arm_compute v19.02
Change-Id: I853a3ecf38f206da13c1b03640c8adf73c20477c
diff --git a/documentation/graph__yolov3_8cpp_source.xhtml b/documentation/graph__yolov3_8cpp_source.xhtml
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<div class="title">graph_yolov3.cpp</div> </div>
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<div class="contents">
-<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> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_graph_8h.xhtml">arm_compute/graph.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_common_graph_options_8h.xhtml">utils/CommonGraphOptions.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <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> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  GraphYOLOv3Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">"YOLOv3"</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">bool</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  common_params = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a04125f2e4cecaffad8724cee7e1c19b0">consume_common_graph_parameters</a>(common_opts);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// Checks</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a>(common_params.data_type), <span class="stringliteral">"QASYMM8 not supported for this graph"</span>);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  std::cout << common_params << std::endl;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::string data_path = common_params.data_path;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <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="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> input_descriptor = <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a>(tensor_shape, common_params.data_type).<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">set_layout</a>(common_params.data_layout);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  graph << common_params.target</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  << common_params.fast_math_hint</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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="l00080"></a><span class="lineno"> 80</span>  std::pair<SubStream, SubStream> intermediate_layers = darknet53(data_path, weights_layout);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  1U, 1U, 512U,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_53_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_53"</span>)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_mean.npy"</span>),</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_var.npy"</span>),</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"</span>),</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_beta.npy"</span>),</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  0.000001f)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_53/BatchNorm"</span>)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_53/LeakyRelu"</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_54_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_54"</span>)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_mean.npy"</span>),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_var.npy"</span>),</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"</span>),</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_beta.npy"</span>),</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  0.000001f)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_54/BatchNorm"</span>)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_54/LeakyRelu"</span>)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  1U, 1U, 512U,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_55_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_55"</span>)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_mean.npy"</span>),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_var.npy"</span>),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"</span>),</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_beta.npy"</span>),</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  0.000001f)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_55/BatchNorm"</span>)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_55/LeakyRelu"</span>)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_56_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_56"</span>)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_mean.npy"</span>),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_var.npy"</span>),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"</span>),</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_beta.npy"</span>),</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  0.000001f)</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_56/BatchNorm"</span>)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_56/LeakyRelu"</span>)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  1U, 1U, 512U,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_57_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_57"</span>)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_mean.npy"</span>),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_var.npy"</span>),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"</span>),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_beta.npy"</span>),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  0.000001f)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_57/BatchNorm"</span>)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv2d_57/LeakyRelu"</span>);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> route_1(graph);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_58_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_58"</span>)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_mean.npy"</span>),</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_var.npy"</span>),</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"</span>),</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_beta.npy"</span>),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  0.000001f)</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_58/BatchNorm"</span>)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_58/LeakyRelu"</span>)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  1U, 1U, 255U,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_59_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_59_b.npy"</span>, weights_layout),</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_59"</span>)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_59/Linear"</span>)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  << <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)</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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="l00175"></a><span class="lineno"> 175</span>  route_1 << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  1U, 1U, 256U,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_60_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_60"</span>)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_mean.npy"</span>),</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_var.npy"</span>),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"</span>),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_beta.npy"</span>),</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  0.000001f)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_59/BatchNorm"</span>)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_60/LeakyRelu"</span>)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  << <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">"Upsample_60"</span>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <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="l00191"></a><span class="lineno"> 191</span>  concat_1 << <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))</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  1U, 1U, 256U,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_61_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_61"</span>)</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_mean.npy"</span>),</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_var.npy"</span>),</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"</span>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_beta.npy"</span>),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  0.000001f)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_60/BatchNorm"</span>)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv2d_61/LeakyRelu"</span>)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  3U, 3U, 512U,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_62_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_62"</span>)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_mean.npy"</span>),</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_var.npy"</span>),</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"</span>),</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_beta.npy"</span>),</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  0.000001f)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_61/BatchNorm"</span>)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv2d_62/LeakyRelu"</span>)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  1U, 1U, 256U,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_63_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_63"</span>)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_mean.npy"</span>),</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_var.npy"</span>),</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"</span>),</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_beta.npy"</span>),</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  0.000001f)</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_62/BatchNorm"</span>)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_63/LeakyRelu"</span>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  3U, 3U, 512U,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_64_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_64"</span>)</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_mean.npy"</span>),</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_var.npy"</span>),</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"</span>),</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_beta.npy"</span>),</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  0.000001f)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_63/BatchNorm"</span>)</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_64/LeakyRelu"</span>)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  1U, 1U, 256U,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_65_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_65"</span>)</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_mean.npy"</span>),</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_var.npy"</span>),</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"</span>),</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_beta.npy"</span>),</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  0.000001f)</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_65/BatchNorm"</span>)</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_65/LeakyRelu"</span>);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <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="l00263"></a><span class="lineno"> 263</span>  concat_1 << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  3U, 3U, 512U,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_66_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_66"</span>)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_mean.npy"</span>),</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_var.npy"</span>),</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"</span>),</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_beta.npy"</span>),</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  0.000001f)</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_65/BatchNorm"</span>)</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_66/LeakyRelu"</span>)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  1U, 1U, 255U,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_67_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_67_b.npy"</span>, weights_layout),</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_67"</span>)</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_67/Linear"</span>)</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  << <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)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  route_2 << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  1U, 1U, 128U,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_68_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_68"</span>)</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_mean.npy"</span>),</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_var.npy"</span>),</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"</span>),</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_beta.npy"</span>),</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  0.000001f)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_66/BatchNorm"</span>)</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_68/LeakyRelu"</span>)</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  << <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">"Upsample_68"</span>);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <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="l00302"></a><span class="lineno"> 302</span>  concat_2 << <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))</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  1U, 1U, 128U,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_69_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_69"</span>)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_mean.npy"</span>),</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_var.npy"</span>),</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"</span>),</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_beta.npy"</span>),</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  0.000001f)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_67/BatchNorm"</span>)</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_69/LeakyRelu"</span>)</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  3U, 3U, 256U,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_70_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_70"</span>)</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_mean.npy"</span>),</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_var.npy"</span>),</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"</span>),</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_beta.npy"</span>),</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  0.000001f)</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_68/BatchNorm"</span>)</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_70/LeakyRelu"</span>)</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  1U, 1U, 128U,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_71_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_71"</span>)</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_mean.npy"</span>),</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_var.npy"</span>),</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"</span>),</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_beta.npy"</span>),</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  0.000001f)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_69/BatchNorm"</span>)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_71/LeakyRelu"</span>)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  3U, 3U, 256U,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_72_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_72"</span>)</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_mean.npy"</span>),</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_var.npy"</span>),</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"</span>),</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_beta.npy"</span>),</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  0.000001f)</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_70/BatchNorm"</span>)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_72/LeakyRelu"</span>)</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  1U, 1U, 128U,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_73_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_73"</span>)</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_mean.npy"</span>),</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_var.npy"</span>),</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"</span>),</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_beta.npy"</span>),</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  0.000001f)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_71/BatchNorm"</span>)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_73/LeakyRelu"</span>)</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  3U, 3U, 256U,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_74_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_74"</span>)</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_mean.npy"</span>),</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_var.npy"</span>),</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"</span>),</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_beta.npy"</span>),</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  0.000001f)</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_72/BatchNorm"</span>)</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_74/LeakyRelu"</span>)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  1U, 1U, 255U,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_75_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_75_b.npy"</span>, weights_layout),</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_75"</span>)</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_75/Linear"</span>)</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  << <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)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">num_threads</a> = common_params.threads;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">use_tuner</a> = common_params.enable_tuner;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">tuner_file</a> = common_params.tuner_file;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  graph.finalize(common_params.target, config);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  }</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  graph.run();</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  std::pair<SubStream, SubStream> darknet53(<span class="keyword">const</span> std::string &data_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout)</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  3U, 3U, 32U,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_1"</span>)</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_mean.npy"</span>),</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_var.npy"</span>),</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"</span>),</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_beta.npy"</span>),</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  0.000001f)</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_1/BatchNorm"</span>)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_1/LeakyRelu"</span>)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  3U, 3U, 64U,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_2"</span>)</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_mean.npy"</span>),</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_var.npy"</span>),</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"</span>),</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_beta.npy"</span>),</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  0.000001f)</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_2/BatchNorm"</span>)</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_2/LeakyRelu"</span>);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  darknet53_block(data_path, <span class="stringliteral">"3"</span>, weights_layout, 32U);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  3U, 3U, 128U,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_5_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_5"</span>)</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_mean.npy"</span>),</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_var.npy"</span>),</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"</span>),</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_beta.npy"</span>),</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  0.000001f)</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_5/BatchNorm"</span>)</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_5/LeakyRelu"</span>);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  darknet53_block(data_path, <span class="stringliteral">"6"</span>, weights_layout, 64U);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  darknet53_block(data_path, <span class="stringliteral">"8"</span>, weights_layout, 64U);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  3U, 3U, 256U,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_10_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_10"</span>)</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_mean.npy"</span>),</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_var.npy"</span>),</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"</span>),</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_beta.npy"</span>),</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  0.000001f)</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_10/BatchNorm"</span>)</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_10/LeakyRelu"</span>);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  darknet53_block(data_path, <span class="stringliteral">"11"</span>, weights_layout, 128U);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  darknet53_block(data_path, <span class="stringliteral">"13"</span>, weights_layout, 128U);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  darknet53_block(data_path, <span class="stringliteral">"15"</span>, weights_layout, 128U);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  darknet53_block(data_path, <span class="stringliteral">"17"</span>, weights_layout, 128U);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  darknet53_block(data_path, <span class="stringliteral">"19"</span>, weights_layout, 128U);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  darknet53_block(data_path, <span class="stringliteral">"21"</span>, weights_layout, 128U);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  darknet53_block(data_path, <span class="stringliteral">"23"</span>, weights_layout, 128U);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  darknet53_block(data_path, <span class="stringliteral">"25"</span>, weights_layout, 128U);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> layer_36(graph);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  3U, 3U, 512U,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_27_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_27"</span>)</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_mean.npy"</span>),</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_var.npy"</span>),</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"</span>),</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_beta.npy"</span>),</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  0.000001f)</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_27/BatchNorm"</span>)</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_27/LeakyRelu"</span>);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  darknet53_block(data_path, <span class="stringliteral">"28"</span>, weights_layout, 256U);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  darknet53_block(data_path, <span class="stringliteral">"30"</span>, weights_layout, 256U);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  darknet53_block(data_path, <span class="stringliteral">"32"</span>, weights_layout, 256U);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  darknet53_block(data_path, <span class="stringliteral">"34"</span>, weights_layout, 256U);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  darknet53_block(data_path, <span class="stringliteral">"36"</span>, weights_layout, 256U);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  darknet53_block(data_path, <span class="stringliteral">"38"</span>, weights_layout, 256U);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  darknet53_block(data_path, <span class="stringliteral">"40"</span>, weights_layout, 256U);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  darknet53_block(data_path, <span class="stringliteral">"42"</span>, weights_layout, 256U);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <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="l00512"></a><span class="lineno"> 512</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_44_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_44"</span>)</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_mean.npy"</span>),</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_var.npy"</span>),</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"</span>),</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_beta.npy"</span>),</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  0.000001f)</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_44/BatchNorm"</span>)</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_44/LeakyRelu"</span>);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  darknet53_block(data_path, <span class="stringliteral">"45"</span>, weights_layout, 512U);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  darknet53_block(data_path, <span class="stringliteral">"47"</span>, weights_layout, 512U);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  darknet53_block(data_path, <span class="stringliteral">"49"</span>, weights_layout, 512U);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  darknet53_block(data_path, <span class="stringliteral">"51"</span>, weights_layout, 512U);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">return</span> std::pair<SubStream, SubStream>(layer_36, layer_61);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  }</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordtype">void</span> darknet53_block(<span class="keyword">const</span> std::string &data_path, std::string &&param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <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="l00536"></a><span class="lineno"> 536</span>  {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  std::string total_path = <span class="stringliteral">"/cnn_data/yolov3_model/"</span>;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  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="l00539"></a><span class="lineno"> 539</span>  <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="l00540"></a><span class="lineno"> 540</span>  <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="l00541"></a><span class="lineno"> 541</span>  i_a << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  1U, 1U, filter_size,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"conv2d_"</span> + param_path + <span class="stringliteral">"_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_mean.npy"</span>),</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_var.npy"</span>),</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_gamma.npy"</span>),</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_beta.npy"</span>),</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  0.000001f)</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_"</span> + param_path + <span class="stringliteral">"/BatchNorm"</span>)</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d"</span> + param_path + <span class="stringliteral">"/LeakyRelu"</span>)</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  3U, 3U, filter_size * 2,</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"conv2d_"</span> + param_path2 + <span class="stringliteral">"_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_mean.npy"</span>),</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_var.npy"</span>),</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_gamma.npy"</span>),</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_beta.npy"</span>),</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  0.000001f)</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_"</span> + param_path2 + <span class="stringliteral">"/BatchNorm"</span>)</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_"</span> + param_path2 + <span class="stringliteral">"/LeakyRelu"</span>);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  graph << <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);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  }</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> };</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span> </div><div class="line"><a name="l00586"></a><span class="lineno"><a class="line" href="graph__yolov3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 586</a></span> <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="l00587"></a><span class="lineno"> 587</span> {</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphYOLOv3Example>(argc, argv);</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> }</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#l00075">Types.h:75</a></div></div>
+<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> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2018-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_graph_8h.xhtml">arm_compute/graph.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_common_graph_options_8h.xhtml">utils/CommonGraphOptions.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <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> {</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  GraphYOLOv3Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">"YOLOv3"</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordtype">bool</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  common_params = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a04125f2e4cecaffad8724cee7e1c19b0">consume_common_graph_parameters</a>(common_opts);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="comment">// Checks</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a>(common_params.data_type), <span class="stringliteral">"QASYMM8 not supported for this graph"</span>);</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  std::cout << common_params << std::endl;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  std::string data_path = common_params.data_path;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f);</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <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="l00072"></a><span class="lineno"> 72</span>  <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> input_descriptor = <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a>(tensor_shape, common_params.data_type).<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">set_layout</a>(common_params.data_layout);</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  graph << common_params.target</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  << common_params.fast_math_hint</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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="l00080"></a><span class="lineno"> 80</span>  std::pair<SubStream, SubStream> intermediate_layers = darknet53(data_path, weights_layout);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  1U, 1U, 512U,</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_53_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_53"</span>)</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_mean.npy"</span>),</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_var.npy"</span>),</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"</span>),</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_53_beta.npy"</span>),</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  0.000001f)</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_53/BatchNorm"</span>)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_53/LeakyRelu"</span>)</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_54_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_54"</span>)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_mean.npy"</span>),</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_var.npy"</span>),</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"</span>),</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_54_beta.npy"</span>),</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  0.000001f)</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_54/BatchNorm"</span>)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_54/LeakyRelu"</span>)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  1U, 1U, 512U,</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_55_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_55"</span>)</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_mean.npy"</span>),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_var.npy"</span>),</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"</span>),</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_55_beta.npy"</span>),</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  0.000001f)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_55/BatchNorm"</span>)</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_55/LeakyRelu"</span>)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_56_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_56"</span>)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_mean.npy"</span>),</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_var.npy"</span>),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"</span>),</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_56_beta.npy"</span>),</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  0.000001f)</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_56/BatchNorm"</span>)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_56/LeakyRelu"</span>)</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  1U, 1U, 512U,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_57_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_57"</span>)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_mean.npy"</span>),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_var.npy"</span>),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"</span>),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_57_beta.npy"</span>),</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  0.000001f)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_57/BatchNorm"</span>)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv2d_57/LeakyRelu"</span>);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> route_1(graph);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_58_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_58"</span>)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_mean.npy"</span>),</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_var.npy"</span>),</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"</span>),</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_58_beta.npy"</span>),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  0.000001f)</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_58/BatchNorm"</span>)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_58/LeakyRelu"</span>)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  1U, 1U, 255U,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_59_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_59_b.npy"</span>, weights_layout),</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_59"</span>)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_59/Linear"</span>)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  << <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">"Yolo1"</span>)</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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="l00175"></a><span class="lineno"> 175</span>  route_1 << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  1U, 1U, 256U,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_60_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_60"</span>)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_mean.npy"</span>),</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_var.npy"</span>),</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"</span>),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_59_beta.npy"</span>),</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  0.000001f)</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_59/BatchNorm"</span>)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_60/LeakyRelu"</span>)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  << <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">"Upsample_60"</span>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <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="l00191"></a><span class="lineno"> 191</span>  concat_1 << <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">"Route1"</span>)</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  1U, 1U, 256U,</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_61_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_61"</span>)</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_mean.npy"</span>),</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_var.npy"</span>),</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"</span>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_60_beta.npy"</span>),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  0.000001f)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_60/BatchNorm"</span>)</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv2d_61/LeakyRelu"</span>)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  3U, 3U, 512U,</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_62_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_62"</span>)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_mean.npy"</span>),</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_var.npy"</span>),</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"</span>),</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_61_beta.npy"</span>),</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  0.000001f)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_61/BatchNorm"</span>)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv2d_62/LeakyRelu"</span>)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  1U, 1U, 256U,</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_63_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_63"</span>)</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_mean.npy"</span>),</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_var.npy"</span>),</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"</span>),</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_62_beta.npy"</span>),</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  0.000001f)</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_62/BatchNorm"</span>)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_63/LeakyRelu"</span>)</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  3U, 3U, 512U,</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_64_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_64"</span>)</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_mean.npy"</span>),</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_var.npy"</span>),</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"</span>),</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_63_beta.npy"</span>),</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  0.000001f)</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_63/BatchNorm"</span>)</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_64/LeakyRelu"</span>)</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  1U, 1U, 256U,</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_65_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_65"</span>)</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_mean.npy"</span>),</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_var.npy"</span>),</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"</span>),</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_64_beta.npy"</span>),</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  0.000001f)</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_65/BatchNorm"</span>)</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_65/LeakyRelu"</span>);</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <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="l00263"></a><span class="lineno"> 263</span>  concat_1 << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  3U, 3U, 512U,</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_66_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_66"</span>)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_mean.npy"</span>),</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_var.npy"</span>),</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"</span>),</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_65_beta.npy"</span>),</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  0.000001f)</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_65/BatchNorm"</span>)</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_66/LeakyRelu"</span>)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  1U, 1U, 255U,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_67_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_67_b.npy"</span>, weights_layout),</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_67"</span>)</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_67/Linear"</span>)</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  << <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">"Yolo2"</span>)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  route_2 << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  1U, 1U, 128U,</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_68_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_68"</span>)</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_mean.npy"</span>),</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_var.npy"</span>),</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"</span>),</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_66_beta.npy"</span>),</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  0.000001f)</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_66/BatchNorm"</span>)</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_68/LeakyRelu"</span>)</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  << <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">"Upsample_68"</span>);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <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="l00302"></a><span class="lineno"> 302</span>  concat_2 << <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">"Route2"</span>)</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  1U, 1U, 128U,</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_69_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_69"</span>)</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_mean.npy"</span>),</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_var.npy"</span>),</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"</span>),</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_67_beta.npy"</span>),</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  0.000001f)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_67/BatchNorm"</span>)</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_69/LeakyRelu"</span>)</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  3U, 3U, 256U,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_70_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_70"</span>)</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_mean.npy"</span>),</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_var.npy"</span>),</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"</span>),</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_68_beta.npy"</span>),</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  0.000001f)</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_68/BatchNorm"</span>)</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_70/LeakyRelu"</span>)</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  1U, 1U, 128U,</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_71_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_71"</span>)</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_mean.npy"</span>),</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_var.npy"</span>),</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"</span>),</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_69_beta.npy"</span>),</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  0.000001f)</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_69/BatchNorm"</span>)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_71/LeakyRelu"</span>)</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  3U, 3U, 256U,</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_72_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_72"</span>)</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_mean.npy"</span>),</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_var.npy"</span>),</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"</span>),</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_70_beta.npy"</span>),</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  0.000001f)</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_70/BatchNorm"</span>)</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_72/LeakyRelu"</span>)</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  1U, 1U, 128U,</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_73_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_73"</span>)</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_mean.npy"</span>),</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_var.npy"</span>),</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"</span>),</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_71_beta.npy"</span>),</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  0.000001f)</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_71/BatchNorm"</span>)</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_73/LeakyRelu"</span>)</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  3U, 3U, 256U,</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_74_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_74"</span>)</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_mean.npy"</span>),</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_var.npy"</span>),</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"</span>),</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_72_beta.npy"</span>),</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  0.000001f)</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_72/BatchNorm"</span>)</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_74/LeakyRelu"</span>)</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  1U, 1U, 255U,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_75_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_75_b.npy"</span>, weights_layout),</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_75"</span>)</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_75/Linear"</span>)</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  << <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">"Yolo3"</span>)</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> </div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">num_threads</a> = common_params.threads;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">use_tuner</a> = common_params.enable_tuner;</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">tuner_file</a> = common_params.tuner_file;</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span> </div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  graph.finalize(common_params.target, config);</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> </div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  }</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  graph.run();</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  std::pair<SubStream, SubStream> darknet53(<span class="keyword">const</span> std::string &data_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout)</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  {</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  3U, 3U, 32U,</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_1/Conv2D"</span>)</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_mean.npy"</span>),</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_var.npy"</span>),</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"</span>),</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_1_beta.npy"</span>),</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  0.000001f)</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_1/BatchNorm"</span>)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_1/LeakyRelu"</span>)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  3U, 3U, 64U,</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_2/Conv2D"</span>)</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_mean.npy"</span>),</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_var.npy"</span>),</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"</span>),</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_2_beta.npy"</span>),</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  0.000001f)</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_2/BatchNorm"</span>)</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_2/LeakyRelu"</span>);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  darknet53_block(data_path, <span class="stringliteral">"3"</span>, weights_layout, 32U);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  3U, 3U, 128U,</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_5_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_5/Conv2D"</span>)</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_mean.npy"</span>),</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_var.npy"</span>),</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"</span>),</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_5_beta.npy"</span>),</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  0.000001f)</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_5/BatchNorm"</span>)</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_5/LeakyRelu"</span>);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  darknet53_block(data_path, <span class="stringliteral">"6"</span>, weights_layout, 64U);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  darknet53_block(data_path, <span class="stringliteral">"8"</span>, weights_layout, 64U);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  3U, 3U, 256U,</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_10_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_10/Conv2D"</span>)</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_mean.npy"</span>),</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_var.npy"</span>),</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"</span>),</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_10_beta.npy"</span>),</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  0.000001f)</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_10/BatchNorm"</span>)</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_10/LeakyRelu"</span>);</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  darknet53_block(data_path, <span class="stringliteral">"11"</span>, weights_layout, 128U);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  darknet53_block(data_path, <span class="stringliteral">"13"</span>, weights_layout, 128U);</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  darknet53_block(data_path, <span class="stringliteral">"15"</span>, weights_layout, 128U);</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  darknet53_block(data_path, <span class="stringliteral">"17"</span>, weights_layout, 128U);</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  darknet53_block(data_path, <span class="stringliteral">"19"</span>, weights_layout, 128U);</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  darknet53_block(data_path, <span class="stringliteral">"21"</span>, weights_layout, 128U);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  darknet53_block(data_path, <span class="stringliteral">"23"</span>, weights_layout, 128U);</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  darknet53_block(data_path, <span class="stringliteral">"25"</span>, weights_layout, 128U);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> layer_36(graph);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  3U, 3U, 512U,</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_27_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_27/Conv2D"</span>)</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_mean.npy"</span>),</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_var.npy"</span>),</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"</span>),</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_27_beta.npy"</span>),</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  0.000001f)</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_27/BatchNorm"</span>)</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_27/LeakyRelu"</span>);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  darknet53_block(data_path, <span class="stringliteral">"28"</span>, weights_layout, 256U);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  darknet53_block(data_path, <span class="stringliteral">"30"</span>, weights_layout, 256U);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  darknet53_block(data_path, <span class="stringliteral">"32"</span>, weights_layout, 256U);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  darknet53_block(data_path, <span class="stringliteral">"34"</span>, weights_layout, 256U);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  darknet53_block(data_path, <span class="stringliteral">"36"</span>, weights_layout, 256U);</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  darknet53_block(data_path, <span class="stringliteral">"38"</span>, weights_layout, 256U);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  darknet53_block(data_path, <span class="stringliteral">"40"</span>, weights_layout, 256U);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  darknet53_block(data_path, <span class="stringliteral">"42"</span>, weights_layout, 256U);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <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="l00512"></a><span class="lineno"> 512</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  3U, 3U, 1024U,</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/conv2d_44_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  PadStrideInfo(2, 2, 1, 1))</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_44/Conv2D"</span>)</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_mean.npy"</span>),</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_var.npy"</span>),</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"</span>),</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/yolov3_model/batch_normalization_44_beta.npy"</span>),</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  0.000001f)</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_44/BatchNorm"</span>)</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_44/LeakyRelu"</span>);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  darknet53_block(data_path, <span class="stringliteral">"45"</span>, weights_layout, 512U);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  darknet53_block(data_path, <span class="stringliteral">"47"</span>, weights_layout, 512U);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  darknet53_block(data_path, <span class="stringliteral">"49"</span>, weights_layout, 512U);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  darknet53_block(data_path, <span class="stringliteral">"51"</span>, weights_layout, 512U);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> </div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <span class="keywordflow">return</span> std::pair<SubStream, SubStream>(layer_36, layer_61);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  }</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordtype">void</span> darknet53_block(<span class="keyword">const</span> std::string &data_path, std::string &&param_path, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <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="l00536"></a><span class="lineno"> 536</span>  {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  std::string total_path = <span class="stringliteral">"/cnn_data/yolov3_model/"</span>;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  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="l00539"></a><span class="lineno"> 539</span>  <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="l00540"></a><span class="lineno"> 540</span>  <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="l00541"></a><span class="lineno"> 541</span>  i_a << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  1U, 1U, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2237230a1357685ba2472c2d6fca17fa">filter_size</a>,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"conv2d_"</span> + param_path + <span class="stringliteral">"_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_"</span> + param_path + <span class="stringliteral">"/Conv2D"</span>)</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_mean.npy"</span>),</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_var.npy"</span>),</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_gamma.npy"</span>),</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path + <span class="stringliteral">"_beta.npy"</span>),</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  0.000001f)</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_"</span> + param_path + <span class="stringliteral">"/BatchNorm"</span>)</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_"</span> + param_path + <span class="stringliteral">"/LeakyRelu"</span>)</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  3U, 3U, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a2237230a1357685ba2472c2d6fca17fa">filter_size</a> * 2,</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"conv2d_"</span> + param_path2 + <span class="stringliteral">"_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_"</span> + param_path2 + <span class="stringliteral">"/Conv2D"</span>)</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_mean.npy"</span>),</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_var.npy"</span>),</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_gamma.npy"</span>),</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"batch_normalization_"</span> + param_path2 + <span class="stringliteral">"_beta.npy"</span>),</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  0.000001f)</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2d_"</span> + param_path2 + <span class="stringliteral">"/BatchNorm"</span>)</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2d_"</span> + param_path2 + <span class="stringliteral">"/LeakyRelu"</span>);</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  graph << <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">""</span>).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"add_"</span> + param_path + <span class="stringliteral">"_"</span> + param_path2);</div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  }</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> };</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00588"></a><span class="lineno"><a class="line" href="graph__yolov3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 588</a></span> <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="l00589"></a><span class="lineno"> 589</span> {</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphYOLOv3Example>(argc, argv);</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> }</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#l00076">Types.h:76</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab14324184f90f342227699c161654b1b"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_input_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified graph parameters. ...</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00408">GraphUtils.h:408</a></div></div>
-<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">arm_compute::graph::TensorDescriptor</a></div><div class="ttdoc">Tensor metadata class. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00038">TensorDescriptor.h:38</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab14324184f90f342227699c161654b1b"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_input_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00438">GraphUtils.h:438</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 &&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#l00210">ToolchainSupport.h:210</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 &&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#l00210">ToolchainSupport.h:210</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#l00125">CommonGraphOptions.h:125</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#l00081">Types.h:81</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#l00958">Layers.h:958</a></div></div>
-<div class="ttc" id="_error_8h_xhtml_ad39a3601153da57978bb5124ace35d36"><div class="ttname"><a href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_EXIT_ON_MSG(cond,...)</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#l00277">Error.h:277</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#l00984">Layers.h:984</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#l00134">Convolution.cpp:134</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#l00125">CommonGraphOptions.h:125</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#l00082">Types.h:82</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#l00990">Layers.h:990</a></div></div>
+<div class="ttc" id="_error_8h_xhtml_ad39a3601153da57978bb5124ace35d36"><div class="ttname"><a href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_EXIT_ON_MSG(cond,...)</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#l00277">Error.h:277</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#l01016">Layers.h:1016</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="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a04125f2e4cecaffad8724cee7e1c19b0"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a04125f2e4cecaffad8724cee7e1c19b0">arm_compute::utils::consume_common_graph_parameters</a></div><div class="ttdeci">CommonGraphParams consume_common_graph_parameters(CommonGraphOptions &options)</div><div class="ttdoc">Consumes the common graph options and creates a structure containing any information. </div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8cpp_source.xhtml#l00169">CommonGraphOptions.cpp:169</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#l00070">Utils.h:70</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00094">Layers.h:94</a></div></div>
-<div class="ttc" id="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 &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#l00063">ToolchainSupport.h:63</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#l00303">Layers.h:303</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#l00506">GraphUtils.h:506</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a04125f2e4cecaffad8724cee7e1c19b0"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a04125f2e4cecaffad8724cee7e1c19b0">arm_compute::utils::consume_common_graph_parameters</a></div><div class="ttdeci">CommonGraphParams consume_common_graph_parameters(CommonGraphOptions &options)</div><div class="ttdoc">Consumes the common graph options and creates a structure containing any information.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8cpp_source.xhtml#l00169">CommonGraphOptions.cpp:169</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#l00070">Utils.h:70</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00094">Layers.h:94</a></div></div>
+<div class="ttc" id="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 &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#l00063">ToolchainSupport.h:63</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#l00303">Layers.h:303</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#l00569">GraphUtils.h:569</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#l00996">Utils.h:996</a></div></div>
-<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2497d23622ec1343e507331ae1388f00"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">arm_compute::graph::TensorDescriptor::set_layout</a></div><div class="ttdeci">TensorDescriptor & set_layout(DataLayout data_layout)</div><div class="ttdoc">Sets tensor descriptor data layout. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00086">TensorDescriptor.h:86</a></div></div>
-<div class="ttc" id="structarm__compute_1_1utils_1_1_common_graph_params_xhtml"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">arm_compute::utils::CommonGraphParams</a></div><div class="ttdoc">Structure holding all the common graph parameters. </div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00088">CommonGraphOptions.h:88</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#l01014">Utils.h:1014</a></div></div>
+<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2497d23622ec1343e507331ae1388f00"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">arm_compute::graph::TensorDescriptor::set_layout</a></div><div class="ttdeci">TensorDescriptor & set_layout(DataLayout data_layout)</div><div class="ttdoc">Sets tensor descriptor data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00086">TensorDescriptor.h:86</a></div></div>
+<div class="ttc" id="structarm__compute_1_1utils_1_1_common_graph_params_xhtml"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">arm_compute::utils::CommonGraphParams</a></div><div class="ttdoc">Structure holding all the common graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00088">CommonGraphOptions.h:88</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_cast_8h_source.xhtml#l00031">Cast.h:31</a></div></div>
<div class="ttc" id="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< graph::ITensorAccessor > get_output_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &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#l00454">GraphUtils.h:454</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#l00079">Types.h:79</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#l00070">Layers.h:70</a></div></div>
-<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00386">GraphUtils.h:386</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, if -1 the backend will stay as it is. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00080">Types.h:80</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#l00586">graph_yolov3.cpp:586</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="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< graph::ITensorAccessor > get_output_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &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#l00484">GraphUtils.h:484</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#l00080">Types.h:80</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#l00070">Layers.h:70</a></div></div>
+<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00416">GraphUtils.h:416</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#l00081">Types.h:81</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#l00588">graph_yolov3.cpp:588</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">arm_compute::graph::frontend::Stream</a></div><div class="ttdoc">Stream frontend class to construct simple graphs in a stream fashion.</div><div class="ttdef"><b>Definition:</b> <a href="_stream_8h_source.xhtml#l00045">Stream.h:45</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1frontend_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00031">ILayer.h:31</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00118">Layers.h:118</a></div></div>
-<div class="ttc" id="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#l00482">Layers.h:482</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#l00111">Types.h:111</a></div></div>
-<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer & set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
-<div class="ttc" id="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#l00217">Layers.h:217</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00118">Layers.h:118</a></div></div>
+<div class="ttc" id="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#l00514">Layers.h:514</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#l00111">Types.h:111</a></div></div>
+<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer & set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
+<div class="ttc" id="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#l00217">Layers.h:217</a></div></div>
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<li class="navelem"><a class="el" href="dir_d28a4824dc47e487b107a5db32ef43c4.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__yolov3_8cpp.xhtml">graph_yolov3.cpp</a></li>
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