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update documents for Paddle inclusion (#10613)

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Introduce PaddlePaddle articles and include PP references in other articles
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Karol Blaszczak
2022-02-23 10:53:37 +01:00
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docs/HOWTO/Custom_Layers_Guide.md
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# Custom Operations Guide {#openvino_docs_HOWTO_Custom_Layers_Guide}
The Intel® Distribution of OpenVINO™ toolkit supports neural network models trained with multiple frameworks including
TensorFlow*, Caffe*, MXNet*, Kaldi* and ONNX* file format. The list of supported operations (layers) is different for
The Intel® Distribution of OpenVINO™ toolkit supports neural network models trained with multiple frameworks, including
TensorFlow, Caffe, MXNet, Kaldi, PaddlePaddle, and ONNX. The list of supported operations (layers) is different for
each of the supported frameworks. To see the operations supported by your framework, refer to
[Supported Framework Layers](../MO_DG/prepare_model/Supported_Frameworks_Layers.md).
Custom operations, that is those not included in the list, are not recognized by Model Optimizer out-of-the-box. Therefore, creating Intermediate Representation (IR) for a model using them requires additional steps. This guide illustrates the workflow for running inference on topologies featuring custom operations, allowing you to plug in your own implementation for existing or completely new operations.
> **NOTE**: *Layer* is a legacy term for *operation* which came from Caffe\* framework. Currently it is not used.
> **NOTE**: *Layer* is a legacy term for *operation* which came from Caffe framework. Currently it is not used.
> Refer to the [Deep Learning Network Intermediate Representation and Operation Sets in OpenVINO™](../MO_DG/IR_and_opsets.md)
> for more information on the topic.
## Terms Used in This Guide
- *Intermediate Representation (IR)* — OpenVINO's Neural Network format used by Inference Engine. It abstracts different frameworks and describs model topology, operations parameters, and weights.
- *Intermediate Representation (IR)* — OpenVINO's Neural Network format used by Inference Engine. It abstracts different frameworks and describes model topology, operations parameters, and weights.
- *Operation* — an abstract concept of a math function selected for a specific purpose. Operations supported by
OpenVINO™ are listed in the supported operation set provided in the [Available Operations Sets](../ops/opset.md).
@@ -28,8 +28,8 @@ Custom operations, that is those not included in the list, are not recognized by
## Custom Operation Support Overview
There are three steps to support inference of a model with custom operation(s):
1. Add support for a custom operation in the [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) so
the Model Optimizer can generate the IR with the operation.
1. Add support for a custom operation in [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) so
that it can generate the IR with the operation.
2. Create an operation set and implement a custom nGraph operation in it as described in the
[Custom nGraph Operation](../OV_Runtime_UG/Extensibility_DG/AddingNGraphOps.md).
3. Implement a customer operation in one of the [OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md)
@@ -59,7 +59,7 @@ operation. Refer to the "Operation Extractor" section of
> **NOTE**: In some cases you may need to implement some transformation to support the operation. This topic is covered in the "Graph Transformation Extensions" section of [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md).
## Custom Operations Extensions for the Inference Engine
## Custom Operation Extensions for the Inference Engine
Inference Engine provides an extension mechanism to support new operations. This mechanism is described in [Inference Engine Extensibility Mechanism](../OV_Runtime_UG/Extensibility_DG/Intro.md).
@@ -80,8 +80,8 @@ operation and correctly infer output tensor shape and type.
## Enabling Magnetic Resonance Image Reconstruction Model
This chapter provides step-by-step instructions on how to enable the magnetic resonance image reconstruction model implemented in the [repository](https://github.com/rmsouza01/Hybrid-CS-Model-MRI/) using a custom operation on CPU. The example is prepared for a model generated from the repository with hash `2ede2f96161ce70dcdc922371fe6b6b254aafcc8`.
### Download and Convert the Model to a Frozen TensorFlow\* Model Format
The original pre-trained model is provided in the hdf5 format which is not supported by OpenVINO directly and needs to be converted to TensorFlow\* frozen model format first.
### Download and Convert the Model to a Frozen TensorFlow Model Format
The original pre-trained model is provided in the hdf5 format which is not supported by OpenVINO directly and needs to be converted to TensorFlow frozen model format first.
1. Download repository `https://github.com/rmsouza01/Hybrid-CS-Model-MRI`:<br>
```bash
@@ -117,11 +117,11 @@ Keras==2.2.4) which should be executed from the root of the cloned repository:<b
f.write(graph_def.SerializeToString())
```
As a result the TensorFlow\* frozen model file "wnet_20.pb" is generated.
As a result the TensorFlow frozen model file "wnet_20.pb" is generated.
### Convert the Frozen TensorFlow\* Model to Intermediate Representation
### Convert the Frozen TensorFlow Model to Intermediate Representation
Firstly, open the model in TensorBoard or other TensorFlow* model visualization tool. The model supports dynamic
Firstly, open the model in TensorBoard or other TensorFlow model visualization tool. The model supports dynamic
batch dimension because the value for the batch dimension is not hardcoded in the model. Model Optimizer need to set all
dynamic dimensions to some specific value to create the IR, therefore specify the command line parameter `-b 1` to set
the batch dimension equal to 1. The actual batch size dimension can be changed at runtime using the Inference Engine API
@@ -132,8 +132,8 @@ for more details and command line parameters used for the model conversion.
mo --input_model <PATH_TO_MODEL>/wnet_20.pb -b 1
```
> **NOTE**: This conversion guide is applicable for the 2021.3 release of OpenVINO and that starting from 2021.4
> the OpenVINO supports this model out of the box.
> **NOTE**: This conversion guide is applicable for the 2021.3 release of OpenVINO and starting from 2021.4
> OpenVINO has supported this model out of the box.
Model Optimizer produces the following error:
```bash
@@ -172,12 +172,12 @@ additional parameter `--log_level DEBUG`. It is worth to mention the following l
This is a part of the log of the partial inference phase of the model conversion. See the "Partial Inference" section on
the [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md) for
more information about this phase. Model Optimizer inferred output shape for the unknown operation of type "Complex"
using a "fallback" to TensorFlow\*. However, it is not enough to generate the IR because Model Optimizer doesn't know
using a "fallback" to TensorFlow. However, it is not enough to generate the IR because Model Optimizer doesn't know
which attributes of the operation should be saved to IR. So it is necessary to implement Model Optimizer extensions to
support these operations.
Before going into the extension development it is necessary to understand what these unsupported operations do according
to the TensorFlow\* framework specification.
to the TensorFlow framework specification.
* "Complex" - returns a tensor of complex type constructed from two real input tensors specifying real and imaginary
part of a complex number.
@@ -342,8 +342,9 @@ python3 mri_reconstruction_demo.py \
## Converting Models:
- [Convert Your Caffe* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
- [Convert Your TensorFlow* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
- [Convert Your MXNet* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
- [Convert Your Kaldi* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
- [Convert Your ONNX* Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
- [Convert Your Caffe Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Caffe.md)
- [Convert Your TensorFlow Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md)
- [Convert Your MXNet Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_MxNet.md)
- [Convert Your Kaldi Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md)
- [Convert Your ONNX Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md)
- [Convert Your PaddlePaddle Model](../MO_DG/prepare_model/convert_model/Convert_Model_From_Paddle.md)