/// Copyright (c) 2021 ARM Limited and Contributors. All rights reserved.
///
/// SPDX-License-Identifier: MIT
///

namespace armnn
{
/**
@page parsers Parsers

@tableofcontents
Execute models from different machine learning platforms efficiently with our parsers. Simply choose a parser according
to the model you want to run e.g. If you've got a model in onnx format (<model_name>.onnx) use our onnx-parser.

If you would like to run a Tensorflow Lite (TfLite) model you probably also want to take a look at our @ref delegate.

All parsers are written in C++ but it is also possible to use them in python. For more information on our python
bindings take a look into the @ref md_python_pyarmnn_README section.

<br/><br/>


@section S5_onnx_parser ArmNN Onnx Parser

`armnnOnnxParser` is a library for loading neural networks defined in ONNX protobuf files into the Arm NN runtime.

## ONNX operators that the Arm NN SDK supports

This reference guide provides a list of ONNX operators the Arm NN SDK currently supports.

The Arm NN SDK ONNX parser currently only supports fp32 operators.

### Fully supported

- Add
  - See the ONNX [Add documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Add) for more information

- AveragePool
  - See the ONNX [AveragePool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#AveragePool) for more information.

- Constant
  - See the ONNX [Constant documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Constant) for more information.

- Clip
  - See the ONNX [Clip documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Clip) for more information.

- Flatten
  - See the ONNX [Flatten documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Flatten) for more information.

- GlobalAveragePool
  - See the ONNX [GlobalAveragePool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#GlobalAveragePool) for more information.

- LeakyRelu
  - See the ONNX [LeakyRelu documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#LeakyRelu) for more information.

- MaxPool
  - See the ONNX [max_pool documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#MaxPool) for more information.

- Relu
  - See the ONNX [Relu documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Relu) for more information.

- Reshape
  - See the ONNX [Reshape documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Reshape) for more information.

- Sigmoid
  - See the ONNX [Sigmoid documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Sigmoid) for more information.

- Tanh
  - See the ONNX [Tanh documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Tanh) for more information.


### Partially supported

- Conv
  - The parser only supports 2D convolutions with a group = 1 or group = #Nb_of_channel (depthwise convolution)
- BatchNormalization
  - The parser does not support training mode. See the ONNX [BatchNormalization documentation](https://github.com/onnx/onnx/blob/master/docs/Operators.md#BatchNormalization) for more information.
- MatMul
  - The parser only supports constant weights in a fully connected layer.

## Tested networks

Arm tested these operators with the following ONNX fp32 neural networks:
- Mobilenet_v2. See the ONNX [MobileNet documentation](https://github.com/onnx/models/tree/master/vision/classification/mobilenet) for more information.
- Simple MNIST. This is no longer directly documented by ONNX. The model and test data may be downloaded [from the ONNX model zoo](https://onnxzoo.blob.core.windows.net/models/opset_8/mnist/mnist.tar.gz).

More machine learning operators will be supported in future releases.
<br/><br/><br/><br/>


@section S6_tf_lite_parser ArmNN Tf Lite Parser

`armnnTfLiteParser` is a library for loading neural networks defined by TensorFlow Lite FlatBuffers files
into the Arm NN runtime.

## TensorFlow Lite operators that the Arm NN SDK supports

This reference guide provides a list of TensorFlow Lite operators the Arm NN SDK currently supports.

### Fully supported
The Arm NN SDK TensorFlow Lite parser currently supports the following operators:

- ABS
- ADD
- ARG_MAX
- ARG_MIN
- AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- BATCH_TO_SPACE
- CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- DEPTH_TO_SPACE
- DEPTHWISE_CONV_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- DEQUANTIZE
- DIV
- ELU
- EXP
- FULLY_CONNECTED, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- GATHER
- HARD_SWISH
- LEAKY_RELU
- LOGICAL_NOT
- LOGISTIC
- L2_NORMALIZATION
- MAX_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
- MAXIMUM
- MEAN
- MINIMUM
- MUL
- NEG
- PACK
- PAD
- PRELU
- QUANTIZE
- RELU
- RELU6
- REDUCE_MAX
- REDUCE_MIN
- RESHAPE
- RESIZE_BILINEAR
- RESIZE_NEAREST_NEIGHBOR
- RSQRT
- SLICE
- SOFTMAX
- SPACE_TO_BATCH
- SPLIT
- SPLIT_V
- SQUEEZE
- STRIDED_SLICE
- SUB
- SUM
- TANH
- TRANSPOSE
- TRANSPOSE_CONV
- UNPACK

### Custom Operator
- TFLite_Detection_PostProcess

## Tested networks
Arm tested these operators with the following TensorFlow Lite neural network:
- [Quantized MobileNet](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)
- [Quantized SSD MobileNet](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz)
- DeepSpeech v1 converted from [TensorFlow model](https://github.com/mozilla/DeepSpeech/releases/tag/v0.4.1)
- DeepSpeaker
- [DeepLab v3+](https://www.tensorflow.org/lite/models/segmentation/overview)
- FSRCNN
- EfficientNet-lite
- RDN converted from [TensorFlow model](https://github.com/hengchuan/RDN-TensorFlow)
- Quantized RDN (CpuRef)
- [Quantized Inception v3](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz)
- [Quantized Inception v4](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) (CpuRef)
- Quantized ResNet v2 50 (CpuRef)
- Quantized Yolo v3 (CpuRef)

More machine learning operators will be supported in future releases.

**/
}