arm_compute v17.12
diff --git a/documentation/graph__alexnet_8cpp_source.xhtml b/documentation/graph__alexnet_8cpp_source.xhtml
index accfea6..ac751cd 100644
--- a/documentation/graph__alexnet_8cpp_source.xhtml
+++ b/documentation/graph__alexnet_8cpp_source.xhtml
@@ -4,7 +4,7 @@
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<title>Compute Library: examples/graph_alexnet.cpp Source File</title>
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@@ -38,7 +40,7 @@
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<div id="projectname">Compute Library
-  <span id="projectnumber">17.10</span>
+  <span id="projectnumber">17.12</span>
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@@ -115,222 +117,53 @@
<div class="title">graph_alexnet.cpp</div> </div>
</div><!--header-->
<div class="contents">
-<a href="graph__alexnet_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div>
-<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017 ARM Limited.</span></div>
-<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div>
-<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div>
-<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div>
-<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div>
-<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div>
-<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div>
-<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div>
-<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div>
-<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div>
-<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div>
-<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div>
-<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div>
-<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div>
-<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div>
-<div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div>
-<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div>
-<div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div>
-<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div>
-<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef ARM_COMPUTE_CL </span><span class="comment">/* Needed by Utils.cpp to handle OpenCL exceptions properly */</span><span class="preprocessor"></span></div>
-<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor"></span><span class="preprocessor">#error "This example needs to be built with -DARM_COMPUTE_CL"</span></div>
-<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor"></span><span class="preprocessor">#endif </span><span class="comment">/* ARM_COMPUTE_CL */</span><span class="preprocessor"></span></div>
-<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor"></span></div>
-<div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_logger_8h.xhtml">arm_compute/core/Logger.h</a>"</span></div>
-<div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_graph_8h.xhtml">arm_compute/graph/Graph.h</a>"</span></div>
-<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_nodes_8h.xhtml">arm_compute/graph/Nodes.h</a>"</span></div>
-<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_c_l_scheduler_8h.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>"</span></div>
-<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="_c_p_p_scheduler_8h.xhtml">arm_compute/runtime/CPP/CPPScheduler.h</a>"</span></div>
-<div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_scheduler_8h.xhtml">arm_compute/runtime/Scheduler.h</a>"</span></div>
-<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div>
-<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div>
-<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></div>
-<div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div>
-<div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <cstdlib></span></div>
-<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <iostream></span></div>
-<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <memory></span></div>
-<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> <span class="keyword">using namespace </span>arm_compute::graph;</div>
-<div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">using namespace </span>arm_compute::graph_utils;</div>
-<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div>
-<div class="line"><a name="l00054"></a><span class="lineno"><a class="line" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0"> 54</a></span> std::unique_ptr<ITensorAccessor> <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(<span class="keyword">const</span> std::string &path, <span class="keyword">const</span> std::string &data_file)</div>
-<div class="line"><a name="l00055"></a><span class="lineno"> 55</span> {</div>
-<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span>(path.empty())</div>
-<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div>
-<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<DummyAccessor>();</div>
-<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  }</div>
-<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div>
-<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);</div>
-<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  }</div>
-<div class="line"><a name="l00064"></a><span class="lineno"> 64</span> }</div>
-<div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div>
-<div class="line"><a name="l00071"></a><span class="lineno"><a class="line" href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35"> 71</a></span> <span class="keywordtype">void</span> <a class="code" href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div>
-<div class="line"><a name="l00072"></a><span class="lineno"> 72</span> {</div>
-<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  std::string data_path; </div>
-<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div>
-<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// Parse arguments</span></div>
-<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">if</span>(argc < 2)</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="comment">// Print help</span></div>
-<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div>
-<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div>
-<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  }</div>
-<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</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>  <span class="comment">//Do something with argv[1]</span></div>
-<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  data_path = argv[1];</div>
-<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div>
-<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  std::cout << <span class="stringliteral">"No number of batches where specified, thus will use the default : "</span> << batches << <span class="stringliteral">"\n\n"</span>;</div>
-<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div>
-<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">else</span></div>
-<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  {</div>
-<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">//Do something with argv[1] and argv[2]</span></div>
-<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  data_path = argv[1];</div>
-<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  batches = std::strtol(argv[2], <span class="keyword">nullptr</span>, 0);</div>
-<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  }</div>
-<div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div>
-<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// Check if OpenCL is available and initialize the scheduler</span></div>
-<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">TargetHint</a> hint = <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09">TargetHint::NEON</a>;</div>
-<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a>())</div>
-<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  {</div>
-<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">arm_compute::CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div>
-<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  hint = <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda">TargetHint::OPENCL</a>;</div>
-<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div>
-<div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div>
-<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</div>
-<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">arm_compute::Logger::get</a>().<a class="code" href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">set_logger</a>(std::cout, <a class="code" href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::LoggerVerbosity::INFO</a>);</div>
-<div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div>
-<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  graph << hint</div>
-<div class="line"><a name="l00109"></a><span class="lineno"> 109</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>(227U, 227U, 3U, batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>())</div>
-<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="comment">// Layer 1</span></div>
-<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
-<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  11U, 11U, 96U,</div>
-<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv1_w.npy"</span>),</div>
-<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv1_b.npy"</span>),</div>
-<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(4, 4, 0, 0))</div>
-<div class="line"><a name="l00116"></a><span class="lineno"> 116</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer.xhtml">NormalizationLayer</a>(<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>, 5, 0.0001f, 0.75f))</div>
-<div class="line"><a name="l00118"></a><span class="lineno"> 118</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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div>
-<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// Layer 2</span></div>
-<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a4c5d06b02c97731aaa976179c62dcf76">ConvolutionMethodHint::DIRECT</a></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>  5U, 5U, 256U,</div>
-<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv2_w.npy"</span>),</div>
-<div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv2_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, 2, 2), 2)</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer.xhtml">NormalizationLayer</a>(<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>, 5, 0.0001f, 0.75f))</div>
-<div class="line"><a name="l00128"></a><span class="lineno"> 128</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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div>
-<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Layer 3</span></div>
-<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
-<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  3U, 3U, 384U,</div>
-<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv3_w.npy"</span>),</div>
-<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv3_b.npy"</span>),</div>
-<div class="line"><a name="l00134"></a><span class="lineno"> 134</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="l00135"></a><span class="lineno"> 135</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// Layer 4</span></div>
-<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
-<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  3U, 3U, 384U,</div>
-<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv4_w.npy"</span>),</div>
-<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv4_b.npy"</span>),</div>
-<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), 2)</div>
-<div class="line"><a name="l00142"></a><span class="lineno"> 142</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// Layer 5</span></div>
-<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
-<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  3U, 3U, 256U,</div>
-<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv5_w.npy"</span>),</div>
-<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv5_b.npy"</span>),</div>
-<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), 2)</div>
-<div class="line"><a name="l00149"></a><span class="lineno"> 149</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00150"></a><span class="lineno"> 150</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>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div>
-<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">// Layer 6</span></div>
-<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
-<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  4096U,</div>
-<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc6_w.npy"</span>),</div>
-<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc6_b.npy"</span>))</div>
-<div class="line"><a name="l00156"></a><span class="lineno"> 156</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <span class="comment">// Layer 7</span></div>
-<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
-<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  4096U,</div>
-<div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc7_w.npy"</span>),</div>
-<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc7_b.npy"</span>))</div>
-<div class="line"><a name="l00162"></a><span class="lineno"> 162</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div>
-<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// Layer 8</span></div>
-<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
-<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  1000U,</div>
-<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc8_w.npy"</span>),</div>
-<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc8_b.npy"</span>))</div>
-<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="comment">// Softmax</span></div>
-<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div>
-<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>());</div>
-<div class="line"><a name="l00171"></a><span class="lineno"> 171</span> </div>
-<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// Run graph</span></div>
-<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</div>
-<div class="line"><a name="l00174"></a><span class="lineno"> 174</span> }</div>
-<div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div>
-<div class="line"><a name="l00181"></a><span class="lineno"><a class="line" href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52"> 181</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div>
-<div class="line"><a name="l00182"></a><span class="lineno"> 182</span> {</div>
-<div class="line"><a name="l00183"></a><span class="lineno"> 183</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__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a>);</div>
-<div class="line"><a name="l00184"></a><span class="lineno"> 184</span> }</div>
-<div class="ttc" id="_logger_8h_xhtml"><div class="ttname"><a href="_logger_8h.xhtml">Logger.h</a></div></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#l00037">FullyConnectedLayer.h:37</a></div></div>
+<a href="graph__alexnet_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_graph_8h.xhtml">arm_compute/graph/Graph.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_nodes_8h.xhtml">arm_compute/graph/Nodes.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></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> <span class="preprocessor">#include <cstdlib></span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include <iostream></span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <memory></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph.xhtml">arm_compute::graph</a>;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35"> 42</a></span> <span class="keywordtype">void</span> <a class="code" href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  std::string data_path; <span class="comment">/* Path to the trainable data */</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  std::string image; <span class="comment">/* Image data */</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::string label; <span class="comment">/* Label data */</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  constexpr <span class="keywordtype">float</span> mean_r = 122.68f; <span class="comment">/* Mean value to subtract from red channel */</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  constexpr <span class="keywordtype">float</span> mean_g = 116.67f; <span class="comment">/* Mean value to subtract from green channel */</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  constexpr <span class="keywordtype">float</span> mean_b = 104.01f; <span class="comment">/* Mean value to subtract from blue channel */</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL). By default it is NEON</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</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="l00054"></a><span class="lineno"> 54</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2">ConvolutionMethodHint</a> convolution_hint = target_hint == <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09">TargetHint::NEON</a> ? <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a5174aac3927faa9ee34befb7fc87a9e3">ConvolutionMethodHint::GEMM</a> : <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a4c5d06b02c97731aaa976179c62dcf76">ConvolutionMethodHint::DIRECT</a>;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordflow">if</span>(argc < 2)</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>  <span class="comment">// Print help</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</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="l00061"></a><span class="lineno"> 61</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</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="l00066"></a><span class="lineno"> 66</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  data_path = argv[2];</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</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="l00072"></a><span class="lineno"> 72</span>  std::cout << <span class="stringliteral">"No image provided: using random values\n\n"</span>;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  {</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  data_path = argv[2];</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  image = argv[3];</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</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="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  data_path = argv[2];</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  image = argv[3];</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  label = argv[4];</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  }</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>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> </div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  graph << target_hint</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</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>(227U, 227U, 3U, 1U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>),</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</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="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">ConvolutionLayer</a>(</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  11U, 11U, 96U,</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/alexnet_model/conv1_w.npy"</span>),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv1_b.npy"</span>),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(4, 4, 0, 0))</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a227ecc6e858c8d1f61664f1967173bea">NormalizationLayer</a>(<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>, 5, 0.0001f, 0.75f))</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  << convolution_hint</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">ConvolutionLayer</a>(</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  5U, 5U, 256U,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv2_w.npy"</span>),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv2_b.npy"</span>),</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 2, 2), 2)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a227ecc6e858c8d1f61664f1967173bea">NormalizationLayer</a>(<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">NormType::CROSS_MAP</a>, 5, 0.0001f, 0.75f))</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">ConvolutionLayer</a>(</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  3U, 3U, 384U,</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/alexnet_model/conv3_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/alexnet_model/conv3_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="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">ConvolutionLayer</a>(</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  3U, 3U, 384U,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/conv4_w.npy"</span>),</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/alexnet_model/conv4_b.npy"</span>),</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), 2)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">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/alexnet_model/conv5_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/alexnet_model/conv5_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), 2)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a">FullyConnectedLayer</a>(</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  4096U,</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc6_w.npy"</span>),</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc6_b.npy"</span>))</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a">FullyConnectedLayer</a>(</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  4096U,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc7_w.npy"</span>),</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/alexnet_model/fc7_b.npy"</span>))</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a">FullyConnectedLayer</a>(</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  1000U,</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/alexnet_model/fc8_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/alexnet_model/fc8_b.npy"</span>))</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">// Softmax</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  << <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a4a9567bc4a6c28a527c973010eaf9a25">SoftmaxLayer</a>()</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</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="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> }</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00164"></a><span class="lineno"><a class="line" href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52"> 164</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a>(<span class="keywordtype">int</span> argc, <span class="keyword">const</span> <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</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__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a>);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</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="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a9a2c9c31d675b34f6ec35cc1ca89e047a4a9567bc4a6c28a527c973010eaf9a25"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a4a9567bc4a6c28a527c973010eaf9a25">arm_compute::graph::OperationType::SoftmaxLayer</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="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">arm_compute::graph_utils::DummyAccessor</a></div><div class="ttdoc">Dummy accessor class. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00060">GraphUtils.h:60</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a9a2c9c31d675b34f6ec35cc1ca89e047a227ecc6e858c8d1f61664f1967173bea"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a227ecc6e858c8d1f61664f1967173bea">arm_compute::graph::OperationType::NormalizationLayer</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( ) </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="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
-<div class="ttc" id="graph__alexnet_8cpp_xhtml_aa2e0960766de068c13ddff2ab22a8c35"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a></div><div class="ttdeci">void main_graph_alexnet(int argc, const char **argv)</div><div class="ttdoc">Example demonstrating how to implement AlexNet's network using the Compute Library's graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00071">graph_alexnet.cpp:71</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F16 per channel </div></div>
-<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00561">Types.h:561</a></div></div>
+<div class="ttc" id="graph__alexnet_8cpp_xhtml_aa2e0960766de068c13ddff2ab22a8c35"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a></div><div class="ttdeci">void main_graph_alexnet(int argc, const char **argv)</div><div class="ttdoc">Example demonstrating how to implement AlexNet&#39;s network using the Compute Library&#39;s graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00042">graph_alexnet.cpp:42</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
+<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00700">Types.h:700</a></div></div>
<div class="ttc" id="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">Graph.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_aa4f4d7a58287017588fc338965873f14"><div class="ttname"><a href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a></div><div class="ttdeci">bool opencl_is_available()</div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09">arm_compute::graph::TargetHint::NEON</a></div><div class="ttdoc">Run node on a NEON capable device. </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#l00511">Types.h:511</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_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__alexnet_8cpp_xhtml_a217dbf8b442f20279ea00b898af96f52"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a></div><div class="ttdeci">int main(int argc, const char **argv)</div><div class="ttdoc">Main program for AlexNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00181">graph_alexnet.cpp:181</a></div></div>
-<div class="ttc" id="_c_l_scheduler_8h_xhtml"><div class="ttname"><a href="_c_l_scheduler_8h.xhtml">CLScheduler.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalization_layer.xhtml">arm_compute::graph::NormalizationLayer</a></div><div class="ttdoc">Normalization layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_normalization_layer_8h_source.xhtml#l00036">NormalizationLayer.h:36</a></div></div>
+<div class="ttc" id="graph__alexnet_8cpp_xhtml_a217dbf8b442f20279ea00b898af96f52"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a></div><div class="ttdeci">int main(int argc, const char **argv)</div><div class="ttdoc">Main program for AlexNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00164">graph_alexnet.cpp:164</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="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
-<div class="ttc" id="graph__alexnet_8cpp_xhtml_acbea98d13e0adbf27ecc036feeb610f0"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a></div><div class="ttdeci">std::unique_ptr< ITensorAccessor > get_accessor(const std::string &path, const std::string &data_file)</div><div class="ttdoc">Generates appropriate accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00054">graph_alexnet.cpp:54</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a60f9a6836b628a7171914c4afe43b4a7"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler & get()</div><div class="ttdoc">Access the scheduler singleton. </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#l00406">Types.h:406</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="_scheduler_8h_xhtml"><div class="ttname"><a href="_scheduler_8h.xhtml">Scheduler.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda">arm_compute::graph::TargetHint::OPENCL</a></div><div class="ttdoc">Run node on an OpenCL capable device (GPU) </div></div>
-<div class="ttc" id="_c_p_p_scheduler_8h_xhtml"><div class="ttname"><a href="_c_p_p_scheduler_8h.xhtml">CPPScheduler.h</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#l00041">Graph.h:41</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aea068ae5aae640d018c4300bc7619575">arm_compute::graph::OperationType::PoolingLayer</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="namespacearm__compute_1_1graph_xhtml_a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a658061ff1dac70c02116fae6c044da1a">arm_compute::graph::OperationType::FullyConnectedLayer</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml">arm_compute::graph</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_l_map_8h_source.xhtml#l00034">CLMap.h:34</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="namespacearm__compute_1_1graph_xhtml_a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047a041485a3394541feee82a34d40249d70">arm_compute::graph::OperationType::ActivationLayer</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="namespacearm__compute_1_1graph_xhtml_a9a92cf6a83b4d54786334cc37a7391a2a4c5d06b02c97731aaa976179c62dcf76"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a4c5d06b02c97731aaa976179c62dcf76">arm_compute::graph::ConvolutionMethodHint::DIRECT</a></div><div class="ttdoc">Direct convolution. </div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3"><div class="ttname"><a href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::LoggerVerbosity::INFO</a></div><div class="ttdoc">Log info. </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#l00050">Types.h:50</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's metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</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="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00038">GraphUtils.h:38</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a2c9c31d675b34f6ec35cc1ca89e047aa252659b59a03bc61e5ec827ab4448b7">arm_compute::graph::OperationType::ConvolutionLayer</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="_nodes_8h_xhtml"><div class="ttname"><a href="_nodes_8h.xhtml">Nodes.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate. </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#l00041">ConvolutionLayer.h:41</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#l00445">Types.h:445</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="namespacearm__compute_xhtml_ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5"><div class="ttname"><a href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">arm_compute::NormType::CROSS_MAP</a></div><div class="ttdoc">Normalization applied cross maps. </div></div>
-<div class="ttc" id="classarm__compute_1_1_logger_xhtml_a6b2499aec6ae645d88670be75dcef769"><div class="ttname"><a href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">arm_compute::Logger::get</a></div><div class="ttdeci">static Logger & get()</div></div>
-<div class="ttc" id="classarm__compute_1_1_logger_xhtml_a8ce097d129855b3ce7f3fcd6c30551ba"><div class="ttname"><a href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">arm_compute::Logger::set_logger</a></div><div class="ttdeci">void set_logger(std::ostream &ostream, LoggerVerbosity verbosity)</div></div>
-<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a46ecf9ef0fe80ba2ed35acfc29856b7d"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">arm_compute::CLScheduler::default_init</a></div><div class="ttdeci">void default_init(ICLTuner *cl_tuner=nullptr)</div><div class="ttdoc">Initialises the context and command queue used by the scheduler to default values and sets a default ...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00083">CLScheduler.h:83</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#l00038">Tensor.h:38</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a9a92cf6a83b4d54786334cc37a7391a2a5174aac3927faa9ee34befb7fc87a9e3"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a5174aac3927faa9ee34befb7fc87a9e3">arm_compute::graph::ConvolutionMethodHint::GEMM</a></div><div class="ttdoc">Convolution using GEMM. </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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