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<a href="_build_guide_native_8md.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> # Delegate Build Guide</div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> </div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> This guide assumes that Arm NN has been built with the Arm NN TF Lite Delegate with the [Arm NN Build Tool](../build-tool/README.md).<br></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> The Arm NN TF Lite Delegate can also be obtained from downloading the [Pre-Built Binaries on the GitHub homepage](../README.md).</div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> **Table of Contents:**</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> - [Running DelegateUnitTests](#running-delegateunittests)</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> - [Run the TF Lite Benchmark Tool](#run-the-tflite-model-benchmark-tool)</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>  - [Download the TFLite Model Benchmark Tool](#download-the-tflite-model-benchmark-tool)</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>  - [Execute the benchmarking tool with the Arm NN TF Lite Delegate](#execute-the-benchmarking-tool-with-the-arm-nn-tf-lite-delegate)</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> - [Integrate the Arm NN TfLite Delegate into your project](#integrate-the-arm-nn-tflite-delegate-into-your-project)</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> ## Running DelegateUnitTests</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> To ensure that the build was successful you can run the unit tests for the delegate that can be found in</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> the build directory for the delegate. [Doctest](https://github.com/onqtam/doctest) was used to create those tests. Using test filters you can</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> filter out tests that your build is not configured for. In this case, we run all test suites that have `CpuAcc` in their name.</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> ```bash</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> cd <PATH_TO_ARMNN_BUILD_DIRECTORY>/delegate/build</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> ./DelegateUnitTests --test-suite=*CpuAcc*</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> ```</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> If you have built for Gpu acceleration as well you might want to change your test-suite filter:</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> ```bash</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> ./DelegateUnitTests --test-suite=*CpuAcc*,*GpuAcc*</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> ```</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> ## Run the TFLite Model Benchmark Tool</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> The [TFLite Model Benchmark](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark) Tool has a useful command line interface to test the TF Lite Delegate.</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> We can use this to demonstrate the use of the Arm NN TF Lite Delegate and its options.</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> Some examples of this can be viewed in this [YouTube demonstration](https://www.youtube.com/watch?v=NResQ1kbm-M&t=920s).</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> ### Download the TFLite Model Benchmark Tool</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> Binary builds of the benchmarking tool for various platforms are available [here](https://www.tensorflow.org/lite/performance/measurement#native_benchmark_binary). In this example I will target an aarch64 Linux environment. I will also download a sample uint8 tflite model from the [Arm ML Model Zoo](https://github.com/ARM-software/ML-zoo).</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> ```bash</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> mkdir $BASEDIR/benchmarking</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> cd $BASEDIR/benchmarking</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> # Get the benchmarking binary.</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> wget https://storage.googleapis.com/tensorflow-nightly-public/prod/tensorflow/release/lite/tools/nightly/latest/linux_aarch64_benchmark_model -O benchmark_model</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> # Make it executable.</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> chmod +x benchmark_model</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> # and a sample model from model zoo.</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> wget https://github.com/ARM-software/ML-zoo/blob/master/models/image_classification/mobilenet_v2_1.0_224/tflite_uint8/mobilenet_v2_1.0_224_quantized_1_default_1.tflite?raw=true -O mobilenet_v2_1.0_224_quantized_1_default_1.tflite</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> ```</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> ### Execute the benchmarking tool with the Arm NN TF Lite Delegate</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> You are already at $BASEDIR/benchmarking from the previous stage.</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> ```bash</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> LD_LIBRARY_PATH=<PATH_TO_ARMNN_BUILD_DIRECTORY> ./benchmark_model --graph=mobilenet_v2_1.0_224_quantized_1_default_1.tflite --external_delegate_path="<PATH_TO_ARMNN_BUILD_DIRECTORY>/delegate/libarmnnDelegate.so" --external_delegate_options="backends:CpuAcc;logging-severity:info"</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> ```</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> The "external_delegate_options" here are specific to the Arm NN delegate. They are used to specify a target Arm NN backend or to enable/disable various options in Arm NN. A full description can be found in the parameters of function tflite_plugin_create_delegate.</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> </div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> ## Integrate the Arm NN TfLite Delegate into your project</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> The delegate can be integrated into your c++ project by creating a TfLite Interpreter and</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> instructing it to use the Arm NN delegate for the graph execution. This should look similar</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> to the following code snippet.</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> ```objectivec</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> // Create TfLite Interpreter</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> std::unique_ptr<Interpreter> armnnDelegateInterpreter;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  (&armnnDelegateInterpreter)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> // Create the Arm NN Delegate</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> armnnDelegate::DelegateOptions delegateOptions(backends);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  armnnDelegate::TfLiteArmnnDelegateDelete);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> // Instruct the Interpreter to use the armnnDelegate</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get());</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> ```</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> </div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> For further information on using TfLite Delegates please visit the [TensorFlow website](https://www.tensorflow.org/lite/guide).</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> For more details of the kind of options you can pass to the Arm NN delegate please check the parameters of function tflite_plugin_create_delegate.</div></div><!-- fragment --></div><!-- contents -->
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