arm_compute v18.05
diff --git a/documentation/graph__mobilenet_8cpp_source.xhtml b/documentation/graph__mobilenet_8cpp_source.xhtml
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--- a/documentation/graph__mobilenet_8cpp_source.xhtml
+++ b/documentation/graph__mobilenet_8cpp_source.xhtml
@@ -40,7 +40,7 @@
<tr style="height: 56px;">
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<div id="projectname">Compute Library
-  <span id="projectnumber">18.03</span>
+  <span id="projectnumber">18.05</span>
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@@ -117,51 +117,52 @@
<div class="title">graph_mobilenet.cpp</div> </div>
</div><!--header-->
<div class="contents">
-<a href="graph__mobilenet_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>GraphMobilenetExample : <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>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</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="l00055"></a><span class="lineno"> 55</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="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2">ConvolutionMethodHint</a> convolution_hint = <a class="code" href="namespacearm__compute.xhtml#a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3">ConvolutionMethodHint::GEMM</a>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordtype">int</span> model_id = (argc > 2) ? std::strtol(argv[2], <span class="keyword">nullptr</span>, 10) : 0;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(model_id > 1, <span class="stringliteral">"Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)"</span>);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordtype">float</span> depth_scale = (model_id == 0) ? 1.f : 0.75;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> spatial_size = (model_id == 0) ? 224 : 160;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  std::string model_path = (model_id == 0) ? <span class="stringliteral">"/cnn_data/mobilenet_v1_1_224_model/"</span> : <span class="stringliteral">"/cnn_data/mobilenet_v1_075_160_model/"</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</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>  <span class="comment">// Print help</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [model] [path_to_data] [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  std::cout << <span class="stringliteral">"No model ID provided: using MobileNetV1_1.0_224\n\n"</span>;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  {</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" [model] [path_to_data] [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::cout << <span class="stringliteral">"No model ID provided: using MobileNetV1_1.0_224\n\n"</span>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" "</span> << argv[2] << <span class="stringliteral">" [path_to_data] [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\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 == 4)</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[3];</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" "</span> << argv[2] << <span class="stringliteral">" "</span> << argv[3] << <span class="stringliteral">" [image] [labels]\n\n"</span>;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  std::cout << <span class="stringliteral">"No image provided: using random values\n\n"</span>;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 5)</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>  data_path = argv[3];</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  image = argv[4];</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</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="l00095"></a><span class="lineno"> 95</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</span>;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">else</span></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>  data_path = argv[3];</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  image = argv[4];</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  label = argv[5];</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// Add model path to data path</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">if</span>(!data_path.empty())</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  data_path += model_path;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> </div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  graph << target_hint</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  << convolution_hint</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</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>(spatial_size, spatial_size, 3U, 1U), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</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>))</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>  3U, 3U, 32U * depth_scale,</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">"Conv2d_0_weights.npy"</span>),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</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>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  0.001f, <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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f))</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_1"</span>, 64 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_2"</span>, 128 * depth_scale, <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#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_3"</span>, 128 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_4"</span>, 256 * depth_scale, <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#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_5"</span>, 256 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_6"</span>, 512 * depth_scale, <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#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_7"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_8"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_9"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_10"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_11"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_12"</span>, 1024 * depth_scale, <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#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_13"</span>, 1024 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</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#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>))</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  1U, 1U, 1001U,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_weights.npy"</span>),</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_biases.npy"</span>),</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_reshape_layer.xhtml">ReshapeLayer</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1001U))</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</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="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</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="l00149"></a><span class="lineno"> 149</span>  graph.graph_init(int_target_hint == 2);</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> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  graph.run();</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  }</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph{};</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>  <a class="code" href="classarm__compute_1_1graph_1_1_branch_layer.xhtml">BranchLayer</a> get_dwsc_node(<span class="keyword">const</span> std::string &data_path, std::string &&param_path,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> dwc_pad_stride_info, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info)</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>  std::string total_path = param_path + <span class="stringliteral">"_"</span>;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="classarm__compute_1_1graph_1_1_sub_graph.xhtml">SubGraph</a> sg;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  sg << <a class="code" href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer.xhtml">DepthwiseConvolutionLayer</a>(</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  3U, 3U,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_depthwise_weights.npy"</span>),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  dwc_pad_stride_info,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="keyword">true</span>)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</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, total_path + <span class="stringliteral">"depthwise_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  0.001f, <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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f))</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  1U, 1U, conv_filt,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_weights.npy"</span>),</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  conv_pad_stride_info)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  << <a class="code" href="classarm__compute_1_1graph_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</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, total_path + <span class="stringliteral">"pointwise_BatchNorm_moving_mean.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, total_path + <span class="stringliteral">"pointwise_BatchNorm_moving_variance.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, total_path + <span class="stringliteral">"pointwise_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a73a37a4970294106ed22e8f916ef3810">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  0.001f, <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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f));</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1_branch_layer.xhtml">BranchLayer</a>(std::move(sg));</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  }</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> };</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00203"></a><span class="lineno"><a class="line" href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 203</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1_reshape_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_reshape_layer.xhtml">arm_compute::graph::ReshapeLayer</a></div><div class="ttdoc">Reshape layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_reshape_layer_8h_source.xhtml#l00037">ReshapeLayer.h:37</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__mobilenet_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>GraphMobilenetExample : <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>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> </div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</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="l00054"></a><span class="lineno"> 54</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="l00055"></a><span class="lineno"> 55</span>  <a class="code" href="namespacearm__compute.xhtml#a683661ae75dcb7aef16b9c9bde31517d">ConvolutionMethod</a> convolution_hint = <a class="code" href="namespacearm__compute.xhtml#a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3">ConvolutionMethod::GEMM</a>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="namespacearm__compute_1_1graph.xhtml#a75ce9b1baad4303a53124d6f0795821f">DepthwiseConvolutionMethod</a> depthwise_convolution_hint = DepthwiseConvolutionMethod::OPTIMIZED_3x3;</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">// Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordtype">int</span> model_id = (argc > 2) ? std::strtol(argv[2], <span class="keyword">nullptr</span>, 10) : 0;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(model_id > 1, <span class="stringliteral">"Invalid model ID. Model must be 0 (MobileNetV1_1.0_224) or 1 (MobileNetV1_0.75_160)"</span>);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">int</span> layout_id = (argc > 3) ? std::strtol(argv[3], <span class="keyword">nullptr</span>, 10) : 0;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a>(layout_id > 1, <span class="stringliteral">"Invalid layout ID. Layout must be 0 (NCHW) or 1 (NHWC)"</span>);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> </div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">float</span> depth_scale = (model_id == 0) ? 1.f : 0.75;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> spatial_size = (model_id == 0) ? 224 : 160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  std::string model_path = (model_id == 0) ? <span class="stringliteral">"/cnn_data/mobilenet_v1_1_224_model/"</span> : <span class="stringliteral">"/cnn_data/mobilenet_v1_075_160_model/"</span>;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  TensorDescriptor input_descriptor_nchw = TensorDescriptor(TensorShape(spatial_size, spatial_size, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  TensorDescriptor input_descriptor_nhwc = TensorDescriptor(TensorShape(3U, spatial_size, spatial_size, 1U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>).set_layout(<a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  TensorDescriptor input_descriptor = (layout_id == 0) ? input_descriptor_nchw : input_descriptor_nhwc;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">if</span>(argc < 2)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  {</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Print help</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::cout << <span class="stringliteral">"No model ID provided: using MobileNetV1_1.0_224\n\n"</span>;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  std::cout << <span class="stringliteral">"No data layout provided: using NCHW\n\n"</span>;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  }</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::cout << <span class="stringliteral">"No model ID provided: using MobileNetV1_1.0_224\n\n"</span>;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  std::cout << <span class="stringliteral">"No data layout provided: using NCHW\n\n"</span>;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</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>  std::cout << <span class="stringliteral">"Usage: "</span> << argv[0] << <span class="stringliteral">" "</span> << argv[1] << <span class="stringliteral">" "</span> << argv[2] << <span class="stringliteral">" [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::cout << <span class="stringliteral">"No data layout provided: using NCHW\n\n"</span>;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  {</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  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">" [path_to_data] [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  std::cout << <span class="stringliteral">"No data folder provided: using random values\n\n"</span>;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 5)</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  {</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  data_path = argv[4];</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</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">" [image] [labels] [fast_math_hint]\n\n"</span>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  std::cout << <span class="stringliteral">"No image provided: using random values\n\n"</span>;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</span>;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  }</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 6)</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>  data_path = argv[4];</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  image = argv[5];</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</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="l00111"></a><span class="lineno"> 111</span>  std::cout << <span class="stringliteral">"No text file with labels provided: skipping output accessor\n\n"</span>;</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>  <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 7)</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  {</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  data_path = argv[4];</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  image = argv[5];</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  label = argv[6];</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</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="l00119"></a><span class="lineno"> 119</span>  std::cout << <span class="stringliteral">"No fast math info provided: disabling fast math\n\n"</span>;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="keywordflow">else</span></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>  data_path = argv[4];</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  image = argv[5];</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  label = argv[6];</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  fast_math_hint = (std::strtol(argv[7], <span class="keyword">nullptr</span>, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  }</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// Add model path to data path</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordflow">if</span>(!data_path.empty())</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>  data_path += model_path;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  }</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  graph << target_hint</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  << convolution_hint</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << depthwise_convolution_hint</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  << fast_math_hint</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</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>))</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  3U, 3U, 32U * depth_scale,</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0"</span>)</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</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="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  0.001f)</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0/BatchNorm"</span>)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0/Relu6"</span>);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_1"</span>, 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_2"</span>, 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_3"</span>, 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_4"</span>, 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_5"</span>, 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_6"</span>, 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_7"</span>, 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_8"</span>, 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_9"</span>, 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_10"</span>, 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_11"</span>, 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_12"</span>, 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  graph << get_dwsc_node(data_path, <span class="stringliteral">"Conv2d_13"</span>, 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), PadStrideInfo(1, 1, 0, 0));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</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">"Logits/AvgPool_1a"</span>)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  1U, 1U, 1001U,</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_biases.npy"</span>),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Logits/Conv2d_1c_1x1"</span>)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(TensorShape(1001U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Reshape"</span>)</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_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">"Softmax"</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_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="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  GraphConfig config;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  config.use_tuner = (target == 2);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  graph.finalize(target_hint, config);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  graph.run();</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> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="keyword">private</span>:</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_stream.xhtml">Stream</a> graph{ 0, <span class="stringliteral">"MobileNetV1"</span> };</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> </div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a> get_dwsc_node(<span class="keyword">const</span> std::string &data_path, std::string &&param_path,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  std::string total_path = param_path + <span class="stringliteral">"_"</span>;</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_sub_stream.xhtml">SubStream</a> sg(graph);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  sg << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">DepthwiseConvolutionLayer</a>(</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  3U, 3U,</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_depthwise_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  dwc_pad_stride_info)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/depthwise"</span>)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</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="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_gamma.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, total_path + <span class="stringliteral">"depthwise_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  0.001f)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/BatchNorm"</span>)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/Relu6"</span>)</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_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  1U, 1U, conv_filt,</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  conv_pad_stride_info)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/Conv2D"</span>)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</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="l00220"></a><span class="lineno"> 220</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  0.001f)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/BatchNorm"</span>)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</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#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/Relu6"</span>);</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a>(std::move(sg));</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> };</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00243"></a><span class="lineno"><a class="line" href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 243</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> {</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</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_1frontend_1_1_reshape_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">arm_compute::graph::frontend::ReshapeLayer</a></div><div class="ttdoc">Reshape Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00360">Layers.h:360</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff"><div class="ttname"><a href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">arm_compute::DimensionRoundingType::CEIL</a></div><div class="ttdoc">Ceil rounding. </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="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="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">arm_compute::graph::frontend::DepthwiseConvolutionLayer</a></div><div class="ttdoc">Depthwise Convolution Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00217">Layers.h:217</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="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
-<div class="ttc" id="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">Graph.h</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00701">Types.h:701</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a9216738b309b6b230b7ba8bca5ba7477"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a9216738b309b6b230b7ba8bca5ba7477">arm_compute::graph_utils::set_target_hint</a></div><div class="ttdeci">graph::TargetHint set_target_hint(int target)</div><div class="ttdoc">Utility function to return the TargetHint. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00276">GraphUtils.h:276</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_branch_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_branch_layer.xhtml">arm_compute::graph::BranchLayer</a></div><div class="ttdoc">Branch Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="_branch_layer_8h_source.xhtml#l00043">BranchLayer.h:43</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="namespacearm__compute_xhtml_a683661ae75dcb7aef16b9c9bde31517d"><div class="ttname"><a href="namespacearm__compute.xhtml#a683661ae75dcb7aef16b9c9bde31517d">arm_compute::ConvolutionMethod</a></div><div class="ttdeci">ConvolutionMethod</div><div class="ttdoc">Available ConvolutionMethod. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01220">Types.h:1220</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="namespacearm__compute_1_1graph_xhtml_a9a92cf6a83b4d54786334cc37a7391a2"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2">arm_compute::graph::ConvolutionMethodHint</a></div><div class="ttdeci">ConvolutionMethodHint</div><div class="ttdoc">Convolution method hint to the graph executor. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</a></div></div>
+<div class="ttc" id="graph_8h_xhtml"><div class="ttname"><a href="graph_8h.xhtml">graph.h</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a75ce9b1baad4303a53124d6f0795821f"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a75ce9b1baad4303a53124d6f0795821f">arm_compute::graph::DepthwiseConvolutionMethod</a></div><div class="ttdeci">DepthwiseConvolutionMethod</div><div class="ttdoc">Supported Depthwise Convolution layer methods. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00110">Types.h:110</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="graph__mobilenet_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for MobileNetV1. </div><div class="ttdef"><b>Definition:</b> <a href="graph__mobilenet_8cpp_source.xhtml#l00203">graph_mobilenet.cpp:203</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_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="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="graph__mobilenet_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for MobileNetV1. </div><div class="ttdef"><b>Definition:</b> <a href="graph__mobilenet_8cpp_source.xhtml#l00243">graph_mobilenet.cpp:243</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="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width. </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="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier ( ) </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"><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="core_2_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="core_2_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="core_2_error_8h_source.xhtml#l00297">Error.h:297</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">arm_compute::graph::PoolingLayer</a></div><div class="ttdoc">Pooling layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_pooling_layer_8h_source.xhtml#l00037">PoolingLayer.h:37</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833b"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">arm_compute::graph::TargetHint</a></div><div class="ttdeci">TargetHint</div><div class="ttdoc">< Execution hint to the graph executor </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00076">Types.h:76</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00044">TensorInfo.h:44</a></div></div>
-<div class="ttc" id="_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_1_depthwise_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_depthwise_convolution_layer.xhtml">arm_compute::graph::DepthwiseConvolutionLayer</a></div><div class="ttdoc">Convolution layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_depthwise_convolution_layer_8h_source.xhtml#l00042">DepthwiseConvolutionLayer.h:42</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">arm_compute::graph::ConvolutionLayer</a></div><div class="ttdoc">Convolution layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_convolution_layer_8h_source.xhtml#l00042">ConvolutionLayer.h:42</a></div></div>
-<div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00588">Types.h:588</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels. </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_a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3"><div class="ttname"><a href="namespacearm__compute.xhtml#a683661ae75dcb7aef16b9c9bde31517da5174aac3927faa9ee34befb7fc87a9e3">arm_compute::ConvolutionMethod::GEMM</a></div><div class="ttdoc">Convolution using GEMM. </div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a></div><div class="ttdoc">Tensor class. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8h_source.xhtml#l00039">Tensor.h:39</a></div></div>
-<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_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>
+<div class="ttc" id="_error_8h_xhtml_a5bbdcf574d3f5e412fa6a1117911e67b"><div class="ttname"><a href="_error_8h.xhtml#a5bbdcf574d3f5e412fa6a1117911e67b">ARM_COMPUTE_ERROR_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_MSG(cond,...)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00319">Error.h:319</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__mobilenet_8cpp.xhtml">graph_mobilenet.cpp</a></li>
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<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.11 </li>
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