/// Copyright (c) 2020 ARM Limited.
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namespace armnn
{
/**
@page converter_and_quantizer The ArmNN Converter and Quantizer
@tableofcontents

@section S10_converter The ArmNN Converter

The `ArmnnConverter` is a program for converting neural networks from other formats to Arm NN format.
Currently the program supports models in Caffe, Onnx, Tensorflow Protocol Buffers and Tensorflow Lite FlatBuffers formats. Run the program with no arguments to see command-line help.

For more information about the layers that are supported, see <a href="parsers.xhtml">parsers</a>.
<br/><br/><br/><br/>

@section S11_quantizer The ArmNN Quantizer

The `ArmnnQuantizer` is a program for loading a 32-bit float network into ArmNN and converting it into a quantized asymmetric 8-bit or quantized symmetric 16-bit network.
It supports static quantization by default, dynamic quantization is enabled if CSV file of raw input tensors is provided. Run the program with no arguments to see command-line help.


|Cmd:|||
| ---|---|---|
| -h | --help               | Display help messages |
| -f | --infile             | Input file containing float 32 ArmNN Input Graph |
| -s | --scheme             | Quantization scheme, "QAsymm8" or "QSymm16". Default value: QAsymm8 |
| -c | --csvfile            | CSV file containing paths for raw input tensors for dynamic quantization. If unset, static quantization is used |
| -p | --preserve-data-type | Preserve the input and output data types. If unset, input and output data types are not preserved |
| -d | --outdir             | Directory that output file will be written to |
| -o | --outfile            | ArmNN output file name |

<br/>
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.sh
./ArmnnQuantizer -f /path/to/armnn/input/graph/ -s "QSymm16" -c /path/to/csv/file -p 1 -d /path/to/output -o outputFileName
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**/
}