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[LICENSE] add ALv2 licenses (#233)
* add ALv2 licenses Signed-off-by: Sheng Zha <[email protected]> * add copyright notices back Signed-off-by: Sheng Zha <[email protected]> * update license for notebook Signed-off-by: Sheng Zha <[email protected]>
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‎LICENSE

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‎PyTorchCustomOperator/README.md

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<!--- SPDX-License-Identifier: Apache-2.0 -->
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## How to export Pytorch model with custom op to ONNX and run it in ONNX Runtime
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This document describes the required steps for extending TorchScript with a custom operator, exporting the operator to ONNX format, and adding the operator to ONNX Runtime for model inference.

‎PyTorchCustomOperator/ort_custom_op/custom_op.cc

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/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include <iostream>
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#include "Eigen/Dense"
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#include "onnxruntime_cxx_api.h"

‎PyTorchCustomOperator/ort_custom_op/custom_op.h

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/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include <iostream>
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#include "onnxruntime_cxx_api.h"
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‎PyTorchCustomOperator/ort_custom_op/custom_op_test.cc

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/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include <iostream>
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#include "custom_op.h"
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#include "onnxruntime_cxx_api.h"

‎PyTorchCustomOperator/pytorch_custom_op/custom_group_norm.cpp

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/*
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include <torch/script.h>
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‎PyTorchCustomOperator/pytorch_custom_op/export_custom_op.py

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# SPDX-License-Identifier: Apache-2.0
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‎PyTorchCustomOperator/pytorch_custom_op/setup.py

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# SPDX-License-Identifier: Apache-2.0
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setup(name='custom_group_norm',
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include_dirs = ['<PATH_TO_EIGEN_HEADER>'])],
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license='Apache License v2.0',
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cmdclass={'build_ext': cpp_extension.BuildExtension})

‎README.md

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<!--- SPDX-License-Identifier: Apache-2.0 -->
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# [ONNX](https://github.com/onnx/onnx) Tutorials
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[Open Neural Network Exchange (ONNX)](http://onnx.ai/) is an open standard format for representing machine learning models. ONNX is supported by [a community of partners](https://onnx.ai/supported-tools) who have implemented it in many frameworks and tools.
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## Getting ONNX models
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* Pre-trained models: Many pre-trained ONNX models are provided for common scenarios in the [ONNX Model Zoo](https://github.com/onnx/models).
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* Pre-trained models: Many pre-trained ONNX models are provided for common scenarios in the [ONNX Model Zoo](https://github.com/onnx/models).
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* Services: Customized ONNX models are generated for your data by cloud based services (see below)
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* Convert models from various frameworks (see below)
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* [Azure Machine Learning automated ML](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml#use-with-onnx-in-c-apps)
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### Converting to ONNX format
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| Framework / Tool | Installation | Tutorial |
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| Framework / Tool | Installation | Tutorial |
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| --- | --- | --- |
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| [Caffe](https://github.com/BVLC/caffe) | [apple/coremltools](https://github.com/apple/coremltools) and [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/caffe_coreml_onnx.ipynb) |
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| [Caffe2](http://caffe2.ai) | [part of caffe2 package](https://github.com/pytorch/pytorch/tree/master/caffe2/python/onnx) | [Example](tutorials/Caffe2OnnxExport.ipynb) |
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| [Caffe](https://github.com/BVLC/caffe) | [apple/coremltools](https://github.com/apple/coremltools) and [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/caffe_coreml_onnx.ipynb) |
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| [Caffe2](http://caffe2.ai) | [part of caffe2 package](https://github.com/pytorch/pytorch/tree/master/caffe2/python/onnx) | [Example](tutorials/Caffe2OnnxExport.ipynb) |
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| [Chainer](https://chainer.org/) | [chainer/onnx-chainer](https://github.com/chainer/onnx-chainer) | [Example](tutorials/ChainerOnnxExport.ipynb) |
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| [Cognitive Toolkit (CNTK)](https://www.microsoft.com/en-us/cognitive-toolkit/) | [built-in](https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your-machine) | [Example](tutorials/CntkOnnxExport.ipynb) |
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| [CoreML (Apple)](https://developer.apple.com/documentation/coreml) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/coreml_onnx.ipynb) |
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| [Cognitive Toolkit (CNTK)](https://www.microsoft.com/en-us/cognitive-toolkit/) | [built-in](https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your-machine) | [Example](tutorials/CntkOnnxExport.ipynb) |
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| [CoreML (Apple)](https://developer.apple.com/documentation/coreml) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/coreml_onnx.ipynb) |
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| [Keras](https://github.com/keras-team/keras) | [onnx/keras-onnx](https://github.com/onnx/keras-onnx) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/keras_onnx.ipynb) | n/a |
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| [LibSVM](https://github.com/cjlin1/libsvm) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/libsvm_onnx.ipynb) | n/a |
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| [LightGBM](https://github.com/Microsoft/LightGBM) | [onnx/onnxmltools](https://github.com/onnx/onnxmltools) | [Example](https://github.com/onnx/onnx-docker/blob/master/onnx-ecosystem/converter_scripts/lightgbm_onnx.ipynb) | n/a |
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## Scoring ONNX Models
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Once you have an ONNX model, it can be scored with a variety of tools.
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| Framework / Tool | Installation | Tutorial |
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| Framework / Tool | Installation | Tutorial |
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| --- | --- | --- |
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| [Caffe2](http://caffe2.ai) | [Caffe2](https://github.com/pytorch/pytorch/tree/master/caffe2/python/onnx) | [Example](tutorials/OnnxCaffe2Import.ipynb) |
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| [Cognitive Toolkit (CNTK)](https://www.microsoft.com/en-us/cognitive-toolkit/) | [built-in](https://docs.microsoft.com/en-us/cognitive-toolkit/setup-cntk-on-your-machine) | [Example](tutorials/OnnxCntkImport.ipynb)|
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* [Serving ONNX models with Cortex](https://towardsdatascience.com/how-to-deploy-onnx-models-in-production-60bd6abfd3ae)
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* [Serving ONNX models with MXNet Model Server](tutorials/ONNXMXNetServer.ipynb)
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* [Serving ONNX models with ONNX Runtime Server](tutorials/OnnxRuntimeServerSSDModel.ipynb)
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* [ONNX model hosting with AWS SageMaker and MXNet](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_onnx_eia/mxnet_onnx_eia.ipynb)
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* [ONNX model hosting with AWS SageMaker and MXNet](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_onnx_eia/mxnet_onnx_eia.ipynb)
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* [Serving ONNX models with ONNX Runtime on Azure ML](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment/onnx)
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* [FER Facial Expression Recognition](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb)
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* [MNIST Handwritten Digits](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb)
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* [Resnet50 Image Classification](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb)
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### ONNX as an intermediary format
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* [Convert a PyTorch model to Tensorflow using ONNX](tutorials/PytorchTensorflowMnist.ipynb)
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