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<div id="projectname">Compute Library
-  <span id="projectnumber">18.03</span>
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<div class="title">graph_lenet.cpp</div> </div>
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<div class="contents">
-<a href="graph__lenet_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) 2017-2018 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> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</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="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">class </span>GraphLenetExample : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">void</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::string data_path; </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> int_target_hint = argc > 1 ? std::strtol(argv[1], <span class="keyword">nullptr</span>, 10) : 0;</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>(int_target_hint);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">if</span>(argc < 2)</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">// Print help</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [path_to_data] [batches]\n\n"</span>;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</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> <span class="keywordflow">if</span>(argc == 2)</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>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" [path_to_data] [batches]\n\n"</span>;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  {</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">//Do something with argv[1]</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">" [path_to_data] [batches]\n\n"</span>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</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="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></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="comment">//Do something with argv[1] and argv[2]</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  data_path = argv[2];</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  batches = std::strtol(argv[3], <span class="keyword">nullptr</span>, 0);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="comment">//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  graph << target_hint</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</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>(28U, 28U, 1U, 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="l00082"></a><span class="lineno"> 82</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  5U, 5U, 20U,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_w.npy"</span>),</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_b.npy"</span>),</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</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>, 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="l00088"></a><span class="lineno"> 88</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  5U, 5U, 50U,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_w.npy"</span>),</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_b.npy"</span>),</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</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>, 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="l00094"></a><span class="lineno"> 94</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  500U,</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/lenet_model/ip1_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/lenet_model/ip1_b.npy"</span>))</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>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>))</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  10U,</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/lenet_model/ip2_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/lenet_model/ip2_b.npy"</span>))</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</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>(0));</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  graph.graph_init(int_target_hint == 2);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  graph.run();</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph{};</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> };</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 124</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphLenetExample>(argc, argv);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</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#l00039">TensorShape.h:39</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>
+<a href="graph__lenet_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) 2017-2018 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.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</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> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</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__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="keyword">class </span>GraphLenetExample : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> {</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">void</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::string data_path; </div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> target = argc > 1 ? std::strtol(argv[1], <span class="keyword">nullptr</span>, 10) : 0;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">Target</a> target_hint = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab6dc388200717b5fae17342af13f5e41">set_target_hint</a>(target);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">FastMathHint</a> fast_math_hint = FastMathHint::DISABLED;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">if</span>(argc < 2)</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="comment">// Print help</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [path_to_data] [batches] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</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">else</span> <span class="keywordflow">if</span>(argc == 2)</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>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" [path_to_data] [batches] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  {</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="comment">//Do something with argv[1]</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">" [path_to_data] [batches] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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="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>  batches = std::strtol(argv[3], <span class="keyword">nullptr</span>, 0);</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">" [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">"No fast math info provided: disabling fast math\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>  <span class="comment">//Do something with argv[1] and argv[2]</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  data_path = argv[2];</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  batches = std::strtol(argv[3], <span class="keyword">nullptr</span>, 0);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  fast_math_hint = (std::strtol(argv[4], <span class="keyword">nullptr</span>, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;</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> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx</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>  << fast_math_hint</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">get_input_accessor</a>(<span class="stringliteral">""</span>))</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  5U, 5U, 20U,</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_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#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv1_b.npy"</span>),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv1"</span>)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool1"</span>)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  5U, 5U, 50U,</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_w.npy"</span>),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/conv2_b.npy"</span>),</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2"</span>)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool2"</span>)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  500U,</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip1_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#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip1_b.npy"</span>))</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"ip1"</span>)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"relu"</span>)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  10U,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip2_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#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/lenet_model/ip2_b.npy"</span>))</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"ip2"</span>)</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">SoftmaxLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"prob"</span>)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">get_output_accessor</a>(<span class="stringliteral">""</span>));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  GraphConfig config;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  config.use_tuner = (target == 2);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  graph.finalize(target_hint, config);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  graph.run();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  }</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> </div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph{ 0, <span class="stringliteral">"LeNet"</span> };</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> };</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00141"></a><span class="lineno"><a class="line" href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 141</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphLenetExample>(argc, argv);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00336">Layers.h:336</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab6dc388200717b5fae17342af13f5e41"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab6dc388200717b5fae17342af13f5e41">arm_compute::graph_utils::set_target_hint</a></div><div class="ttdeci">graph::Target 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#l00370">GraphUtils.h:370</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="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#l00096">GraphUtils.h:96</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#l00330">GraphUtils.h:330</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="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="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">Graph.h</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#l00701">Types.h:701</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#l00276">GraphUtils.h:276</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#l00234">GraphUtils.h:234</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_input_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">arm_compute::graph::frontend::InputLayer</a></div><div class="ttdoc">Input Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00045">Layers.h:45</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_8h_xhtml"><div class="ttname"><a href="graph_8h.xhtml">graph.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00062">Utils.h:62</a></div></div>
-<div class="ttc" id="graph__lenet_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for LeNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00124">graph_lenet.cpp:124</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#l00491">Types.h:491</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#l00044">Graph.h:44</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00033">Utils.cpp:33</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#l00039">GraphUtils.h:39</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="_nodes_8h_xhtml"><div class="ttname"><a href="_nodes_8h.xhtml">Nodes.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a9984cc47279cdb732b7b83caf0627de6"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">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, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true)</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#l00299">GraphUtils.h:299</a></div></div>
+<div class="ttc" id="graph__lenet_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__lenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for LeNet. </div><div class="ttdef"><b>Definition:</b> <a href="graph__lenet_8cpp_source.xhtml#l00141">graph_lenet.cpp:141</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00094">Layers.h:94</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a31488d29805a596498c0234ae392d35d"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">arm_compute::graph::Target</a></div><div class="ttdeci">Target</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_1graph_1_1frontend_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">arm_compute::graph::frontend::ConvolutionLayer</a></div><div class="ttdoc">Convolution Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00158">Layers.h:158</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_ac85a46f3ebd3ab09f576a994ac2dce11"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">arm_compute::graph::FastMathHint</a></div><div class="ttdeci">FastMathHint</div><div class="ttdoc">Enable or disable fast math for Convolution layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00118">Types.h:118</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_cast_8h_source.xhtml#l00031">Cast.h:31</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#l00041">GraphUtils.h:41</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">arm_compute::graph::frontend::SoftmaxLayer</a></div><div class="ttdoc">Softmax Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00411">Layers.h:411</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">arm_compute::graph::frontend::OutputLayer</a></div><div class="ttdoc">Output Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00070">Layers.h:70</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">arm_compute::graph::frontend::FullyConnectedLayer</a></div><div class="ttdoc">Fully Connected Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00281">Layers.h:281</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">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, DataLayout file_layout=DataLayout::NCHW)</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#l00275">GraphUtils.h:275</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">arm_compute::graph::frontend::Stream</a></div><div class="ttdoc">Stream frontend class to construct simple graphs in a stream fashion. </div><div class="ttdef"><b>Definition:</b> <a href="_stream_8h_source.xhtml#l00045">Stream.h:45</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_1_1frontend_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00031">ILayer.h:31</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#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#l00588">Types.h:588</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>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer & set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
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<li class="navelem"><a class="el" href="dir_d28a4824dc47e487b107a5db32ef43c4.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__lenet_8cpp.xhtml">graph_lenet.cpp</a></li>
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