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<div class="title">graph_ssd_mobilenet.cpp</div> </div>
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<a href="graph__ssd__mobilenet_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2018-2019 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.xhtml">arm_compute</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_1utils.xhtml">arm_compute::utils</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_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="comment">/** Example demonstrating how to implement MobileNetSSD&#39;s network using the Compute Library&#39;s graph API */</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;<span class="keyword">class </span>GraphSSDMobilenetExample : <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="l00037"></a><span class="lineno"> 37</span>&#160;{</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; GraphSSDMobilenetExample()</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">&quot;MobileNetSSD&quot;</span>)</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="comment">// Add topk option</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160; keep_topk_opt = cmd_parser.add_option&lt;<a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;int&gt;</a>&gt;(<span class="stringliteral">&quot;topk&quot;</span>, 100);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; keep_topk_opt-&gt;<a class="code" href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">set_help</a>(<span class="stringliteral">&quot;Top k detections results per image. Used for data type F32.&quot;</span>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="comment">// Add output option</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; detection_boxes_opt = cmd_parser.add_option&lt;<a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a>&gt;(<span class="stringliteral">&quot;detection_boxes_opt&quot;</span>, <span class="stringliteral">&quot;&quot;</span>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; detection_boxes_opt-&gt;<a class="code" href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">set_help</a>(<span class="stringliteral">&quot;Filename containing the reference values for the graph output detection_boxes. Used for data type QASYMM8.&quot;</span>);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; detection_classes_opt = cmd_parser.add_option&lt;<a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a>&gt;(<span class="stringliteral">&quot;detection_classes_opt&quot;</span>, <span class="stringliteral">&quot;&quot;</span>);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; detection_classes_opt-&gt;<a class="code" href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">set_help</a>(<span class="stringliteral">&quot;Filename containing the reference values for the output detection_classes. Used for data type QASYMM8.&quot;</span>);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160; detection_scores_opt = cmd_parser.add_option&lt;<a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a>&gt;(<span class="stringliteral">&quot;detection_scores_opt&quot;</span>, <span class="stringliteral">&quot;&quot;</span>);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; detection_scores_opt-&gt;<a class="code" href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">set_help</a>(<span class="stringliteral">&quot;Filename containing the reference values for the output detection_scores. Used for data type QASYMM8.&quot;</span>);</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; num_detections_opt = cmd_parser.add_option&lt;<a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a>&gt;(<span class="stringliteral">&quot;num_detections_opt&quot;</span>, <span class="stringliteral">&quot;&quot;</span>);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; num_detections_opt-&gt;<a class="code" href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">set_help</a>(<span class="stringliteral">&quot;Filename containing the reference values for the output num_detections. Used with datatype QASYMM8.&quot;</span>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; GraphSSDMobilenetExample(<span class="keyword">const</span> GraphSSDMobilenetExample &amp;) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; GraphSSDMobilenetExample &amp;operator=(<span class="keyword">const</span> GraphSSDMobilenetExample &amp;) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; GraphSSDMobilenetExample(GraphSSDMobilenetExample &amp;&amp;) = <span class="keywordflow">default</span>; <span class="comment">// NOLINT</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; GraphSSDMobilenetExample &amp;operator=(GraphSSDMobilenetExample &amp;&amp;) = <span class="keywordflow">default</span>; <span class="comment">// NOLINT</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; ~GraphSSDMobilenetExample() <span class="keyword">override</span> = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</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="l00061"></a><span class="lineno"> 61</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; cmd_parser.validate();</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</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="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; <span class="keywordflow">if</span>(common_params.help)</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; cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</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;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; std::cout &lt;&lt; common_params &lt;&lt; std::endl;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(300, 300, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, common_params.data_layout);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</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>(common_params.data_layout);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <span class="comment">// Set graph hints</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; graph &lt;&lt; common_params.<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2a7ca82c5e74421cb45f17e936abf964">target</a></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; &lt;&lt; common_params.fast_math_hint;</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="comment">// Create core graph</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#af5982a092e9eb743fce2d6392bdd8897">arm_compute::is_data_type_float</a>(common_params.data_type))</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; {</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; create_graph_float(input_descriptor);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; }</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; {</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; create_graph_qasymm(input_descriptor);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; }</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</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="l00100"></a><span class="lineno"> 100</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="l00101"></a><span class="lineno"> 101</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="l00102"></a><span class="lineno"> 102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; graph.finalize(common_params.target, config);</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; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; <span class="comment">// Run graph</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; graph.run();</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; }</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</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="l00115"></a><span class="lineno"> 115</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="l00116"></a><span class="lineno"> 116</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;int&gt;</a> *keep_topk_opt{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</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="l00118"></a><span class="lineno"> 118</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="l00119"></a><span class="lineno"> 119</span>&#160;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a> *detection_boxes_opt{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a> *detection_classes_opt{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a> *detection_scores_opt{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption&lt;std::string&gt;</a> *num_detections_opt{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_node_A_float(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">IStream</a> &amp;master_graph, <span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> dwc_pad_stride_info, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; {</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; <span class="keyword">const</span> std::string total_path = param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> sg(master_graph);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; sg &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">DepthwiseConvolutionLayer</a>(</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160; 3U, 3U,</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</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;dw_w.npy&quot;</span>),</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; dwc_pad_stride_info)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</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;/dw&quot;</span>)</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</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, total_path + <span class="stringliteral">&quot;dw_bn_mean.npy&quot;</span>),</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</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;dw_bn_var.npy&quot;</span>),</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</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;dw_scale_w.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, total_path + <span class="stringliteral">&quot;dw_scale_b.npy&quot;</span>), 0.00001f)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</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;/dw/bn&quot;</span>)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<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;dw/relu&quot;</span>)</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</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="l00146"></a><span class="lineno"> 146</span>&#160; 1U, 1U, conv_filt,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</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;w.npy&quot;</span>),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; conv_pad_stride_info)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</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;/pw&quot;</span>)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</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, total_path + <span class="stringliteral">&quot;bn_mean.npy&quot;</span>),</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</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;bn_var.npy&quot;</span>),</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</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;scale_w.npy&quot;</span>),</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</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;scale_b.npy&quot;</span>), 0.00001f)</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</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;/pw/bn&quot;</span>)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<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;pw/relu&quot;</span>);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</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(sg));</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; }</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; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_node_B_float(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">IStream</a> &amp;master_graph, <span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info_1, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info_2)</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; {</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <span class="keyword">const</span> std::string total_path = param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> sg(master_graph);</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; sg &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="l00169"></a><span class="lineno"> 169</span>&#160; 1, 1, conv_filt / 2,</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</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;1_w.npy&quot;</span>),</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; conv_pad_stride_info_1)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;1/conv&quot;</span>)</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</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, total_path + <span class="stringliteral">&quot;1_bn_mean.npy&quot;</span>),</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</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;1_bn_var.npy&quot;</span>),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</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;1_scale_w.npy&quot;</span>),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</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;1_scale_b.npy&quot;</span>), 0.00001f)</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>(total_path + <span class="stringliteral">&quot;1/bn&quot;</span>)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;1/relu&quot;</span>);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; sg &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="l00182"></a><span class="lineno"> 182</span>&#160; 3, 3, conv_filt,</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</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;2_w.npy&quot;</span>),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; conv_pad_stride_info_2)</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;2/conv&quot;</span>)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</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, total_path + <span class="stringliteral">&quot;2_bn_mean.npy&quot;</span>),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</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;2_bn_var.npy&quot;</span>),</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</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;2_scale_w.npy&quot;</span>),</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, total_path + <span class="stringliteral">&quot;2_scale_b.npy&quot;</span>), 0.00001f)</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;2/bn&quot;</span>)</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;2/relu&quot;</span>);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160;</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</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(sg));</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; }</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_node_C_float(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">IStream</a> &amp;master_graph, <span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; {</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <span class="keyword">const</span> std::string total_path = param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> sg(master_graph);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; sg &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="l00203"></a><span class="lineno"> 203</span>&#160; 1U, 1U, conv_filt,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</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;w.npy&quot;</span>),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</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;b.npy&quot;</span>),</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; conv_pad_stride_info)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</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;/conv&quot;</span>);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; <span class="keywordflow">if</span>(common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</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; sg &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_permute_layer.xhtml">PermuteLayer</a>(<a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(2U, 0U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</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;/perm&quot;</span>);</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; sg &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">FlattenLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/flat&quot;</span>);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</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(sg));</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;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <span class="keywordtype">void</span> create_graph_float(<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> &amp;input_descriptor)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; {</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; <span class="keyword">const</span> std::array&lt;float, 3&gt; mean_rgb{ { 127.5f, 127.5f, 127.5f } };</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; std::unique_ptr&lt;IPreprocessor&gt; preprocessor = arm_compute::support::cpp14::make_unique&lt;CaffePreproccessor&gt;(mean_rgb, <span class="keyword">true</span>, 0.007843f);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; std::string data_path = common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a30a81dbc66a8e9eeb693a75046b4655d">data_path</a>;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; <span class="comment">// Add model path to data path</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; <span class="keywordflow">if</span>(!data_path.empty())</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; {</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; data_path += <span class="stringliteral">&quot;/cnn_data/ssd_mobilenet_model/&quot;</span>;</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; }</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; graph &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor,</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">get_input_accessor</a>(common_params, std::move(preprocessor)));</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_11(graph);</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160; conv_11 &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="l00237"></a><span class="lineno"> 237</span>&#160; 3U, 3U, 32U,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;conv0_w.npy&quot;</span>),</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv0&quot;</span>);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; conv_11 &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, <span class="stringliteral">&quot;conv0_bn_mean.npy&quot;</span>),</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;conv0_bn_var.npy&quot;</span>),</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, <span class="stringliteral">&quot;conv0_scale_w.npy&quot;</span>),</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;conv0_scale_b.npy&quot;</span>), 0.00001f)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</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;conv0/bn&quot;</span>)</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv0/relu&quot;</span>);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv1&quot;</span>, 64, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv2&quot;</span>, 128, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv3&quot;</span>, 128, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv4&quot;</span>, 256, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv5&quot;</span>, 256, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv6&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv7&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv8&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv9&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv10&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>&#160; conv_11 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv11&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160;</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_13(conv_11);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; conv_13 &lt;&lt; get_node_A_float(conv_11, data_path, <span class="stringliteral">&quot;conv12&quot;</span>, 1024, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; conv_13 &lt;&lt; get_node_A_float(conv_13, data_path, <span class="stringliteral">&quot;conv13&quot;</span>, 1024, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_14(conv_13);</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; conv_14 &lt;&lt; get_node_B_float(conv_13, data_path, <span class="stringliteral">&quot;conv14&quot;</span>, 512, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1));</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160;</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_15(conv_14);</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; conv_15 &lt;&lt; get_node_B_float(conv_14, data_path, <span class="stringliteral">&quot;conv15&quot;</span>, 256, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1));</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16(conv_15);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; conv_16 &lt;&lt; get_node_B_float(conv_15, data_path, <span class="stringliteral">&quot;conv16&quot;</span>, 256, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1));</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160;</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_17(conv_16);</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; conv_17 &lt;&lt; get_node_B_float(conv_16, data_path, <span class="stringliteral">&quot;conv17&quot;</span>, 128, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 1, 1));</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; <span class="comment">//mbox_loc</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_11_mbox_loc(conv_11);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>&#160; conv_11_mbox_loc &lt;&lt; get_node_C_float(conv_11, data_path, <span class="stringliteral">&quot;conv11_mbox_loc&quot;</span>, 12, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>&#160;</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_13_mbox_loc(conv_13);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; conv_13_mbox_loc &lt;&lt; get_node_C_float(conv_13, data_path, <span class="stringliteral">&quot;conv13_mbox_loc&quot;</span>, 24, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160;</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_sub_stream.xhtml">SubStream</a> conv_14_2_mbox_loc(conv_14);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; conv_14_2_mbox_loc &lt;&lt; get_node_C_float(conv_14, data_path, <span class="stringliteral">&quot;conv14_2_mbox_loc&quot;</span>, 24, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</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> conv_15_2_mbox_loc(conv_15);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>&#160; conv_15_2_mbox_loc &lt;&lt; get_node_C_float(conv_15, data_path, <span class="stringliteral">&quot;conv15_2_mbox_loc&quot;</span>, 24, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>&#160;</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16_2_mbox_loc(conv_16);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>&#160; conv_16_2_mbox_loc &lt;&lt; get_node_C_float(conv_16, data_path, <span class="stringliteral">&quot;conv16_2_mbox_loc&quot;</span>, 24, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>&#160;</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_sub_stream.xhtml">SubStream</a> conv_17_2_mbox_loc(conv_17);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>&#160; conv_17_2_mbox_loc &lt;&lt; get_node_C_float(conv_17, data_path, <span class="stringliteral">&quot;conv17_2_mbox_loc&quot;</span>, 24, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> mbox_loc(graph);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; mbox_loc &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(conv_11_mbox_loc), std::move(conv_13_mbox_loc), conv_14_2_mbox_loc, std::move(conv_15_2_mbox_loc),</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; std::move(conv_16_2_mbox_loc), std::move(conv_17_2_mbox_loc));</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>&#160;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>&#160; <span class="comment">//mbox_conf</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_11_mbox_conf(conv_11);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; conv_11_mbox_conf &lt;&lt; get_node_C_float(conv_11, data_path, <span class="stringliteral">&quot;conv11_mbox_conf&quot;</span>, 63, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_13_mbox_conf(conv_13);</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>&#160; conv_13_mbox_conf &lt;&lt; get_node_C_float(conv_13, data_path, <span class="stringliteral">&quot;conv13_mbox_conf&quot;</span>, 126, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>&#160;</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_sub_stream.xhtml">SubStream</a> conv_14_2_mbox_conf(conv_14);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>&#160; conv_14_2_mbox_conf &lt;&lt; get_node_C_float(conv_14, data_path, <span class="stringliteral">&quot;conv14_2_mbox_conf&quot;</span>, 126, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>&#160;</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_15_2_mbox_conf(conv_15);</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; conv_15_2_mbox_conf &lt;&lt; get_node_C_float(conv_15, data_path, <span class="stringliteral">&quot;conv15_2_mbox_conf&quot;</span>, 126, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16_2_mbox_conf(conv_16);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; conv_16_2_mbox_conf &lt;&lt; get_node_C_float(conv_16, data_path, <span class="stringliteral">&quot;conv16_2_mbox_conf&quot;</span>, 126, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160;</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_17_2_mbox_conf(conv_17);</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; conv_17_2_mbox_conf &lt;&lt; get_node_C_float(conv_17, data_path, <span class="stringliteral">&quot;conv17_2_mbox_conf&quot;</span>, 126, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>&#160;</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> mbox_conf(graph);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; mbox_conf &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(conv_11_mbox_conf), std::move(conv_13_mbox_conf), std::move(conv_14_2_mbox_conf),</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; std::move(conv_15_2_mbox_conf), std::move(conv_16_2_mbox_conf), std::move(conv_17_2_mbox_conf));</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; mbox_conf &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(21U, 1917U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;mbox_conf/reshape&quot;</span>);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; mbox_conf &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;mbox_conf/softmax&quot;</span>);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; mbox_conf &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">FlattenLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;mbox_conf/flat&quot;</span>);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>&#160;</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; priorbox_variances = { 0.1f, 0.1f, 0.2f, 0.2f };</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> priorbox_offset = 0.5f;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>&#160; <span class="keyword">const</span> std::vector&lt;float&gt; priorbox_aspect_ratios = { 2.f, 3.f };</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; <span class="comment">//mbox_priorbox branch</span></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> conv_11_mbox_priorbox(conv_11);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>&#160;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>&#160; conv_11_mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">PriorBoxLayer</a>(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a>(graph),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a>({ 60.f }, priorbox_variances, priorbox_offset, <span class="keyword">true</span>, <span class="keyword">false</span>, {}, { 2.f }))</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</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;conv11/priorbox&quot;</span>);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_13_mbox_priorbox(conv_13);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; conv_13_mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">PriorBoxLayer</a>(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a>(graph),</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a>({ 105.f }, priorbox_variances, priorbox_offset, <span class="keyword">true</span>, <span class="keyword">false</span>, { 150.f }, priorbox_aspect_ratios))</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>(<span class="stringliteral">&quot;conv13/priorbox&quot;</span>);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160;</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_14_2_mbox_priorbox(conv_14);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; conv_14_2_mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">PriorBoxLayer</a>(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a>(graph),</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a>({ 150.f }, priorbox_variances, priorbox_offset, <span class="keyword">true</span>, <span class="keyword">false</span>, { 195.f }, priorbox_aspect_ratios))</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</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;conv14/priorbox&quot;</span>);</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>&#160;</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_sub_stream.xhtml">SubStream</a> conv_15_2_mbox_priorbox(conv_15);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; conv_15_2_mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">PriorBoxLayer</a>(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a>(graph),</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a>({ 195.f }, priorbox_variances, priorbox_offset, <span class="keyword">true</span>, <span class="keyword">false</span>, { 240.f }, priorbox_aspect_ratios))</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</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;conv15/priorbox&quot;</span>);</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; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16_2_mbox_priorbox(conv_16);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; conv_16_2_mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">PriorBoxLayer</a>(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a>(graph),</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a>({ 240.f }, priorbox_variances, priorbox_offset, <span class="keyword">true</span>, <span class="keyword">false</span>, { 285.f }, priorbox_aspect_ratios))</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</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;conv16/priorbox&quot;</span>);</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; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_17_2_mbox_priorbox(conv_17);</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; conv_17_2_mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">PriorBoxLayer</a>(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a>(graph),</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; <a class="code" href="classarm__compute_1_1_prior_box_layer_info.xhtml">PriorBoxLayerInfo</a>({ 285.f }, priorbox_variances, priorbox_offset, <span class="keyword">true</span>, <span class="keyword">false</span>, { 300.f }, priorbox_aspect_ratios))</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</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;conv17/priorbox&quot;</span>);</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>&#160;</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> mbox_priorbox(graph);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160;</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; mbox_priorbox &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; (common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>) ? <a class="code" href="structarm__compute_1_1graph_1_1descriptors_1_1_concat_layer_descriptor.xhtml">arm_compute::graph::descriptors::ConcatLayerDescriptor</a>(<a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>) : <a class="code" href="structarm__compute_1_1graph_1_1descriptors_1_1_concat_layer_descriptor.xhtml">arm_compute::graph::descriptors::ConcatLayerDescriptor</a>(</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>),</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; std::move(conv_11_mbox_priorbox), std::move(conv_13_mbox_priorbox), std::move(conv_14_2_mbox_priorbox),</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; std::move(conv_15_2_mbox_priorbox), std::move(conv_16_2_mbox_priorbox), std::move(conv_17_2_mbox_priorbox));</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160;</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_classes = 21;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; <span class="keyword">const</span> <span class="keywordtype">bool</span> share_location = <span class="keyword">true</span>;</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830">DetectionOutputLayerCodeType</a> detection_type = <a class="code" href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830a1150a8d7752b01d30d91fe18fe9d8a54">DetectionOutputLayerCodeType::CENTER_SIZE</a>;</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> keep_top_k = keep_topk_opt-&gt;value();</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> nms_threshold = 0.45f;</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> label_id_background = 0;</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> conf_thrs = 0.25f;</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> top_k = 100;</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160;</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> detection_ouput(mbox_loc);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; detection_ouput &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_detection_output_layer.xhtml">DetectionOutputLayer</a>(std::move(mbox_conf), std::move(mbox_priorbox),</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; <a class="code" href="classarm__compute_1_1_detection_output_layer_info.xhtml">DetectionOutputLayerInfo</a>(num_classes, share_location, detection_type, keep_top_k, nms_threshold, top_k, label_id_background, conf_thrs));</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160; detection_ouput &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#aff280480ba1a9075fed13fbb15ca0063">get_detection_output_accessor</a>(common_params, { input_descriptor.<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a1fcd64682b37ed3c2098d0094ce788d8">shape</a> }));</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; }</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160;</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_node_A_qasymm(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">IStream</a> &amp;master_graph, <span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> dwc_pad_stride_info, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; std::pair&lt;QuantizationInfo, QuantizationInfo&gt; depth_quant_info, std::pair&lt;QuantizationInfo, QuantizationInfo&gt; point_quant_info)</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; {</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; <span class="keyword">const</span> std::string total_path = param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> sg(master_graph);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160;</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; sg &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">DepthwiseConvolutionLayer</a>(</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; 3U, 3U,</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</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;dw_w.npy&quot;</span>),</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;dw_b.npy&quot;</span>),</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160; dwc_pad_stride_info, 1, depth_quant_info.first, depth_quant_info.second)</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</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;/dw&quot;</span>)</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/dw/relu6&quot;</span>);</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>&#160;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>&#160; sg &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="l00402"></a><span class="lineno"> 402</span>&#160; 1U, 1U, conv_filt,</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</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;w.npy&quot;</span>),</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</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;b.npy&quot;</span>),</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; conv_pad_stride_info, 1, point_quant_info.first, point_quant_info.second)</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</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;/pw&quot;</span>)</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/pw/relu6&quot;</span>);</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>&#160;</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</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(sg));</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160; }</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160;</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_node_B_qasymm(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">IStream</a> &amp;master_graph, <span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info_1x1, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info_3x3,</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160; <span class="keyword">const</span> std::pair&lt;QuantizationInfo, QuantizationInfo&gt; quant_info_1x1, <span class="keyword">const</span> std::pair&lt;QuantizationInfo, QuantizationInfo&gt; quant_info_3x3)</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>&#160; {</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>&#160; <span class="keyword">const</span> std::string total_path = param_path + <span class="stringliteral">&quot;_&quot;</span>;</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> sg(master_graph);</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>&#160;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>&#160; sg &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="l00421"></a><span class="lineno"> 421</span>&#160; 1, 1, conv_filt / 2,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</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;1x1_w.npy&quot;</span>),</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</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;1x1_b.npy&quot;</span>),</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>&#160; conv_pad_stride_info_1x1, 1, quant_info_1x1.first, quant_info_1x1.second)</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;1x1/conv&quot;</span>)</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;1x1/conv/relu6&quot;</span>);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; sg &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="l00429"></a><span class="lineno"> 429</span>&#160; 3, 3, conv_filt,</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</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;3x3_w.npy&quot;</span>),</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</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;3x3_b.npy&quot;</span>),</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; conv_pad_stride_info_3x3, 1, quant_info_3x3.first, quant_info_3x3.second)</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;3x3/conv&quot;</span>)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">&quot;3x3/conv/relu6&quot;</span>);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160;</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</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(sg));</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>&#160; }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>&#160;</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_node_C_qasymm(<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">IStream</a> &amp;master_graph, <span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info,</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; <span class="keyword">const</span> std::pair&lt;QuantizationInfo, QuantizationInfo&gt; quant_info, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> reshape_shape)</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; {</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; <span class="keyword">const</span> std::string total_path = param_path + <span class="stringliteral">&quot;_&quot;</span>;</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_sub_stream.xhtml">SubStream</a> sg(master_graph);</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>&#160; sg &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="l00446"></a><span class="lineno"> 446</span>&#160; 1U, 1U, conv_filt,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</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;w.npy&quot;</span>),</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</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;b.npy&quot;</span>),</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; conv_pad_stride_info, 1, quant_info.first, quant_info.second)</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</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;/conv&quot;</span>);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; <span class="keywordflow">if</span>(common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a> == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>&#160; {</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>&#160; sg &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_permute_layer.xhtml">PermuteLayer</a>(<a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">PermutationVector</a>(2U, 0U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>&#160; }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>&#160; sg &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(reshape_shape).<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;/reshape&quot;</span>);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>&#160;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</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(sg));</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; }</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; <span class="keywordtype">void</span> create_graph_qasymm(<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> &amp;input_descriptor)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; std::string data_path = common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a30a81dbc66a8e9eeb693a75046b4655d">data_path</a>;</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160;</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; <span class="comment">// Add model path to data path</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>&#160; <span class="keywordflow">if</span>(!data_path.empty())</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>&#160; {</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>&#160; data_path += <span class="stringliteral">&quot;/cnn_data/ssd_mobilenet_qasymm8_model/&quot;</span>;</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>&#160; }</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>&#160;</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>&#160; <span class="comment">// Quantization info are saved as pair for each (pointwise/depthwise) convolution layer: &lt;weight_quant_info, output_quant_info&gt;</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>&#160; <span class="keyword">const</span> std::vector&lt;std::pair&lt;QuantizationInfo, QuantizationInfo&gt;&gt; conv_quant_info =</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>&#160; {</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.03624850884079933f, 163), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.22219789028167725f, 113) }, <span class="comment">// conv0</span></div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0028752065263688564f, 113), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.05433657020330429f, 128) }, <span class="comment">// conv13_2_1_1</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0014862528769299388f, 125), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.05037643015384674f, 131) }, <span class="comment">// conv13_2_3_3</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00233650766313076f, 113), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.04468846693634987f, 126) }, <span class="comment">// conv13_3_1_1</span></div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.002501056529581547f, 120), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.06026708707213402f, 111) }, <span class="comment">// conv13_3_3_3</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.002896666992455721f, 121), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.037775348871946335f, 117) }, <span class="comment">// conv13_4_1_1</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0023875406477600336f, 122), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.03881589323282242f, 108) }, <span class="comment">// conv13_4_3_3</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0022081052884459496f, 77), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.025450613349676132f, 125) }, <span class="comment">// conv13_5_1_1</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00604657270014286f, 121), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.033533502370119095f, 109) } <span class="comment">// conv13_5_3_3</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>&#160; };</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>&#160;</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>&#160; <span class="keyword">const</span> std::vector&lt;std::pair&lt;QuantizationInfo, QuantizationInfo&gt;&gt; depth_quant_info =</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>&#160; {</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.03408717364072f, 131), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.29286590218544006f, 108) }, <span class="comment">// dwsc1</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.027518004179000854f, 107), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.20796941220760345, 117) }, <span class="comment">// dwsc2</span></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.052489638328552246f, 85), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.4303881824016571f, 142) }, <span class="comment">// dwsc3</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.016570359468460083f, 79), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.10512150079011917f, 116) }, <span class="comment">// dwsc4</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.060739465057849884f, 65), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.15331414341926575f, 94) }, <span class="comment">// dwsc5</span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.01324534136801958f, 124), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.13010895252227783f, 153) }, <span class="comment">// dwsc6</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.032326459884643555f, 124), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.11565316468477249, 156) }, <span class="comment">// dwsc7</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.029948478564620018f, 155), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.11413891613483429f, 146) }, <span class="comment">// dwsc8</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.028054025024175644f, 129), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.1142905130982399f, 140) }, <span class="comment">// dwsc9</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.025204822421073914f, 129), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.14668069779872894f, 149) }, <span class="comment">// dwsc10</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.019332280382514f, 110), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.1480235457420349f, 91) }, <span class="comment">// dwsc11</span></div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0319712869822979f, 88), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.10424695909023285f, 117) }, <span class="comment">// dwsc12</span></div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.04378943517804146f, 164), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.23176774382591248f, 138) } <span class="comment">// dwsc13</span></div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; };</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160;</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; <span class="keyword">const</span> std::vector&lt;std::pair&lt;QuantizationInfo, QuantizationInfo&gt;&gt; point_quant_info =</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; {</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.028777318075299263f, 144), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.2663874328136444f, 121) }, <span class="comment">// pw1</span></div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.015796702355146408f, 127), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.1739964485168457f, 111) }, <span class="comment">// pw2</span></div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.009349990636110306f, 127), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.1805974692106247f, 104) }, <span class="comment">// pw3</span></div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.012920888140797615f, 106), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.1205204650759697f, 100) }, <span class="comment">// pw4</span></div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.008119508624076843f, 145), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.12272439152002335f, 97) }, <span class="comment">// pw5</span></div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0070041813887655735f, 115), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0947074219584465f, 101) }, <span class="comment">// pw6</span></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.004827278666198254f, 115), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0842885747551918f, 110) }, <span class="comment">// pw7</span></div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.004755120258778334f, 128), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.08283159881830215f, 116) }, <span class="comment">// pw8</span></div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.007527193054556847f, 142), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.12555131316184998f, 137) }, <span class="comment">// pw9</span></div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.006050156895071268f, 109), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.10871313512325287f, 124) }, <span class="comment">// pw10</span></div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00490700313821435f, 127), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.10364262014627457f, 140) }, <span class="comment">// pw11</span></div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.006063731852918863, 124), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.11241862177848816f, 125) }, <span class="comment">// pw12</span></div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.007901716977357864f, 139), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.49889302253723145f, 141) } <span class="comment">// pw13</span></div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; };</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; <span class="comment">// Quantization info taken from the TfLite SSD MobileNet example</span></div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> in_quant_info = <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0078125f, 128);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; <span class="comment">// Create core graph</span></div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; graph &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="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#afe5692937b0558d4cffe2d4fee57d581">set_quantization_info</a>(in_quant_info),</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a96b4a087acee7543a7624102a67fc14d">image</a>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>));</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; graph &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="l00525"></a><span class="lineno"> 525</span>&#160; 3U, 3U, 32U,</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;conv0_w.npy&quot;</span>),</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, <span class="stringliteral">&quot;conv0_b.npy&quot;</span>),</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), 1, conv_quant_info.at(0).first, conv_quant_info.at(0).second)</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; .set_name(<span class="stringliteral">&quot;conv0&quot;</span>);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; graph &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv0/relu&quot;</span>);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv1&quot;</span>, 64U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(0),</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160; point_quant_info.at(0));</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv2&quot;</span>, 128U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(1),</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; point_quant_info.at(1));</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv3&quot;</span>, 128U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(2),</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; point_quant_info.at(2));</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv4&quot;</span>, 256U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(3),</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; point_quant_info.at(3));</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv5&quot;</span>, 256U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(4),</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; point_quant_info.at(4));</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv6&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(5),</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; point_quant_info.at(5));</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv7&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(6),</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160; point_quant_info.at(6));</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv8&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(7),</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; point_quant_info.at(7));</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv9&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(8),</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; point_quant_info.at(8));</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv10&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(9),</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160; point_quant_info.at(9));</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>&#160; graph &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv11&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(10),</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>&#160; point_quant_info.at(10));</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>&#160;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_13(graph);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; conv_13 &lt;&lt; get_node_A_qasymm(graph, data_path, <span class="stringliteral">&quot;conv12&quot;</span>, 1024U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(11),</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; point_quant_info.at(11));</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>&#160; conv_13 &lt;&lt; get_node_A_qasymm(conv_13, data_path, <span class="stringliteral">&quot;conv13&quot;</span>, 1024U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_quant_info.at(12),</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>&#160; point_quant_info.at(12));</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_14(conv_13);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; conv_14 &lt;&lt; get_node_B_qasymm(conv_13, data_path, <span class="stringliteral">&quot;conv13_2&quot;</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), conv_quant_info.at(1),</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; conv_quant_info.at(2));</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_15(conv_14);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; conv_15 &lt;&lt; get_node_B_qasymm(conv_14, data_path, <span class="stringliteral">&quot;conv13_3&quot;</span>, 256U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), conv_quant_info.at(3),</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>&#160; conv_quant_info.at(4));</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16(conv_15);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>&#160; conv_16 &lt;&lt; get_node_B_qasymm(conv_15, data_path, <span class="stringliteral">&quot;conv13_4&quot;</span>, 256U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), conv_quant_info.at(5),</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>&#160; conv_quant_info.at(6));</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_17(conv_16);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160; conv_17 &lt;&lt; get_node_B_qasymm(conv_16, data_path, <span class="stringliteral">&quot;conv13_5&quot;</span>, 128U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), conv_quant_info.at(7),</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>&#160; conv_quant_info.at(8));</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>&#160;</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>&#160; <span class="comment">// box_predictor</span></div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160; <span class="keyword">const</span> std::vector&lt;std::pair&lt;QuantizationInfo, QuantizationInfo&gt;&gt; box_enc_pred_quant_info =</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>&#160; {</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.005202020984143019f, 136), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.08655580133199692f, 183) }, <span class="comment">// boxpredictor0_bep</span></div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.003121797926723957f, 132), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.03218776360154152f, 140) }, <span class="comment">// boxpredictor1_bep</span></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.002995674265548587f, 130), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.029072262346744537f, 125) }, <span class="comment">// boxpredictor2_bep</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0023131705820560455f, 130), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.026488754898309708f, 127) }, <span class="comment">// boxpredictor3_bep</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0013905081432312727f, 132), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0199890099465847f, 137) }, <span class="comment">// boxpredictor4_bep</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00216794665902853f, 121), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.019798893481492996f, 151) } <span class="comment">// boxpredictor5_bep</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;</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>&#160; <span class="keyword">const</span> std::vector&lt;TensorShape&gt; box_reshape = <span class="comment">// NHWC</span></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_1_tensor_shape.xhtml">TensorShape</a>(4U, 1U, 1083U), <span class="comment">// boxpredictor0_bep_reshape</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 1U, 600U), <span class="comment">// boxpredictor1_bep_reshape</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 1U, 150U), <span class="comment">// boxpredictor2_bep_reshape</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 1U, 54U), <span class="comment">// boxpredictor3_bep_reshape</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 1U, 24U), <span class="comment">// boxpredictor4_bep_reshape</span></div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 1U, 6U) <span class="comment">// boxpredictor5_bep_reshape</span></div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>&#160; };</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>&#160;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_11_box_enc_pre(graph);</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>&#160; conv_11_box_enc_pre &lt;&lt; get_node_C_qasymm(graph, data_path, <span class="stringliteral">&quot;BoxPredictor_0_BEP&quot;</span>, 12U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(0), box_reshape.at(0));</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>&#160;</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_sub_stream.xhtml">SubStream</a> conv_13_box_enc_pre(conv_13);</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>&#160; conv_13_box_enc_pre &lt;&lt; get_node_C_qasymm(conv_13, data_path, <span class="stringliteral">&quot;BoxPredictor_1_BEP&quot;</span>, 24U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(1), box_reshape.at(1));</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>&#160;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_14_2_box_enc_pre(conv_14);</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>&#160; conv_14_2_box_enc_pre &lt;&lt; get_node_C_qasymm(conv_14, data_path, <span class="stringliteral">&quot;BoxPredictor_2_BEP&quot;</span>, 24U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(2), box_reshape.at(2));</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>&#160;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_15_2_box_enc_pre(conv_15);</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>&#160; conv_15_2_box_enc_pre &lt;&lt; get_node_C_qasymm(conv_15, data_path, <span class="stringliteral">&quot;BoxPredictor_3_BEP&quot;</span>, 24U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(3), box_reshape.at(3));</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>&#160;</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16_2_box_enc_pre(conv_16);</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>&#160; conv_16_2_box_enc_pre &lt;&lt; get_node_C_qasymm(conv_16, data_path, <span class="stringliteral">&quot;BoxPredictor_4_BEP&quot;</span>, 24U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(4), box_reshape.at(4));</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>&#160;</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_17_2_box_enc_pre(conv_17);</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>&#160; conv_17_2_box_enc_pre &lt;&lt; get_node_C_qasymm(conv_17, data_path, <span class="stringliteral">&quot;BoxPredictor_5_BEP&quot;</span>, 24U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(5), box_reshape.at(5));</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>&#160;</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> box_enc_pre(graph);</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> bep_concate_qinfo = <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.08655580133199692f, 183);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>&#160; box_enc_pre &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(<a class="code" href="structarm__compute_1_1graph_1_1descriptors_1_1_concat_layer_descriptor.xhtml">arm_compute::graph::descriptors::ConcatLayerDescriptor</a>(<a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">DataLayoutDimension::HEIGHT</a>, bep_concate_qinfo),</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>&#160; std::move(conv_11_box_enc_pre), std::move(conv_13_box_enc_pre), conv_14_2_box_enc_pre, std::move(conv_15_2_box_enc_pre),</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>&#160; std::move(conv_16_2_box_enc_pre), std::move(conv_17_2_box_enc_pre))</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</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;BoxPredictor/concat&quot;</span>);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>&#160; box_enc_pre &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 1917U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;BoxPredictor/reshape&quot;</span>);</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>&#160;</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>&#160; <span class="comment">// class_predictor</span></div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>&#160; <span class="keyword">const</span> std::vector&lt;std::pair&lt;QuantizationInfo, QuantizationInfo&gt;&gt; class_pred_quant_info =</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>&#160; {</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.002744135679677129f, 125), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.05746262148022652f, 234) }, <span class="comment">// boxpredictor0_cp</span></div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0024326108396053314f, 80), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.03764628246426582f, 217) }, <span class="comment">// boxpredictor1_cp</span></div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0013898586621508002f, 141), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.034081317484378815f, 214) }, <span class="comment">// boxpredictor2_cp</span></div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0014176908880472183f, 133), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.033889178186655045f, 215) }, <span class="comment">// boxpredictor3_cp</span></div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.001090311910957098f, 125), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.02646234817802906f, 230) }, <span class="comment">// boxpredictor4_cp</span></div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>&#160; { <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.001134163816459477f, 115), <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.026926767081022263f, 218) } <span class="comment">// boxpredictor5_cp</span></div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>&#160; };</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>&#160;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>&#160; <span class="keyword">const</span> std::vector&lt;TensorShape&gt; class_reshape =</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>&#160; {</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(91U, 1083U), <span class="comment">// boxpredictor0_cp_reshape</span></div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(91U, 600U), <span class="comment">// boxpredictor1_cp_reshape</span></div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(91U, 150U), <span class="comment">// boxpredictor2_cp_reshape</span></div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(91U, 54U), <span class="comment">// boxpredictor3_cp_reshape</span></div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(91U, 24U), <span class="comment">// boxpredictor4_cp_reshape</span></div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>&#160; <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(91U, 6U) <span class="comment">// boxpredictor5_cp_reshape</span></div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>&#160; };</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>&#160;</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_sub_stream.xhtml">SubStream</a> conv_11_class_pre(graph);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>&#160; conv_11_class_pre &lt;&lt; get_node_C_qasymm(graph, data_path, <span class="stringliteral">&quot;BoxPredictor_0_CP&quot;</span>, 273U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), class_pred_quant_info.at(0), class_reshape.at(0));</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>&#160;</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_13_class_pre(conv_13);</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>&#160; conv_13_class_pre &lt;&lt; get_node_C_qasymm(conv_13, data_path, <span class="stringliteral">&quot;BoxPredictor_1_CP&quot;</span>, 546U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), class_pred_quant_info.at(1), class_reshape.at(1));</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>&#160;</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_14_2_class_pre(conv_14);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>&#160; conv_14_2_class_pre &lt;&lt; get_node_C_qasymm(conv_14, data_path, <span class="stringliteral">&quot;BoxPredictor_2_CP&quot;</span>, 546U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), class_pred_quant_info.at(2), class_reshape.at(2));</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>&#160;</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_15_2_class_pre(conv_15);</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>&#160; conv_15_2_class_pre &lt;&lt; get_node_C_qasymm(conv_15, data_path, <span class="stringliteral">&quot;BoxPredictor_3_CP&quot;</span>, 546U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), class_pred_quant_info.at(3), class_reshape.at(3));</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>&#160;</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_16_2_class_pre(conv_16);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>&#160; conv_16_2_class_pre &lt;&lt; get_node_C_qasymm(conv_16, data_path, <span class="stringliteral">&quot;BoxPredictor_4_CP&quot;</span>, 546U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), class_pred_quant_info.at(4), class_reshape.at(4));</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>&#160;</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> conv_17_2_class_pre(conv_17);</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>&#160; conv_17_2_class_pre &lt;&lt; get_node_C_qasymm(conv_17, data_path, <span class="stringliteral">&quot;BoxPredictor_5_CP&quot;</span>, 546U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), class_pred_quant_info.at(5), class_reshape.at(5));</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>&#160;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> cp_concate_qinfo = <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0584389753639698f, 230);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> class_pred(graph);</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>&#160; class_pred &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1descriptors_1_1_concat_layer_descriptor.xhtml">arm_compute::graph::descriptors::ConcatLayerDescriptor</a>(<a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">DataLayoutDimension::WIDTH</a>, cp_concate_qinfo),</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>&#160; std::move(conv_11_class_pre), std::move(conv_13_class_pre), std::move(conv_14_2_class_pre),</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>&#160; std::move(conv_15_2_class_pre), std::move(conv_16_2_class_pre), std::move(conv_17_2_class_pre))</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</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;ClassPrediction/concat&quot;</span>);</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>&#160;</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> logistic_out_qinfo = <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00390625f, 0);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>&#160; class_pred &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>), logistic_out_qinfo).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;ClassPrediction/logistic&quot;</span>);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>&#160;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> max_detections = 10;</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> max_classes_per_detection = 1;</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> nms_score_threshold = 0.30000001192092896f;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> nms_iou_threshold = 0.6000000238418579f;</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>&#160; <span class="keyword">const</span> <span class="keywordtype">int</span> num_classes = 90;</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> x_scale = 10.f;</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> y_scale = 10.f;</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> h_scale = 5.f;</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>&#160; <span class="keyword">const</span> <span class="keywordtype">float</span> w_scale = 5.f;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>&#160; std::array&lt;float, 4&gt; scales = { y_scale, x_scale, w_scale, h_scale };</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>&#160; <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> anchors_qinfo = <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.006453060545027256f, 0);</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; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> detection_ouput(box_enc_pre);</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>&#160; detection_ouput &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_detection_post_process_layer.xhtml">DetectionPostProcessLayer</a>(std::move(class_pred),</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>&#160; <a class="code" href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">DetectionPostProcessLayerInfo</a>(max_detections, max_classes_per_detection, nms_score_threshold, nms_iou_threshold, num_classes, scales),</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;anchors.npy&quot;</span>), anchors_qinfo)</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</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;DetectionPostProcess&quot;</span>);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>&#160;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> ouput_0(detection_ouput);</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>&#160; ouput_0 &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#a10e9c53263d766cbd37e4e37f5e8091e">get_npy_output_accessor</a>(detection_boxes_opt-&gt;value(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(4U, 10U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), 0);</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>&#160;</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> ouput_1(detection_ouput);</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>&#160; ouput_1 &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#a10e9c53263d766cbd37e4e37f5e8091e">get_npy_output_accessor</a>(detection_classes_opt-&gt;value(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(10U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), 1);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>&#160;</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> ouput_2(detection_ouput);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>&#160; ouput_2 &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#a10e9c53263d766cbd37e4e37f5e8091e">get_npy_output_accessor</a>(detection_scores_opt-&gt;value(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(10U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), 2);</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>&#160;</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> ouput_3(detection_ouput);</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>&#160; ouput_3 &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#a10e9c53263d766cbd37e4e37f5e8091e">get_npy_output_accessor</a>(num_detections_opt-&gt;value(), <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>), 3);</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>&#160; }</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>&#160;};</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>&#160;<span class="comment">/** Main program for MobileNetSSD</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>&#160;<span class="comment"> * Model is based on:</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span>&#160;<span class="comment"> * http://arxiv.org/abs/1512.02325</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>&#160;<span class="comment"> * SSD: Single Shot MultiBox Detector</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>&#160;<span class="comment"> * Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg</span></div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>&#160;<span class="comment"> * Provenance: https://github.com/chuanqi305/MobileNet-SSD</span></div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00710"></a><span class="lineno"> 710</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="l00711"></a><span class="lineno"> 711</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>&#160;<span class="comment"> * @param[in] argc Number of arguments</span></div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>&#160;<span class="comment"> * @param[in] argv Arguments</span></div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00715"></a><span class="lineno"><a class="line" href="graph__ssd__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 715</a></span>&#160;<span class="keywordtype">int</span> <a class="code" href="graph__ssd__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>&#160;{</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>&#160; <span class="keywordflow">return</span> arm_compute::utils::run_example&lt;GraphSSDMobilenetExample&gt;(argc, argv);</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>&#160;}</div><div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a1fcd64682b37ed3c2098d0094ce788d8"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a1fcd64682b37ed3c2098d0094ce788d8">arm_compute::graph::TensorDescriptor::shape</a></div><div class="ttdeci">TensorShape shape</div><div class="ttdoc">Tensor shape.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00109">TensorDescriptor.h:109</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="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</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="namespacearm__compute_xhtml_ad818ba0ecd4a87d8f1bb0d5b17f07830a1150a8d7752b01d30d91fe18fe9d8a54"><div class="ttname"><a href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830a1150a8d7752b01d30d91fe18fe9d8a54">arm_compute::DetectionOutputLayerCodeType::CENTER_SIZE</a></div><div class="ttdoc">Use box centers and size.</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="namespacearm__compute_1_1graph__utils_xhtml_aff280480ba1a9075fed13fbb15ca0063"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#aff280480ba1a9075fed13fbb15ca0063">arm_compute::graph_utils::get_detection_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_detection_output_accessor(const arm_compute::utils::CommonGraphParams &amp;graph_parameters, std::vector&lt; TensorShape &gt; tensor_shapes, 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#l00577">GraphUtils.h:577</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="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">arm_compute::graph::frontend::DepthwiseConvolutionLayer</a></div><div class="ttdoc">Depthwise Convolution Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00439">Layers.h:439</a></div></div>
<div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02ad770ba3ce18fa409965dfdf5e7c348e6">arm_compute::DataLayoutDimension::HEIGHT</a></div><div class="ttdoc">height</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00048">Types.h:48</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="namespacearm__compute_1_1graph__utils_xhtml_a10e9c53263d766cbd37e4e37f5e8091e"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a10e9c53263d766cbd37e4e37f5e8091e">arm_compute::graph_utils::get_npy_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_npy_output_accessor(const std::string &amp;npy_path, TensorShape shape, DataType data_type, DataLayout data_layout=DataLayout::NCHW, std::ostream &amp;output_stream=std::cout)</div><div class="ttdoc">Generates appropriate npy output accessor according to the specified npy_path.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00611">GraphUtils.h:611</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_command_line_parser_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">arm_compute::utils::CommandLineParser</a></div><div class="ttdoc">Class to parse command line arguments.</div><div class="ttdef"><b>Definition:</b> <a href="_command_line_parser_8h_source.xhtml#l00044">CommandLineParser.h:44</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml">arm_compute::graph::frontend::IStream</a></div><div class="ttdoc">Stream interface.</div><div class="ttdef"><b>Definition:</b> <a href="_i_stream_8h_source.xhtml#l00042">IStream.h:42</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="namespacearm__compute_xhtml_ad818ba0ecd4a87d8f1bb0d5b17f07830"><div class="ttname"><a href="namespacearm__compute.xhtml#ad818ba0ecd4a87d8f1bb0d5b17f07830">arm_compute::DetectionOutputLayerCodeType</a></div><div class="ttdeci">DetectionOutputLayerCodeType</div><div class="ttdoc">Available Detection Output code types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00964">Types.h:964</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01615">Types.h:1615</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="graph__ssd__mobilenet_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__ssd__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for MobileNetSSD.</div><div class="ttdef"><b>Definition:</b> <a href="graph__ssd__mobilenet_8cpp_source.xhtml#l00715">graph_ssd_mobilenet.cpp:715</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="classarm__compute_1_1_quantization_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_quantization_info.xhtml">arm_compute::QuantizationInfo</a></div><div class="ttdoc">Quantization information.</div><div class="ttdef"><b>Definition:</b> <a href="_quantization_info_8h_source.xhtml#l00069">QuantizationInfo.h:69</a></div></div>
<div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_detection_post_process_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_detection_post_process_layer.xhtml">arm_compute::graph::frontend::DetectionPostProcessLayer</a></div><div class="ttdoc">DetectionOutputPostProcess Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00541">Layers.h:541</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00074">Utils.h:74</a></div></div>
<div class="ttc" id="classarm__compute_1_1_prior_box_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_prior_box_layer_info.xhtml">arm_compute::PriorBoxLayerInfo</a></div><div class="ttdoc">PriorBox layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00836">Types.h:836</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00686">Types.h:686</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</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="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">arm_compute::ActivationLayerInfo::ActivationFunction::LOGISTIC</a></div><div class="ttdoc">Logistic ( )</div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_afe5692937b0558d4cffe2d4fee57d581"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#afe5692937b0558d4cffe2d4fee57d581">arm_compute::graph::TensorDescriptor::set_quantization_info</a></div><div class="ttdeci">TensorDescriptor &amp; set_quantization_info(QuantizationInfo tensor_quant_info)</div><div class="ttdoc">Sets tensor descriptor quantization info.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00097">TensorDescriptor.h:97</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="structarm__compute_1_1utils_1_1_common_graph_params_xhtml_a96b4a087acee7543a7624102a67fc14d"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a96b4a087acee7543a7624102a67fc14d">arm_compute::utils::CommonGraphParams::image</a></div><div class="ttdeci">std::string image</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00102">CommonGraphOptions.h:102</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="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2a7ca82c5e74421cb45f17e936abf964"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2a7ca82c5e74421cb45f17e936abf964">arm_compute::graph::TensorDescriptor::target</a></div><div class="ttdeci">Target target</div><div class="ttdoc">Target.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00113">TensorDescriptor.h:113</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="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_prior_box_layer.xhtml">arm_compute::graph::frontend::PriorBoxLayer</a></div><div class="ttdoc">PriorBox Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00991">Layers.h:991</a></div></div>
<div class="ttc" id="classarm__compute_1_1_detection_output_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_detection_output_layer_info.xhtml">arm_compute::DetectionOutputLayerInfo</a></div><div class="ttdoc">Detection Output layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00973">Types.h:973</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="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier ( )</div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#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_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div>
<div class="ttc" id="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="classarm__compute_1_1graph_1_1frontend_1_1_detection_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_detection_output_layer.xhtml">arm_compute::graph::frontend::DetectionOutputLayer</a></div><div class="ttdoc">DetectionOutput Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00512">Layers.h:512</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="classarm__compute_1_1_detection_post_process_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_detection_post_process_layer_info.xhtml">arm_compute::DetectionPostProcessLayerInfo</a></div><div class="ttdoc">Detection Output layer info.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01092">Types.h:1092</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="structarm__compute_1_1graph_1_1descriptors_1_1_concat_layer_descriptor_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1descriptors_1_1_concat_layer_descriptor.xhtml">arm_compute::graph::descriptors::ConcatLayerDescriptor</a></div><div class="ttdoc">Concatenate layer descriptor.</div><div class="ttdef"><b>Definition:</b> <a href="_layer_descriptors_8h_source.xhtml#l00036">LayerDescriptors.h:36</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02a49da85b69bc6285eeee286ca49fa7195">arm_compute::DataLayoutDimension::WIDTH</a></div><div class="ttdoc">width</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="structarm__compute_1_1utils_1_1_common_graph_params_xhtml_a30a81dbc66a8e9eeb693a75046b4655d"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a30a81dbc66a8e9eeb693a75046b4655d">arm_compute::utils::CommonGraphParams::data_path</a></div><div class="ttdeci">std::string data_path</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00101">CommonGraphOptions.h:101</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">arm_compute::graph::frontend::FlattenLayer</a></div><div class="ttdoc">Flatten Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00626">Layers.h:626</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer &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_1utils_1_1_simple_option_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_simple_option.xhtml">arm_compute::utils::SimpleOption&lt; int &gt;</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_option_xhtml_a48a2672f362eeed9a3e93403f4d3de37"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">arm_compute::utils::Option::set_help</a></div><div class="ttdeci">void set_help(std::string help)</div><div class="ttdoc">Set the help message for the option.</div><div class="ttdef"><b>Definition:</b> <a href="_option_8h_source.xhtml#l00125">Option.h:125</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_af5982a092e9eb743fce2d6392bdd8897"><div class="ttname"><a href="namespacearm__compute.xhtml#af5982a092e9eb743fce2d6392bdd8897">arm_compute::is_data_type_float</a></div><div class="ttdeci">bool is_data_type_float(DataType dt)</div><div class="ttdoc">Check if a given data type is of floating point type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01097">Utils.h:1097</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 class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_permute_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_permute_layer.xhtml">arm_compute::graph::frontend::PermuteLayer</a></div><div class="ttdoc">Permute Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00855">Layers.h:855</a></div></div>
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