Google Git
Sign in
android / platform / external / ComputeLibrary / e8521c234 / . / documentation / graph__inception__v3_8cpp_source.xhtml
blob: 264993af39e7729269c7e9db5b588f56f5ad84b0 [file] [log] [blame]
<!-- HTML header for doxygen 1.8.15-->
<!-- Remember to use version doxygen 1.8.15 +-->
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.15"/>
<meta name="robots" content="NOINDEX, NOFOLLOW" /> <!-- Prevent indexing by search engines -->
<title>Compute Library: examples/graph_inception_v3.cpp Source File</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="navtree.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="resize.js"></script>
<script type="text/javascript" src="navtreedata.js"></script>
<script type="text/javascript" src="navtree.js"></script>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(document).ready(initResizable);
/* @license-end */</script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
extensions: ["tex2jax.js"],
jax: ["input/TeX","output/HTML-CSS"],
});
</script><script type="text/javascript" async="async" src="http://cdn.mathjax.org/mathjax/latest/MathJax.js"></script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<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">
&#160;<span id="projectnumber">20.02.1</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.15 -->
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
var searchBox = new SearchBox("searchBox", "search",false,'Search');
/* @license-end */
</script>
<script type="text/javascript" src="menudata.js"></script>
<script type="text/javascript" src="menu.js"></script>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(function() {
initMenu('',true,false,'search.php','Search');
$(document).ready(function() { init_search(); });
});
/* @license-end */</script>
<div id="main-nav"></div>
</div><!-- top -->
<div id="side-nav" class="ui-resizable side-nav-resizable">
<div id="nav-tree">
<div id="nav-tree-contents">
<div id="nav-sync" class="sync"></div>
</div>
</div>
<div id="splitbar" style="-moz-user-select:none;"
class="ui-resizable-handle">
</div>
</div>
<script type="text/javascript">
/* @license magnet:?xt=urn:btih:cf05388f2679ee054f2beb29a391d25f4e673ac3&amp;dn=gpl-2.0.txt GPL-v2 */
$(document).ready(function(){initNavTree('graph__inception__v3_8cpp_source.xhtml','');});
/* @license-end */
</script>
<div id="doc-content">
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
</div>
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0"
name="MSearchResults" id="MSearchResults">
</iframe>
</div>
<div class="header">
<div class="headertitle">
<div class="title">graph_inception_v3.cpp</div> </div>
</div><!--header-->
<div class="contents">
<a href="graph__inception__v3_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>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2017-2020 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<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>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<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>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_8h.xhtml">arm_compute/graph.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>&quot;</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_common_graph_options_8h.xhtml">utils/CommonGraphOptions.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="comment">/** Example demonstrating how to implement InceptionV3&#39;s network using the Compute Library&#39;s graph API */</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">class </span>InceptionV3Example : <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>&#160;{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; InceptionV3Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">&quot;InceptionV3&quot;</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <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>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; cmd_parser.validate();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; 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>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; std::cout &lt;&lt; common_params &lt;&lt; std::endl;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; std::string data_path = common_params.data_path;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; std::unique_ptr&lt;IPreprocessor&gt; preprocessor = arm_compute::support::cpp14::make_unique&lt;TFPreproccessor&gt;();</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <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>&#160; <span class="keyword">const</span> TensorShape tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(TensorShape(299U, 299U, 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>&#160; <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>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <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>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; graph &lt;&lt; common_params.target</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; &lt;&lt; common_params.fast_math_hint</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; &lt;&lt; <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, std::move(preprocessor), <span class="keyword">false</span>))</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(3U, 3U, 32U,</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(2, 2, 0, 0))</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_1a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; 0.001f)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_1a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; &lt;&lt; <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">&quot;Conv2d_1a_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(3U, 3U, 32U,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; 0.001f)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; &lt;&lt; <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">&quot;Conv2d_2a_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(3U, 3U, 64U,</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2b_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; 0.001f)</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2b_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; &lt;&lt; <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">&quot;Conv2d_2b_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; &lt;&lt; <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, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;MaxPool_3a_3x3/MaxPool&quot;</span>)</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(1U, 1U, 80U,</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_3b_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; 0.001f)</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_3b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; &lt;&lt; <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">&quot;Conv2d_3b_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(3U, 3U, 192U,</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_4a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keyword">nullptr</span>, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; 0.001f)</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_4a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; &lt;&lt; <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">&quot;Conv2d_4a_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; &lt;&lt; <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, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;MaxPool_5a_3x3/MaxPool&quot;</span>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; graph &lt;&lt; get_inception_node_A(data_path, <span class="stringliteral">&quot;Mixed_5b&quot;</span>, weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; 32U)</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_5b/concat&quot;</span>);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; graph &lt;&lt; get_inception_node_A(data_path, <span class="stringliteral">&quot;Mixed_5c&quot;</span>, weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; 64U, <span class="keyword">true</span>)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_5c/concat&quot;</span>);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; graph &lt;&lt; get_inception_node_A(data_path, <span class="stringliteral">&quot;Mixed_5d&quot;</span>, weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; 64U)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_5d/concat&quot;</span>);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; graph &lt;&lt; get_inception_node_B(data_path, <span class="stringliteral">&quot;Mixed_6a&quot;</span>, weights_layout, 384U, std::make_tuple(64U, 96U, 96U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6a/concat&quot;</span>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160;</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6b&quot;</span>, weights_layout, 192U, std::make_tuple(128U, 128U, 192U),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6b/concat&quot;</span>);</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6c&quot;</span>, weights_layout, 192U, std::make_tuple(160U, 160U, 192U),</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6c/concat&quot;</span>);</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6d&quot;</span>, weights_layout, 192U, std::make_tuple(160U, 160U, 192U),</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6d/concat&quot;</span>);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6e&quot;</span>, weights_layout, 192U, std::make_tuple(192U, 192U, 192U),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6e/concat&quot;</span>);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; graph &lt;&lt; get_inception_node_D(data_path, <span class="stringliteral">&quot;Mixed_7a&quot;</span>, weights_layout, std::make_tuple(192U, 320U),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; std::make_tuple(192U, 192U, 192U, 192U))</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_7a/concat&quot;</span>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; graph &lt;&lt; get_inception_node_E(data_path, <span class="stringliteral">&quot;Mixed_7b&quot;</span>, weights_layout, 320U, std::make_tuple(384U, 384U, 384U),</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; std::make_tuple(448U, 384U, 384U, 384U), 192U)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_7b/concat&quot;</span>);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; graph &lt;&lt; get_inception_node_E(data_path, <span class="stringliteral">&quot;Mixed_7c&quot;</span>, weights_layout, 320U, std::make_tuple(384U, 384U, 384U),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; std::make_tuple(448U, 384U, 384U, 384U), 192U, <span class="keyword">true</span>)</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_7c/concat&quot;</span>);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; graph &lt;&lt; <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>, 8, operation_layout, PadStrideInfo(1, 1, 0, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Logits/AvgPool_1a_8x8/AvgPool&quot;</span>)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(1U, 1U, 1001U, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy&quot;</span>),</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Logits/Conv2d_1c_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(TensorShape(1001U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Predictions/Reshape&quot;</span>)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">SoftmaxLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Predictions/Softmax&quot;</span>)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; &lt;&lt; <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#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; 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="l00201"></a><span class="lineno"> 201</span>&#160; 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="l00202"></a><span class="lineno"> 202</span>&#160; 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="l00203"></a><span class="lineno"> 203</span>&#160; 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="l00204"></a><span class="lineno"> 204</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a0292b7cb87d1bcd5c093c4b9d3b9c0bc">convert_to_uint8</a> = (common_params.data_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; graph.finalize(common_params.target, config);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; }</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; graph.run();</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; }</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_inception_node_A(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> a_filt,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; std::tuple&lt;unsigned int, unsigned int&gt; b_filters,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int&gt; c_filters,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d_filt,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <span class="keywordtype">bool</span> is_name_different = <span class="keyword">false</span>)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; std::string total_path = <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160;</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; <span class="comment">// This is due to a naming issue in the tf model</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; std::string conv_id0 = <span class="stringliteral">&quot;_0a_&quot;</span>;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; std::string conv_id1 = <span class="stringliteral">&quot;2d_0b_&quot;</span>;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; <span class="keywordflow">if</span>(is_name_different)</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; {</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160; conv_id0 = <span class="stringliteral">&quot;_0b_&quot;</span>;</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; conv_id1 = <span class="stringliteral">&quot;_1_0c_&quot;</span>;</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; }</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; 1U, 1U, a_filt,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; 0.001f)</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b(graph);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; i_b &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; 1U, 1U, std::get&lt;0&gt;(b_filters),</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1/convolution&quot;</span>)</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; 0.001f)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id0 + <span class="stringliteral">&quot;1x1/Relu&quot;</span>)</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; 5U, 5U, std::get&lt;1&gt;(b_filters),</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; PadStrideInfo(1, 1, 2, 2))</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5/convolution&quot;</span>)</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; 0.001f)</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id1 + <span class="stringliteral">&quot;5x5/Relu&quot;</span>);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>&#160;</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c(graph);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; i_c &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160; 1U, 1U, std::get&lt;0&gt;(c_filters),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160; 0.001f)</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; 3U, 3U, std::get&lt;1&gt;(c_filters),</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; 0.001f)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; 3U, 3U, std::get&lt;2&gt;(c_filters),</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; 0.001f)</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm&quot;</span>)</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_3x3/Relu&quot;</span>);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_d(graph);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160; i_d &lt;&lt; <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>, 3, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, PadStrideInfo(1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>),</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; <span class="keyword">true</span>))</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/AvgPool_0a_3x3/AvgPool&quot;</span>)</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; 1U, 1U, d_filt,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160; 0.001f)</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; }</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_inception_node_B(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> a_filt,</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int&gt; b_filters)</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; {</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; std::string total_path = <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>&#160; 3U, 3U, a_filt,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; PadStrideInfo(2, 2, 0, 0))</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_1a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; 0.001f)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_1a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b(graph);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; i_b &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; 1U, 1U, std::get&lt;0&gt;(b_filters),</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; 0.001f)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; 3U, 3U, std::get&lt;1&gt;(b_filters),</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160; 0.001f)</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>&#160; 3U, 3U, std::get&lt;2&gt;(b_filters),</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; PadStrideInfo(2, 2, 0, 0))</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; 0.001f)</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c(graph);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160; i_c &lt;&lt; <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, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, PadStrideInfo(2, 2, 0, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/MaxPool_1a_3x3/MaxPool&quot;</span>);</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_a), std::move(i_b), std::move(i_c));</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; }</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160;</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_inception_node_C(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> a_filt,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int&gt; b_filters,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int, unsigned int, unsigned int&gt; c_filters,</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d_filt)</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; {</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>&#160; std::string total_path = <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; 1U, 1U, a_filt,</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; 0.001f)</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>&#160;</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b(graph);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; i_b &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; 1U, 1U, std::get&lt;0&gt;(b_filters),</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; 0.001f)</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; 7U, 1U, std::get&lt;1&gt;(b_filters),</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; PadStrideInfo(1, 1, 3, 0))</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x7/convolution&quot;</span>)</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; 0.001f)</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x7/Relu&quot;</span>)</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; 1U, 7U, std::get&lt;2&gt;(b_filters),</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; PadStrideInfo(1, 1, 0, 3))</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0c_7x1/convolution&quot;</span>)</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; 0.001f)</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0c_7x1/Relu&quot;</span>);</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160;</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c(graph);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; i_c &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; 1U, 1U, std::get&lt;0&gt;(c_filters),</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; 0.001f)</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; 1U, 7U, std::get&lt;1&gt;(c_filters),</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_7x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; PadStrideInfo(1, 1, 0, 3))</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_7x1/convolution&quot;</span>)</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; 0.001f)</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_7x1/Relu&quot;</span>)</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; 7U, 1U, std::get&lt;2&gt;(c_filters),</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x7_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; PadStrideInfo(1, 1, 3, 0))</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x7/convolution&quot;</span>)</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; 0.001f)</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x7/Relu&quot;</span>)</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; 1U, 7U, std::get&lt;3&gt;(c_filters),</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_7x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; PadStrideInfo(1, 1, 0, 3))</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_7x1/convolution&quot;</span>)</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160; 0.001f)</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_7x1/Relu&quot;</span>)</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; 7U, 1U, std::get&lt;4&gt;(c_filters),</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0e_1x7_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160; PadStrideInfo(1, 1, 3, 0))</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0e_1x7/convolution&quot;</span>)</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160; 0.001f)</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0e_1x7/Relu&quot;</span>);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160;</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_d(graph);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; i_d &lt;&lt; <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>, 3, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, PadStrideInfo(1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>),</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160; <span class="keyword">true</span>))</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/AvgPool_0a_3x3/AvgPool&quot;</span>)</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160; 1U, 1U, d_filt,</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160; 0.001f)</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>&#160;</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160; }</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>&#160;</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_inception_node_D(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>&#160; std::tuple&lt;unsigned int, unsigned int&gt; a_filters,</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int, unsigned int&gt; b_filters)</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>&#160; {</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>&#160; std::string total_path = <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>&#160; i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>&#160; 1U, 1U, std::get&lt;0&gt;(a_filters),</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>&#160; 0.001f)</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>&#160; 3U, 3U, std::get&lt;1&gt;(a_filters),</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>&#160; PadStrideInfo(2, 2, 0, 0))</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_1a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>&#160; 0.001f)</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_1a_3x3/Relu&quot;</span>);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>&#160;</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b(graph);</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>&#160; i_b &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>&#160; 1U, 1U, std::get&lt;0&gt;(b_filters),</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>&#160; 0.001f)</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>&#160; 7U, 1U, std::get&lt;1&gt;(b_filters),</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>&#160; PadStrideInfo(1, 1, 3, 0))</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x7/convolution&quot;</span>)</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>&#160; 0.001f)</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x7/Relu&quot;</span>)</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>&#160; 1U, 7U, std::get&lt;2&gt;(b_filters),</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>&#160; PadStrideInfo(1, 1, 0, 3))</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0c_7x1/convolution&quot;</span>)</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>&#160; 0.001f)</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0c_7x1/Relu&quot;</span>)</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>&#160; 3U, 3U, std::get&lt;3&gt;(b_filters),</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>&#160; PadStrideInfo(2, 2, 0, 0))</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>&#160; 0.001f)</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_3x3/Relu&quot;</span>);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>&#160;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c(graph);</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>&#160; i_c &lt;&lt; <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, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, PadStrideInfo(2, 2, 0, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/MaxPool_1a_3x3/MaxPool&quot;</span>);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>&#160;</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_a), std::move(i_b), std::move(i_c));</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>&#160; }</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>&#160;</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_inception_node_E(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> a_filt,</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int&gt; b_filters,</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>&#160; std::tuple&lt;unsigned int, unsigned int, unsigned int, unsigned int&gt; c_filters,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d_filt,</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>&#160; <span class="keywordtype">bool</span> is_name_different = <span class="keyword">false</span>)</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>&#160; {</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>&#160; <span class="comment">// This is due to a naming issue in the tf model</span></div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>&#160; std::string conv_id = <span class="stringliteral">&quot;_0b_&quot;</span>;</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>&#160; <span class="keywordflow">if</span>(is_name_different)</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>&#160; {</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>&#160; conv_id = <span class="stringliteral">&quot;_0c_&quot;</span>;</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>&#160; }</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>&#160;</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>&#160; std::string total_path = <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>&#160; i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>&#160; 1U, 1U, a_filt,</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>&#160; 0.001f)</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>&#160;</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b(graph);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>&#160; i_b &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>&#160; 1U, 1U, std::get&lt;0&gt;(b_filters),</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>&#160; 0.001f)</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>&#160;</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b1(i_b);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>&#160; i_b1 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>&#160; 3U, 1U, std::get&lt;1&gt;(b_filters),</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>&#160; PadStrideInfo(1, 1, 1, 0))</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x3/convolution&quot;</span>)</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>&#160; 0.001f)</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x3/Relu&quot;</span>);</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>&#160;</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b2(i_b);</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>&#160; i_b2 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>&#160; 1U, 3U, std::get&lt;2&gt;(b_filters),</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>&#160; PadStrideInfo(1, 1, 0, 1))</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1/convolution&quot;</span>)</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>&#160; 0.001f)</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1/Relu&quot;</span>);</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>&#160;</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>&#160; <span class="comment">// Merge b1 and b2</span></div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>&#160; i_b &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_b1), std::move(i_b2)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/concat&quot;</span>);</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>&#160;</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c(graph);</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>&#160; i_c &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>&#160; 1U, 1U, std::get&lt;0&gt;(c_filters),</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>&#160; 0.001f)</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>&#160; 3U, 3U, std::get&lt;1&gt;(c_filters),</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>&#160; 0.001f)</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0b_3x3/Relu&quot;</span>);</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>&#160;</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c1(i_c);</div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>&#160; i_c1 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>&#160; 3U, 1U, std::get&lt;2&gt;(c_filters),</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>&#160; PadStrideInfo(1, 1, 1, 0))</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x3/convolution&quot;</span>)</div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>&#160; 0.001f)</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x3/Relu&quot;</span>);</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>&#160;</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c2(i_c);</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>&#160; i_c2 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>&#160; 1U, 3U, std::get&lt;3&gt;(c_filters),</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>&#160; PadStrideInfo(1, 1, 0, 1))</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_3x1/convolution&quot;</span>)</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>&#160; 0.001f)</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_3x1/Relu&quot;</span>);</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>&#160;</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>&#160; <span class="comment">// Merge i_c1 and i_c2</span></div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>&#160; i_c &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_c1), std::move(i_c2)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/concat&quot;</span>);</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>&#160;</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_d(graph);</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>&#160; i_d &lt;&lt; <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>, 3, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, PadStrideInfo(1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>),</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span>&#160; <span class="keyword">true</span>))</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/AvgPool_0a_3x3/AvgPool&quot;</span>)</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>&#160; 1U, 1U, d_filt,</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span>&#160; <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>&#160; 0.001f)</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>&#160; &lt;&lt; <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>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>&#160;</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>&#160; <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>&#160; }</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>&#160;};</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>&#160;<span class="comment">/** Main program for Inception V3</span></div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>&#160;<span class="comment"> * Model is based on:</span></div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>&#160;<span class="comment"> * https://arxiv.org/abs/1512.00567</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>&#160;<span class="comment"> * &quot;Rethinking the Inception Architecture for Computer Vision&quot;</span></div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>&#160;<span class="comment"> * Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna</span></div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>&#160;<span class="comment"> * Provenance: download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz</span></div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>&#160;<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="l00864"></a><span class="lineno"> 864</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>&#160;<span class="comment"> * @param[in] argc Number of arguments</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>&#160;<span class="comment"> * @param[in] argv Arguments</span></div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00868"></a><span class="lineno"><a class="line" href="graph__inception__v3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 868</a></span>&#160;<span class="keywordtype">int</span> <a class="code" href="graph__inception__v3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>&#160;{</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>&#160; <span class="keywordflow">return</span> arm_compute::utils::run_example&lt;InceptionV3Example&gt;(argc, argv);</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span>&#160;}</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="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#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&lt; graph::ITensorAccessor &gt; get_input_accessor(const arm_compute::utils::CommonGraphParams &amp;graph_parameters, std::unique_ptr&lt; IPreprocessor &gt; 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="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">arm_compute::graph::frontend::ReshapeLayer</a></div><div class="ttdoc">Reshape Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01067">Layers.h:1067</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="namespacearm__compute_xhtml_a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff"><div class="ttname"><a href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">arm_compute::DimensionRoundingType::CEIL</a></div><div class="ttdoc">Ceil rounding.</div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a0292b7cb87d1bcd5c093c4b9d3b9c0bc"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a0292b7cb87d1bcd5c093c4b9d3b9c0bc">arm_compute::graph::GraphConfig::convert_to_uint8</a></div><div class="ttdeci">bool convert_to_uint8</div><div class="ttdoc">Convert graph to a synthetic uint8 graph.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</a></div></div>
<div class="ttc" id="classarm__compute_1_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_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 &amp;options, CommonParams &amp;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="graph__inception__v3_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__inception__v3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for Inception V3.</div><div class="ttdef"><b>Definition:</b> <a href="graph__inception__v3_8cpp_source.xhtml#l00868">graph_inception_v3.cpp:868</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_1utils_1_1_common_graph_params_xhtml_aa56f0562febf49bc0e29a4257551191b"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">arm_compute::utils::CommonGraphParams::data_layout</a></div><div class="ttdeci">arm_compute::DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00096">CommonGraphOptions.h:96</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="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</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="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 &amp; 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>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00044">GraphUtils.h:44</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">arm_compute::graph::frontend::SoftmaxLayer</a></div><div class="ttdoc">Softmax Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01200">Layers.h:1200</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a"><div class="ttname"><a href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">arm_compute::PoolingType::AVG</a></div><div class="ttdoc">Average Pooling.</div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ae3d177d243f5fb34544105a4ee4e1f58"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">arm_compute::graph_utils::get_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_output_accessor(const arm_compute::utils::CommonGraphParams &amp;graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &amp;output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00543">GraphUtils.h:543</a></div></div>
<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&lt; graph::ITensorAccessor &gt; get_weights_accessor(const std::string &amp;path, const std::string &amp;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#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_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00149">Layers.h:149</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#l00117">Types.h:117</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 &amp; set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">arm_compute::graph::frontend::ConcatLayer</a></div><div class="ttdoc">Concat Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00248">Layers.h:248</a></div></div>
</div><!-- fragment --></div><!-- contents -->
</div><!-- doc-content -->
<!-- start footer part -->
<div id="nav-path" class="navpath"><!-- id is needed for treeview function! -->
<ul>
<li class="navelem"><a class="el" href="dir_d28a4824dc47e487b107a5db32ef43c4.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__inception__v3_8cpp.xhtml">graph_inception_v3.cpp</a></li>
<li class="footer">Generated on Thu Mar 5 2020 16:06:57 for Compute Library by
<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/></a> 1.8.15 </li>
</ul>
</div>
</body>
</html>