Documentation/md_tests__model_accuracy_tool-_armnn__r_e_a_d_m_e.html - platform/external/armnn - Git at Google

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 <div class="title">The ModelAccuracyTool-Armnn </div>  </div>
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 <div class="contents">
 <div class="textblock"><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:</p><ul>
 <li>-DFLATC_DIR=/path/to/flatbuffers/x86build/</li>
 <li>-DBUILD_ACCURACY_TOOL=1</li>
 <li>-DBUILD_ARMNN_SERIALIZER=1</li>
 </ul>
 <table class="doxtable">
 <tr>
 <th>Cmd:</th><th></th><th></th></tr>
 <tr>
 <td>-h </td><td>&ndash;help </td><td>Display help messages </td></tr>
 <tr>
 <td>-m </td><td>&ndash;model-path </td><td>Path to armnn format model file </td></tr>
 <tr>
 <td>-f </td><td>&ndash;model-format </td><td>The model format. Supported values: caffe, tensorflow, tflite </td></tr>
 <tr>
 <td>-i </td><td>&ndash;input-name </td><td>Identifier of the input tensors in the network separated by comma </td></tr>
 <tr>
 <td>-o </td><td>&ndash;output-name </td><td>Identifier of the output tensors in the network separated by comma </td></tr>
 <tr>
 <td>-d </td><td>&ndash;data-dir </td><td>Path to directory containing the ImageNet test data </td></tr>
 <tr>
 <td>-p </td><td>&ndash;model-output-labels </td><td>Path to model output labels file. </td></tr>
 <tr>
 <td>-v </td><td>&ndash;validation-labels-path </td><td>Path to ImageNet Validation <a class="el" href="struct_label.html">Label</a> file </td></tr>
 <tr>
 <td>-l </td><td>&ndash;data-layout ] </td><td>Data layout. Supported value: NHWC, NCHW. Default: NHWC </td></tr>
 <tr>
 <td>-c </td><td>&ndash;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>&ndash;validation-range </td><td>The range of the images to be evaluated. Specified in the form &lt;begin index&gt;=""&gt;:&lt;end index&gt;=""&gt;. 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>&ndash;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 />
  <code>./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</code> </p>
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	<div class="header">
	<div class="headertitle">
	<div class="title">The ModelAccuracyTool-Armnn </div> </div>
	</div><!--header-->
	<div class="contents">
	<div class="textblock"><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:</p><ul>
	<li>-DFLATC_DIR=/path/to/flatbuffers/x86build/</li>
	<li>-DBUILD_ACCURACY_TOOL=1</li>
	<li>-DBUILD_ARMNN_SERIALIZER=1</li>
	</ul>
	<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 <a class="el" href="struct_label.html">Label</a> 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 />
	<code>./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</code> </p>
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