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117 <div class="title">MobileNetV1Network.h</div> </div>
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120 <a href="_mobile_net_v1_network_8h.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) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#ifndef __ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENETV1_H__</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENETV1_H__</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_assets_library_8h.xhtml">tests/AssetsLibrary.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_globals_8h.xhtml">tests/Globals.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="tests_2_utils_8h.xhtml">tests/Utils.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</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="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <memory></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a>;</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1test.xhtml">arm_compute::test</a>;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></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> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> {</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">namespace </span>networks</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">template</span> <<span class="keyword">typename</span> TensorType,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">typename</span> <a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml">Accessor</a>,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">typename</span> ActivationLayerFunction,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keyword">typename</span> BatchNormalizationLayerFunction,</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <span class="keyword">typename</span> ConvolutionLayerFunction,</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keyword">typename</span> DirectConvolutionLayerFunction,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">typename</span> DepthwiseConvolutionFunction,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">typename</span> ReshapeFunction,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keyword">typename</span> PoolingLayerFunction,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keyword">typename</span> SoftmaxLayerFunction></div><div class="line"><a name="l00055"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml"> 55</a></span> <span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml">MobileNetV1Network</a></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> {</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="keyword">public</span>:</div><div class="line"><a name="l00063"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3b81d78cb73291bea06a00d70ad09b5d"> 63</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3b81d78cb73291bea06a00d70ad09b5d">init</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input_spatial_size, <span class="keywordtype">int</span> batches)</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>  _batches = batches;</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  _input_spatial_size = input_spatial_size;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> </div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// Currently supported sizes</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(input_spatial_size != 128 && input_spatial_size != 224);</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Initialize input, output</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  input.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(input_spatial_size, input_spatial_size, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  output.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1001<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="comment">// Initialize weights and biases</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  w_conv3x3.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 32<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  mean_conv3x3.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(32<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  var_conv3x3.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(32<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  beta_conv3x3.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(32<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  gamma_conv3x3.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(32<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  depthwise_conv_block_init(0, 32, 32);</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  depthwise_conv_block_init(1, 32, 64);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  depthwise_conv_block_init(2, 64, 64);</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  depthwise_conv_block_init(3, 64, 128);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  depthwise_conv_block_init(4, 128, 256);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  depthwise_conv_block_init(5, 256, 512);</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  depthwise_conv_block_init(6, 512, 512);</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  depthwise_conv_block_init(7, 512, 512);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  depthwise_conv_block_init(8, 512, 512);</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  depthwise_conv_block_init(9, 512, 512);</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  depthwise_conv_block_init(10, 512, 512);</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  depthwise_conv_block_init(11, 512, 1024);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  depthwise_conv_block_init(12, 1024, 1024);</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  w_conv1c.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1024<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1001<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  b_conv1c.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1001<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// Init reshaped output</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  reshape_out.allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1001<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, _batches), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00100"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a7740c7ab195c03ac140f1f75f633470f"> 100</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a7740c7ab195c03ac140f1f75f633470f">build</a>()</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Configure Layers</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  conv3x3.configure(&input, &w_conv3x3, <span class="keyword">nullptr</span>, &conv_out[0], <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  conv3x3_bn.configure(&conv_out[0], <span class="keyword">nullptr</span>, &mean_conv3x3, &var_conv3x3, &beta_conv3x3, &gamma_conv3x3, 0.001f);</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  conv3x3_act.configure(&conv_out[0], <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f));</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  depthwise_conv_block_build(0, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  depthwise_conv_block_build(1, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  depthwise_conv_block_build(2, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  depthwise_conv_block_build(3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  depthwise_conv_block_build(4, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  depthwise_conv_block_build(5, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  depthwise_conv_block_build(6, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  depthwise_conv_block_build(7, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  depthwise_conv_block_build(8, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  depthwise_conv_block_build(9, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  depthwise_conv_block_build(10, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  depthwise_conv_block_build(11, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 1, 0, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  depthwise_conv_block_build(12, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa56c1e354d36beb85b0d881c5b2e24cbe">DimensionRoundingType::FLOOR</a>), <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  pool.configure(&conv_out[13], &pool_out, <a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>));</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  conv1c.configure(&pool_out, &w_conv1c, &b_conv1c, &conv_out[14], <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 0, 0));</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  reshape.configure(&conv_out[14], &reshape_out);</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  smx.configure(&reshape_out, &output);</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  }</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00126"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef"> 126</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">allocate</a>()</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  {</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  input.allocator()->allocate();</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  output.allocator()->allocate();</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  w_conv3x3.allocator()->allocate();</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  mean_conv3x3.allocator()->allocate();</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  var_conv3x3.allocator()->allocate();</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  beta_conv3x3.allocator()->allocate();</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  gamma_conv3x3.allocator()->allocate();</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> </div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(w_conv.size() != w_dwc.size());</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < w_conv.size(); ++i)</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  w_dwc[i].allocator()->allocate();</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  bn_mean[2 * i].allocator()->allocate();</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  bn_var[2 * i].allocator()->allocate();</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  bn_beta[2 * i].allocator()->allocate();</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  bn_gamma[2 * i].allocator()->allocate();</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  w_conv[i].allocator()->allocate();</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  bn_mean[2 * i + 1].allocator()->allocate();</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  bn_var[2 * i + 1].allocator()->allocate();</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  bn_beta[2 * i + 1].allocator()->allocate();</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  bn_gamma[2 * i + 1].allocator()->allocate();</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  w_conv1c.allocator()->allocate();</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  b_conv1c.allocator()->allocate();</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  <span class="comment">// Allocate intermediate buffers</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &o : conv_out)</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  {</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  o.allocator()->allocate();</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &o : dwc_out)</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  {</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  o.allocator()->allocate();</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  }</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  pool_out.allocator()->allocate();</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  reshape_out.allocator()->allocate();</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  }</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> </div><div class="line"><a name="l00168"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0"> 168</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">fill_random</a>()</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  {</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> seed_idx = 0;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  std::uniform_real_distribution<> distribution(-1, 1);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(input), distribution, seed_idx++);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(w_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(mean_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(var_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(beta_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(gamma_conv3x3), distribution, seed_idx++);</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(w_conv.size() != w_dwc.size());</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < w_conv.size(); ++i)</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  {</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(w_dwc[i]), distribution, seed_idx++);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_mean[2 * i]), distribution, seed_idx++);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_var[2 * i]), distribution, seed_idx++);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_beta[2 * i]), distribution, seed_idx++);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_gamma[2 * i]), distribution, seed_idx++);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(w_conv[i]), distribution, seed_idx++);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_mean[2 * i + 1]), distribution, seed_idx++);</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_var[2 * i + 1]), distribution, seed_idx++);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_beta[2 * i + 1]), distribution, seed_idx++);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(bn_gamma[2 * i + 1]), distribution, seed_idx++);</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  }</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(w_conv1c), distribution, seed_idx++);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill(Accessor(b_conv1c), distribution, seed_idx++);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  }</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> </div><div class="line"><a name="l00202"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3a41262ce9aed70a248ecefae646013b"> 202</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3a41262ce9aed70a248ecefae646013b">feed</a>(std::string name)</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  {</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">library</a>->fill_layer_data(Accessor(input), name);</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00211"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26"> 211</a></span>  std::vector<unsigned int> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">get_classifications</a>()</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  {</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  std::vector<unsigned int> classified_labels;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  Accessor output_accessor(output);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(<a class="code" href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">Window::DimX</a>, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, 1, 1));</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d = 1; d < output_accessor.<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">num_dimensions</a>(); ++d)</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  {</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">set</a>(d, <a class="code" href="classarm__compute_1_1_window_1_1_dimension.xhtml">Window::Dimension</a>(0, output_accessor.<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>()[d], 1));</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <a class="code" href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">int</span> max_idx = 0;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordtype">float</span> val = 0;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keyword">const</span> <span class="keywordtype">void</span> *<span class="keyword">const</span> out_ptr = output_accessor(<span class="keywordtype">id</span>);</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> l = 0; l < output_accessor.<a class="code" href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">shape</a>().<a class="code" href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">x</a>(); ++l)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  {</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <span class="keywordtype">float</span> curr_val = <span class="keyword">reinterpret_cast<</span><span class="keyword">const </span><span class="keywordtype">float</span> *<span class="keyword">></span>(out_ptr)[l];</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keywordflow">if</span>(curr_val > val)</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  {</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  max_idx = l;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  val = curr_val;</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  }</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  }</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  classified_labels.push_back(max_idx);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  });</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keywordflow">return</span> classified_labels;</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  }</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00243"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204"> 243</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204">clear</a>()</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  {</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  input.allocator()->free();</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  output.allocator()->free();</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  w_conv3x3.allocator()->free();</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  mean_conv3x3.allocator()->free();</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  var_conv3x3.allocator()->free();</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  beta_conv3x3.allocator()->free();</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  gamma_conv3x3.allocator()->free();</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(w_conv.size() != w_dwc.size());</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordflow">for</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < w_conv.size(); ++i)</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  {</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  w_dwc[i].allocator()->free();</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  bn_mean[2 * i].allocator()->free();</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  bn_var[2 * i].allocator()->free();</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  bn_beta[2 * i].allocator()->free();</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  bn_gamma[2 * i].allocator()->free();</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  w_conv[i].allocator()->free();</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  bn_mean[2 * i + 1].allocator()->free();</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  bn_var[2 * i + 1].allocator()->free();</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  bn_beta[2 * i + 1].allocator()->free();</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  bn_gamma[2 * i + 1].allocator()->free();</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  }</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  w_conv1c.allocator()->free();</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  b_conv1c.allocator()->free();</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> </div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  <span class="comment">// Free intermediate buffers</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &o : conv_out)</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  {</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  o.allocator()->free();</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  }</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="keywordflow">for</span>(<span class="keyword">auto</span> &o : dwc_out)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  {</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  o.allocator()->free();</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  pool_out.allocator()->free();</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  reshape_out.allocator()->free();</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  }</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00285"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a13a43e6d814de94978c515cb084873b1"> 285</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>()</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  conv3x3.run();</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  conv3x3_bn.run();</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  conv3x3_act.run();</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  depthwise_conv_block_run(0);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  depthwise_conv_block_run(1);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  depthwise_conv_block_run(2);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  depthwise_conv_block_run(3);</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  depthwise_conv_block_run(4);</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  depthwise_conv_block_run(5);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  depthwise_conv_block_run(6);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  depthwise_conv_block_run(7);</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  depthwise_conv_block_run(8);</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  depthwise_conv_block_run(9);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  depthwise_conv_block_run(10);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  depthwise_conv_block_run(11);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  depthwise_conv_block_run(12);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  pool.run();</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  conv1c.run();</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  reshape.run();</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  smx.run();</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  }</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> </div><div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#ad55f80ed3cd8b6c4f247763b747016af"> 310</a></span>  <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#ad55f80ed3cd8b6c4f247763b747016af">sync</a>()</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  sync_if_necessary<TensorType>();</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  sync_tensor_if_necessary<TensorType>(output);</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  }</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> </div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keywordtype">void</span> depthwise_conv_block_init(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ifm, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ofm)</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  {</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  <span class="comment">// Depthwise Convolution weights</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  w_dwc[idx].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 3<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, ifm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="comment">// Batch normalization parameters</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  bn_mean[2 * idx].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ifm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  bn_var[2 * idx].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ifm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  bn_beta[2 * idx].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ifm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  bn_gamma[2 * idx].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ifm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <span class="comment">// Convolution weights</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  w_conv[idx].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, 1<a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">U</a>, ifm, ofm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="comment">// Batch normalization parameters</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  bn_mean[2 * idx + 1].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ofm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  bn_var[2 * idx + 1].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ofm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  bn_beta[2 * idx + 1].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ofm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  bn_gamma[2 * idx + 1].allocator()->init(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(ofm), 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>));</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <span class="keywordtype">void</span> depthwise_conv_block_build(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> dwc_ps, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a> conv_ps)</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  {</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  <span class="comment">// Configure depthwise convolution block</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  dwc3x3[idx].configure(&conv_out[idx], &w_dwc[idx], <span class="keyword">nullptr</span>, &dwc_out[idx], dwc_ps);</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  bn[2 * idx].configure(&dwc_out[idx], <span class="keyword">nullptr</span>, &bn_mean[2 * idx], &bn_var[2 * idx], &bn_beta[2 * idx], &bn_gamma[2 * idx], 0.001f);</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  act[2 * idx].configure(&dwc_out[idx], <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f));</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  <span class="comment">// Configure pointwise convolution block</span></div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  conv1x1[idx].configure(&dwc_out[idx], &w_conv[idx], <span class="keyword">nullptr</span>, &conv_out[idx + 1], conv_ps);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  bn[2 * idx + 1].configure(&conv_out[idx + 1], <span class="keyword">nullptr</span>, &bn_mean[2 * idx + 1], &bn_var[2 * idx + 1], &bn_beta[2 * idx + 1], &bn_gamma[2 * idx + 1], 0.001f);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  act[2 * idx + 1].configure(&conv_out[idx], <span class="keyword">nullptr</span>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, 6.f));</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  }</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordtype">void</span> depthwise_conv_block_run(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> idx)</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  {</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  dwc3x3[idx].run();</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  bn[2 * idx].run();</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  act[2 * idx].run();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  conv1x1[idx].run();</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  bn[2 * idx + 1].run();</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  act[2 * idx + 1].run();</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  }</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> </div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <span class="keyword">private</span>:</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> _batches{ 0 };</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> _input_spatial_size{ 0 };</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> </div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  ConvolutionLayerFunction conv3x3{};</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  BatchNormalizationLayerFunction conv3x3_bn{};</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  ActivationLayerFunction conv3x3_act{};</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  std::array<ActivationLayerFunction, 26> act{ {} };</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  std::array<BatchNormalizationLayerFunction, 26> bn{ {} };</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  std::array<DepthwiseConvolutionFunction, 13> dwc3x3{ {} };</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  std::array<DirectConvolutionLayerFunction, 13> conv1x1{ {} };</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  DirectConvolutionLayerFunction conv1c{};</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  PoolingLayerFunction pool{};</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  ReshapeFunction reshape{};</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  SoftmaxLayerFunction smx{};</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  TensorType w_conv3x3{}, mean_conv3x3{}, var_conv3x3{}, beta_conv3x3{}, gamma_conv3x3{};</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  std::array<TensorType, 13> w_conv{ {} };</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::array<TensorType, 13> w_dwc{ {} };</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  std::array<TensorType, 26> bn_mean{ {} };</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  std::array<TensorType, 26> bn_var{ {} };</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  std::array<TensorType, 26> bn_beta{ {} };</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  std::array<TensorType, 26> bn_gamma{ {} };</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  TensorType w_conv1c{}, b_conv1c{};</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  TensorType input{}, output{};</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  std::array<TensorType, 15> conv_out{ {} };</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  std::array<TensorType, 13> dwc_out{ {} };</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  TensorType pool_out{};</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  TensorType reshape_out{};</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> };</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> } <span class="comment">// namespace networks</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> } <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> <span class="preprocessor">#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENETV1_H__</span></div><div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_ac8bb3912a3ce86b15842e79d0b421204"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#ac8bb3912a3ce86b15842e79d0b421204">arm_compute::test::networks::MobileNetV1Network::clear</a></div><div class="ttdeci">void clear()</div><div class="ttdoc">Clear all allocated memory from the tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00243">MobileNetV1Network.h:243</a></div></div>
121 <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div>
122 <div class="ttc" id="namespacearm__compute_1_1test_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml">arm_compute::test</a></div><div class="ttdef"><b>Definition:</b> <a href="02__tests_8dox_source.xhtml#l00003">02_tests.dox:3</a></div></div>
123 <div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
124 <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>
125 <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
126 <div class="ttc" id="_error_8h_xhtml_a54a6080c9f4df1f908e57a9bbb46f5da"><div class="ttname"><a href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true then an error message is printed and an exception thrown. </div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00328">Error.h:328</a></div></div>
127 <div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml_aba5871b3e4a65d057ec1c28fce8b00ba"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml#aba5871b3e4a65d057ec1c28fce8b00ba">arm_compute::test::Accessor::shape</a></div><div class="ttdeci">TensorShape shape() const override</div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_accessor_8h_source.xhtml#l00087">Accessor.h:87</a></div></div>
128 <div class="ttc" id="classarm__compute_1_1_window_1_1_dimension_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window_1_1_dimension.xhtml">arm_compute::Window::Dimension</a></div><div class="ttdoc">Describe one of the image&#39;s dimensions with a start, end and step. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00068">Window.h:68</a></div></div>
129 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00809">Types.h:809</a></div></div>
130 <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">This file contains all available output stages for GEMMLowp on OpenCL. </div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00001">00_introduction.dox:1</a></div></div>
131 <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_afb5cd37bb08f1029691590372e6330f0"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#afb5cd37bb08f1029691590372e6330f0">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const </div><div class="ttdoc">Alias to access the size of the first dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div>
132 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml">arm_compute::test::networks::MobileNetV1Network</a></div><div class="ttdoc">MobileNet model object. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00055">MobileNetV1Network.h:55</a></div></div>
133 <div class="ttc" id="_globals_8h_xhtml"><div class="ttname"><a href="_globals_8h.xhtml">Globals.h</a></div></div>
134 <div class="ttc" id="classarm__compute_1_1test_1_1_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_accessor.xhtml">arm_compute::test::Accessor</a></div><div class="ttdoc">Accessor implementation for Tensor objects. </div><div class="ttdef"><b>Definition:</b> <a href="_accessor_8h_source.xhtml#l00035">Accessor.h:35</a></div></div>
135 <div class="ttc" id="classarm__compute_1_1_window_xhtml_aa96e81276ee4f87ab386cd05a5539a7d"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#aa96e81276ee4f87ab386cd05a5539a7d">arm_compute::Window::DimX</a></div><div class="ttdeci">static constexpr size_t DimX</div><div class="ttdoc">Alias for dimension 0 also known as X dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00043">Window.h:43</a></div></div>
136 <div class="ttc" id="namespacearm__compute_1_1test_xhtml_a71326f0909d77386e29b511e1990a11f"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a71326f0909d77386e29b511e1990a11f">arm_compute::test::library</a></div><div class="ttdeci">std::unique_ptr< AssetsLibrary > library</div><div class="ttdef"><b>Definition:</b> <a href="main_8cpp_source.xhtml#l00059">main.cpp:59</a></div></div>
137 <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aa4c614360da93c0a041b22e537de151eb">arm_compute::Channel::U</a></div><div class="ttdoc">Cb/U channel. </div></div>
138 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_ad55f80ed3cd8b6c4f247763b747016af"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#ad55f80ed3cd8b6c4f247763b747016af">arm_compute::test::networks::MobileNetV1Network::sync</a></div><div class="ttdeci">void sync()</div><div class="ttdoc">Sync the results. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00310">MobileNetV1Network.h:310</a></div></div>
139 <div class="ttc" id="namespacearm__compute_xhtml_a6c0dcc38187027dcb89cd9724bc5a823"><div class="ttname"><a href="namespacearm__compute.xhtml#a6c0dcc38187027dcb89cd9724bc5a823">arm_compute::execute_window_loop</a></div><div class="ttdeci">void execute_window_loop(const Window &w, L &&lambda_function, Ts &&...iterators)</div><div class="ttdoc">Iterate through the passed window, automatically adjusting the iterators and calling the lambda_funct...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00122">Helpers.inl:122</a></div></div>
140 <div class="ttc" id="classarm__compute_1_1_coordinates_xhtml"><div class="ttname"><a href="classarm__compute_1_1_coordinates.xhtml">arm_compute::Coordinates</a></div><div class="ttdoc">Coordinates of an item. </div><div class="ttdef"><b>Definition:</b> <a href="_coordinates_8h_source.xhtml#l00037">Coordinates.h:37</a></div></div>
141 <div class="ttc" id="_assets_library_8h_xhtml"><div class="ttname"><a href="_assets_library_8h.xhtml">AssetsLibrary.h</a></div></div>
142 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_a1466ef70729f3c8b5da5ebfec3f53f26"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a1466ef70729f3c8b5da5ebfec3f53f26">arm_compute::test::networks::MobileNetV1Network::get_classifications</a></div><div class="ttdeci">std::vector< unsigned int > get_classifications()</div><div class="ttdoc">Get the classification results. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00211">MobileNetV1Network.h:211</a></div></div>
143 <div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00571">Types.h:571</a></div></div>
144 <div class="ttc" id="classarm__compute_1_1_window_xhtml_acd3d2bba51cb84d34dd7656ad2375a6e"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#acd3d2bba51cb84d34dd7656ad2375a6e">arm_compute::Window::set</a></div><div class="ttdeci">void set(size_t dimension, const Dimension &dim)</div><div class="ttdoc">Set the values of a given dimension. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00041">Window.inl:41</a></div></div>
145 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_a7740c7ab195c03ac140f1f75f633470f"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a7740c7ab195c03ac140f1f75f633470f">arm_compute::test::networks::MobileNetV1Network::build</a></div><div class="ttdeci">void build()</div><div class="ttdoc">Build the model. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00100">MobileNetV1Network.h:100</a></div></div>
146 <div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div>
147 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_a3b778cda9ac3fad08e7217edbcb942e0"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3b778cda9ac3fad08e7217edbcb942e0">arm_compute::test::networks::MobileNetV1Network::fill_random</a></div><div class="ttdeci">void fill_random()</div><div class="ttdoc">Fills the trainable parameters and input with random data. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00168">MobileNetV1Network.h:168</a></div></div>
148 <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">arm_compute::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a></div><div class="ttdoc">Upper Bounded Rectifier ( ) </div></div>
149 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::test::networks::MobileNetV1Network::run</a></div><div class="ttdeci">void run()</div><div class="ttdoc">Runs the model. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00285">MobileNetV1Network.h:285</a></div></div>
150 <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>
151 <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_a0f59f175e7682c7ed5f4ea30ef687834"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#a0f59f175e7682c7ed5f4ea30ef687834">arm_compute::Dimensions::num_dimensions</a></div><div class="ttdeci">unsigned int num_dimensions() const </div><div class="ttdoc">Returns the effective dimensionality of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00122">Dimensions.h:122</a></div></div>
152 <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</a></div></div>
153 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_acaefe811b78a2fdc4a0dba0c4029c3ef"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#acaefe811b78a2fdc4a0dba0c4029c3ef">arm_compute::test::networks::MobileNetV1Network::allocate</a></div><div class="ttdeci">void allocate()</div><div class="ttdoc">Allocate the network. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00126">MobileNetV1Network.h:126</a></div></div>
154 <div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00688">Types.h:688</a></div></div>
155 <div class="ttc" id="classarm__compute_1_1_window_xhtml"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml">arm_compute::Window</a></div><div class="ttdoc">Describe a multidimensional execution window. </div><div class="ttdef"><b>Definition:</b> <a href="_window_8h_source.xhtml#l00039">Window.h:39</a></div></div>
156 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_a3a41262ce9aed70a248ecefae646013b"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3a41262ce9aed70a248ecefae646013b">arm_compute::test::networks::MobileNetV1Network::feed</a></div><div class="ttdeci">void feed(std::string name)</div><div class="ttdoc">Feed input to network from file. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00202">MobileNetV1Network.h:202</a></div></div>
157 <div class="ttc" id="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network_xhtml_a3b81d78cb73291bea06a00d70ad09b5d"><div class="ttname"><a href="classarm__compute_1_1test_1_1networks_1_1_mobile_net_v1_network.xhtml#a3b81d78cb73291bea06a00d70ad09b5d">arm_compute::test::networks::MobileNetV1Network::init</a></div><div class="ttdeci">void init(unsigned int input_spatial_size, int batches)</div><div class="ttdoc">Initialize the network. </div><div class="ttdef"><b>Definition:</b> <a href="_mobile_net_v1_network_8h_source.xhtml#l00063">MobileNetV1Network.h:63</a></div></div>
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