||||
Compressing a Model to FP16 <openvino_docs_MO_DG_FP16_Compression>
|
||||
Convert Models Represented as Python Objects <openvino_docs_MO_DG_Python_API>
|
||||
Model Optimizer Frequently Asked Questions <openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ>
|
||||
Supported Model Formats <Supported_Model_Formats_MO_DG>
|
||||
|
||||
.. meta::
|
||||
:description: Model conversion (MO) furthers the transition between training and
|
||||
|
||||
@ -1,8 +1,13 @@
|
||||
.. {#openvino_docs_MO_DG_Python_API}
|
||||
|
||||
Convert Models Represented as Python Objects
|
||||
============================================
|
||||
[LEGACY] Convert Models Represented as Python Objects
|
||||
=============================================================
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Model Preparation <openvino_docs_model_processing_introduction>` article.
|
||||
|
||||
Model conversion API is represented by ``convert_model()`` method in openvino.tools.mo namespace. ``convert_model()`` is compatible with types from openvino.runtime, like PartialShape, Layout, Type, etc.
|
||||
|
||||
|
||||
@ -1,8 +1,11 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model}
|
||||
|
||||
Cutting Off Parts of a Model
|
||||
============================
|
||||
[LEGACY] Cutting Off Parts of a Model
|
||||
================================================
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
Sometimes, it is necessary to remove parts of a model when converting it to OpenVINO IR. This chapter describes how to do it, using model conversion API parameters. Model cutting applies mostly to TensorFlow models, which is why TensorFlow will be used in this chapter's examples, but it may be also useful for other frameworks.
|
||||
|
||||
|
||||
@ -1,8 +1,13 @@
|
||||
.. {#openvino_docs_MO_DG_Additional_Optimization_Use_Cases}
|
||||
|
||||
Embedding Preprocessing Computation
|
||||
===================================
|
||||
[LEGACY] Embedding Preprocessing Computation
|
||||
=====================================================
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>` article.
|
||||
|
||||
Input data for inference can be different from the training dataset and requires
|
||||
additional preprocessing before inference. To accelerate the whole pipeline including
|
||||
|
||||
@ -1,9 +1,14 @@
|
||||
.. {#openvino_docs_MO_DG_FP16_Compression}
|
||||
|
||||
Compressing a Model to FP16
|
||||
===========================
|
||||
[LEGACY] Compressing a Model to FP16
|
||||
=============================================
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>` article.
|
||||
|
||||
By default, when IR is saved all relevant floating-point weights are compressed to ``FP16`` data type during model conversion.
|
||||
It results in creating a "compressed ``FP16`` model", which occupies about half of
|
||||
the original space in the file system. The compression may introduce a minor drop in accuracy,
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ}
|
||||
|
||||
Model Optimizer Frequently Asked Questions
|
||||
==========================================
|
||||
[LEGACY] Model Optimizer Frequently Asked Questions
|
||||
===========================================================
|
||||
|
||||
|
||||
.. important::
|
||||
|
||||
@ -1,7 +1,13 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model}
|
||||
|
||||
Setting Input Shapes
|
||||
====================
|
||||
[LEGACY] Setting Input Shapes
|
||||
====================================
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Setting Input Shapes <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Converting_Model>` article.
|
||||
|
||||
With model conversion API you can increase your model's efficiency by providing an additional shape definition, with these two parameters: `input_shape` and `static_shape`.
|
||||
|
||||
|
||||
@ -1,19 +1,24 @@
|
||||
.. {#Supported_Model_Formats_MO_DG}
|
||||
|
||||
Supported Model Formats
|
||||
=======================
|
||||
[LEGACY] Supported Model Formats
|
||||
=====================================
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Supported Model Formats <Supported_Model_Formats>` article.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
|
||||
openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow
|
||||
openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX
|
||||
openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch
|
||||
openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite
|
||||
openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle
|
||||
openvino_docs_MO_DG_prepare_model_convert_model_tutorials
|
||||
Converting a TensorFlow Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>
|
||||
Converting an ONNX Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>
|
||||
Converting a PyTorch Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch>
|
||||
Converting a TensorFlow Lite Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>
|
||||
Converting a PaddlePaddle Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>
|
||||
Model Conversion Tutorials <openvino_docs_MO_DG_prepare_model_convert_model_tutorials>
|
||||
|
||||
.. meta::
|
||||
:description: Learn about supported model formats and the methods used to convert, read, and compile them in OpenVINO™.
|
||||
|
||||
@ -1,13 +1,20 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX}
|
||||
|
||||
Converting an ONNX Model
|
||||
========================
|
||||
|
||||
[LEGACY] Converting an ONNX Model
|
||||
=============================================
|
||||
|
||||
.. meta::
|
||||
:description: Learn how to convert a model from the
|
||||
ONNX format to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting an ONNX Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>` article.
|
||||
|
||||
|
||||
.. note:: ONNX models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
|
||||
|
||||
Converting an ONNX Model
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle}
|
||||
|
||||
Converting a PaddlePaddle Model
|
||||
===============================
|
||||
[LEGACY] Converting a PaddlePaddle Model
|
||||
======================================================
|
||||
|
||||
|
||||
.. meta::
|
||||
@ -9,6 +9,13 @@ Converting a PaddlePaddle Model
|
||||
PaddlePaddle format to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a PaddlePaddle Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_Paddle>` article.
|
||||
|
||||
|
||||
This page provides general instructions on how to convert a model from a PaddlePaddle format to the OpenVINO IR format using Model Optimizer. The instructions are different depending on PaddlePaddle model format.
|
||||
|
||||
.. note:: PaddlePaddle models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
|
||||
|
||||
Converting a PyTorch Model
|
||||
==========================
|
||||
[LEGACY] Converting a PyTorch Model
|
||||
============================================
|
||||
|
||||
|
||||
.. meta::
|
||||
@ -9,6 +9,12 @@ Converting a PyTorch Model
|
||||
PyTorch format to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a PyTorch Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch>` article.
|
||||
|
||||
This page provides instructions on how to convert a model from the PyTorch format to the OpenVINO IR format.
|
||||
|
||||
The conversion is a required step to run inference using OpenVINO API.
|
||||
|
||||
@ -1,13 +1,18 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow}
|
||||
|
||||
Converting a TensorFlow Model
|
||||
=============================
|
||||
|
||||
[LEGACY] Converting a TensorFlow Model
|
||||
============================================
|
||||
|
||||
.. meta::
|
||||
:description: Learn how to convert a model from a
|
||||
TensorFlow format to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a TensorFlow Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow>` article.
|
||||
|
||||
|
||||
.. note:: TensorFlow models are supported via :doc:`FrontEnd API <openvino_docs_MO_DG_TensorFlow_Frontend>`. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
|
||||
|
||||
|
||||
@ -1,13 +1,18 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite}
|
||||
|
||||
Converting a TensorFlow Lite Model
|
||||
==================================
|
||||
[LEGACY] Converting a TensorFlow Lite Model
|
||||
=====================================================
|
||||
|
||||
|
||||
.. meta::
|
||||
:description: Learn how to convert a model from a
|
||||
TensorFlow Lite format to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a TensorFlow Lite Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>` article.
|
||||
|
||||
To convert a TensorFlow Lite model, use the ``mo`` script and specify the path to the input ``.tflite`` model file:
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
.. {#openvino_docs_MO_DG_prepare_model_convert_model_tutorials}
|
||||
|
||||
Model Conversion Tutorials
|
||||
==========================
|
||||
[LEGACY] Model Conversion Tutorials
|
||||
====================================================
|
||||
|
||||
|
||||
.. toctree::
|
||||
@ -39,6 +39,12 @@ Model Conversion Tutorials
|
||||
:description: Get to know conversion methods for specific TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described in the tutorials has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This section provides a set of tutorials that demonstrate conversion methods for specific
|
||||
TensorFlow, ONNX, and PyTorch models. Note that these instructions do not cover all use
|
||||
cases and may not reflect your particular needs.
|
||||
|
||||
@ -10,6 +10,12 @@ Converting a TensorFlow Attention OCR Model
|
||||
OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This tutorial explains how to convert the Attention OCR (AOCR) model from the `TensorFlow Attention OCR repository <https://github.com/emedvedev/attention-ocr>`__ to the Intermediate Representation (IR).
|
||||
|
||||
Extracting a Model from ``aocr`` Library
|
||||
|
||||
@ -9,6 +9,12 @@ Converting a TensorFlow BERT Model
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
Pretrained models for BERT (Bidirectional Encoder Representations from Transformers) are
|
||||
`publicly available <https://github.com/google-research/bert>`__.
|
||||
|
||||
|
||||
@ -8,6 +8,11 @@ Converting a PyTorch BERT-NER Model
|
||||
:description: Learn how to convert a BERT-NER model
|
||||
from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
The goal of this article is to present a step-by-step guide on how to convert PyTorch BERT-NER model to OpenVINO IR. First, you need to download the model and convert it to ONNX.
|
||||
|
||||
|
||||
@ -9,6 +9,12 @@ Converting a TensorFlow CRNN Model
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This tutorial explains how to convert a CRNN model to OpenVINO™ Intermediate Representation (IR).
|
||||
|
||||
There are several public versions of TensorFlow CRNN model implementation available on GitHub. This tutorial explains how to convert the model from
|
||||
|
||||
@ -9,6 +9,12 @@ Converting a PyTorch Cascade RCNN R-101 Model
|
||||
model from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
The goal of this article is to present a step-by-step guide on how to convert a PyTorch Cascade RCNN R-101 model to OpenVINO IR. First, you need to download the model and convert it to ONNX.
|
||||
|
||||
Downloading and Converting Model to ONNX
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a TensorFlow DeepSpeech Model
|
||||
:description: Learn how to convert a DeepSpeech model
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
`DeepSpeech project <https://github.com/mozilla/DeepSpeech>`__ provides an engine to train speech-to-text models.
|
||||
|
||||
Downloading the Pretrained DeepSpeech Model
|
||||
|
||||
@ -9,6 +9,12 @@ Converting TensorFlow EfficientDet Models
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This tutorial explains how to convert EfficientDet public object detection models to the Intermediate Representation (IR).
|
||||
|
||||
.. _efficientdet-to-ir:
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a PyTorch F3Net Model
|
||||
:description: Learn how to convert a F3Net model
|
||||
from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
`F3Net <https://github.com/weijun88/F3Net>`__ : Fusion, Feedback and Focus for Salient Object Detection
|
||||
|
||||
Cloning the F3Net Repository
|
||||
|
||||
@ -8,7 +8,12 @@ Converting TensorFlow FaceNet Models
|
||||
:description: Learn how to convert a FaceNet model
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Supported Model Formats <Supported_Model_Formats>` article.
|
||||
|
||||
`Public pre-trained FaceNet models <https://github.com/davidsandberg/facenet#pre-trained-models>`__ contain both training
|
||||
and inference part of graph. Switch between this two states is manageable with placeholder value.
|
||||
Intermediate Representation (IR) models are intended for inference, which means that train part is redundant.
|
||||
|
||||
@ -8,7 +8,12 @@ Converting an ONNX Faster R-CNN Model
|
||||
:description: Learn how to convert a Faster R-CNN model
|
||||
from ONNX to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
The instructions below are applicable **only** to the Faster R-CNN model converted to the ONNX file format from the `maskrcnn-benchmark model <https://github.com/facebookresearch/maskrcnn-benchmark>`__:
|
||||
|
||||
1. Download the pretrained model file from `onnx/models <https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn>`__ (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a TensorFlow GNMT Model
|
||||
:description: Learn how to convert a GNMT model
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This tutorial explains how to convert Google Neural Machine Translation (GNMT) model to the Intermediate Representation (IR).
|
||||
|
||||
There are several public versions of TensorFlow GNMT model implementation available on GitHub. This tutorial explains how to convert the GNMT model from the `TensorFlow Neural Machine Translation (NMT) repository <https://github.com/tensorflow/nmt>`__ to the IR.
|
||||
|
||||
@ -8,6 +8,11 @@ Converting an ONNX GPT-2 Model
|
||||
:description: Learn how to convert a pre-trained GPT-2
|
||||
model from ONNX to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
`Public pre-trained GPT-2 model <https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2>`__ is a large
|
||||
transformer-based language model with a simple objective: predict the next word, given all of the previous words within some text.
|
||||
|
||||
@ -8,6 +8,11 @@ Converting an ONNX Mask R-CNN Model
|
||||
:description: Learn how to convert a pre-trained Mask
|
||||
R-CNN model from ONNX to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
The instructions below are applicable **only** to the Mask R-CNN model converted to the ONNX file format from the `maskrcnn-benchmark model <https://github.com/facebookresearch/maskrcnn-benchmark>`__.
|
||||
|
||||
|
||||
@ -9,6 +9,11 @@ Converting a TensorFlow Neural Collaborative Filtering Model
|
||||
Filtering Model from TensorFlow to the OpenVINO Intermediate
|
||||
Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This tutorial explains how to convert Neural Collaborative Filtering (NCF) model to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
@ -10,6 +10,12 @@ Converting TensorFlow Object Detection API Models
|
||||
Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
* Starting with the 2022.1 release, model conversion API can convert the TensorFlow Object Detection API Faster and Mask RCNNs topologies differently. By default, model conversion adds operation "Proposal" to the generated IR. This operation needs an additional input to the model with name "image_info" which should be fed with several values describing the preprocessing applied to the input image (refer to the :doc:`Proposal <openvino_docs_ops_detection_Proposal_4>` operation specification for more information). However, this input is redundant for the models trained and inferred with equal size images. Model conversion API can generate IR for such models and insert operation :doc:`DetectionOutput <openvino_docs_ops_detection_DetectionOutput_1>` instead of ``Proposal``. The `DetectionOutput` operation does not require additional model input "image_info". Moreover, for some models the produced inference results are closer to the original TensorFlow model. In order to trigger new behavior, the attribute "operation_to_add" in the corresponding JSON transformation configuration file should be set to value "DetectionOutput" instead of default one "Proposal".
|
||||
* Starting with the 2021.1 release, model conversion API converts the TensorFlow Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the OpenVINO Runtime using dedicated reshape API. Refer to the :doc:`Using Shape Inference <openvino_docs_OV_UG_ShapeInference>` guide for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
|
||||
* To generate IRs for TF 1 SSD topologies, model conversion API creates a number of ``PriorBoxClustered`` operations instead of a constant node with prior boxes calculated for the particular input image size. This change allows you to reshape the topology in the OpenVINO Runtime using dedicated API. The reshaping is supported for all SSD topologies except FPNs, which contain hardcoded shapes for some operations preventing from changing topology input shape.
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a PyTorch QuartzNet Model
|
||||
:description: Learn how to convert a QuartzNet model
|
||||
from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
`NeMo project <https://github.com/NVIDIA/NeMo>`__ provides the QuartzNet model.
|
||||
|
||||
Downloading the Pre-trained QuartzNet Model
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a PyTorch RCAN Model
|
||||
:description: Learn how to convert a RCAN model
|
||||
from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
`RCAN <https://github.com/yulunzhang/RCAN>`__ : Image Super-Resolution Using Very Deep Residual Channel Attention Networks
|
||||
|
||||
Downloading and Converting the Model to ONNX
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a PyTorch RNN-T Model
|
||||
:description: Learn how to convert a RNN-T model
|
||||
from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This guide covers conversion of RNN-T model from `MLCommons <https://github.com/mlcommons>`__ repository. Follow
|
||||
the instructions below to export a PyTorch model into ONNX, before converting it to IR:
|
||||
|
||||
|
||||
@ -9,6 +9,12 @@ Converting a TensorFlow RetinaNet Model
|
||||
from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This tutorial explains how to convert a RetinaNet model to the Intermediate Representation (IR).
|
||||
|
||||
`Public RetinaNet model <https://github.com/fizyr/keras-retinanet>`__ does not contain pretrained TensorFlow weights.
|
||||
|
||||
@ -9,6 +9,11 @@ Converting TensorFlow Slim Image Classification Model Library Models
|
||||
Classification model from TensorFlow to the OpenVINO
|
||||
Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
`TensorFlow-Slim Image Classification Model Library <https://github.com/tensorflow/models/tree/master/research/slim/README.md>`__ is a library to define, train and evaluate classification models in TensorFlow. The library contains Python scripts defining the classification topologies together with checkpoint files for several pre-trained classification topologies. To convert a TensorFlow-Slim library model, complete the following steps:
|
||||
|
||||
|
||||
@ -9,6 +9,12 @@ Converting TensorFlow Wide and Deep Family Models
|
||||
models from TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
The Wide and Deep models is a combination of wide and deep parts for memorization and generalization of object features respectively.
|
||||
These models can contain different types of object features such as numerical, categorical, sparse and sequential features. These feature types are specified
|
||||
through Tensorflow tf.feature_column API. Table below presents what feature types are supported by the OpenVINO toolkit.
|
||||
|
||||
@ -8,7 +8,12 @@ Converting a TensorFlow XLNet Model
|
||||
:description: Learn how to convert an XLNet model from
|
||||
TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
Pretrained models for XLNet (Bidirectional Encoder Representations from Transformers) are
|
||||
`publicly available <https://github.com/zihangdai/xlnet>`__.
|
||||
|
||||
|
||||
@ -9,6 +9,12 @@ Converting a PyTorch YOLACT Model
|
||||
from PyTorch to the OpenVINO Intermediate Representation.
|
||||
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
You Only Look At CoefficienTs (YOLACT) is a simple, fully convolutional model for real-time instance segmentation.
|
||||
The PyTorch implementation is publicly available in `this GitHub repository <https://github.com/dbolya/yolact>`__.
|
||||
The YOLACT++ model is not supported, because it uses deformable convolutional layers that cannot be represented in ONNX format.
|
||||
|
||||
@ -8,6 +8,11 @@ Converting TensorFlow YOLO Models
|
||||
:description: Learn how to convert YOLO models from
|
||||
TensorFlow to the OpenVINO Intermediate Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
This document explains how to convert real-time object detection YOLOv1, YOLOv2, YOLOv3 and YOLOv4 public models to the Intermediate Representation (IR). All YOLO models are originally implemented in the DarkNet framework and consist of two files:
|
||||
|
||||
|
||||
@ -9,7 +9,12 @@ Converting a TensorFlow Language Model on One Billion Word Benchmark
|
||||
Model on One Billion Word Benchmark to the OpenVINO Intermediate
|
||||
Representation.
|
||||
|
||||
.. danger::
|
||||
|
||||
The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
|
||||
|
||||
This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
|
||||
|
||||
Downloading a Pre-trained Language Model on One Billion Word Benchmark
|
||||
######################################################################
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user