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Feature/azaytsev/mo devguide changes (openvinotoolkit#6405) * MO devguide edits * MO devguide edits * MO devguide edits * MO devguide edits * MO devguide edits * Experimenting with videos * Experimenting with videos * Experimenting with videos * Experimenting with videos * Experimenting with videos * Experimenting with videos * Experimenting with videos * Experimenting with videos * Experimenting with videos * Additional edits * Additional edits * Updated the workflow diagram * Minor fix * Experimenting with videos * Updated the workflow diagram * Removed Prepare_Trained_Model, changed the title for Config_Model_Optimizer * Rolled back * Revert "Rolled back" This reverts commit 6a4a3e1. * Revert "Removed Prepare_Trained_Model, changed the title for Config_Model_Optimizer" This reverts commit 0810bd5. * Fixed ie_docs.xml, Removed Prepare_Trained_Model, changed the title for Config_Model_Optimizer * Fixed ie_docs.xml * Minor fix * <details> tag issue * <details> tag issue * Fix <details> tag issue * Fix <details> tag issue * Fix <details> tag issue # Conflicts: # thirdparty/ade
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| # Model Optimizer Developer Guide {#openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide} | ||
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| ## Introduction | ||
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| Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. | ||
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| Model Optimizer process assumes you have a network model trained using a supported deep learning framework. The scheme below illustrates the typical workflow for deploying a trained deep learning model: | ||
| Model Optimizer process assumes you have a network model trained using supported deep learning frameworks: Caffe*, TensorFlow*, Kaldi*, MXNet* or converted to the ONNX* format. Model Optimizer produces an Intermediate Representation (IR) of the network, which can be inferred with the [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md). | ||
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| > **NOTE**: Model Optimizer does not infer models. Model Optimizer is an offline tool that runs before the inference takes place. | ||
| The scheme below illustrates the typical workflow for deploying a trained deep learning model: | ||
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| Model Optimizer produces an Intermediate Representation (IR) of the network, which can be read, loaded, and inferred with the Inference Engine. The Inference Engine API offers a unified API across a number of supported Intel® platforms. The Intermediate Representation is a pair of files describing the model: | ||
| The IR is a pair of files describing the model: | ||
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| * <code>.xml</code> - Describes the network topology | ||
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| * <code>.bin</code> - Contains the weights and biases binary data. | ||
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| Below is a simple command running Model Optimizer to generate an IR for the input model: | ||
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| ```sh | ||
| python3 mo.py --input_model INPUT_MODEL | ||
| ``` | ||
| To learn about all Model Optimizer parameters and conversion technics, see the [Converting a Model to IR](prepare_model/convert_model/Converting_Model.md) page. | ||
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| > **TIP**: You can quick start with the Model Optimizer inside the OpenVINO™ [Deep Learning Workbench](@ref | ||
| > openvino_docs_get_started_get_started_dl_workbench) (DL Workbench). | ||
| > [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is the OpenVINO™ toolkit UI that enables you to | ||
| > import a model, analyze its performance and accuracy, visualize the outputs, optimize and prepare the model for | ||
| > deployment on various Intel® platforms. | ||
| ## What's New in the Model Optimizer in this Release? | ||
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| * Common changes: | ||
| * Implemented several optimization transformations to replace sub-graphs of operations with HSwish, Mish, Swish and SoftPlus operations. | ||
| * Model Optimizer generates IR keeping shape-calculating sub-graphs **by default**. Previously, this behavior was triggered if the "--keep_shape_ops" command line parameter was provided. The key is ignored in this release and will be deleted in the next release. To trigger the legacy behavior to generate an IR for a fixed input shape (folding ShapeOf operations and shape-calculating sub-graphs to Constant), use the "--static_shape" command line parameter. Changing model input shape using the Inference Engine API in runtime may fail for such an IR. | ||
| * Fixed Model Optimizer conversion issues resulted in non-reshapeable IR using the Inference Engine reshape API. | ||
| * Enabled transformations to fix non-reshapeable patterns in the original networks: | ||
| * Hardcoded Reshape | ||
| * In Reshape(2D)->MatMul pattern | ||
| * Reshape->Transpose->Reshape when the pattern can be fused to the ShuffleChannels or DepthToSpace operation | ||
| * Hardcoded Interpolate | ||
| * In Interpolate->Concat pattern | ||
| * Added a dedicated requirements file for TensorFlow 2.X as well as the dedicated install prerequisites scripts. | ||
| * Replaced the SparseToDense operation with ScatterNDUpdate-4. | ||
| * ONNX*: | ||
| * Enabled an ability to specify the model output **tensor** name using the "--output" command line parameter. | ||
| * Added support for the following operations: | ||
| * Acosh | ||
| * Asinh | ||
| * Atanh | ||
| * DepthToSpace-11, 13 | ||
| * DequantizeLinear-10 (zero_point must be constant) | ||
| * HardSigmoid-1,6 | ||
| * QuantizeLinear-10 (zero_point must be constant) | ||
| * ReduceL1-11, 13 | ||
| * ReduceL2-11, 13 | ||
| * Resize-11, 13 (except mode="nearest" with 5D+ input, mode="tf_crop_and_resize", and attributes exclude_outside and extrapolation_value with non-zero values) | ||
| * ScatterND-11, 13 | ||
| * SpaceToDepth-11, 13 | ||
| * TensorFlow*: | ||
| * Added support for the following operations: | ||
| * Acosh | ||
| * Asinh | ||
| * Atanh | ||
| * CTCLoss | ||
| * EuclideanNorm | ||
| * ExtractImagePatches | ||
| * FloorDiv | ||
| * MXNet*: | ||
| * Added support for the following operations: | ||
| * Acosh | ||
| * Asinh | ||
| * Atanh | ||
| * Kaldi*: | ||
| * Fixed bug with ParallelComponent support. Now it is fully supported with no restrictions. | ||
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| > **NOTE:** | ||
| > [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019). | ||
| ## Table of Contents | ||
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| * [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md) | ||
| * [Configuring Model Optimizer](prepare_model/Config_Model_Optimizer.md) | ||
| * [Converting a Model to Intermediate Representation (IR)](prepare_model/convert_model/Converting_Model.md) | ||
| * [Converting a Model Using General Conversion Parameters](prepare_model/convert_model/Converting_Model_General.md) | ||
| * [Converting Your Caffe* Model](prepare_model/convert_model/Convert_Model_From_Caffe.md) | ||
| * [Converting Your TensorFlow* Model](prepare_model/convert_model/Convert_Model_From_TensorFlow.md) | ||
| * [Converting BERT from TensorFlow](prepare_model/convert_model/tf_specific/Convert_BERT_From_Tensorflow.md) | ||
| * [Converting GNMT from TensorFlow](prepare_model/convert_model/tf_specific/Convert_GNMT_From_Tensorflow.md) | ||
| * [Converting YOLO from DarkNet to TensorFlow and then to IR](prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md) | ||
| * [Converting Wide and Deep Models from TensorFlow](prepare_model/convert_model/tf_specific/Convert_WideAndDeep_Family_Models.md) | ||
| * [Converting FaceNet from TensorFlow](prepare_model/convert_model/tf_specific/Convert_FaceNet_From_Tensorflow.md) | ||
| * [Converting DeepSpeech from TensorFlow](prepare_model/convert_model/tf_specific/Convert_DeepSpeech_From_Tensorflow.md) | ||
| * [Converting Language Model on One Billion Word Benchmark from TensorFlow](prepare_model/convert_model/tf_specific/Convert_lm_1b_From_Tensorflow.md) | ||
| * [Converting Neural Collaborative Filtering Model from TensorFlow*](prepare_model/convert_model/tf_specific/Convert_NCF_From_Tensorflow.md) | ||
| * [Converting TensorFlow* Object Detection API Models](prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md) | ||
| * [Converting TensorFlow*-Slim Image Classification Model Library Models](prepare_model/convert_model/tf_specific/Convert_Slim_Library_Models.md) | ||
| * [Converting CRNN Model from TensorFlow*](prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md) | ||
| * [Converting Your MXNet* Model](prepare_model/convert_model/Convert_Model_From_MxNet.md) | ||
| * [Converting a Style Transfer Model from MXNet](prepare_model/convert_model/mxnet_specific/Convert_Style_Transfer_From_MXNet.md) | ||
| * [Converting Your Kaldi* Model](prepare_model/convert_model/Convert_Model_From_Kaldi.md) | ||
| * [Converting Your ONNX* Model](prepare_model/convert_model/Convert_Model_From_ONNX.md) | ||
| * [Converting Faster-RCNN ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_Faster_RCNN.md) | ||
| * [Converting Mask-RCNN ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_Mask_RCNN.md) | ||
| * [Converting GPT2 ONNX* Model](prepare_model/convert_model/onnx_specific/Convert_GPT2.md) | ||
| * [Converting Your PyTorch* Model](prepare_model/convert_model/Convert_Model_From_PyTorch.md) | ||
| * [Converting F3Net PyTorch* Model](prepare_model/convert_model/pytorch_specific/Convert_F3Net.md) | ||
| * [Converting QuartzNet PyTorch* Model](prepare_model/convert_model/pytorch_specific/Convert_QuartzNet.md) | ||
| * [Converting YOLACT PyTorch* Model](prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md) | ||
| * [Model Optimizations Techniques](prepare_model/Model_Optimization_Techniques.md) | ||
| * [Cutting parts of the model](prepare_model/convert_model/Cutting_Model.md) | ||
| * [Sub-graph Replacement in Model Optimizer](prepare_model/customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md) | ||
| * [Supported Framework Layers](prepare_model/Supported_Frameworks_Layers.md) | ||
| * [Intermediate Representation and Operation Sets](IR_and_opsets.md) | ||
| * [Operations Specification](../ops/opset.md) | ||
| * [Intermediate Representation suitable for INT8 inference](prepare_model/convert_model/IR_suitable_for_INT8_inference.md) | ||
| * [Model Optimizer Extensibility](prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md) | ||
| * [Extending Model Optimizer with New Primitives](prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_New_Primitives.md) | ||
| * [Extending Model Optimizer with Caffe Python Layers](prepare_model/customize_model_optimizer/Extending_Model_Optimizer_with_Caffe_Python_Layers.md) | ||
| * [Extending Model Optimizer with Custom MXNet* Operations](prepare_model/customize_model_optimizer/Extending_MXNet_Model_Optimizer_with_New_Primitives.md) | ||
| * [Legacy Mode for Caffe* Custom Layers](prepare_model/customize_model_optimizer/Legacy_Mode_for_Caffe_Custom_Layers.md) | ||
| * [Model Optimizer Frequently Asked Questions](prepare_model/Model_Optimizer_FAQ.md) | ||
| ## Videos | ||
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| * [Known Issues](Known_Issues_Limitations.md) | ||
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| **Typical Next Step:** [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md) | ||
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| ## Video: Model Optimizer Concept | ||
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| [](https://www.youtube.com/watch?v=Kl1ptVb7aI8) | ||
| \htmlonly | ||
| <iframe width="560" height="315" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | ||
| \endhtmlonly | ||
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| ## Video: Model Optimizer Basic Operation | ||
| [](https://www.youtube.com/watch?v=BBt1rseDcy0) | ||
| \htmlonly | ||
| <iframe width="560" height="315" src="https://www.youtube.com/embed/BBt1rseDcy0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | ||
| <table> | ||
| <tr> | ||
| <td><iframe width="220" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></td> | ||
| <td><iframe width="220" src="https://www.youtube.com/embed/BBt1rseDcy0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></td> | ||
| <td><iframe width="220" src="https://www.youtube.com/embed/RF8ypHyiKrY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></td> | ||
| </tr> | ||
| <tr> | ||
| <td><strong>Model Optimizer Concept</strong>. <br>Duration: 3:56</td> | ||
| <td><strong>Model Optimizer Basic<br> Operation</strong>. <br>Duration: 2:57.</td> | ||
| <td><strong>Choosing the Right Precision</strong>. <br>Duration: 4:18.</td> | ||
| </tr> | ||
| </table> | ||
| \endhtmlonly | ||
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| ## Video: Choosing the Right Precision | ||
| [](https://www.youtube.com/watch?v=RF8ypHyiKrY) | ||
| \htmlonly | ||
| <iframe width="560" height="315" src="https://www.youtube.com/embed/RF8ypHyiKrY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> | ||
| \endhtmlonly |
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