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See Also sections in MO Guide (#2770) 

* convert to doxygen comments

* layouts and code comments

* separate layout

* Changed layouts

* Removed FPGA from the documentation

* Updated according to CVS-38225

* some changes

* Made changes to benchmarks according to review comments

* Added logo info to the Legal_Information, updated Ubuntu, CentOS supported versions

* Updated supported Intel® Core™ processors list

* Fixed table formatting

* update api layouts

* Added new index page with overview

* Changed CMake and Python versions

* Fixed links

* some layout changes

* some layout changes

* some layout changes

* COnverted svg images to png

* layouts

* update layout

* Added a label for nGraph_Python_API.md

* fixed links

* Fixed image

* removed links to ../IE_DG/Introduction.md

* Removed links to tools overview page as removed

* some changes

* Remove link to Integrate_your_kernels_into_IE.md

* remove openvino_docs_IE_DG_Graph_debug_capabilities from layout as it was removed

* update layouts

* Post-release fixes and installation path changes

* Added PIP installation and Build from Source to the layout

* Fixed formatting issue, removed broken link

* Renamed section EXAMPLES to RESOURCES according to review comments

* add mo faq navigation by url param

* Removed DLDT description

* Pt 1

* Update Deep_Learning_Model_Optimizer_DevGuide.md

* Extra file

* Update IR_and_opsets.md

* Update Known_Issues_Limitations.md

* Update Config_Model_Optimizer.md

* Update Convert_Model_From_Kaldi.md

* Update Convert_Model_From_Kaldi.md

* Update Convert_Model_From_MxNet.md

* Update Convert_Model_From_ONNX.md

* Update Convert_Model_From_TensorFlow.md

* Update Converting_Model_General.md

* Update Cutting_Model.md

* Update IR_suitable_for_INT8_inference.md

* Update Aspire_Tdnn_Model.md

* Update Convert_Model_From_Caffe.md

* Update Convert_Model_From_TensorFlow.md

* Update Convert_Model_From_MxNet.md

* Update Convert_Model_From_Kaldi.md

* Added references to other fws from each fw

* Fixed broken links

* Fixed broken links

* fixes

* fixes

* Fixed wrong links

Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com>
Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
Co-authored-by: Tyukaev <nikolay.tyukaev@intel.com>
This commit is contained in:
Alina Alborova 2020-11-06 17:24:07 +03:00 committed by GitHub
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4
docs/MO_DG/IR_and_opsets.md
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@ -242,4 +242,8 @@ To differentiate versions of the same operation type, like `ReLU`, the suffix `-
`N` usually refers to the first `opsetN` where this version of the operation is introduced.
It is not guaranteed that new operations will be named according to that rule, the naming convention might be changed, but not for old operations which are frozen completely.
---
## See Also
* [Cut Off Parts of a Model](prepare_model/convert_model/Cutting_Model.md)

5
docs/MO_DG/Known_Issues_Limitations.md
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@ -45,3 +45,8 @@ Possible workaround is to upgrade default protobuf compiler (libprotoc 2.5.0) to
libprotoc 2.6.1.
[protobuf_issue]: https://github.com/google/protobuf/issues/4272
---
## See Also
* [Known Issues and Limitations in the Inference Engine](../IE_DG/Known_Issues_Limitations.md)

8
docs/MO_DG/prepare_model/Config_Model_Optimizer.md
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@ -260,6 +260,14 @@ python3 -m easy_install dist/protobuf-3.6.1-py3.6-win-amd64.egg
set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
```
---
## See Also
docs\MO_DG\prepare_model\Config_Model_Optimizer.md
docs\install_guides\installing-openvino-raspbian.md
* [Converting a Model to Intermediate Representation (IR)](convert_model/Converting_Model.md)
* [Install OpenVINO™ toolkit for Raspbian* OS](../../install_guides/installing-openvino-raspbian.md)
* [Install Intel® Distribution of OpenVINO™ toolkit for Windows* 10](../../install_guides/installing-openvino-windows.md)
* [Install Intel® Distribution of OpenVINO™ toolkit for Windows* with FPGA Support](../../install_guides/installing-openvino-windows-fpga.md)
* [Install Intel® Distribution of OpenVINO™ toolkit for macOS*](../../install_guides/installing-openvino-macos.md)
* [Configuration Guide for the Intel® Distribution of OpenVINO™ toolkit 2020.4 and the Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA SG2 (IEI's Mustang-F100-A10) on Linux* ](../../install_guides/VisionAcceleratorFPGA_Configure.md)

10
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md
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@ -144,3 +144,13 @@ In this document, you learned:
* Basic information about how the Model Optimizer works with Caffe\* models
* Which Caffe\* models are supported
* How to convert a trained Caffe\* model using the Model Optimizer with both framework-agnostic and Caffe-specific command-line options
---
## See Also
* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
* [Converting a Model Using General Conversion Parameters](Converting_Model_General.md)
* [Custom Layers in the Model Optimizer ](../customize_model_optimizer/Customize_Model_Optimizer.md)

9
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
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@ -106,3 +106,12 @@ must be copied to `Parameter_0_for_Offset_fastlstm2.r_trunc__2Offset_fastlstm2.r
## Supported Kaldi\* Layers
Refer to [Supported Framework Layers ](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
---
## See Also
* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
* [Custom Layers Guide](../../../HOWTO/Custom_Layers_Guide.md)

9
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md
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@ -103,3 +103,12 @@ In this document, you learned:
* Basic information about how the Model Optimizer works with MXNet\* models
* Which MXNet\* models are supported
* How to convert a trained MXNet\* model using the Model Optimizer with both framework-agnostic and MXNet-specific command-line options
---
## See Also
* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
* [Custom Layers in the Model Optimizer](../customize_model_optimizer/Customize_Model_Optimizer.md)

9
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md
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@ -78,3 +78,12 @@ There are no ONNX\* specific parameters, so only [framework-agnostic parameters]
## Supported ONNX\* Layers
Refer to [Supported Framework Layers](../Supported_Frameworks_Layers.md) for the list of supported standard layers.
---
## See Also
* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
* [Convert TensorFlow* BERT Model to the Intermediate Representation ](tf_specific/Convert_BERT_From_Tensorflow.md)

9
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md
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@ -375,3 +375,12 @@ In this document, you learned:
* Which TensorFlow models are supported
* How to freeze a TensorFlow model
* How to convert a trained TensorFlow model using the Model Optimizer with both framework-agnostic and TensorFlow-specific command-line options
---
## See Also
* [Converting a Caffe* Model](Convert_Model_From_Caffe.md)
* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
* [Converting a Model Using General Conversion Parameters](Converting_Model_General.md)

10
docs/MO_DG/prepare_model/convert_model/Converting_Model_General.md
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@ -233,3 +233,13 @@ Otherwise, it will be casted to data type passed to `--data_type` parameter (by
```sh
python3 mo.py --input_model FaceNet.pb --input "placeholder_layer_name->[0.1 1.2 2.3]"
```
---
## See Also
* [Converting a Cafee* Model](Convert_Model_From_Caffe.md)
* [Converting a TensorFlow* Model](Convert_Model_From_TensorFlow.md)
* [Converting an MXNet* Model](Convert_Model_From_MxNet.md)
* [Converting an ONNX* Model](Convert_Model_From_ONNX.md)
* [Converting a Kaldi* Model](Convert_Model_From_Kaldi.md)
* [Using Shape Inference](../../../IE_DG/ShapeInference.md)

9
docs/MO_DG/prepare_model/convert_model/Cutting_Model.md
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@ -389,4 +389,11 @@ In this case, when `--input_shape` is specified and the node contains multiple i
The correct command line is:
```sh
python3 mo.py --input_model=inception_v1.pb --input=0:InceptionV1/InceptionV1/Conv2d_1a_7x7/convolution --input_shape=[1,224,224,3]
```
```
---
## See Also
* [Sub-Graph Replacement in the Model Optimizer](../customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md)
* [Extending the Model Optimizer with New Primitives](../customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md)
* [Converting a Model Using General Conversion Parameters](Converting_Model_General.md)

9
docs/MO_DG/prepare_model/convert_model/IR_suitable_for_INT8_inference.md
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@ -34,4 +34,11 @@ Weights compression leaves `FakeQuantize` output arithmetically the same and wei
See the visualization of `Convolution` with the compressed weights:
![](../../img/compressed_int8_Convolution_weights.png)
Both Model Optimizer and Post-Training Optimization tool generate a compressed IR by default. To generate an expanded INT8 IR, use `--disable_weights_compression`.
Both Model Optimizer and Post-Training Optimization tool generate a compressed IR by default. To generate an expanded INT8 IR, use `--disable_weights_compression`.
---
## See Also
* [Quantization](@ref pot_compression_algorithms_quantization_README)
* [Optimization Guide](../../../optimization_guide/dldt_optimization_guide.md)
* [Low Precision Optimization Guide](@ref pot_docs_LowPrecisionOptimizationGuide)

5
docs/MO_DG/prepare_model/convert_model/kaldi_specific/Aspire_Tdnn_Model.md
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@ -110,3 +110,8 @@ speech_sample -i feats.ark,ivector_online_ie.ark -m final.xml -d CPU -o predicti
Results can be decoded as described in "Use of Sample in Kaldi* Speech Recognition Pipeline" chapter
in [the Speech Recognition Sample description](../../../../../inference-engine/samples/speech_sample/README.md).
---
## See Also
* [Converting a Kaldi Model](../Convert_Model_From_Kaldi.md)