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<div id="projectname">Compute Library
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<div class="title">graph_resnet50.cpp</div> </div>
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<div class="contents">
-<a href="graph__resnet50_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>GraphResNet50Example : <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; <span class="comment">/* Path to the trainable data */</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  std::string image; <span class="comment">/* Image data */</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  std::string label; <span class="comment">/* Label data */</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">false</span> <span class="comment">/* Do not convert to BGR */</span>);</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">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</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="l00057"></a><span class="lineno"> 57</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="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</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>  <span class="comment">// Print help</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [path_to_data] [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</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>  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="l00069"></a><span class="lineno"> 69</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</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>  data_path = argv[2];</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</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="l00075"></a><span class="lineno"> 75</span>  std::cout << <span class="stringliteral">"No image provided: using random values\n\n"</span>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</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>  data_path = argv[2];</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  image = argv[3];</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</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="l00082"></a><span class="lineno"> 82</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  data_path = argv[2];</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  image = argv[3];</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  label = argv[4];</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> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  graph << target_hint</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(224U, 224U, 3U, 1U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">get_input_accessor</a>(image, std::move(preprocessor), <span class="keyword">false</span> <span class="comment">/* Do not convert to BGR */</span>))</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  7U, 7U, 64U,</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/resnet50_model/conv1_weights.npy"</span>),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</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>(2, 2, 3, 3))</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"</span>),</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/resnet50_model/conv1_BatchNorm_moving_variance.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/resnet50_model/conv1_BatchNorm_gamma.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#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  0.0000100099996416f)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</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="l00106"></a><span class="lineno"> 106</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, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</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>  add_residual_block(data_path, <span class="stringliteral">"block1"</span>, 64, 3, 2);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  add_residual_block(data_path, <span class="stringliteral">"block2"</span>, 128, 4, 2);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  add_residual_block(data_path, <span class="stringliteral">"block3"</span>, 256, 6, 2);</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  add_residual_block(data_path, <span class="stringliteral">"block4"</span>, 512, 3, 1);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  graph << <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#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>))</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  1U, 1U, 1000U,</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/resnet50_model/logits_weights.npy"</span>),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/logits_biases.npy"</span>),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</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="l00119"></a><span class="lineno"> 119</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_flatten_layer.xhtml">FlattenLayer</a>()</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</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="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</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="l00124"></a><span class="lineno"> 124</span>  graph.graph_init(int_target_hint == 2);</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_1_graph.xhtml">Graph</a> graph{};</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>  <span class="keywordtype">void</span> add_residual_block(<span class="keyword">const</span> std::string &data_path, <span class="keyword">const</span> std::string &name, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> base_depth, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_units; ++i)</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  std::stringstream unit;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  unit << <span class="stringliteral">"/cnn_data/resnet50_model/"</span> << name << <span class="stringliteral">"_unit_"</span> << (i + 1) << <span class="stringliteral">"_bottleneck_v1_"</span>;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  std::string unit_name = unit.str();</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> middle_stride = 1;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordflow">if</span>(i == (num_units - 1))</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  {</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  middle_stride = stride;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  }</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="classarm__compute_1_1graph_1_1_sub_graph.xhtml">SubGraph</a> right;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  right << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  1U, 1U, base_depth,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv1_weights.npy"</span>),</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</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="l00156"></a><span class="lineno"> 156</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv1_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv1_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv1_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv1_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  0.0000100099996416f)</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> </div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  3U, 3U, base_depth,</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv2_weights.npy"</span>),</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(middle_stride, middle_stride, 1, 1))</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv2_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv2_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv2_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv2_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  0.0000100099996416f)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</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="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  1U, 1U, base_depth * 4,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv3_weights.npy"</span>),</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</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="l00182"></a><span class="lineno"> 182</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv3_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv3_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv3_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"conv3_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  0.0000100099996416f);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">if</span>(i == 0)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  {</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="classarm__compute_1_1graph_1_1_sub_graph.xhtml">SubGraph</a> left;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  left << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  1U, 1U, base_depth * 4,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"shortcut_weights.npy"</span>),</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</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="l00197"></a><span class="lineno"> 197</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"shortcut_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"shortcut_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"shortcut_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, unit_name + <span class="stringliteral">"shortcut_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  0.0000100099996416f);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1_residual_layer.xhtml">ResidualLayer</a>(std::move(left), std::move(right));</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(middle_stride > 1)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="classarm__compute_1_1graph_1_1_sub_graph.xhtml">SubGraph</a> left;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  left << <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>, 1, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(middle_stride, middle_stride, 0, 0), <span class="keyword">true</span>))</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</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#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">ActivationLayerInfo::ActivationFunction::LINEAR</a>, 1.f, 0.f));</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1_residual_layer.xhtml">ResidualLayer</a>(std::move(left), std::move(right));</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  }</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  {</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1_residual_layer.xhtml">ResidualLayer</a>(std::move(right));</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  }</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  graph << <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="l00220"></a><span class="lineno"> 220</span>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> };</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00229"></a><span class="lineno"><a class="line" href="graph__resnet50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 229</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__resnet50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphResNet50Example>(argc, argv);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1_residual_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_residual_layer.xhtml">arm_compute::graph::ResidualLayer</a></div><div class="ttdoc">Branch Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_residual_layer_8h_source.xhtml#l00042">ResidualLayer.h:42</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#l00039">TensorShape.h:39</a></div></div>
+<a href="graph__resnet50_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> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</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="l00028"></a><span class="lineno"> 28</span> </div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include <cstdlib></span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</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="l00032"></a><span class="lineno"> 32</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="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="keyword">class </span>GraphResNet50Example : <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="l00041"></a><span class="lineno"> 41</span> {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</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="l00044"></a><span class="lineno"> 44</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  std::string data_path; <span class="comment">/* Path to the trainable data */</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  std::string image; <span class="comment">/* Image data */</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  std::string label; <span class="comment">/* Label data */</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> </div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">const</span> std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">false</span> <span class="comment">/* Do not convert to BGR */</span>);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</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="l00056"></a><span class="lineno"> 56</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="l00057"></a><span class="lineno"> 57</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">FastMathHint</a> fast_math_hint = FastMathHint::DISABLED;</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">// Parse arguments</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</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>  <span class="comment">// Print help</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  }</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</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>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" [path_to_data] [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  }</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</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>  data_path = argv[2];</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</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] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::cout << <span class="stringliteral">"No image provided: using random values\n\n"</span>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</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>  data_path = argv[2];</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  image = argv[3];</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</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] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</span>;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 5)</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  {</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  data_path = argv[2];</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  image = argv[3];</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  label = argv[4];</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</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">" "</span> << argv[4] << <span class="stringliteral">" [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  std::cout << <span class="stringliteral">"No fast math info provided: disabling fast math\n\n"</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="keywordflow">else</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  data_path = argv[2];</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  image = argv[3];</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  label = argv[4];</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  fast_math_hint = (std::strtol(argv[5], <span class="keyword">nullptr</span>, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  }</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  graph << target_hint</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  << fast_math_hint</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">get_input_accessor</a>(image, std::move(preprocessor), <span class="keyword">false</span> <span class="comment">/* Do not convert to BGR */</span>))</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  7U, 7U, 64U,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_weights.npy"</span>),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  PadStrideInfo(2, 2, 3, 3))</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv1/convolution"</span>)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  0.0000100099996416f)</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv1/BatchNorm"</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_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">"conv1/Relu"</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_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool1/MaxPool"</span>);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  add_residual_block(data_path, <span class="stringliteral">"block1"</span>, 64, 3, 2);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  add_residual_block(data_path, <span class="stringliteral">"block2"</span>, 128, 4, 2);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  add_residual_block(data_path, <span class="stringliteral">"block3"</span>, 256, 6, 2);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  add_residual_block(data_path, <span class="stringliteral">"block4"</span>, 512, 3, 1);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  graph << <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#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool5"</span>)</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  1U, 1U, 1000U,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/logits_weights.npy"</span>),</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnet50_model/logits_biases.npy"</span>),</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"logits/convolution"</span>)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">FlattenLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"predictions/Reshape"</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_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">"predictions/Softmax"</span>)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</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>(label, 5));</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  GraphConfig config;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  config.use_tuner = (target == 2);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  graph.finalize(target_hint, config);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  graph.run();</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph{ 0, <span class="stringliteral">"ResNet50"</span> };</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="keywordtype">void</span> add_residual_block(<span class="keyword">const</span> std::string &data_path, <span class="keyword">const</span> std::string &name, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> base_depth, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride)</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  {</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_units; ++i)</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>  std::stringstream unit_path_ss;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  unit_path_ss << <span class="stringliteral">"/cnn_data/resnet50_model/"</span> << name << <span class="stringliteral">"_unit_"</span> << (i + 1) << <span class="stringliteral">"_bottleneck_v1_"</span>;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  std::stringstream unit_name_ss;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  unit_name_ss << name << <span class="stringliteral">"/unit"</span> << (i + 1) << <span class="stringliteral">"/bottleneck_v1/"</span>;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  std::string unit_path = unit_path_ss.str();</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  std::string unit_name = unit_name_ss.str();</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> middle_stride = 1;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordflow">if</span>(i == (num_units - 1))</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  middle_stride = stride;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> right(graph);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  right << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  1U, 1U, base_depth,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_weights.npy"</span>),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/convolution"</span>)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  0.0000100099996416f)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/BatchNorm"</span>)</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</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>(unit_name + <span class="stringliteral">"conv1/Relu"</span>)</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  3U, 3U, base_depth,</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_weights.npy"</span>),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  PadStrideInfo(middle_stride, middle_stride, 1, 1))</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/convolution"</span>)</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  0.0000100099996416f)</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/BatchNorm"</span>)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</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>(unit_name + <span class="stringliteral">"conv1/Relu"</span>)</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  1U, 1U, base_depth * 4,</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_weights.npy"</span>),</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv3/convolution"</span>)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  0.0000100099996416f)</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/BatchNorm"</span>);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordflow">if</span>(i == 0)</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  {</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> left(graph);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  left << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  1U, 1U, base_depth * 4,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"shortcut_weights.npy"</span>),</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"shortcut/convolution"</span>)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"shortcut_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"shortcut_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"shortcut_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"shortcut_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  0.0000100099996416f)</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"shortcut/BatchNorm"</span>);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a>(<a class="code" href="namespacearm__compute.xhtml#afa20b6a7f4383003babd690f026f22dca9eeb52badb613229884838847294b90d">BranchMergeMethod::ADD</a>, std::move(left), std::move(right)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  }</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(middle_stride > 1)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> left(graph);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  left << <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>, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), <span class="keyword">true</span>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"shortcut/MaxPool"</span>);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a>(<a class="code" href="namespacearm__compute.xhtml#afa20b6a7f4383003babd690f026f22dca9eeb52badb613229884838847294b90d">BranchMergeMethod::ADD</a>, std::move(left), std::move(right)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> left(graph);</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a>(<a class="code" href="namespacearm__compute.xhtml#afa20b6a7f4383003babd690f026f22dca9eeb52badb613229884838847294b90d">BranchMergeMethod::ADD</a>, std::move(left), std::move(right)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  graph << <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>(unit_name + <span class="stringliteral">"Relu"</span>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  }</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  }</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> };</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00257"></a><span class="lineno"><a class="line" href="graph__resnet50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 257</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__resnet50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> {</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphResNet50Example>(argc, argv);</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">arm_compute::graph::frontend::BranchLayer</a></div><div class="ttdoc">Branch Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00435">Layers.h:435</a></div></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="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">arm_compute::graph::frontend::SubStream</a></div><div class="ttdoc">Sub stream class. </div><div class="ttdef"><b>Definition:</b> <a href="_sub_stream_8h_source.xhtml#l00047">SubStream.h:47</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_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">arm_compute::graph::BatchNormalizationLayer</a></div><div class="ttdoc">BatchNormalization layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_batch_normalization_layer_8h_source.xhtml#l00038">BatchNormalizationLayer.h:38</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#l00300">GraphUtils.h:300</a></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_a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe"><div class="ttname"><a href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">arm_compute::DimensionRoundingType::FLOOR</a></div><div class="ttdoc">Floor rounding. </div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_flatten_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_flatten_layer.xhtml">arm_compute::graph::FlattenLayer</a></div><div class="ttdoc">Flatten layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_flatten_layer_8h_source.xhtml#l00037">FlattenLayer.h:37</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="graph__resnet50_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__resnet50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for ResNet50. </div><div class="ttdef"><b>Definition:</b> <a href="graph__resnet50_8cpp_source.xhtml#l00229">graph_resnet50.cpp:229</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="graph__resnet50_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__resnet50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for ResNet50. </div><div class="ttdef"><b>Definition:</b> <a href="graph__resnet50_8cpp_source.xhtml#l00257">graph_resnet50.cpp:257</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="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#l00256">GraphUtils.h:256</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="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="namespacearm__compute_xhtml_afa20b6a7f4383003babd690f026f22dca9eeb52badb613229884838847294b90d"><div class="ttname"><a href="namespacearm__compute.xhtml#afa20b6a7f4383003babd690f026f22dca9eeb52badb613229884838847294b90d">arm_compute::FixedPointOp::ADD</a></div><div class="ttdoc">Addition. </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="namespacearm__compute_xhtml_a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a"><div class="ttname"><a href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">arm_compute::PoolingType::AVG</a></div><div class="ttdoc">Average Pooling. </div></div>
-<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">arm_compute::ActivationLayerInfo::ActivationFunction::LINEAR</a></div><div class="ttdoc">Linear ( ) </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="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="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="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00118">Layers.h:118</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_1_sub_graph_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_sub_graph.xhtml">arm_compute::graph::SubGraph</a></div><div class="ttdoc">SubGraph class. </div><div class="ttdef"><b>Definition:</b> <a href="_sub_graph_8h_source.xhtml#l00042">SubGraph.h:42</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">arm_compute::graph::frontend::FlattenLayer</a></div><div class="ttdoc">Flatten Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00264">Layers.h:264</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__resnet50_8cpp.xhtml">graph_resnet50.cpp</a></li>
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<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
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