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<div class="title">Other Tools </div> </div>
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<div class="contents">
<div class="toc"><h3>Table of Contents</h3>
<ul><li class="level1"><a href="#S14_image_csv_file_generator">The ImageCSVFileGenerator</a></li>
<li class="level1"><a href="#S15_image_tensor_generator">The ImageTensorGenerator</a></li>
<li class="level1"><a href="#S16_model_accuracy_tool_armnn">The ModelAccuracyTool-ArmNN</a></li>
</ul>
</div>
<div class="textblock"><h1><a class="anchor" id="S14_image_csv_file_generator"></a>
The ImageCSVFileGenerator</h1>
<p>The <code>ImageCSVFileGenerator</code> is a program for creating a CSV file that contains a list of .raw tensor files. These .raw tensor files can be generated using the<code>ImageTensorGenerator</code>.</p>
<table class="doxtable">
<tr>
<th>Cmd:</th><th></th><th></th></tr>
<tr>
<td>-h </td><td>–help </td><td>Display help messages </td></tr>
<tr>
<td>-i </td><td>–indir </td><td>Directory that .raw files are stored in </td></tr>
<tr>
<td>-o </td><td>–outfile </td><td>Output CSV file path </td></tr>
</table>
<p>Example usage: <br />
</p><div class="fragment"><div class="line">./ImageCSVFileGenerator -i /path/to/directory/ -o /output/path/csvfile.csv</div></div><!-- fragment --><p> <br />
<br />
<br />
<br />
</p>
<h1><a class="anchor" id="S15_image_tensor_generator"></a>
The ImageTensorGenerator</h1>
<p>The <code>ImageTensorGenerator</code> is a program for pre-processing a .jpg image before generating a .raw tensor file from it.</p>
<p>Build option: To build ModelAccuracyTool, pass the following options to Cmake: -DBUILD_ARMNN_QUANTIZER=1</p>
<table class="doxtable">
<tr>
<th>Cmd:</th><th></th><th></th></tr>
<tr>
<td>-h </td><td>–help </td><td>Display help messages </td></tr>
<tr>
<td>-f </td><td>–model-format </td><td>Format of the intended model file that uses the images.Different formats have different image normalization styles.Accepted values (caffe, tensorflow, tflite) </td></tr>
<tr>
<td>-i </td><td>–infile </td><td>Input image file to generate tensor from </td></tr>
<tr>
<td>-o </td><td>–outfile </td><td>Output raw tensor file path </td></tr>
<tr>
<td>-z </td><td>–output-type </td><td>The data type of the output tensors.If unset, defaults to "float" for all defined inputs. Accepted values (float, int or qasymm8) </td></tr>
<tr>
<td></td><td>–new-width </td><td>Resize image to new width. Keep original width if unspecified </td></tr>
<tr>
<td></td><td>–new-height </td><td>Resize image to new height. Keep original height if unspecified </td></tr>
<tr>
<td>-l </td><td>–layout </td><td>Output data layout, "NHWC" or "NCHW". Default value: NHWC </td></tr>
</table>
<p>Example usage: <br />
</p><div class="fragment"><div class="line">.sh ./ImageTensorGenerator -i /path/to/image/dog.jpg -o /output/path/dog.raw --new-width 224 --new-height 224</div></div><!-- fragment --><p> <br />
<br />
<br />
<br />
</p>
<h1><a class="anchor" id="S16_model_accuracy_tool_armnn"></a>
The ModelAccuracyTool-ArmNN</h1>
<p>The <code>ModelAccuracyTool-Armnn</code> is a program for measuring the Top 5 accuracy results of a model against an image dataset.</p>
<p>Prerequisites:</p><ol type="1">
<li>The model is in .armnn format model file. The <code>ArmnnConverter</code> can be used to convert a model to this format.</li>
</ol>
<p>Build option: To build ModelAccuracyTool, pass the following options to Cmake: -DFLATC_DIR=/path/to/flatbuffers/x86build/ -DBUILD_ACCURACY_TOOL=1 -DBUILD_ARMNN_SERIALIZER=1</p>
<table class="doxtable">
<tr>
<th>Cmd:</th><th></th><th></th></tr>
<tr>
<td>-h </td><td>–help </td><td>Display help messages </td></tr>
<tr>
<td>-m </td><td>–model-path </td><td>Path to armnn format model file </td></tr>
<tr>
<td>-f </td><td>–model-format </td><td>The model format. Supported values: caffe, tensorflow, tflite </td></tr>
<tr>
<td>-i </td><td>–input-name </td><td>Identifier of the input tensors in the network separated by comma </td></tr>
<tr>
<td>-o </td><td>–output-name </td><td>Identifier of the output tensors in the network separated by comma </td></tr>
<tr>
<td>-d </td><td>–data-dir </td><td>Path to directory containing the ImageNet test data </td></tr>
<tr>
<td>-p </td><td>–model-output-labels </td><td>Path to model output labels file. </td></tr>
<tr>
<td>-v </td><td>–validation-labels-path </td><td>Path to ImageNet Validation Label file </td></tr>
<tr>
<td>-l </td><td>–data-layout ] </td><td>Data layout. Supported value: NHWC, NCHW. Default: NHWC </td></tr>
<tr>
<td>-c </td><td>–compute </td><td>Which device to run layers on by default. Possible choices: CpuRef, CpuAcc, GpuAcc. Default: CpuAcc, CpuRef </td></tr>
<tr>
<td>-r </td><td>–validation-range </td><td>The range of the images to be evaluated. Specified in the form <begin index>="">:<end index>="">. The index starts at 1 and the range is inclusive. By default the evaluation will be performed on all images. </td></tr>
<tr>
<td>-b </td><td>–blacklist-path </td><td>Path to a blacklist file where each line denotes the index of an image to be excluded from evaluation. </td></tr>
</table>
<p>Example usage: <br />
</p><div class="fragment"><div class="line">./ModelAccuracyTool -m /path/to/model/model.armnn -f tflite -i input -o output -d /path/to/test/directory/ -p /path/to/model-output-labels -v /path/to/file/val.txt -c CpuRef -r 1:100</div></div><!-- fragment --><p> <br />
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