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<div class="title">graph_resnet_v2_50.cpp</div> </div>
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<a href="graph__resnet__v2__50_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2018-2020 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_8h.xhtml">arm_compute/graph.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>&quot;</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_common_graph_options_8h.xhtml">utils/CommonGraphOptions.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>&quot;</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;<span class="comment">/** Example demonstrating how to implement ResNetV2_50 network using the Compute Library&#39;s graph API */</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">class </span>GraphResNetV2_50Example : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160; GraphResNetV2_50Example()</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160; : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">&quot;ResNetV2_50&quot;</span>)</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160; {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160; }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160; <span class="keywordtype">bool</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160; <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160; cmd_parser.parse(argc, argv);</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160; cmd_parser.validate();</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160; <span class="comment">// Consume common parameters</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160; common_params = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a2593e1f13f425f627658900657f73dc3">consume_common_graph_parameters</a>(common_opts);</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160; <span class="comment">// Return when help menu is requested</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160; <span class="keywordflow">if</span>(common_params.help)</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; {</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160; cmd_parser.print_help(argv[0]);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160; <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160; }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160; <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160; std::cout &lt;&lt; common_params &lt;&lt; std::endl;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160; <span class="comment">// Get trainable parameters data path</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160; std::string data_path = common_params.data_path;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; std::string model_path = <span class="stringliteral">&quot;/cnn_data/resnet_v2_50_model/&quot;</span>;</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160; <span class="keywordflow">if</span>(!data_path.empty())</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160; {</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160; data_path += model_path;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160; }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160; <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160; std::unique_ptr&lt;IPreprocessor&gt; preprocessor = arm_compute::support::cpp14::make_unique&lt;TFPreproccessor&gt;();</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160; <span class="comment">// Create input descriptor</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <span class="keyword">const</span> <span class="keyword">auto</span> operation_layout = common_params.data_layout;</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160; <span class="keyword">const</span> TensorShape tensor_shape = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">permute_shape</a>(TensorShape(224U, 224U, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>, operation_layout);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a> input_descriptor = <a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml">TensorDescriptor</a>(tensor_shape, common_params.data_type).<a class="code" href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">set_layout</a>(operation_layout);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160; <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160; <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160; graph &lt;&lt; common_params.target</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160; &lt;&lt; common_params.fast_math_hint</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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> <span class="comment">/* Do not convert to BGR */</span>))</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160; 7U, 7U, 64U,</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;conv1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;conv1_biases.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; PadStrideInfo(2, 2, 3, 3))</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;conv1/convolution&quot;</span>)</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;pool1/MaxPool&quot;</span>);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; add_residual_block(data_path, <span class="stringliteral">&quot;block1&quot;</span>, weights_layout, 64, 3, 2);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; add_residual_block(data_path, <span class="stringliteral">&quot;block2&quot;</span>, weights_layout, 128, 4, 2);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160; add_residual_block(data_path, <span class="stringliteral">&quot;block3&quot;</span>, weights_layout, 256, 6, 2);</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160; add_residual_block(data_path, <span class="stringliteral">&quot;block4&quot;</span>, weights_layout, 512, 3, 1);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160; graph &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;postnorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;postnorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;postnorm_gamma.npy&quot;</span>),</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;postnorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160; 0.000009999999747378752f)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;postnorm/BatchNorm&quot;</span>)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;postnorm/Relu&quot;</span>)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>, operation_layout)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;pool5&quot;</span>)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160; 1U, 1U, 1001U,</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;logits_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;logits_biases.npy&quot;</span>),</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;logits/convolution&quot;</span>)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">FlattenLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;predictions/Reshape&quot;</span>)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">SoftmaxLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;predictions/Softmax&quot;</span>)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">get_output_accessor</a>(common_params, 5));</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">num_threads</a> = common_params.threads;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">use_tuner</a> = common_params.enable_tuner;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">tuner_mode</a> = common_params.tuner_mode;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">tuner_file</a> = common_params.tuner_file;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160; config.<a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a0292b7cb87d1bcd5c093c4b9d3b9c0bc">convert_to_uint8</a> = (common_params.data_type == <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160; graph.finalize(common_params.target, config);</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160; <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160; }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160; <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160;<span class="keyword"> </span>{</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160; <span class="comment">// Run graph</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160; graph.run();</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160; }</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">CommandLineParser</a> cmd_parser;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160; <a class="code" href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">CommonGraphOptions</a> common_opts;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160; <a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">CommonGraphParams</a> common_params;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160; <span class="keywordtype">void</span> add_residual_block(<span class="keyword">const</span> std::string &amp;data_path, <span class="keyword">const</span> std::string &amp;name, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout,</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> base_depth, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_units, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> stride)</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160; {</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160; <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i &lt; num_units; ++i)</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160; {</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160; <span class="comment">// Generate unit names</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160; std::stringstream unit_path_ss;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160; unit_path_ss &lt;&lt; name &lt;&lt; <span class="stringliteral">&quot;_unit_&quot;</span> &lt;&lt; (i + 1) &lt;&lt; <span class="stringliteral">&quot;_bottleneck_v2_&quot;</span>;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160; std::stringstream unit_name_ss;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160; unit_name_ss &lt;&lt; name &lt;&lt; <span class="stringliteral">&quot;/unit&quot;</span> &lt;&lt; (i + 1) &lt;&lt; <span class="stringliteral">&quot;/bottleneck_v2/&quot;</span>;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160;</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160; std::string unit_path = unit_path_ss.str();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160; std::string unit_name = unit_name_ss.str();</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160; <span class="keyword">const</span> TensorShape last_shape = graph.<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml#a7e4fdf6dbe73cb6da5cc36976cff9fa7">graph</a>().<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#af8baf1f3da6d42a94d0569395ece882a">node</a>(graph.<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml#a110fbabe5b6b600f0f5b1fec06ab1484">tail_node</a>())-&gt;output(0)-&gt;desc().shape;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_in = last_shape[<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a>(common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>)];</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> depth_out = base_depth * 4;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160; <span class="comment">// All units have stride 1 apart from last one</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160; <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> middle_stride = (i == (num_units - 1)) ? stride : 1;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160; <span class="comment">// Preact</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> preact(graph);</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160; preact &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;preact_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;preact_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;preact_gamma.npy&quot;</span>),</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;preact_beta.npy&quot;</span>),</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160; 0.000009999999747378752f)</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;preact/BatchNorm&quot;</span>)</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;preact/Relu&quot;</span>);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160; <span class="comment">// Create bottleneck path</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> shortcut(graph);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160; <span class="keywordflow">if</span>(depth_in == depth_out)</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160; {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160; <span class="keywordflow">if</span>(middle_stride != 1)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160; {</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160; shortcut &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 1, common_params.<a class="code" href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">data_layout</a>, PadStrideInfo(middle_stride, middle_stride, 0, 0), <span class="keyword">true</span>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;shortcut/MaxPool&quot;</span>);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160; }</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160; }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160; <span class="keywordflow">else</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160; {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160; shortcut.forward_tail(preact.tail_node());</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160; shortcut &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160; 1U, 1U, depth_out,</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;shortcut_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;shortcut_biases.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;shortcut/convolution&quot;</span>);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>&#160; }</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>&#160;</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>&#160; <span class="comment">// Create residual path</span></div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>&#160; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> residual(preact);</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>&#160; residual &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>&#160; 1U, 1U, base_depth,</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv1_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv1/convolution&quot;</span>)</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv1_BatchNorm_gamma.npy&quot;</span>),</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>&#160; 0.000009999999747378752f)</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv1/BatchNorm&quot;</span>)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv1/Relu&quot;</span>)</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>&#160; 3U, 3U, base_depth,</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv2_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>&#160; std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>&#160; PadStrideInfo(middle_stride, middle_stride, 1, 1))</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv2/convolution&quot;</span>)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv2_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv2_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv2_BatchNorm_gamma.npy&quot;</span>),</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv2_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>&#160; 0.000009999999747378752f)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv2/BatchNorm&quot;</span>)</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv1/Relu&quot;</span>)</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>&#160; &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>&#160; 1U, 1U, depth_out,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv3_weights.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>&#160; <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, unit_path + <span class="stringliteral">&quot;conv3_biases.npy&quot;</span>, weights_layout),</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>&#160; PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>&#160; .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;conv3/convolution&quot;</span>);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>&#160; graph &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">EltwiseLayer</a>(std::move(shortcut), std::move(residual), EltwiseOperation::Add).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(unit_name + <span class="stringliteral">&quot;add&quot;</span>);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>&#160; }</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>&#160; }</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>&#160;};</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>&#160;<span class="comment">/** Main program for ResNetV2_50</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>&#160;<span class="comment"> * Model is based on:</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>&#160;<span class="comment"> * https://arxiv.org/abs/1603.05027</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>&#160;<span class="comment"> * &quot;Identity Mappings in Deep Residual Networks&quot;</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>&#160;<span class="comment"> * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>&#160;<span class="comment"> * Provenance: download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>&#160;<span class="comment"> * @note To list all the possible arguments execute the binary appended with the --help option</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>&#160;<span class="comment"> * @param[in] argc Number of arguments</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>&#160;<span class="comment"> * @param[in] argv Arguments</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00248"></a><span class="lineno"><a class="line" href="graph__resnet__v2__50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 248</a></span>&#160;<span class="keywordtype">int</span> <a class="code" href="graph__resnet__v2__50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>&#160;{</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>&#160; <span class="keywordflow">return</span> arm_compute::utils::run_example&lt;GraphResNetV2_50Example&gt;(argc, argv);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00882">Layers.h:882</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">arm_compute::graph::frontend::SubStream</a></div><div class="ttdoc">Sub stream class.</div><div class="ttdef"><b>Definition:</b> <a href="_sub_stream_8h_source.xhtml#l00047">SubStream.h:47</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml">arm_compute::graph::GraphConfig</a></div><div class="ttdoc">Graph configuration structure Device target types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00078">Types.h:78</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a249f3f713c6ea8f564e760559cf509f4"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a249f3f713c6ea8f564e760559cf509f4">arm_compute::graph::GraphConfig::tuner_mode</a></div><div class="ttdeci">CLTunerMode tuner_mode</div><div class="ttdoc">Tuner mode to be used by the CL tuner.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00085">Types.h:85</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab14324184f90f342227699c161654b1b"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_input_accessor(const arm_compute::utils::CommonGraphParams &amp;graph_parameters, std::unique_ptr&lt; IPreprocessor &gt; preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00497">GraphUtils.h:497</a></div></div>
<div class="ttc" id="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="structarm__compute_1_1graph_1_1_graph_config_xhtml_a0292b7cb87d1bcd5c093c4b9d3b9c0bc"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a0292b7cb87d1bcd5c093c4b9d3b9c0bc">arm_compute::graph::GraphConfig::convert_to_uint8</a></div><div class="ttdeci">bool convert_to_uint8</div><div class="ttdoc">Convert graph to a synthetic uint8 graph.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</a></div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier ( )</div></div>
<div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe"><div class="ttname"><a href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">arm_compute::DimensionRoundingType::FLOOR</a></div><div class="ttdoc">Floor rounding.</div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a2593e1f13f425f627658900657f73dc3"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a2593e1f13f425f627658900657f73dc3">arm_compute::utils::consume_common_graph_parameters</a></div><div class="ttdeci">void consume_common_graph_parameters(CommonGraphValidateOptions &amp;options, CommonParams &amp;common_params)</div><div class="ttdoc">Consumes the consume_common_graph_parameters graph options and creates a structure containing any inf...</div><div class="ttdef"><b>Definition:</b> <a href="graph__validate__utils_8h_source.xhtml#l00316">graph_validate_utils.h:316</a></div></div>
<div class="ttc" id="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">graph.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_common_graph_options_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">arm_compute::utils::CommonGraphOptions</a></div><div class="ttdoc">Common command line options used to configure the graph examples.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00129">CommonGraphOptions.h:129</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_command_line_parser_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_command_line_parser.xhtml">arm_compute::utils::CommandLineParser</a></div><div class="ttdoc">Class to parse command line arguments.</div><div class="ttdef"><b>Definition:</b> <a href="_command_line_parser_8h_source.xhtml#l00044">CommandLineParser.h:44</a></div></div>
<div class="ttc" id="structarm__compute_1_1utils_1_1_common_graph_params_xhtml_aa56f0562febf49bc0e29a4257551191b"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml#aa56f0562febf49bc0e29a4257551191b">arm_compute::utils::CommonGraphParams::data_layout</a></div><div class="ttdeci">arm_compute::DataLayout data_layout</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00096">CommonGraphOptions.h:96</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a5cabfb35cd0014387f7ec2a0c362c20f"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a5cabfb35cd0014387f7ec2a0c362c20f">arm_compute::graph::GraphConfig::tuner_file</a></div><div class="ttdeci">std::string tuner_file</div><div class="ttdoc">File to load/store tuning values from.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00087">Types.h:87</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_input_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">arm_compute::graph::frontend::InputLayer</a></div><div class="ttdoc">Input Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00045">Layers.h:45</a></div></div>
<div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_stream_xhtml_a110fbabe5b6b600f0f5b1fec06ab1484"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_stream.xhtml#a110fbabe5b6b600f0f5b1fec06ab1484">arm_compute::graph::frontend::IStream::tail_node</a></div><div class="ttdeci">NodeID tail_node()</div><div class="ttdoc">Returns the tail node of the Stream.</div><div class="ttdef"><b>Definition:</b> <a href="_i_stream_8h_source.xhtml#l00065">IStream.h:65</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number unsigned</div></div>
<div class="ttc" id="graph__resnet__v2__50_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__resnet__v2__50_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for ResNetV2_50.</div><div class="ttdef"><b>Definition:</b> <a href="graph__resnet__v2__50_8cpp_source.xhtml#l00248">graph_resnet_v2_50.cpp:248</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00074">Utils.h:74</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00121">Layers.h:121</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">arm_compute::graph::frontend::ConvolutionLayer</a></div><div class="ttdoc">Convolution Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00334">Layers.h:334</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab3a897163a7fe23208f1d9c618062ee2"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab3a897163a7fe23208f1d9c618062ee2">arm_compute::graph_utils::permute_shape</a></div><div class="ttdeci">TensorShape permute_shape(TensorShape tensor_shape, DataLayout in_data_layout, DataLayout out_data_layout)</div><div class="ttdoc">Permutes a given tensor shape given the input and output data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00664">GraphUtils.h:664</a></div></div>
<div class="ttc" id="_common_graph_options_8h_xhtml"><div class="ttname"><a href="_common_graph_options_8h.xhtml">CommonGraphOptions.h</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2497d23622ec1343e507331ae1388f00"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">arm_compute::graph::TensorDescriptor::set_layout</a></div><div class="ttdeci">TensorDescriptor &amp; set_layout(DataLayout data_layout)</div><div class="ttdoc">Sets tensor descriptor data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00086">TensorDescriptor.h:86</a></div></div>
<div class="ttc" id="structarm__compute_1_1utils_1_1_common_graph_params_xhtml"><div class="ttname"><a href="structarm__compute_1_1utils_1_1_common_graph_params.xhtml">arm_compute::utils::CommonGraphParams</a></div><div class="ttdoc">Structure holding all the common graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00090">CommonGraphOptions.h:90</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_safe_ops_8h_source.xhtml#l00032">SafeOps.h:32</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00044">GraphUtils.h:44</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">arm_compute::graph::frontend::SoftmaxLayer</a></div><div class="ttdoc">Softmax Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l01200">Layers.h:1200</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a"><div class="ttname"><a href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">arm_compute::PoolingType::AVG</a></div><div class="ttdoc">Average Pooling.</div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_af8baf1f3da6d42a94d0569395ece882a"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#af8baf1f3da6d42a94d0569395ece882a">arm_compute::graph::Graph::node</a></div><div class="ttdeci">const INode * node(NodeID id) const</div><div class="ttdoc">Get node object given its id.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_8cpp_source.xhtml#l00204">Graph.cpp:204</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ae3d177d243f5fb34544105a4ee4e1f58"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ae3d177d243f5fb34544105a4ee4e1f58">arm_compute::graph_utils::get_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_output_accessor(const arm_compute::utils::CommonGraphParams &amp;graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &amp;output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00543">GraphUtils.h:543</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a9da74af255a3e6ea61180d4a03192a48"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a9da74af255a3e6ea61180d4a03192a48">arm_compute::graph::GraphConfig::use_tuner</a></div><div class="ttdeci">bool use_tuner</div><div class="ttdoc">Use a tuner in tunable backends.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00083">Types.h:83</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">arm_compute::graph::frontend::OutputLayer</a></div><div class="ttdoc">Output Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00095">Layers.h:95</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_weights_accessor(const std::string &amp;path, const std::string &amp;data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00475">GraphUtils.h:475</a></div></div>
<div class="ttc" id="structarm__compute_1_1graph_1_1_graph_config_xhtml_a08963f7335eef295237ab460863bc3d5"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_graph_config.xhtml#a08963f7335eef295237ab460863bc3d5">arm_compute::graph::GraphConfig::num_threads</a></div><div class="ttdeci">int num_threads</div><div class="ttdoc">Number of threads to use (thread capable backends), if 0 the backend will auto-initialize,...</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00086">Types.h:86</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml_a7e4fdf6dbe73cb6da5cc36976cff9fa7"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml#a7e4fdf6dbe73cb6da5cc36976cff9fa7">arm_compute::graph::frontend::Stream::graph</a></div><div class="ttdeci">Graph &amp; graph() override</div><div class="ttdoc">Returns the underlying graph.</div><div class="ttdef"><b>Definition:</b> <a href="_stream_8cpp_source.xhtml#l00063">Stream.cpp:63</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">arm_compute::graph::frontend::Stream</a></div><div class="ttdoc">Stream frontend class to construct simple graphs in a stream fashion.</div><div class="ttdef"><b>Definition:</b> <a href="_stream_8h_source.xhtml#l00045">Stream.h:45</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1frontend_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00031">ILayer.h:31</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00149">Layers.h:149</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_eltwise_layer.xhtml">arm_compute::graph::frontend::EltwiseLayer</a></div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00597">Layers.h:597</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00327">Helpers.inl:327</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate.</div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00117">Types.h:117</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_flatten_layer.xhtml">arm_compute::graph::frontend::FlattenLayer</a></div><div class="ttdoc">Flatten Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00626">Layers.h:626</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer &amp; set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
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