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| <div class="title">graph_vgg19.cpp</div> </div> |
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| <a href="graph__vgg19_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_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="keyword">class </span>GraphVGG19Example : <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="l00035"></a><span class="lineno"> 35</span> {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  GraphVGG19Example()</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, <span class="stringliteral">"VGG19"</span>)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  }</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <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="l00042"></a><span class="lineno"> 42</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// Check if the system has enough RAM to run the example, systems with less than 2GB have</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// to hint the API to minimize memory consumption otherwise it'll run out of memory and</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="comment">// fail throwing the bad_alloc exception</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarm__compute_1_1_m_e_m_info.xhtml">arm_compute::MEMInfo</a> meminfo;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> mem_total = meminfo.<a class="code" href="classarm__compute_1_1_m_e_m_info.xhtml#a417a573b0ef6347fa727ce756b76f1d2">get_total_in_kb</a>();</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">if</span>(mem_total <= <a class="code" href="classarm__compute_1_1_m_e_m_info.xhtml#abd0ddfc9f23b25d70cdcf6e2e0d37782">arm_compute::MEMInfo::TWO_GB_IN_KB</a>)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="classarm__compute_1_1_m_e_m_info.xhtml#a1609b8101beb35633a19f845d22323a8">arm_compute::MEMInfo::set_policy</a>(<a class="code" href="namespacearm__compute.xhtml#a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda">arm_compute::MemoryPolicy::MINIMIZE</a>);</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  }</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</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">// Checks</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(<a class="code" href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a>(common_params.data_type), <span class="stringliteral">"QASYMM8 not supported for this graph"</span>);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="comment">// Print parameter values</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  std::cout << common_params << std::endl;</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">// Get trainable parameters data path</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  std::string data_path = common_params.data_path;</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 a preprocessor object</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keyword">const</span> std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } };</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);</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">// Create input descriptor</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <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>, common_params.data_layout);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</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="l00082"></a><span class="lineno"> 82</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// Set weights trained layout</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> weights_layout = <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  graph << common_params.target</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  << common_params.fast_math_hint</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(input_descriptor, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">get_input_accessor</a>(common_params, std::move(preprocessor)))</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="comment">// Layer 1</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  3U, 3U, 64U,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_1_b.npy"</span>),</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv1_1"</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_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">"conv1_1/Relu"</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_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  3U, 3U, 64U,</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv1_2_b.npy"</span>),</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv1_2"</span>)</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv1_2/Relu"</span>)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  << <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>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool1"</span>)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// Layer 2</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  3U, 3U, 128U,</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_1_b.npy"</span>),</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2_1"</span>)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2_1/Relu"</span>)</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  3U, 3U, 128U,</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv2_2_b.npy"</span>),</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv2_2"</span>)</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv2_2/Relu"</span>)</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  << <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>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool2"</span>)</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// Layer 3</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  3U, 3U, 256U,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_1_b.npy"</span>),</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv3_1"</span>)</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv3_1/Relu"</span>)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  3U, 3U, 256U,</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_2_b.npy"</span>),</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv3_2"</span>)</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv3_2/Relu"</span>)</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  3U, 3U, 256U,</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_3_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_3_b.npy"</span>),</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv3_3"</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_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">"conv3_3/Relu"</span>)</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  3U, 3U, 256U,</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_4_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv3_4_b.npy"</span>),</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  PadStrideInfo(1, 1, 1, 1))</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_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv3_4"</span>)</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv3_4/Relu"</span>)</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool3"</span>)</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">// Layer 4</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  3U, 3U, 512U,</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_1_b.npy"</span>),</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv4_1"</span>)</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"conv4_1/Relu"</span>)</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  3U, 3U, 512U,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_2_b.npy"</span>),</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv4_2"</span>)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv4_2/Relu"</span>)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  3U, 3U, 512U,</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_3_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_3_b.npy"</span>),</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv4_3"</span>)</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv4_3/Relu"</span>)</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  3U, 3U, 512U,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_4_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv4_4_b.npy"</span>),</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv4_4"</span>)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv4_4/Relu"</span>)</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool4"</span>)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// Layer 5</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  3U, 3U, 512U,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_1_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_1_b.npy"</span>),</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv5_1"</span>)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv5_1/Relu"</span>)</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  3U, 3U, 512U,</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_2_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_2_b.npy"</span>),</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv5_2"</span>)</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv5_2/Relu"</span>)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  3U, 3U, 512U,</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_3_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_3_b.npy"</span>),</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv5_3"</span>)</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv5_3/Relu"</span>)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  3U, 3U, 512U,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_4_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/conv5_4_b.npy"</span>),</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  PadStrideInfo(1, 1, 1, 1))</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"conv5_4"</span>)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"conv5_4/Relu"</span>)</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  << <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>, 2, PadStrideInfo(2, 2, 0, 0))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"pool5"</span>)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="comment">// Layer 6</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  4096U,</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc6_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc6_b.npy"</span>))</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"fc6"</span>)</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"Relu"</span>)</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="comment">// Layer 7</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  4096U,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc7_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc7_b.npy"</span>))</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"fc7"</span>)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#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">"Relu_1"</span>)</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="comment">// Layer 8</span></div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  1000U,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc8_w.npy"</span>, weights_layout),</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">"/cnn_data/vgg19_model/fc8_b.npy"</span>))</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">"fc8"</span>)</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="comment">// Softmax</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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">"prob"</span>)</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  << <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_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="l00234"></a><span class="lineno"> 234</span> </div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <a class="code" href="structarm__compute_1_1graph_1_1_graph_config.xhtml">GraphConfig</a> config;</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</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="l00238"></a><span class="lineno"> 238</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="l00239"></a><span class="lineno"> 239</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="l00240"></a><span class="lineno"> 240</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="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  graph.finalize(common_params.target, config);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> </div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  }</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">// Run graph</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  graph.run();</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  }</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</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="l00254"></a><span class="lineno"> 254</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="l00255"></a><span class="lineno"> 255</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="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph;</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> };</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00273"></a><span class="lineno"><a class="line" href="graph__vgg19_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627"> 273</a></span> <span class="keywordtype">int</span> <a class="code" href="graph__vgg19_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> {</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">return</span> arm_compute::utils::run_example<GraphVGG19Example>(argc, argv);</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</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> |
| <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#l00083">Types.h:83</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab14324184f90f342227699c161654b1b"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab14324184f90f342227699c161654b1b">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_input_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00464">GraphUtils.h:464</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="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="_graph_8h_xhtml"><div class="ttname"><a href="_graph_8h.xhtml">graph.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_common_graph_options_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_common_graph_options.xhtml">arm_compute::utils::CommonGraphOptions</a></div><div class="ttdoc">Common command line options used to configure the graph examples.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8h_source.xhtml#l00129">CommonGraphOptions.h:129</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda"><div class="ttname"><a href="namespacearm__compute.xhtml#a4c9101ca1a1f72f48efff28b4b9a6495a704bfa6c1ed5e479c8cfb5bdfc8cccda">arm_compute::MemoryPolicy::MINIMIZE</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_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#l00085">Types.h:85</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_ad39a3601153da57978bb5124ace35d36"><div class="ttname"><a href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_EXIT_ON_MSG(cond,...)</div><div class="ttdoc">If the condition is true, the given message is printed and program exits.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00277">Error.h:277</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_input_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">arm_compute::graph::frontend::InputLayer</a></div><div class="ttdoc">Input Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00045">Layers.h:45</a></div></div> |
| <div class="ttc" id="graph__vgg19_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__vgg19_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for VGG19.</div><div class="ttdef"><b>Definition:</b> <a href="graph__vgg19_8cpp_source.xhtml#l00273">graph_vgg19.cpp:273</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_m_e_m_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_m_e_m_info.xhtml">arm_compute::MEMInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_types_8h_source.xhtml#l00197">CPPTypes.h:197</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_m_e_m_info_xhtml_abd0ddfc9f23b25d70cdcf6e2e0d37782"><div class="ttname"><a href="classarm__compute_1_1_m_e_m_info.xhtml#abd0ddfc9f23b25d70cdcf6e2e0d37782">arm_compute::MEMInfo::TWO_GB_IN_KB</a></div><div class="ttdeci">static const size_t TWO_GB_IN_KB</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_types_8h_source.xhtml#l00215">CPPTypes.h:215</a></div></div> |
| <div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a04125f2e4cecaffad8724cee7e1c19b0"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a04125f2e4cecaffad8724cee7e1c19b0">arm_compute::utils::consume_common_graph_parameters</a></div><div class="ttdeci">CommonGraphParams consume_common_graph_parameters(CommonGraphOptions &options)</div><div class="ttdoc">Consumes the common graph options and creates a structure containing any information.</div><div class="ttdef"><b>Definition:</b> <a href="_common_graph_options_8cpp_source.xhtml#l00183">CommonGraphOptions.cpp:183</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00070">Utils.h:70</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00096">Layers.h:96</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#l00309">Layers.h:309</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#l00618">GraphUtils.h:618</a></div></div> |
| <div class="ttc" id="_common_graph_options_8h_xhtml"><div class="ttname"><a href="_common_graph_options_8h.xhtml">CommonGraphOptions.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a14f46283f316e7f0fad301d5c1507e9f"><div class="ttname"><a href="namespacearm__compute.xhtml#a14f46283f316e7f0fad301d5c1507e9f">arm_compute::is_data_type_quantized_asymmetric</a></div><div class="ttdeci">bool is_data_type_quantized_asymmetric(DataType dt)</div><div class="ttdoc">Check if a given data type is of asymmetric quantized type.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_utils_8h_source.xhtml#l01030">Utils.h:1030</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1graph_1_1_tensor_descriptor_xhtml_a2497d23622ec1343e507331ae1388f00"><div class="ttname"><a href="structarm__compute_1_1graph_1_1_tensor_descriptor.xhtml#a2497d23622ec1343e507331ae1388f00">arm_compute::graph::TensorDescriptor::set_layout</a></div><div class="ttdeci">TensorDescriptor & set_layout(DataLayout data_layout)</div><div class="ttdoc">Sets tensor descriptor data layout.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_descriptor_8h_source.xhtml#l00086">TensorDescriptor.h:86</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_m_e_m_info_xhtml_a417a573b0ef6347fa727ce756b76f1d2"><div class="ttname"><a href="classarm__compute_1_1_m_e_m_info.xhtml#a417a573b0ef6347fa727ce756b76f1d2">arm_compute::MEMInfo::get_total_in_kb</a></div><div class="ttdeci">size_t get_total_in_kb() const</div><div class="ttdoc">Return the total amount of RAM memory in the system expressed in KB.</div><div class="ttdef"><b>Definition:</b> <a href="_m_e_m_utils_8cpp_source.xhtml#l00098">MEMUtils.cpp:98</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="_cast_8h_source.xhtml#l00031">Cast.h:31</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_m_e_m_info_xhtml_a1609b8101beb35633a19f845d22323a8"><div class="ttname"><a href="classarm__compute_1_1_m_e_m_info.xhtml#a1609b8101beb35633a19f845d22323a8">arm_compute::MEMInfo::set_policy</a></div><div class="ttdeci">static void set_policy(MemoryPolicy policy)</div><div class="ttdef"><b>Definition:</b> <a href="_m_e_m_utils_8cpp_source.xhtml#l00081">MEMUtils.cpp:81</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#l01071">Layers.h:1071</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< graph::ITensorAccessor > get_output_accessor(const arm_compute::utils::CommonGraphParams &graph_parameters, size_t top_n=5, bool is_validation=false, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified graph parameters.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00510">GraphUtils.h:510</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#l00082">Types.h:82</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#l00070">Layers.h:70</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_fully_connected_layer.xhtml">arm_compute::graph::frontend::FullyConnectedLayer</a></div><div class="ttdoc">Fully Connected Layer.</div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00599">Layers.h:599</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr< graph::ITensorAccessor > get_weights_accessor(const std::string &path, const std::string &data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00442">GraphUtils.h:442</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#l00084">Types.h:84</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="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#l00114">Types.h:114</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer & set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer.</div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div> |
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| <li class="navelem"><a class="el" href="dir_d28a4824dc47e487b107a5db32ef43c4.xhtml">examples</a></li><li class="navelem"><a class="el" href="graph__vgg19_8cpp.xhtml">graph_vgg19.cpp</a></li> |
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