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gerrit-public.fairphone.software / platform / external / ComputeLibrary / 0e205f7e1083bf26e6ffc33d96005999b541b8f9 / . / documentation / graph__yolov3_8cpp_source.xhtml
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<div class="title">graph_yolov3.cpp</div> </div>
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<a href="graph__yolov3_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_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="comment">/** Example demonstrating how to implement YOLOv3 network using the Compute Library&#39;s graph API */</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">class </span>GraphYOLOv3Example : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; GraphYOLOv3Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">&quot;YOLOv3&quot;</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</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="l00044"></a><span class="lineno"> 44</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160; cmd_parser.validate();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</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="l00051"></a><span class="lineno"> 51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <span class="keywordflow">if</span>(common_params.help)</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; cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160; }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; <span class="comment">// Checks</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160; <a class="code" href="_error_8h.xhtml#a292b758f9eba8b487d71eae4b37326fc">ARM_COMPUTE_EXIT_ON_MSG</a>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a>(common_params.data_type), <span class="stringliteral">&quot;QASYMM8 not supported for this graph&quot;</span>);</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; std::cout &lt;&lt; common_params &lt;&lt; std::endl;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; std::string data_path = common_params.data_path;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160; <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; std::unique_ptr&lt;IPreprocessor&gt; preprocessor = arm_compute::support::cpp14::make_unique&lt;TFPreproccessor&gt;(0.f);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160; <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="keyword">const</span> TensorShape tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(TensorShape(608U, 608U, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, common_params.data_layout);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</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="l00074"></a><span class="lineno"> 74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; graph &lt;&lt; common_params.target</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160; &lt;&lt; common_params.fast_math_hint</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">get_input_accessor</a>(common_params, std::move(preprocessor), <span class="keyword">false</span>));</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; std::pair&lt;SubStream, SubStream&gt; intermediate_layers = darknet53(data_path, weights_layout);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</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="l00083"></a><span class="lineno"> 83</span>&#160; 1U, 1U, 512U,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_53_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_53&quot;</span>)</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_53_mean.npy&quot;</span>),</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_53_var.npy&quot;</span>),</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_53_gamma.npy&quot;</span>),</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_53_beta.npy&quot;</span>),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; 0.000001f)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_53/BatchNorm&quot;</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_53/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</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="l00097"></a><span class="lineno"> 97</span>&#160; 3U, 3U, 1024U,</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_54_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_54&quot;</span>)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_54_mean.npy&quot;</span>),</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_54_var.npy&quot;</span>),</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_54_gamma.npy&quot;</span>),</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_54_beta.npy&quot;</span>),</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; 0.000001f)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_54/BatchNorm&quot;</span>)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_54/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</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="l00111"></a><span class="lineno"> 111</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_55_mean.npy&quot;</span>),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_55_var.npy&quot;</span>),</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_55_gamma.npy&quot;</span>),</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_55_beta.npy&quot;</span>),</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; 0.000001f)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_55/BatchNorm&quot;</span>)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_55/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</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="l00125"></a><span class="lineno"> 125</span>&#160; 3U, 3U, 1024U,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_56_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_56&quot;</span>)</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_56_beta.npy&quot;</span>),</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; 0.000001f)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_56/BatchNorm&quot;</span>)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_56/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_57_beta.npy&quot;</span>),</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; 0.000001f)</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>(<span class="stringliteral">&quot;conv2d_57/BatchNorm&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_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_57/LeakyRelu&quot;</span>);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> route_1(graph);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</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="l00154"></a><span class="lineno"> 154</span>&#160; 3U, 3U, 1024U,</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_58_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_58&quot;</span>)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_58_mean.npy&quot;</span>),</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_58_var.npy&quot;</span>),</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_58_gamma.npy&quot;</span>),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_58_beta.npy&quot;</span>),</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; 0.000001f)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_58/BatchNorm&quot;</span>)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_59_b.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_59&quot;</span>)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">ActivationLayerInfo::ActivationFunction::LINEAR</a>, 1.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_59/Linear&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_y_o_l_o_layer.xhtml">YOLOLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>, 0.1f), 80).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Yolo1&quot;</span>)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; route_1 &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="l00177"></a><span class="lineno"> 177</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_59_mean.npy&quot;</span>),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_59_var.npy&quot;</span>),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_59_gamma.npy&quot;</span>),</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_59_beta.npy&quot;</span>),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; 0.000001f)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_59/BatchNorm&quot;</span>)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_60/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_upsample_layer.xhtml">UpsampleLayer</a>(Size2D(2, 2), <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Upsample_60&quot;</span>);</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_sub_stream.xhtml">SubStream</a> concat_1(route_1);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; concat_1 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(route_1), std::move(intermediate_layers.second)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Route1&quot;</span>)</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</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="l00194"></a><span class="lineno"> 194</span>&#160; 1U, 1U, 256U,</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_61_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_61&quot;</span>)</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_60_mean.npy&quot;</span>),</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_60_var.npy&quot;</span>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_60_gamma.npy&quot;</span>),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_60_beta.npy&quot;</span>),</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; 0.000001f)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_60/BatchNorm&quot;</span>)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_61/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</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="l00208"></a><span class="lineno"> 208</span>&#160; 3U, 3U, 512U,</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_62_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_62&quot;</span>)</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_61_mean.npy&quot;</span>),</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_61_var.npy&quot;</span>),</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_61_gamma.npy&quot;</span>),</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_61_beta.npy&quot;</span>),</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; 0.000001f)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_61/BatchNorm&quot;</span>)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_62/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</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="l00222"></a><span class="lineno"> 222</span>&#160; 1U, 1U, 256U,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_63_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_63&quot;</span>)</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_62_mean.npy&quot;</span>),</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_62_var.npy&quot;</span>),</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_62_gamma.npy&quot;</span>),</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_62_beta.npy&quot;</span>),</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160; 0.000001f)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_62/BatchNorm&quot;</span>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_63/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160; 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&lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_63_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;/cnn_data/yolov3_model/batch_normalization_63_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;/cnn_data/yolov3_model/batch_normalization_63_gamma.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;/cnn_data/yolov3_model/batch_normalization_63_beta.npy&quot;</span>),</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160; 0.000001f)</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_63/BatchNorm&quot;</span>)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_64/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</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="l00250"></a><span class="lineno"> 250</span>&#160; 1U, 1U, 256U,</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_65_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_65&quot;</span>)</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_64_beta.npy&quot;</span>),</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>&#160; 0.000001f)</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_65/BatchNorm&quot;</span>)</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_65/LeakyRelu&quot;</span>);</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> route_2(concat_1);</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>&#160; concat_1 &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="l00265"></a><span class="lineno"> 265</span>&#160; 3U, 3U, 512U,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_66_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_66&quot;</span>)</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_65_mean.npy&quot;</span>),</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_65_var.npy&quot;</span>),</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_65_gamma.npy&quot;</span>),</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_65_beta.npy&quot;</span>),</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>&#160; 0.000001f)</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_65/BatchNorm&quot;</span>)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_67_b.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_67&quot;</span>)</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">ActivationLayerInfo::ActivationFunction::LINEAR</a>, 1.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_67/Linear&quot;</span>)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_66_mean.npy&quot;</span>),</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_66_var.npy&quot;</span>),</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_66_gamma.npy&quot;</span>),</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_66_beta.npy&quot;</span>),</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>&#160; 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<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_upsample_layer.xhtml">UpsampleLayer</a>(Size2D(2, 2), <a class="code" href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">InterpolationPolicy::NEAREST_NEIGHBOR</a>).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Upsample_68&quot;</span>);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> concat_2(route_2);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>&#160; concat_2 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(route_2), std::move(intermediate_layers.first)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Route2&quot;</span>)</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>&#160; 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&lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_67_mean.npy&quot;</span>),</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_67_var.npy&quot;</span>),</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_67_gamma.npy&quot;</span>),</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_67_beta.npy&quot;</span>),</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>&#160; 0.000001f)</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_67/BatchNorm&quot;</span>)</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_69/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</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="l00319"></a><span class="lineno"> 319</span>&#160; 3U, 3U, 256U,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_70_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_70&quot;</span>)</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_68_beta.npy&quot;</span>),</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>&#160; 0.000001f)</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_68/BatchNorm&quot;</span>)</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_70/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</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="l00333"></a><span class="lineno"> 333</span>&#160; 1U, 1U, 128U,</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_71_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>&#160; PadStrideInfo(1, 1, 0, 0))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_71&quot;</span>)</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_69_mean.npy&quot;</span>),</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_69_var.npy&quot;</span>),</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_69_gamma.npy&quot;</span>),</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_69_beta.npy&quot;</span>),</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>&#160; 0.000001f)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_69/BatchNorm&quot;</span>)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_71/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</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="l00347"></a><span class="lineno"> 347</span>&#160; 3U, 3U, 256U,</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_72_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_72&quot;</span>)</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_70_mean.npy&quot;</span>),</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_70_var.npy&quot;</span>),</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_70_gamma.npy&quot;</span>),</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_70_beta.npy&quot;</span>),</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>&#160; 0.000001f)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_70/BatchNorm&quot;</span>)</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_72/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</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="l00361"></a><span class="lineno"> 361</span>&#160; 1U, 1U, 128U,</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_73_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_73&quot;</span>)</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_71_mean.npy&quot;</span>),</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_71_var.npy&quot;</span>),</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_71_gamma.npy&quot;</span>),</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_71_beta.npy&quot;</span>),</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>&#160; 0.000001f)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_71/BatchNorm&quot;</span>)</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_73/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</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="l00375"></a><span class="lineno"> 375</span>&#160; 3U, 3U, 256U,</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_74_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>&#160; PadStrideInfo(1, 1, 1, 1))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_74&quot;</span>)</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_72_mean.npy&quot;</span>),</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_72_var.npy&quot;</span>),</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_72_gamma.npy&quot;</span>),</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_72_beta.npy&quot;</span>),</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>&#160; 0.000001f)</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_72/BatchNorm&quot;</span>)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_74/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>&#160; 1U, 1U, 255U,</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_75_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_75_b.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_75&quot;</span>)</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">ActivationLayerInfo::ActivationFunction::LINEAR</a>, 1.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_75/Linear&quot;</span>)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_y_o_l_o_layer.xhtml">YOLOLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaa72ee60fba0509af07cbbd91398d8db9d">ActivationLayerInfo::ActivationFunction::LOGISTIC</a>, 0.1f), 80).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Yolo3&quot;</span>)</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>&#160;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>&#160; <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</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="l00401"></a><span class="lineno"> 401</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="l00402"></a><span class="lineno"> 402</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">tuner_mode</a> = common_params.tuner_mode;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</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="l00404"></a><span class="lineno"> 404</span>&#160;</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>&#160; graph.finalize(common_params.target, config);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>&#160;</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</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="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>&#160; <span class="comment">// Run graph</span></div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>&#160; graph.run();</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>&#160; }</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>&#160;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</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="l00417"></a><span class="lineno"> 417</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="l00418"></a><span class="lineno"> 418</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="l00419"></a><span class="lineno"> 419</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="l00420"></a><span class="lineno"> 420</span>&#160;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>&#160; std::pair&lt;SubStream, SubStream&gt; darknet53(<span class="keyword">const</span> std::string &amp;data_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout)</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>&#160; {</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>&#160; 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="l00424"></a><span class="lineno"> 424</span>&#160; 3U, 3U, 32U,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_1_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>&#160; PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_1/Conv2D&quot;</span>)</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="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, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_1_mean.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, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_1_var.npy&quot;</span>),</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_1_gamma.npy&quot;</span>),</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_1_beta.npy&quot;</span>),</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>&#160; 0.000001f)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_1/BatchNorm&quot;</span>)</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_1/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</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="l00438"></a><span class="lineno"> 438</span>&#160; 3U, 3U, 64U,</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_2_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>&#160; PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_2/Conv2D&quot;</span>)</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_2_beta.npy&quot;</span>),</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>&#160; 0.000001f)</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_2/BatchNorm&quot;</span>)</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_2/LeakyRelu&quot;</span>);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>&#160; 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.<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_5/Conv2D&quot;</span>)</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_5_mean.npy&quot;</span>),</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_5_var.npy&quot;</span>),</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_5_gamma.npy&quot;</span>),</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_5_beta.npy&quot;</span>),</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>&#160; 0.000001f)</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_5/BatchNorm&quot;</span>)</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_10_mean.npy&quot;</span>),</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_10_var.npy&quot;</span>),</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_10_gamma.npy&quot;</span>),</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_10_beta.npy&quot;</span>),</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>&#160; 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<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_27_var.npy&quot;</span>),</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_27_gamma.npy&quot;</span>),</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_27_beta.npy&quot;</span>),</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>&#160; 0.000001f)</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>&#160; 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darknet53_block(data_path, <span class="stringliteral">&quot;30&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;32&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;34&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;36&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;38&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;40&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;42&quot;</span>, weights_layout, 256U);</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> layer_61(graph);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</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="l00515"></a><span class="lineno"> 515</span>&#160; 3U, 3U, 1024U,</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/conv2d_44_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>&#160; PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_44/Conv2D&quot;</span>)</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_44_mean.npy&quot;</span>),</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_44_var.npy&quot;</span>),</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, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_44_gamma.npy&quot;</span>),</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/yolov3_model/batch_normalization_44_beta.npy&quot;</span>),</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>&#160; 0.000001f)</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_44/BatchNorm&quot;</span>)</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_44/LeakyRelu&quot;</span>);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;45&quot;</span>, weights_layout, 512U);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;47&quot;</span>, weights_layout, 512U);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;49&quot;</span>, weights_layout, 512U);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>&#160; darknet53_block(data_path, <span class="stringliteral">&quot;51&quot;</span>, weights_layout, 512U);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>&#160;</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>&#160; <span class="keywordflow">return</span> std::pair&lt;SubStream, SubStream&gt;(layer_36, layer_61);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>&#160; }</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>&#160;</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>&#160; <span class="keywordtype">void</span> darknet53_block(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2237230a1357685ba2472c2d6fca17fa">filter_size</a>)</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>&#160; {</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>&#160; std::string total_path = <span class="stringliteral">&quot;/cnn_data/yolov3_model/&quot;</span>;</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>&#160; std::string param_path2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">arm_compute::support::cpp11::to_string</a>(<a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#abdba606a789b8d664774f17d18f45cfe">arm_compute::support::cpp11::stoi</a>(param_path) + 1);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b(graph);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>&#160; i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>&#160; 1U, 1U, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2237230a1357685ba2472c2d6fca17fa">filter_size</a>,</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</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;conv2d_&quot;</span> + param_path + <span class="stringliteral">&quot;_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_&quot;</span> + param_path + <span class="stringliteral">&quot;/Conv2D&quot;</span>)</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;batch_normalization_&quot;</span> + param_path + <span class="stringliteral">&quot;_mean.npy&quot;</span>),</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</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;batch_normalization_&quot;</span> + param_path + <span class="stringliteral">&quot;_var.npy&quot;</span>),</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</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;batch_normalization_&quot;</span> + param_path + <span class="stringliteral">&quot;_gamma.npy&quot;</span>),</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</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;batch_normalization_&quot;</span> + param_path + <span class="stringliteral">&quot;_beta.npy&quot;</span>),</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>&#160; 0.000001f)</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_&quot;</span> + param_path + <span class="stringliteral">&quot;/BatchNorm&quot;</span>)</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_&quot;</span> + param_path + <span class="stringliteral">&quot;/LeakyRelu&quot;</span>)</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</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="l00558"></a><span class="lineno"> 558</span>&#160; 3U, 3U, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2237230a1357685ba2472c2d6fca17fa">filter_size</a> * 2,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</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;conv2d_&quot;</span> + param_path2 + <span class="stringliteral">&quot;_w.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>&#160; PadStrideInfo(1, 1, 1, 1))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_&quot;</span> + param_path2 + <span class="stringliteral">&quot;/Conv2D&quot;</span>)</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</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;batch_normalization_&quot;</span> + param_path2 + <span class="stringliteral">&quot;_mean.npy&quot;</span>),</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</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;batch_normalization_&quot;</span> + param_path2 + <span class="stringliteral">&quot;_var.npy&quot;</span>),</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</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;batch_normalization_&quot;</span> + param_path2 + <span class="stringliteral">&quot;_gamma.npy&quot;</span>),</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</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;batch_normalization_&quot;</span> + param_path2 + <span class="stringliteral">&quot;_beta.npy&quot;</span>),</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>&#160; 0.000001f)</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_&quot;</span> + param_path2 + <span class="stringliteral">&quot;/BatchNorm&quot;</span>)</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a>, 0.1f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv2d_&quot;</span> + param_path2 + <span class="stringliteral">&quot;/LeakyRelu&quot;</span>);</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; graph &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">EltwiseLayer</a>(std::move(i_a), std::move(i_b), EltwiseOperation::Add).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;&quot;</span>).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;add_&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span> + param_path2);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>&#160; }</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;<span class="comment"></span></div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>&#160;<span class="comment">/** Main program for YOLOv3</span></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>&#160;<span class="comment"> * Model is based on:</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>&#160;<span class="comment"> * https://arxiv.org/abs/1804.02767</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>&#160;<span class="comment"> * &quot;YOLOv3: An Incremental Improvement&quot;</span></div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>&#160;<span class="comment"> * Joseph Redmon, Ali Farhadi</span></div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</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="l00584"></a><span class="lineno"> 584</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>&#160;<span class="comment"> * @param[in] argc Number of arguments</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>&#160;<span class="comment"> * @param[in] argv Arguments</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>&#160;<span class="comment"> * @return Return code</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00590"></a><span class="lineno"><a class="line" href="graph__yolov3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 590</a></span>&#160;<span class="keywordtype">int</span> <a class="code" href="graph__yolov3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</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; <span class="keywordflow">return</span> arm_compute::utils::run_example&lt;GraphYOLOv3Example&gt;(argc, argv);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">arm_compute::graph::frontend::SubStream</a></div><div class="ttdoc">Sub stream class.</div><div class="ttdef"><b>Definition:</b> <a href="_sub_stream_8h_source.xhtml#l00047">SubStream.h:47</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml">arm_compute::graph::GraphConfig</a></div><div class="ttdoc">Graph configuration structure Device target types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00078">Types.h:78</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a249f3f713c6ea8f564e760559cf509f4"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">arm_compute::graph::GraphConfig::tuner_mode</a></div><div class="ttdeci">CLTunerMode tuner_mode</div><div class="ttdoc">Tuner mode to be used by the CL tuner.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</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="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="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1support_1_1cpp11_xhtml_acc5dddee1cbe93a4eaf0a9f74ee96bb7"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp11.xhtml#acc5dddee1cbe93a4eaf0a9f74ee96bb7">arm_compute::support::cpp11::to_string</a></div><div class="ttdeci">std::string to_string(T &amp;&amp;value)</div><div class="ttdoc">Convert integer and float values to string.</div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00272">ToolchainSupport.h:272</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#l00317">graph_validate_utils.h:317</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_xhtml_a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2"><div class="ttname"><a href="namespacearm__compute.xhtml#a966a9c417ce5e94dca08d9b5e745c0c9a7f5ccbc3d30c2cd3fd04d567946cbde2">arm_compute::InterpolationPolicy::NEAREST_NEIGHBOR</a></div><div class="ttdoc">Output values are defined to match the source pixel whose center is nearest to the sample position.</div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_command_line_parser_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">arm_compute::utils::CommandLineParser</a></div><div class="ttdoc">Class to parse command line arguments.</div><div class="ttdef"><b>Definition:</b> <a href="_command_line_parser_8h_source.xhtml#l00044">CommandLineParser.h:44</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a5cabfb35cd0014387f7ec2a0c362c20f"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">arm_compute::graph::GraphConfig::tuner_file</a></div><div class="ttdeci">std::string tuner_file</div><div class="ttdoc">File to load/store tuning values from.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00086">Types.h:86</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_upsample_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_upsample_layer.xhtml">arm_compute::graph::frontend::UpsampleLayer</a></div><div class="ttdoc">Upsample Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01225">Layers.h:1225</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_1graph_1_1frontend_1_1_y_o_l_o_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_y_o_l_o_layer.xhtml">arm_compute::graph::frontend::YOLOLayer</a></div><div class="ttdoc">YOLO Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01251">Layers.h:1251</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a2237230a1357685ba2472c2d6fca17fa"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a2237230a1357685ba2472c2d6fca17fa">arm_compute::test::validation::filter_size</a></div><div class="ttdeci">filter_size</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_8cpp_source.xhtml#l00119">Convolution.cpp:119</a></div></div>
<div class="ttc" id="_error_8h_xhtml_a292b758f9eba8b487d71eae4b37326fc"><div class="ttname"><a href="_error_8h.xhtml#a292b758f9eba8b487d71eae4b37326fc">ARM_COMPUTE_EXIT_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_EXIT_ON_MSG(cond, msg)</div><div class="ttdoc">If the condition is true, the given message is printed and program exits.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00379">Error.h:379</a></div></div>
<div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00074">Utils.h:74</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00121">Layers.h:121</a></div></div>
<div class="ttc" id="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="namespacearm__compute_1_1support_1_1cpp11_xhtml_abdba606a789b8d664774f17d18f45cfe"><div class="ttname"><a href="namespacearm__compute_1_1support_1_1cpp11.xhtml#abdba606a789b8d664774f17d18f45cfe">arm_compute::support::cpp11::stoi</a></div><div class="ttdeci">int stoi(const std::string &amp;str, std::size_t *pos=0, NumericBase base=NumericBase::BASE_10)</div><div class="ttdoc">Convert string values to integer.</div><div class="ttdef"><b>Definition:</b> <a href="_toolchain_support_8h_source.xhtml#l00067">ToolchainSupport.h:67</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">arm_compute::graph::frontend::ConvolutionLayer</a></div><div class="ttdoc">Convolution Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00334">Layers.h:334</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab3a897163a7fe23208f1d9c618062ee2"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">arm_compute::graph_utils::permute_shape</a></div><div class="ttdeci">TensorShape permute_shape(TensorShape tensor_shape, DataLayout in_data_layout, DataLayout out_data_layout)</div><div class="ttdoc">Permutes a given tensor shape given the input and output data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00664">GraphUtils.h:664</a></div></div>
<div class="ttc" id="_common_graph_options_8h_xhtml"><div class="ttname"><a href="_common_graph_options_8h.xhtml">CommonGraphOptions.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01044">Utils.h:1044</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2497d23622ec1343e507331ae1388f00"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">arm_compute::graph::TensorDescriptor::set_layout</a></div><div class="ttdeci">TensorDescriptor &amp; set_layout(DataLayout data_layout)</div><div class="ttdoc">Sets tensor descriptor data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00086">TensorDescriptor.h:86</a></div></div>
<div class="ttc" id="structarm__compute_1_1utils_1_1_common_graph_params_xhtml"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">arm_compute::utils::CommonGraphParams</a></div><div class="ttdoc">Structure holding all the common graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00090">CommonGraphOptions.h:90</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_safe_ops_8h_source.xhtml#l00032">SafeOps.h:32</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00044">GraphUtils.h:44</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaac7e80a3de04936f4e423e1b564fdca10">arm_compute::ActivationLayerInfo::ActivationFunction::LEAKY_RELU</a></div><div class="ttdoc">Leaky Rectifier ( )</div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ae3d177d243f5fb34544105a4ee4e1f58"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">arm_compute::graph_utils::get_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_output_accessor(const arm_compute::utils::CommonGraphParams &amp;graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &amp;output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00543">GraphUtils.h:543</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a9da74af255a3e6ea61180d4a03192a48"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">arm_compute::graph::GraphConfig::use_tuner</a></div><div class="ttdeci">bool use_tuner</div><div class="ttdoc">Use a tuner in tunable backends.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00083">Types.h:83</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaaac544aacc3615aada24897a215f5046">arm_compute::ActivationLayerInfo::ActivationFunction::LINEAR</a></div><div class="ttdoc">Linear ( )</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">arm_compute::graph::frontend::OutputLayer</a></div><div class="ttdoc">Output Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00095">Layers.h:95</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_weights_accessor(const std::string &amp;path, const std::string &amp;data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00475">GraphUtils.h:475</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a08963f7335eef295237ab460863bc3d5"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">arm_compute::graph::GraphConfig::num_threads</a></div><div class="ttdeci">int num_threads</div><div class="ttdoc">Number of threads to use (thread capable backends), if 0 the backend will auto-initialize,...</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00085">Types.h:85</a></div></div>
<div class="ttc" id="graph__yolov3_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__yolov3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for YOLOv3.</div><div class="ttdef"><b>Definition:</b> <a href="graph__yolov3_8cpp_source.xhtml#l00590">graph_yolov3.cpp:590</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">arm_compute::graph::frontend::Stream</a></div><div class="ttdoc">Stream frontend class to construct simple graphs in a stream fashion.</div><div class="ttdef"><b>Definition:</b> <a href="_stream_8h_source.xhtml#l00045">Stream.h:45</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1frontend_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00031">ILayer.h:31</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00149">Layers.h:149</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">arm_compute::graph::frontend::EltwiseLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00597">Layers.h:597</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00116">Types.h:116</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer &amp; set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">arm_compute::graph::frontend::ConcatLayer</a></div><div class="ttdoc">Concat Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00248">Layers.h:248</a></div></div>
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