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[DOCS] convert_model() as a default conversion path - for 23.0 (#17751)

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Porting: https://github.com/openvinotoolkit/openvino/pull/17454

Updating MO documentation to make convert_model() a default conversion path.
This commit is contained in:
Sebastian Golebiewski
2023-05-31 19:22:54 +02:00
committed by GitHub
parent 20dc436b6f
commit f40f0fa58b
94 changed files with 1384 additions and 995 deletions
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4
samples/cpp/benchmark/sync_benchmark/README.md
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@@ -67,7 +67,7 @@ To run the sample, you need to specify a model:
.. note::
Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
@@ -127,6 +127,6 @@ See Also
* :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
* :doc:`Using OpenVINO Samples <openvino_docs_OV_UG_Samples_Overview>`
* :doc:`Model Downloader <omz_tools_downloader>`
* :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
* :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

4
samples/cpp/benchmark/throughput_benchmark/README.md
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@@ -70,7 +70,7 @@ To run the sample, you need to specify a model:
.. note::
Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
@@ -130,6 +130,6 @@ See Also
* :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
* :doc:`Using OpenVINO Samples <openvino_docs_OV_UG_Samples_Overview>`
* :doc:`Model Downloader <omz_tools_downloader>`
* :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
* :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

4
samples/cpp/benchmark_app/README.md
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@@ -125,7 +125,7 @@ Advanced Usage
.. note::
By default, OpenVINO samples, tools and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channel order in the sample or demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model to Intermediate Representation (IR).
By default, OpenVINO samples, tools and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channel order in the sample or demo application or reconvert your model using ``mo`` with ``reverse_input_channels`` argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model to Intermediate Representation (IR).
Per-layer performance and logging
+++++++++++++++++++++++++++++++++
@@ -405,7 +405,7 @@ See Also
####################
* :doc:`Using OpenVINO Samples <openvino_docs_OV_UG_Samples_Overview>`
* :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
* :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
* :doc:`Model Downloader <omz_tools_downloader>`
@endsphinxdirective

6
samples/cpp/classification_sample_async/README.md
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@@ -88,9 +88,9 @@ To run the sample, you need to specify a model and image:
.. note::
- By default, OpenVINO™ Toolkit Samples and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with ``--reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- By default, OpenVINO™ Toolkit Samples and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using ``mo`` with ``reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
@@ -196,7 +196,7 @@ See Also
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

6
samples/cpp/hello_classification/README.md
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@@ -74,8 +74,8 @@ To run the sample, you need to specify a model and image:
.. note::
- By default, OpenVINO™ Toolkit Samples and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with ``--reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- By default, OpenVINO™ Toolkit Samples and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using ``mo`` with ``reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
Example
@@ -149,7 +149,7 @@ See Also
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

6
samples/cpp/hello_nv12_input_classification/README.md
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@@ -70,8 +70,8 @@ The sample accepts an uncompressed image in the NV12 color format. To run the sa
.. note::
- Because the sample reads raw image files, you should provide a correct image size along with the image path. The sample expects the logical size of the image, not the buffer size. For example, for 640x480 BGR/RGB image the corresponding NV12 logical image size is also 640x480, whereas the buffer size is 640x720.
- By default, this sample expects that model input has BGR channels order. If you trained your model to work with RGB order, you need to reconvert your model using the Model Optimizer tool with ``--reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- By default, this sample expects that model input has BGR channels order. If you trained your model to work with RGB order, you need to reconvert your model using ``mo`` with ``reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
Example
@@ -147,7 +147,7 @@ See Also
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

6
samples/cpp/hello_reshape_ssd/README.md
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@@ -65,8 +65,8 @@ To run the sample, you need to specify a model and image:
.. note::
- By default, OpenVINO™ Toolkit Samples and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with ``--reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- By default, OpenVINO™ Toolkit Samples and Demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using ``mo`` with ``reverse_input_channels`` argument specified. For more information about the argument, refer to **When to Reverse Input Channels** section of :doc:`Embedding Preprocessing Computation <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample accepts models in ONNX format (\*.onnx) that do not require preprocessing.
Example
@@ -137,7 +137,7 @@ See Also
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

4
samples/cpp/model_creation_sample/README.md
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@@ -83,7 +83,7 @@ Running
.. note::
- you can use LeNet model weights in the sample folder: ``lenet.bin`` with FP32 weights file
- The ``lenet.bin`` with FP32 weights file was generated by the :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` tool from the public LeNet model with the ``--input_shape [64,1,28,28]`` parameter specified.
- The ``lenet.bin`` with FP32 weights file was generated by :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>` from the public LeNet model with the ``input_shape [64,1,28,28]`` parameter specified.
The original model is available in the `Caffe* repository <https://github.com/BVLC/caffe/tree/master/examples/mnist>`__ on GitHub\*.
@@ -214,7 +214,7 @@ See Also
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective

6
samples/cpp/speech_sample/README.md
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@@ -154,7 +154,7 @@ Usage message:
Model Preparation
+++++++++++++++++
You can use the following model optimizer command to convert a Kaldi nnet1 or nnet2 neural model to OpenVINO™ toolkit Intermediate Representation format:
You can use the following model conversion command to convert a Kaldi nnet1 or nnet2 neural model to OpenVINO™ toolkit Intermediate Representation format:
.. code-block:: sh
@@ -181,7 +181,7 @@ Here, the floating point Kaldi-generated reference neural network scores (``dev9
.. note::
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using the :doc:`Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (\*.xml + \*.bin) using :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
- The sample supports input and output in numpy file format (.npz)
@@ -275,7 +275,7 @@ See Also
- :doc:`Integrate the OpenVINO™ Runtime with Your Application <openvino_docs_OV_UG_Integrate_OV_with_your_application>`
- :doc:`Using OpenVINO™ Toolkit Samples <openvino_docs_OV_UG_Samples_Overview>`
- :doc:`Model Downloader <omz_tools_downloader>`
- :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
- :doc:`Convert a Model <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
@endsphinxdirective