| <a href="graph__mobilenet_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2019 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.xhtml">arm_compute</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_1utils.xhtml">arm_compute::utils</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_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</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="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">class </span>GraphMobilenetExample : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  GraphMobilenetExample()</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">"MobileNetV1"</span>)</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="comment">// Add model id option</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  model_id_opt = cmd_parser.add_option<<a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption<int></a>>(<span class="stringliteral">"model-id"</span>, 0);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  model_id_opt-><a class="code" href="classarm__compute_1_1utils_1_1_option.xhtml#a48a2672f362eeed9a3e93403f4d3de37">set_help</a>(<span class="stringliteral">"Mobilenet model id (0: 1.0_224, else: 0.75_160"</span>);</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  }</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  GraphMobilenetExample(<span class="keyword">const</span> GraphMobilenetExample &) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  GraphMobilenetExample &operator=(<span class="keyword">const</span> GraphMobilenetExample &) = <span class="keyword">delete</span>;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  GraphMobilenetExample(GraphMobilenetExample &&) = <span class="keywordflow">default</span>; <span class="comment">// NOLINT</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  GraphMobilenetExample &operator=(GraphMobilenetExample &&) = <span class="keywordflow">default</span>; <span class="comment">// NOLINT</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  ~GraphMobilenetExample() <span class="keyword">override</span> = <span class="keywordflow">default</span>;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</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="l00052"></a><span class="lineno"> 52</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</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="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> </div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  std::cout << common_params << std::endl;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="comment">// Get model parameters</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="keywordtype">int</span> model_id = model_id_opt->value();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> </div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> spatial_size = (model_id == 0 || common_params.data_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>) ? 224 : 160;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> </div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(spatial_size, spatial_size, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, common_params.data_layout);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</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="l00078"></a><span class="lineno"> 78</span> </div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="comment">// Set graph hints</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  graph << common_params.<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2a7ca82c5e74421cb45f17e936abf964">target</a></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  << DepthwiseConvolutionMethod::Optimized3x3 <span class="comment">// TODO(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  << common_params.fast_math_hint;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="comment">// Create core graph</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#af5982a092e9eb743fce2d6392bdd8897">arm_compute::is_data_type_float</a>(common_params.data_type))</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  create_graph_float(input_descriptor, model_id);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  }</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  create_graph_qasymm(input_descriptor);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  }</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// Create common tail</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1001U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Reshape"</span>)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">SoftmaxLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Softmax"</span>)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">num_threads</a> = common_params.threads;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">use_tuner</a> = common_params.enable_tuner;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">tuner_mode</a> = common_params.tuner_mode;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">tuner_file</a> = common_params.tuner_file;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  graph.finalize(common_params.target, config);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  graph.run();</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  }</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <a class="code" href="classarm__compute_1_1utils_1_1_simple_option.xhtml">SimpleOption<int></a> *model_id_opt{ <span class="keyword">nullptr</span> };</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">void</span> create_graph_float(<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> &input_descriptor, <span class="keywordtype">int</span> model_id)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordtype">float</span> depth_scale = (model_id == 0) ? 1.f : 0.75;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  std::string model_path = (model_id == 0) ? <span class="stringliteral">"/cnn_data/mobilenet_v1_1_224_model/"</span> : <span class="stringliteral">"/cnn_data/mobilenet_v1_075_160_model/"</span>;</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  std::string data_path = common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a30a81dbc66a8e9eeb693a75046b4655d">data_path</a>;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Add model path to data path</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordflow">if</span>(!data_path.empty())</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  {</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  data_path += model_path;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  }</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  <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="l00142"></a><span class="lineno"> 142</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  3U, 3U, 32U * depth_scale,</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">"Conv2d_0_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0"</span>)</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_moving_variance.npy"</span>),</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Conv2d_0_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  0.001f)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0/BatchNorm"</span>)</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0/Relu6"</span>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_1"</span>, 64 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_2"</span>, 128 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_3"</span>, 128 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_4"</span>, 256 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_5"</span>, 256 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_6"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_7"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_8"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_9"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_10"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_11"</span>, 512 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_12"</span>, 1024 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  graph << get_dwsc_node_float(data_path, <span class="stringliteral">"Conv2d_13"</span>, 1024 * depth_scale, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Logits/AvgPool_1a"</span>)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  1U, 1U, 1001U,</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_biases.npy"</span>),</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0))</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Logits/Conv2d_1c_1x1"</span>);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  }</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span> </div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keywordtype">void</span> create_graph_qasymm(<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> &input_descriptor)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  std::string data_path = common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a30a81dbc66a8e9eeb693a75046b4655d">data_path</a>;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// Add model path to data path</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordflow">if</span>(!data_path.empty())</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  {</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  data_path += <span class="stringliteral">"/cnn_data/mobilenet_qasymm8_model/"</span>;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  }</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// Quantization info taken from the AndroidNN QASYMM8 MobileNet example</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> in_quant_info = <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0078125f, 128);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keyword">const</span> std::vector<QuantizationInfo> conv_weights_quant_info =</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  {</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.02182667888700962f, 151), <span class="comment">// conv0</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.004986600950360298f, 74) <span class="comment">// conv14</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  };</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keyword">const</span> std::vector<QuantizationInfo> conv_out_quant_info =</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  {</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.023528477177023888f, 0), <span class="comment">// conv0</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.16609922051429749f, 66) <span class="comment">// conv14</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  };</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> std::vector<QuantizationInfo> depth_weights_quant_info =</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.29219913482666016f, 110), <span class="comment">// dwsc1</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.40277284383773804f, 130), <span class="comment">// dwsc2</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.06053730100393295f, 160), <span class="comment">// dwsc3</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.01675807684659958f, 123), <span class="comment">// dwsc4</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.04105526953935623f, 129), <span class="comment">// dwsc5</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.013460792601108551f, 122), <span class="comment">// dwsc6</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.036934755742549896f, 132), <span class="comment">// dwsc7</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.042609862983226776f, 94), <span class="comment">// dwsc8</span></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.028358859941363335f, 127), <span class="comment">// dwsc9</span></div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.024329448118805885f, 134), <span class="comment">// dwsc10</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.019366811960935593f, 106), <span class="comment">// dwsc11</span></div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.007835594937205315f, 126), <span class="comment">// dwsc12</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.12616927921772003f, 211) <span class="comment">// dwsc13</span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  };</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keyword">const</span> std::vector<QuantizationInfo> point_weights_quant_info =</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  {</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.030420949682593346f, 121), <span class="comment">// dwsc1</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.015148180536925793f, 104), <span class="comment">// dwsc2</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.013755458407104015f, 94), <span class="comment">// dwsc3</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.007601846940815449f, 151), <span class="comment">// dwsc4</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.006431614048779011f, 122), <span class="comment">// dwsc5</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00917122047394514f, 109), <span class="comment">// dwsc6</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.005300046876072884f, 140), <span class="comment">// dwsc7</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.0049632852897048f, 127), <span class="comment">// dwsc8</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.007770895957946777f, 89), <span class="comment">// dwsc9</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.009658650495111942f, 99), <span class="comment">// dwsc10</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.005446993745863438f, 153), <span class="comment">// dwsc11</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.00817922968417406f, 130), <span class="comment">// dwsc12</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a>(0.018048152327537537f, 95) <span class="comment">// dwsc13</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  };</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> </div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  graph << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor.<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#afe5692937b0558d4cffe2d4fee57d581">set_quantization_info</a>(in_quant_info),</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#a96b4a087acee7543a7624102a67fc14d">image</a>))</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_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  3U, 3U, 32U,</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">"Conv2d_0_weights.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">"Conv2d_0_bias.npy"</span>),</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>),</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  1, conv_weights_quant_info.at(0), conv_out_quant_info.at(0))</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  .set_name(<span class="stringliteral">"Conv2d_0"</span>)</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_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"Conv2d_0/Relu6"</span>);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_1"</span>, 64U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0));</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_2"</span>, 128U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1),</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  point_weights_quant_info.at(1));</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_3"</span>, 128U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2),</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  point_weights_quant_info.at(2));</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_4"</span>, 256U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3),</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  point_weights_quant_info.at(3));</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_5"</span>, 256U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4),</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  point_weights_quant_info.at(4));</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_6"</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5),</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  point_weights_quant_info.at(5));</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_7"</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6),</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  point_weights_quant_info.at(6));</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_8"</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7),</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  point_weights_quant_info.at(7));</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_9"</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8),</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  point_weights_quant_info.at(8));</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_10"</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9),</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  point_weights_quant_info.at(9));</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_11"</span>, 512U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10),</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  point_weights_quant_info.at(10));</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_12"</span>, 1024U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2U, 2U, 0U, 1U, 0U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11),</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  point_weights_quant_info.at(11));</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  graph << get_dwsc_node_qasymm(data_path, <span class="stringliteral">"Conv2d_13"</span>, 1024U, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 1U, 1U, 1U, 1U, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  point_weights_quant_info.at(12))</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>)).set_name(<span class="stringliteral">"Logits/AvgPool_1a"</span>)</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  1U, 1U, 1001U,</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_weights.npy"</span>),</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"Logits_Conv2d_1c_1x1_bias.npy"</span>),</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1), conv_out_quant_info.at(1))</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  .set_name(<span class="stringliteral">"Logits/Conv2d_1c_1x1"</span>);</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> </div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_dwsc_node_float(<span class="keyword">const</span> std::string &data_path, std::string &&param_path,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> dwc_pad_stride_info, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info)</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  {</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  std::string total_path = param_path + <span class="stringliteral">"_"</span>;</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> sg(graph);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  sg << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">DepthwiseConvolutionLayer</a>(</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  3U, 3U,</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_depthwise_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  dwc_pad_stride_info)</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/depthwise"</span>)</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</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="l00294"></a><span class="lineno"> 294</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_moving_mean.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, total_path + <span class="stringliteral">"depthwise_BatchNorm_moving_variance.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, total_path + <span class="stringliteral">"depthwise_BatchNorm_gamma.npy"</span>),</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  0.001f)</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/BatchNorm"</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_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/Relu6"</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_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  1U, 1U, conv_filt,</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_weights.npy"</span>, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>),</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  std::unique_ptr<arm_compute::graph::ITensorAccessor>(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  conv_pad_stride_info)</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/Conv2D"</span>)</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</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="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_moving_mean.npy"</span>),</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_BatchNorm_moving_variance.npy"</span>),</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, total_path + <span class="stringliteral">"pointwise_BatchNorm_gamma.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, total_path + <span class="stringliteral">"pointwise_BatchNorm_beta.npy"</span>),</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  0.001f)</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/BatchNorm"</span>)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/Relu6"</span>);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(sg));</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  }</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> </div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a> get_dwsc_node_qasymm(<span class="keyword">const</span> std::string &data_path, std::string &&param_path,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> conv_filt,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> dwc_pad_stride_info, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_pad_stride_info,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> depth_weights_quant_info, <a class="code" href="classarm__compute_1_1_quantization_info.xhtml">QuantizationInfo</a> point_weights_quant_info)</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  {</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  std::string total_path = param_path + <span class="stringliteral">"_"</span>;</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> sg(graph);</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  sg << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_depthwise_convolution_layer.xhtml">DepthwiseConvolutionLayer</a>(</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  3U, 3U,</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_weights.npy"</span>),</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"depthwise_bias.npy"</span>),</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  dwc_pad_stride_info, 1, std::move(depth_weights_quant_info))</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/depthwise"</span>)</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"depthwise/Relu6"</span>)</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  1U, 1U, conv_filt,</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_weights.npy"</span>),</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">"pointwise_bias.npy"</span>),</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  conv_pad_stride_info, 1, std::move(point_weights_quant_info))</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/Conv2D"</span>)</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, 6.f)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(total_path + <span class="stringliteral">"pointwise/Relu6"</span>);</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> </div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_concat_layer.xhtml">ConcatLayer</a>(std::move(sg));</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  }</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> };</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00361"></a><span class="lineno"><a class="line" href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 361</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__mobilenet_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> {</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphMobilenetExample>(argc, argv);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> }</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00838">Layers.h:838</a></div></div> |
