arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
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<div id="projectname">Compute Library
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
-<tr class="memitem:ac84f647fbdaaf283cbbf10a56d84b476"><td class="memItemLeft" align="right" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="graph__vgg19_8cpp.xhtml#ac84f647fbdaaf283cbbf10a56d84b476">main_graph_vgg19</a> (int argc, const char **argv)</td></tr>
-<tr class="memdesc:ac84f647fbdaaf283cbbf10a56d84b476"><td class="mdescLeft"> </td><td class="mdescRight">Example demonstrating how to implement VGG19's network using the Compute Library's graph API. <a href="#ac84f647fbdaaf283cbbf10a56d84b476">More...</a><br /></td></tr>
-<tr class="separator:ac84f647fbdaaf283cbbf10a56d84b476"><td class="memSeparator" colspan="2"> </td></tr>
-<tr class="memitem:a217dbf8b442f20279ea00b898af96f52"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="graph__vgg19_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a> (int argc, const char **argv)</td></tr>
-<tr class="memdesc:a217dbf8b442f20279ea00b898af96f52"><td class="mdescLeft"> </td><td class="mdescRight">Main program for VGG19. <a href="#a217dbf8b442f20279ea00b898af96f52">More...</a><br /></td></tr>
-<tr class="separator:a217dbf8b442f20279ea00b898af96f52"><td class="memSeparator" colspan="2"> </td></tr>
+<tr class="memitem:a3c04138a5bfe5d72780bb7e82a18e627"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="graph__vgg19_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a> (int argc, char **argv)</td></tr>
+<tr class="memdesc:a3c04138a5bfe5d72780bb7e82a18e627"><td class="mdescLeft"> </td><td class="mdescRight">Main program for VGG19. <a href="#a3c04138a5bfe5d72780bb7e82a18e627">More...</a><br /></td></tr>
+<tr class="separator:a3c04138a5bfe5d72780bb7e82a18e627"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<h2 class="groupheader">Function Documentation</h2>
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<td class="paramkey"></td>
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- <td class="paramtype">const char ** </td>
+ <td class="paramtype">char ** </td>
<td class="paramname"><em>argv</em> </td>
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</dd>
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-<p>Definition at line <a class="el" href="graph__vgg19_8cpp_source.xhtml#l00228">228</a> of file <a class="el" href="graph__vgg19_8cpp_source.xhtml">graph_vgg19.cpp</a>.</p>
-
-<p>References <a class="el" href="graph__vgg19_8cpp_source.xhtml#l00040">main_graph_vgg19()</a>, and <a class="el" href="utils_2_utils_8cpp_source.xhtml#l00069">arm_compute::utils::run_example()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a>(argc, argv, <a class="code" href="graph__vgg19_8cpp.xhtml#ac84f647fbdaaf283cbbf10a56d84b476">main_graph_vgg19</a>);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> }</div><div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a4c9395db2c8b8d0c336656a7b58fca3e"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a></div><div class="ttdeci">int run_example(int argc, const char **argv, example &func)</div><div class="ttdoc">Run an example and handle the potential exceptions it throws. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00069">Utils.cpp:69</a></div></div>
-<div class="ttc" id="graph__vgg19_8cpp_xhtml_ac84f647fbdaaf283cbbf10a56d84b476"><div class="ttname"><a href="graph__vgg19_8cpp.xhtml#ac84f647fbdaaf283cbbf10a56d84b476">main_graph_vgg19</a></div><div class="ttdeci">void main_graph_vgg19(int argc, const char **argv)</div><div class="ttdoc">Example demonstrating how to implement VGG19&#39;s network using the Compute Library&#39;s graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__vgg19_8cpp_source.xhtml#l00040">graph_vgg19.cpp:40</a></div></div>
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- <td class="memname">void main_graph_vgg19 </td>
- <td>(</td>
- <td class="paramtype">int </td>
- <td class="paramname"><em>argc</em>, </td>
- </tr>
- <tr>
- <td class="paramkey"></td>
- <td></td>
- <td class="paramtype">const char ** </td>
- <td class="paramname"><em>argv</em> </td>
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- <td></td>
- <td>)</td>
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-<p>Example demonstrating how to implement VGG19's network using the Compute Library's graph API. </p>
-<dl class="params"><dt>Parameters</dt><dd>
- <table class="params">
- <tr><td class="paramdir">[in]</td><td class="paramname">argc</td><td>Number of arguments </td></tr>
- <tr><td class="paramdir">[in]</td><td class="paramname">argv</td><td>Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) </td></tr>
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-<p>Definition at line <a class="el" href="graph__vgg19_8cpp_source.xhtml#l00040">40</a> of file <a class="el" href="graph__vgg19_8cpp_source.xhtml">graph_vgg19.cpp</a>.</p>
-
-<p>References <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">arm_compute::graph::ActivationLayer</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">arm_compute::graph::ConvolutionLayer</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a4c5d06b02c97731aaa976179c62dcf76">arm_compute::graph::DIRECT</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a">arm_compute::graph::FullyConnectedLayer</a>, <a class="el" href="_graph_utils_8h_source.xhtml#l00212">arm_compute::graph_utils::get_input_accessor()</a>, <a class="el" href="_graph_utils_8h_source.xhtml#l00254">arm_compute::graph_utils::get_output_accessor()</a>, <a class="el" href="_graph_utils_8h_source.xhtml#l00189">arm_compute::graph_utils::get_weights_accessor()</a>, <a class="el" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::MAX</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575">arm_compute::graph::PoolingLayer</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::RELU</a>, <a class="el" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">Graph::run()</a>, <a class="el" href="_graph_utils_8h_source.xhtml#l00230">arm_compute::graph_utils::set_target_hint()</a>, and <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a4a9567bc4a6c28a527c973010eaf9a25">arm_compute::graph::SoftmaxLayer</a>.</p>
-
-<p>Referenced by <a class="el" href="graph__vgg19_8cpp_source.xhtml#l00228">main()</a>.</p>
-<div class="fragment"><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  std::string data_path; <span class="comment">/* Path to the trainable data */</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  std::string image; <span class="comment">/* Image data */</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  std::string label; <span class="comment">/* Label data */</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  constexpr <span class="keywordtype">float</span> mean_r = 123.68f; <span class="comment">/* Mean value to subtract from red channel */</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  constexpr <span class="keywordtype">float</span> mean_g = 116.779f; <span class="comment">/* Mean value to subtract from green channel */</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  constexpr <span class="keywordtype">float</span> mean_b = 103.939f; <span class="comment">/* Mean value to subtract from blue channel */</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>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">TargetHint</a> target_hint = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9216738b309b6b230b7ba8bca5ba7477">set_target_hint</a>(argc > 1 ? std::strtol(argv[1], <span class="keyword">nullptr</span>, 10) : 0);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2">ConvolutionMethodHint</a> convolution_hint = ConvolutionMethodHint::DIRECT;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">if</span>(argc < 2)</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>  <span class="comment">// Print help</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [path_to_data] [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" [path_to_data] [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</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>  data_path = argv[2];</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" "</span> << argv[2] << <span class="stringliteral">" [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  std::cout << <span class="stringliteral">"No image provided: using random values\n\n"</span>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  }</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</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>  data_path = argv[2];</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  image = argv[3];</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" "</span> << argv[2] << <span class="stringliteral">" "</span> << argv[3] << <span class="stringliteral">" [labels]\n\n"</span>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</span>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  data_path = argv[2];</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  image = argv[3];</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  label = argv[4];</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  }</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  graph << target_hint</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  << convolution_hint</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(224U, 224U, 3U, 1U), 1, DataType::F32),</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#aedce0421da328fb2aaae190aede068e1">get_input_accessor</a>(image, mean_r, mean_g, mean_b))</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  3U, 3U, 64U,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_1_w.npy"</span>),</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_1_b.npy"</span>),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  3U, 3U, 64U,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_2_w.npy"</span>),</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_2_b.npy"</span>),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  3U, 3U, 128U,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_1_w.npy"</span>),</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_1_b.npy"</span>),</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  3U, 3U, 128U,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_2_w.npy"</span>),</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_2_b.npy"</span>),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  3U, 3U, 256U,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_1_w.npy"</span>),</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_1_b.npy"</span>),</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  3U, 3U, 256U,</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_2_w.npy"</span>),</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_2_b.npy"</span>),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  3U, 3U, 256U,</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_3_w.npy"</span>),</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_3_b.npy"</span>),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  3U, 3U, 256U,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_4_w.npy"</span>),</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_4_b.npy"</span>),</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  3U, 3U, 512U,</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_1_w.npy"</span>),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_1_b.npy"</span>),</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  3U, 3U, 512U,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_2_w.npy"</span>),</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_2_b.npy"</span>),</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  3U, 3U, 512U,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_3_w.npy"</span>),</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_3_b.npy"</span>),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  3U, 3U, 512U,</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_4_w.npy"</span>),</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_4_b.npy"</span>),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  3U, 3U, 512U,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_1_w.npy"</span>),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_1_b.npy"</span>),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  3U, 3U, 512U,</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_2_w.npy"</span>),</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_2_b.npy"</span>),</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  3U, 3U, 512U,</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_3_w.npy"</span>),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_3_b.npy"</span>),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  3U, 3U, 512U,</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_4_w.npy"</span>),</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_4_b.npy"</span>),</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  4096U,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc6_w.npy"</span>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc6_b.npy"</span>))</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  4096U,</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc7_w.npy"</span>),</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc7_b.npy"</span>))</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  1000U,</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc8_w.npy"</span>),</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc8_b.npy"</span>))</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="comment">// Softmax</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">get_output_accessor</a>(label, 5));</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> }</div><div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">arm_compute::graph::FullyConnectedLayer</a></div><div class="ttdoc">Fully connected layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_fully_connected_layer_8h_source.xhtml#l00038">FullyConnectedLayer.h:38</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::graph::Graph::run</a></div><div class="ttdeci">void run()</div><div class="ttdoc">Executes the graph. </div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_aaf0c8eff756108c8bb23aecf51d44f79"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">arm_compute::graph_utils::get_output_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_output_accessor(const std::string &labels_path, size_t top_n=5, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified labels_path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00254">GraphUtils.h:254</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00650">Types.h:650</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_aedce0421da328fb2aaae190aede068e1"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#aedce0421da328fb2aaae190aede068e1">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_input_accessor(const std::string &ppm_path, float mean_r, float mean_g, float mean_b)</div><div class="ttdoc">Generates appropriate input accessor according to the specified ppm_path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00212">GraphUtils.h:212</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a9216738b309b6b230b7ba8bca5ba7477"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a9216738b309b6b230b7ba8bca5ba7477">arm_compute::graph_utils::set_target_hint</a></div><div class="ttdeci">graph::TargetHint set_target_hint(int target)</div><div class="ttdoc">Utility function to return the TargetHint. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00230">GraphUtils.h:230</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a73a37a4970294106ed22e8f916ef3810"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_weights_accessor(const std::string &path, const std::string &data_file)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00189">GraphUtils.h:189</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a9a92cf6a83b4d54786334cc37a7391a2"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2">arm_compute::graph::ConvolutionMethodHint</a></div><div class="ttdeci">ConvolutionMethodHint</div><div class="ttdoc">Convolution method hint to the graph executor. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00460">Types.h:460</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">arm_compute::graph::SoftmaxLayer</a></div><div class="ttdoc">Softmax layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_softmax_layer_8h_source.xhtml#l00036">SoftmaxLayer.h:36</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml">arm_compute::graph::Graph</a></div><div class="ttdoc">Graph class. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8h_source.xhtml#l00043">Graph.h:43</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">arm_compute::graph::PoolingLayer</a></div><div class="ttdoc">Pooling layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_pooling_layer_8h_source.xhtml#l00037">PoolingLayer.h:37</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833b"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">arm_compute::graph::TargetHint</a></div><div class="ttdeci">TargetHint</div><div class="ttdoc">< Execution hint to the graph executor </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00076">Types.h:76</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00044">TensorInfo.h:44</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">arm_compute::graph::ActivationLayer</a></div><div class="ttdoc">Activation Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_activation_layer_8h_source.xhtml#l00037">ActivationLayer.h:37</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">arm_compute::graph::ConvolutionLayer</a></div><div class="ttdoc">Convolution layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_convolution_layer_8h_source.xhtml#l00042">ConvolutionLayer.h:42</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00553">Types.h:553</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a></div><div class="ttdoc">Tensor class. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8h_source.xhtml#l00039">Tensor.h:39</a></div></div>
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+<p>Definition at line <a class="el" href="graph__vgg19_8cpp_source.xhtml#l00236">236</a> of file <a class="el" href="graph__vgg19_8cpp_source.xhtml">graph_vgg19.cpp</a>.</p>
+<div class="fragment"><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphVGG19Example>(argc, argv);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> }</div></div><!-- fragment -->
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<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_d28a4824dc47e487b107a5db32ef43c4.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__vgg19_8cpp.xhtml">graph_vgg19.cpp</a></li>
- <li class="footer">Generated on Thu Dec 14 2017 23:48:33 for Compute Library by
+ <li class="footer">Generated on Wed Jan 24 2018 14:30:42 for Compute Library by
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<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
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