arm_compute v20.02
diff --git a/documentation/graph__resnext50_8cpp_source.xhtml b/documentation/graph__resnext50_8cpp_source.xhtml
index 862a813..9acad1a 100644
--- a/documentation/graph__resnext50_8cpp_source.xhtml
+++ b/documentation/graph__resnext50_8cpp_source.xhtml
@@ -40,7 +40,7 @@
<img alt="Compute Library" src="https://raw.githubusercontent.com/ARM-software/ComputeLibrary/gh-pages/ACL_logo.png" style="max-width: 100%;margin-top: 15px;margin-left: 10px"/>
<td style="padding-left: 0.5em;">
<div id="projectname">
-  <span id="projectnumber">19.11.1</span>
+  <span id="projectnumber">20.02</span>
</div>
</td>
</tr>
@@ -100,35 +100,35 @@
<div class="title">graph_resnext50.cpp</div> </div>
</div><!--header-->
<div class="contents">
-<a href="graph__resnext50_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) 2018-2019 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="_common_graph_options_8h.xhtml">utils/CommonGraphOptions.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="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</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_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</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__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment"></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment">/** Example demonstrating how to implement ResNeXt50 network using the Compute Library's graph API */</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">class </span>GraphResNeXt50Example : <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="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  GraphResNeXt50Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">"ResNeXt50"</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">bool</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="l00043"></a><span class="lineno"> 43</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  cmd_parser.validate();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  common_params = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a2593e1f13f425f627658900657f73dc3">consume_common_graph_parameters</a>(common_opts);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// Checks</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_error_8h.xhtml#a292b758f9eba8b487d71eae4b37326fc">ARM_COMPUTE_EXIT_ON_MSG</a>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a>(common_params.data_type), <span class="stringliteral">"QASYMM8 not supported for this graph"</span>);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  std::cout << common_params << std::endl;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::string data_path = common_params.data_path;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> TensorShape tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(TensorShape(224U, 224U, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, common_params.data_layout);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> input_descriptor = <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a>(tensor_shape, common_params.data_type).<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">set_layout</a>(common_params.data_layout);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  graph << common_params.target</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  << common_params.fast_math_hint</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">get_input_accessor</a>(common_params))</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">ScaleLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/bn_data_mul.npy"</span>),</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/bn_data_add.npy"</span>))</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"bn_data/Scale"</span>)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  7U, 7U, 64U,</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/conv0_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/conv0_biases.npy"</span>),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  PadStrideInfo(2, 2, 2, 3, 2, 3, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv0/Convolution"</span>)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv0/Relu"</span>)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool0"</span>);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  add_residual_block(data_path, weights_layout, <span class="comment">/*ofm*/</span> 256, <span class="comment">/*stage*/</span> 1, <span class="comment">/*num_unit*/</span> 3, <span class="comment">/*stride_conv_unit1*/</span> 1);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  add_residual_block(data_path, weights_layout, 512, 2, 4, 2);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  add_residual_block(data_path, weights_layout, 1024, 3, 6, 2);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  add_residual_block(data_path, weights_layout, 2048, 4, 3, 2);</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>  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">"pool1"</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">FlattenLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"predictions/Reshape"</span>)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</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#a10e9c53263d766cbd37e4e37f5e8091e">get_npy_output_accessor</a>(common_params.labels, TensorShape(2048U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">num_threads</a> = common_params.threads;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">use_tuner</a> = common_params.enable_tuner;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">tuner_mode</a> = common_params.tuner_mode;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">tuner_file</a> = common_params.tuner_file;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  graph.finalize(common_params.target, config);</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>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</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>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  graph.run();</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> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> </div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="keywordtype">void</span> add_residual_block(<span class="keyword">const</span> std::string &data_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> base_depth, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stage, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_conv_unit1)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_units; ++i)</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  {</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  std::stringstream unit_path_ss;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  unit_path_ss << <span class="stringliteral">"/cnn_data/resnext50_model/stage"</span> << stage << <span class="stringliteral">"_unit"</span> << (i + 1) << <span class="stringliteral">"_"</span>;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::string unit_path = unit_path_ss.str();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  std::stringstream unit_name_ss;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  unit_name_ss << <span class="stringliteral">"stage"</span> << stage << <span class="stringliteral">"/unit"</span> << (i + 1) << <span class="stringliteral">"/"</span>;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  std::string unit_name = unit_name_ss.str();</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>  PadStrideInfo pad_grouped_conv(1, 1, 1, 1);</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="keywordflow">if</span>(i == 0)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  {</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>);</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> right(graph);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  right << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  1U, 1U, base_depth / 2,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_biases.npy"</span>),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  PadStrideInfo(1, 1, 0, 0))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/convolution"</span>)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/Relu"</span>)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  3U, 3U, base_depth / 2,</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  pad_grouped_conv, 32)</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/convolution"</span>)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">ScaleLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"bn2_mul.npy"</span>),</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"bn2_add.npy"</span>))</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/Scale"</span>)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/Relu"</span>)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> </div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  1U, 1U, base_depth,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_biases.npy"</span>),</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv3/convolution"</span>);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> left(graph);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="keywordflow">if</span>(i == 0)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  left << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  1U, 1U, base_depth,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"sc_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"sc/convolution"</span>)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">ScaleLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"sc_bn_mul.npy"</span>),</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"sc_bn_add.npy"</span>))</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"sc/scale"</span>);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">EltwiseLayer</a>(std::move(left), std::move(right), EltwiseOperation::Add).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"Relu"</span>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  }</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> };</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="comment"></span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="comment">/** Main program for ResNeXt50</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="comment"> *</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="comment"> * Model is based on:</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="comment"> * https://arxiv.org/abs/1611.05431</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="comment"> * "Aggregated Residual Transformations for Deep Neural Networks"</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="comment"> * Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment"> *</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment"> * @note To list all the possible arguments execute the binary appended with the --help option</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment"> *</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"> * @param[in] argc Number of arguments</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment"> * @param[in] argv Arguments</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment"> */</span></div><div class="line"><a name="l00200"></a><span class="lineno"><a class="line" href="graph__resnext50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 200</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__resnext50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphResNeXt50Example>(argc, argv);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00882">Layers.h:882</a></div></div>
+<a href="graph__resnext50_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) 2018-2020 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="_common_graph_options_8h.xhtml">utils/CommonGraphOptions.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="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</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_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</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__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment"></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment">/** Example demonstrating how to implement ResNeXt50 network using the Compute Library's graph API */</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">class </span>GraphResNeXt50Example : <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="l00036"></a><span class="lineno"> 36</span> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  GraphResNeXt50Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">"ResNeXt50"</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">bool</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="l00043"></a><span class="lineno"> 43</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  cmd_parser.validate();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  common_params = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a2593e1f13f425f627658900657f73dc3">consume_common_graph_parameters</a>(common_opts);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// Checks</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_error_8h.xhtml#a292b758f9eba8b487d71eae4b37326fc">ARM_COMPUTE_EXIT_ON_MSG</a>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a>(common_params.data_type), <span class="stringliteral">"QASYMM8 not supported for this graph"</span>);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  std::cout << common_params << std::endl;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::string data_path = common_params.data_path;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keyword">const</span> <span class="keyword">auto</span> operation_layout = common_params.data_layout;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> TensorShape tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(TensorShape(224U, 224U, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, operation_layout);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> input_descriptor = <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a>(tensor_shape, common_params.data_type).<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">set_layout</a>(operation_layout);</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">// Set weights trained layout</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</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>  graph << common_params.target</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  << common_params.fast_math_hint</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">get_input_accessor</a>(common_params))</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">ScaleLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/bn_data_mul.npy"</span>),</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/bn_data_add.npy"</span>))</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"bn_data/Scale"</span>)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  7U, 7U, 64U,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/conv0_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/resnext50_model/conv0_biases.npy"</span>),</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  PadStrideInfo(2, 2, 2, 3, 2, 3, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv0/Convolution"</span>)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv0/Relu"</span>)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool0"</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>  add_residual_block(data_path, weights_layout, <span class="comment">/*ofm*/</span> 256, <span class="comment">/*stage*/</span> 1, <span class="comment">/*num_unit*/</span> 3, <span class="comment">/*stride_conv_unit1*/</span> 1);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  add_residual_block(data_path, weights_layout, 512, 2, 4, 2);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  add_residual_block(data_path, weights_layout, 1024, 3, 6, 2);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  add_residual_block(data_path, weights_layout, 2048, 4, 3, 2);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> </div><div class="line"><a name="l00095"></a><span class="lineno"> 95</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>, operation_layout)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool1"</span>)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">FlattenLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"predictions/Reshape"</span>)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</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#a10e9c53263d766cbd37e4e37f5e8091e">get_npy_output_accessor</a>(common_params.labels, TensorShape(2048U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</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="comment">// Finalize graph</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">num_threads</a> = common_params.threads;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">use_tuner</a> = common_params.enable_tuner;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">tuner_mode</a> = common_params.tuner_mode;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">tuner_file</a> = common_params.tuner_file;</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>  graph.finalize(common_params.target, config);</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>  <span class="keywordflow">return</span> <span class="keyword">true</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> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  graph.run();</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">void</span> add_residual_block(<span class="keyword">const</span> std::string &data_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> base_depth, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stage, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride_conv_unit1)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  {</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < num_units; ++i)</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>  std::stringstream unit_path_ss;</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  unit_path_ss << <span class="stringliteral">"/cnn_data/resnext50_model/stage"</span> << stage << <span class="stringliteral">"_unit"</span> << (i + 1) << <span class="stringliteral">"_"</span>;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  std::string unit_path = unit_path_ss.str();</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>  std::stringstream unit_name_ss;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  unit_name_ss << <span class="stringliteral">"stage"</span> << stage << <span class="stringliteral">"/unit"</span> << (i + 1) << <span class="stringliteral">"/"</span>;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  std::string unit_name = unit_name_ss.str();</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  PadStrideInfo pad_grouped_conv(1, 1, 1, 1);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">if</span>(i == 0)</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  {</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  }</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> right(graph);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  right << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  1U, 1U, base_depth / 2,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv1_biases.npy"</span>),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/convolution"</span>)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/Relu"</span>)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  3U, 3U, base_depth / 2,</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv2_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  pad_grouped_conv, 32)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/convolution"</span>)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">ScaleLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"bn2_mul.npy"</span>),</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"bn2_add.npy"</span>))</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv1/Scale"</span>)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv2/Relu"</span>)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> </div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  1U, 1U, base_depth,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"conv3_biases.npy"</span>),</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"conv3/convolution"</span>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</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_sub_stream.xhtml">SubStream</a> left(graph);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">if</span>(i == 0)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  left << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  1U, 1U, base_depth,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"sc_weights.npy"</span>, weights_layout),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"sc/convolution"</span>)</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">ScaleLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"sc_bn_mul.npy"</span>),</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">"sc_bn_add.npy"</span>))</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"sc/scale"</span>);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">EltwiseLayer</a>(std::move(left), std::move(right), EltwiseOperation::Add).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"add"</span>);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">"Relu"</span>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  }</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> };</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="comment"></span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="comment">/** Main program for ResNeXt50</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="comment"> *</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="comment"> * Model is based on:</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="comment"> * https://arxiv.org/abs/1611.05431</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="comment"> * "Aggregated Residual Transformations for Deep Neural Networks"</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment"> * Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment"> *</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment"> * @note To list all the possible arguments execute the binary appended with the --help option</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"> *</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment"> * @param[in] argc Number of arguments</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment"> * @param[in] argv Arguments</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="comment"> */</span></div><div class="line"><a name="l00201"></a><span class="lineno"><a class="line" href="graph__resnext50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 201</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__resnext50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> {</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphResNeXt50Example>(argc, argv);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00882">Layers.h:882</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="graph__resnext50_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__resnext50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for ResNeXt50.</div><div class="ttdef"><b>Definition:</b> <a href="graph__resnext50_8cpp_source.xhtml#l00200">graph_resnext50.cpp:200</a></div></div>
+<div class="ttc" id="graph__resnext50_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__resnext50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for ResNeXt50.</div><div class="ttdef"><b>Definition:</b> <a href="graph__resnext50_8cpp_source.xhtml#l00201">graph_resnext50.cpp:201</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml">arm_compute::graph::GraphConfig</a></div><div class="ttdoc">Graph configuration structure Device target types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00078">Types.h:78</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="structarm__compute_1_1graph_1_1_graph_config_xhtml_a249f3f713c6ea8f564e760559cf509f4"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">arm_compute::graph::GraphConfig::tuner_mode</a></div><div class="ttdeci">CLTunerMode tuner_mode</div><div class="ttdoc">Tuner mode to be used by the CL tuner.</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="structarm__compute_1_1graph_1_1_graph_config_xhtml_a249f3f713c6ea8f564e760559cf509f4"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">arm_compute::graph::GraphConfig::tuner_mode</a></div><div class="ttdeci">CLTunerMode tuner_mode</div><div class="ttdoc">Tuner mode to be used by the CL tuner.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00085">Types.h:85</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab14324184f90f342227699c161654b1b"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_input_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00497">GraphUtils.h:497</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">arm_compute::graph::TensorDescriptor</a></div><div class="ttdoc">Tensor metadata class.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00038">TensorDescriptor.h:38</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( )</div></div>
<div class="ttc" id="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="namespacearm__compute_1_1utils_xhtml_a2593e1f13f425f627658900657f73dc3"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a2593e1f13f425f627658900657f73dc3">arm_compute::utils::consume_common_graph_parameters</a></div><div class="ttdeci">void consume_common_graph_parameters(CommonGraphValidateOptions &options, CommonParams &common_params)</div><div class="ttdoc">Consumes the consume_common_graph_parameters graph options and creates a structure containing any inf...</div><div class="ttdef"><b>Definition:</b> <a href="graph__validate__utils_8h_source.xhtml#l00317">graph_validate_utils.h:317</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a2593e1f13f425f627658900657f73dc3"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a2593e1f13f425f627658900657f73dc3">arm_compute::utils::consume_common_graph_parameters</a></div><div class="ttdeci">void consume_common_graph_parameters(CommonGraphValidateOptions &options, CommonParams &common_params)</div><div class="ttdoc">Consumes the consume_common_graph_parameters graph options and creates a structure containing any inf...</div><div class="ttdef"><b>Definition:</b> <a href="graph__validate__utils_8h_source.xhtml#l00316">graph_validate_utils.h:316</a></div></div>
<div class="ttc" id="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">graph.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_common_graph_options_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">arm_compute::utils::CommonGraphOptions</a></div><div class="ttdoc">Common command line options used to configure the graph examples.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00129">CommonGraphOptions.h:129</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a10e9c53263d766cbd37e4e37f5e8091e"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a10e9c53263d766cbd37e4e37f5e8091e">arm_compute::graph_utils::get_npy_output_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_npy_output_accessor(const std::string &npy_path, TensorShape shape, DataType data_type, DataLayout data_layout=DataLayout::NCHW, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Generates appropriate npy output accessor according to the specified npy_path.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00611">GraphUtils.h:611</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_command_line_parser_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">arm_compute::utils::CommandLineParser</a></div><div class="ttdoc">Class to parse command line arguments.</div><div class="ttdef"><b>Definition:</b> <a href="_command_line_parser_8h_source.xhtml#l00044">CommandLineParser.h:44</a></div></div>
-<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a5cabfb35cd0014387f7ec2a0c362c20f"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">arm_compute::graph::GraphConfig::tuner_file</a></div><div class="ttdeci">std::string tuner_file</div><div class="ttdoc">File to load/store tuning values from.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00086">Types.h:86</a></div></div>
+<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a5cabfb35cd0014387f7ec2a0c362c20f"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">arm_compute::graph::GraphConfig::tuner_file</a></div><div class="ttdeci">std::string tuner_file</div><div class="ttdoc">File to load/store tuning values from.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00087">Types.h:87</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="_error_8h_xhtml_a292b758f9eba8b487d71eae4b37326fc"><div class="ttname"><a href="_error_8h.xhtml#a292b758f9eba8b487d71eae4b37326fc">ARM_COMPUTE_EXIT_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_EXIT_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, the given message is printed and program exits.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00379">Error.h:379</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="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">arm_compute::graph::frontend::ScaleLayer</a></div><div class="ttdoc">Scale Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01062">Layers.h:1062</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_scale_layer.xhtml">arm_compute::graph::frontend::ScaleLayer</a></div><div class="ttdoc">Scale Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01147">Layers.h:1147</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#l00074">Utils.h:74</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#l00121">Layers.h:121</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#l00334">Layers.h:334</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab3a897163a7fe23208f1d9c618062ee2"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">arm_compute::graph_utils::permute_shape</a></div><div class="ttdeci">TensorShape permute_shape(TensorShape tensor_shape, DataLayout in_data_layout, DataLayout out_data_layout)</div><div class="ttdoc">Permutes a given tensor shape given the input and output data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00664">GraphUtils.h:664</a></div></div>
<div class="ttc" id="_common_graph_options_8h_xhtml"><div class="ttname"><a href="_common_graph_options_8h.xhtml">CommonGraphOptions.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01044">Utils.h:1044</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01139">Utils.h:1139</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2497d23622ec1343e507331ae1388f00"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">arm_compute::graph::TensorDescriptor::set_layout</a></div><div class="ttdeci">TensorDescriptor & set_layout(DataLayout data_layout)</div><div class="ttdoc">Sets tensor descriptor data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00086">TensorDescriptor.h:86</a></div></div>
<div class="ttc" id="structarm__compute_1_1utils_1_1_common_graph_params_xhtml"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">arm_compute::utils::CommonGraphParams</a></div><div class="ttdoc">Structure holding all the common graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00090">CommonGraphOptions.h:90</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="_safe_ops_8h_source.xhtml#l00032">SafeOps.h:32</a></div></div>
@@ -137,12 +137,12 @@
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a9da74af255a3e6ea61180d4a03192a48"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">arm_compute::graph::GraphConfig::use_tuner</a></div><div class="ttdeci">bool use_tuner</div><div class="ttdoc">Use a tuner in tunable backends.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00083">Types.h:83</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">arm_compute::graph::frontend::OutputLayer</a></div><div class="ttdoc">Output Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00095">Layers.h:95</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#l00475">GraphUtils.h:475</a></div></div>
-<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a08963f7335eef295237ab460863bc3d5"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">arm_compute::graph::GraphConfig::num_threads</a></div><div class="ttdeci">int num_threads</div><div class="ttdoc">Number of threads to use (thread capable backends), if 0 the backend will auto-initialize,...</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00085">Types.h:85</a></div></div>
+<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a08963f7335eef295237ab460863bc3d5"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">arm_compute::graph::GraphConfig::num_threads</a></div><div class="ttdeci">int num_threads</div><div class="ttdoc">Number of threads to use (thread capable backends), if 0 the backend will auto-initialize,...</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00086">Types.h:86</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_eltwise_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">arm_compute::graph::frontend::EltwiseLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00597">Layers.h:597</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate.</div></div>
-<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00116">Types.h:116</a></div></div>
+<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">arm_compute::graph::frontend::FlattenLayer</a></div><div class="ttdoc">Flatten Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00626">Layers.h:626</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer & set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
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@@ -151,7 +151,7 @@
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
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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__resnext50_8cpp.xhtml">graph_resnext50.cpp</a></li>
- <li class="footer">Generated on Wed Jan 22 2020 18:07:38 for Compute Library by
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<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.15 </li>
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