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| <a href="_n_e_batch_normalization_layer_kernel_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="_n_e_batch_normalization_layer_kernel_8h.xhtml">arm_compute/core/NEON/kernels/NEBatchNormalizationLayerKernel.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_c_p_p_2_validate_8h.xhtml">arm_compute/core/CPP/Validate.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_helpers_8h.xhtml">arm_compute/core/Helpers.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="_n_e_fixed_point_8h.xhtml">arm_compute/core/NEON/NEFixedPoint.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="_n_e_math_8h.xhtml">arm_compute/core/NEON/NEMath.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_n_e_activation_function_detail_8h.xhtml">arm_compute/core/NEON/kernels/detail/NEActivationFunctionDetail.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_tensor_info_8h.xhtml">arm_compute/core/TensorInfo.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="arm__compute_2core_2_utils_8h.xhtml">arm_compute/core/Utils.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="_validate_8h.xhtml">arm_compute/core/Validate.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="_window_8h.xhtml">arm_compute/core/Window.h</a>"</span></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="preprocessor">#include <map></span></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">using 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></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> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> validate_arguments(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *mean, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *var,</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *beta, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *gamma, <span class="keywordtype">float</span> <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>);</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <a class="code" href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a>(input);</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <a class="code" href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(input, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</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>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.enabled())</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>  <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9ea">ActivationLayerInfo::ActivationFunction</a> act = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.activation();</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  && act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU);</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.b() > <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>.a());</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="keywordflow">if</span>(<span class="keyword">nullptr</span> != output)</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(input, output);</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <a class="code" href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a>(input, output);</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, output);</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>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, mean, var);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(mean, var);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordflow">if</span>(beta != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  {</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, beta);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(mean, beta);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">if</span>(gamma != <span class="keyword">nullptr</span>)</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>  <a class="code" href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a>(input, gamma);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a>(mean, gamma);</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <a class="code" href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(<a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>(), <a class="code" href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>)) != mean-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> }</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> std::pair<Status, Window> <a class="code" href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">validate_and_configure_window</a>(<a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *mean, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *var, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *gamma, <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *beta)</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordflow">if</span>(output != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="comment">// Output tensor auto initialization if not yet initialized</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <a class="code" href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">auto_init_if_empty</a>(*output, *input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>());</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  }</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> num_elems_processed_per_iteration = 16 / input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>();</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> win = <a class="code" href="namespacearm__compute.xhtml#ab7980fa5ee693e3282a76da047a1c3b5">calculate_max_window</a>(*input, <a class="code" href="classarm__compute_1_1_steps.xhtml">Steps</a>(num_elems_processed_per_iteration));</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <a class="code" href="classarm__compute_1_1_access_window_horizontal.xhtml">AccessWindowHorizontal</a> input_access(input, 0, num_elems_processed_per_iteration);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="keywordtype">bool</span> window_changed = <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, input_access);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordflow">if</span>(output != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  {</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="classarm__compute_1_1_access_window_horizontal.xhtml">AccessWindowHorizontal</a> output_access(output, 0, num_elems_processed_per_iteration);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  window_changed |= <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, output_access);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  output_access.set_valid_region(win, input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a74dfd07380a290c34fe7c8e065029b95">valid_region</a>());</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  }</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">// Mean, var, gamma and beta get parallelized for the NHWC case as they follow the channel dimension, which is along the first axis</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">if</span>(input-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>)</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  {</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <a class="code" href="classarm__compute_1_1_access_window_horizontal.xhtml">AccessWindowHorizontal</a> mean_access(mean, 0, num_elems_processed_per_iteration);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="classarm__compute_1_1_access_window_horizontal.xhtml">AccessWindowHorizontal</a> var_access(var, 0, num_elems_processed_per_iteration);</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  window_changed |= <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, mean_access, var_access);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordflow">if</span>(gamma != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="classarm__compute_1_1_access_window_horizontal.xhtml">AccessWindowHorizontal</a> gamma_access(gamma, 0, num_elems_processed_per_iteration);</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  window_changed |= <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, gamma_access);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  }</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">if</span>(beta != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  {</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <a class="code" href="classarm__compute_1_1_access_window_horizontal.xhtml">AccessWindowHorizontal</a> beta_access(beta, 0, num_elems_processed_per_iteration);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  window_changed |= <a class="code" href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">update_window_and_padding</a>(win, beta_access);</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  }</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> err = (window_changed) ? <a class="code" href="_error_8h.xhtml#a046fbca6a9505ce038bc02830c739fed">ARM_COMPUTE_CREATE_ERROR</a>(<a class="code" href="namespacearm__compute.xhtml#a59e56af19e754a6aa26a612ebf91d05fa62be47fdd89da032cf78dfce82239579">ErrorCode::RUNTIME_ERROR</a>, <span class="stringliteral">"Insufficient Padding!"</span>) : <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordflow">return</span> std::make_pair(err, win);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> }</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> } <span class="comment">//namespace</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="keyword">template</span> <<span class="keywordtype">bool</span> fused_activation, <span class="keyword">typename</span> F></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="keywordtype">void</span> NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window)</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>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> input(_input, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> output(_output, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  F activation_functor(_act_info);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="comment">// Hold information about the current feature map we are iterating.</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="comment">// Only compute denominator and NEON vectors once per feature map.</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> = -1;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_mean = reinterpret_cast<const float16_t *>(_mean-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_var = reinterpret_cast<const float16_t *>(_var-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_gamma = (_gamma != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float16_t *>(_gamma-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_beta = (_beta != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float16_t *>(_beta-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> </div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  float16x8_t mean_vec = vdupq_n_f16(0.0);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  float16x8_t var_vec = vdupq_n_f16(0.0);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  float16x8_t gamma_vec = vdupq_n_f16(1.0);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  float16x8_t beta_vec = vdupq_n_f16(0.0);</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  float16x8_t denominator = vdupq_n_f16(0.0);</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  <span class="keyword">const</span> float16x8_t epsilon_vec = vdupq_n_f16(_epsilon);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>, [&](<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="l00154"></a><span class="lineno"> 154</span>  {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> != <span class="keywordtype">id</span>.z())</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>  <span class="comment">// Conctruct vectors</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  mean_vec = vdupq_n_f16(*(input_mean + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  var_vec = vdupq_n_f16(*(input_var + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">if</span>(input_gamma != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  gamma_vec = vdupq_n_f16(*(input_gamma + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  }</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="keywordflow">if</span>(input_beta != <span class="keyword">nullptr</span>)</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>  beta_vec = vdupq_n_f16(*(input_beta + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  }</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="comment">// Calculate denominator</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec));</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> = <span class="keywordtype">id</span>.z();</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  }</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>  <span class="comment">// Calculate x bar and store results</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keyword">const</span> float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), mean_vec);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keyword">const</span> float16x8_t x_bar = vmulq_f16(numerator, denominator);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  float16x8_t res = vaddq_f16(beta_vec, vmulq_f16(x_bar, gamma_vec));</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// Perform fused activation</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">if</span>(fused_activation)</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  {</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  activation_functor(res);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  }</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), res);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  },</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  input, output);</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */</span><span class="preprocessor"></span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> }</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="keyword">template</span> <<span class="keywordtype">bool</span> fused_activation, <span class="keyword">typename</span> F></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> <span class="keywordtype">void</span> NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window)</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="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> input(_input, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> output(_output, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  F activation_functor(_act_info);</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> </div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_mean = reinterpret_cast<const float16_t *>(_mean-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_var = reinterpret_cast<const float16_t *>(_var-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_gamma = (_gamma != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float16_t *>(_gamma-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_beta = (_beta != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float16_t *>(_beta-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</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>  <span class="keyword">const</span> float16x8_t epsilon_vec = vdupq_n_f16(_epsilon);</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>, [&](<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="l00208"></a><span class="lineno"> 208</span>  {</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="comment">// Conctruct vectors</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keyword">const</span> float16x8_t mean_vec = vld1q_f16(input_mean + <span class="keywordtype">id</span>.x());</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="keyword">const</span> float16x8_t var_vec = vld1q_f16(input_var + <span class="keywordtype">id</span>.x());</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keyword">const</span> float16x8_t gamma_vec = (input_gamma != <span class="keyword">nullptr</span>) ? vld1q_f16(input_gamma + <span class="keywordtype">id</span>.x()) : vdupq_n_f16(1.0);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keyword">const</span> float16x8_t beta_vec = (input_beta != <span class="keyword">nullptr</span>) ? vld1q_f16(input_beta + <span class="keywordtype">id</span>.x()) : vdupq_n_f16(0.0);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Calculate denominator</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keyword">const</span> float16x8_t denominator = vinvsqrtq_f16(vaddq_f16(var_vec, epsilon_vec));</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <span class="comment">// Calculate x bar and store results</span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <span class="keyword">const</span> float16x8_t numerator = vsubq_f16(vld1q_f16(reinterpret_cast<const float16_t *>(input.ptr())), mean_vec);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keyword">const</span> float16x8_t x_bar = vmulq_f16(numerator, denominator);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  float16x8_t res = vaddq_f16(beta_vec, vmulq_f16(x_bar, gamma_vec));</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>  <span class="comment">// Perform fused activation</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">if</span>(fused_activation)</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>  activation_functor(res);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  vst1q_f16(reinterpret_cast<float16_t *>(output.ptr()), res);</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>  input, output);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="preprocessor">#endif </span><span class="comment">/* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */</span><span class="preprocessor"></span></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> </div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="keyword">template</span> <<span class="keywordtype">bool</span> fused_activation, <span class="keyword">typename</span> F></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="keywordtype">void</span> NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window)</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>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> input(_input, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> output(_output, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  F activation_functor(_act_info);</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>  <span class="comment">// Hold information about the current feature map we are iterating.</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// Only compute denominator and NEON vectors once per feature map.</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> = -1;</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="keyword">const</span> <span class="keyword">auto</span> input_mean = reinterpret_cast<const float *>(_mean-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_var = reinterpret_cast<const float *>(_var-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_gamma = (_gamma != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float *>(_gamma-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_beta = (_beta != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float *>(_beta-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</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>  float32x4_t mean_vec = vdupq_n_f32(0.0);</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  float32x4_t var_vec = vdupq_n_f32(0.0);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  float32x4_t gamma_vec = vdupq_n_f32(1.0);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  float32x4_t beta_vec = vdupq_n_f32(0.0);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  float32x4_t denominator = vdupq_n_f32(0.0);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keyword">const</span> float32x4_t epsilon_vec = vdupq_n_f32(_epsilon);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>, [&](<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="l00258"></a><span class="lineno"> 258</span>  {</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> != <span class="keywordtype">id</span>.z())</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  {</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="comment">// Conctruct vectors</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  mean_vec = vdupq_n_f32(*(input_mean + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  var_vec = vdupq_n_f32(*(input_var + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">if</span>(input_gamma != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  {</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  gamma_vec = vdupq_n_f32(*(input_gamma + <span class="keywordtype">id</span>.z()));</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>  <span class="keywordflow">if</span>(input_beta != <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  {</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  beta_vec = vdupq_n_f32(*(input_beta + <span class="keywordtype">id</span>.z()));</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  }</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <span class="comment">// Calculate denominator</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  denominator = <a class="code" href="namespacearm__compute.xhtml#ab8970d7aed07d8649f5e3088455948b8">vinvsqrtq_f32</a>(vaddq_f32(var_vec, epsilon_vec));</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">slice</a> = <span class="keywordtype">id</span>.z();</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  }</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>  <span class="comment">// Calculate x bar</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec);</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="keyword">const</span> float32x4_t x_bar = vmulq_f32(numerator, denominator);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec);</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>  <span class="comment">// Perform fused activation</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordflow">if</span>(fused_activation)</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  {</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  activation_functor(res);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  }</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// Store results</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  vst1q_f32(reinterpret_cast<float *>(output.ptr()), res);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  },</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  input, output);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> }</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> </div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> <span class="keyword">template</span> <<span class="keywordtype">bool</span> fused_activation, <span class="keyword">typename</span> F></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> <span class="keywordtype">void</span> NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window)</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> {</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> input(_input, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="classarm__compute_1_1_iterator.xhtml">Iterator</a> output(_output, <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  F activation_functor(_act_info);</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_mean = reinterpret_cast<const float *>(_mean-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_var = reinterpret_cast<const float *>(_var-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0)));</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_gamma = (_gamma != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float *>(_gamma-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keyword">const</span> <span class="keyword">auto</span> input_beta = (_beta != <span class="keyword">nullptr</span>) ? reinterpret_cast<const float *>(_beta-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a>(0, 0))) : <span class="keyword">nullptr</span>;</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>  <span class="keyword">const</span> float32x4_t epsilon_vec = vdupq_n_f32(_epsilon);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>, [&](<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="l00310"></a><span class="lineno"> 310</span>  {</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <span class="comment">// Conctruct vectors</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keyword">const</span> float32x4_t mean_vec = vld1q_f32(input_mean + <span class="keywordtype">id</span>.x());</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keyword">const</span> float32x4_t var_vec = vld1q_f32(input_var + <span class="keywordtype">id</span>.x());</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keyword">const</span> float32x4_t gamma_vec = (input_gamma != <span class="keyword">nullptr</span>) ? vld1q_f32(input_gamma + <span class="keywordtype">id</span>.x()) : vdupq_n_f32(1.0);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="keyword">const</span> float32x4_t beta_vec = (input_beta != <span class="keyword">nullptr</span>) ? vld1q_f32(input_beta + <span class="keywordtype">id</span>.x()) : vdupq_n_f32(0.0);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="comment">// Calculate denominator</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  <span class="keyword">const</span> float32x4_t denominator = <a class="code" href="namespacearm__compute.xhtml#ab8970d7aed07d8649f5e3088455948b8">vinvsqrtq_f32</a>(vaddq_f32(var_vec, epsilon_vec));</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">// Calculate x bar</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">const</span> float32x4_t numerator = vsubq_f32(vld1q_f32(reinterpret_cast<const float *>(input.ptr())), mean_vec);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <span class="keyword">const</span> float32x4_t x_bar = vmulq_f32(numerator, denominator);</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  float32x4_t res = vmlaq_f32(beta_vec, x_bar, gamma_vec);</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="comment">// Perform fused activation</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordflow">if</span>(fused_activation)</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  {</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  activation_functor(res);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  }</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> </div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  <span class="comment">// Store results</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  vst1q_f32(reinterpret_cast<float *>(output.ptr()), res);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  },</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  input, output);</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> }</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="keywordtype">void</span> NEBatchNormalizationLayerKernel::configure_non_fused()</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> {</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> is_nhwc = _input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>;</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">switch</span>(_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  {</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<<span class="keyword">false</span>, ::<a class="code" href="structarm__compute_1_1detail_1_1dummy.xhtml">detail::dummy<float16_t, 8></a>> :</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<<span class="keyword">false</span>, ::<a class="code" href="structarm__compute_1_1detail_1_1dummy.xhtml">detail::dummy<float16_t, 8></a>>;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="preprocessor">#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  _func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<<span class="keyword">false</span>, ::<a class="code" href="structarm__compute_1_1detail_1_1dummy.xhtml">detail::dummy<float, 4></a>> :</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<<span class="keyword">false</span>, ::<a class="code" href="structarm__compute_1_1detail_1_1dummy.xhtml">detail::dummy<float, 4></a>>;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Element size not supported"</span>);</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  <span class="keywordflow">break</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> }</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> </div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="keywordtype">void</span> NEBatchNormalizationLayerKernel::configure_fused()</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>  <span class="comment">// NCHW Fused Batched Normalization with activation functions : FP32</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keyword">static</span> std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw =</div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  {</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::relu<float, 4>> },</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::brelu<float, 4>> },</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nchw<true, ::detail::lubrelu<float, 4>> }</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  };</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="comment">// NHWC Fused Batched Normalization with activation functions : FP32</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">static</span> std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nhwc =</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  {</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::relu<float, 4>> },</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::brelu<float, 4>> },</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp32_nhwc<true, ::detail::lubrelu<float, 4>> }</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  };</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span>  <span class="comment">// NCHW Fused Batched Normalization with activation functions : FP16</span></div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  <span class="keyword">static</span> std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw =</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span>  {</div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::relu<float16_t, 8>> },</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::brelu<float16_t, 8>> },</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<true, ::detail::lubrelu<float16_t, 8>> }</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  };</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  <span class="comment">// NHWC Fused Batched Normalization with activation functions : FP16</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">static</span> std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nhwc =</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  {</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::relu<float16_t, 8>> },</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaacc516ab03b98f1c908ddf6ed4a7c45e9">ActivationLayerInfo::ActivationFunction::BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::brelu<float16_t, 8>> },</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  { <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a>, &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<true, ::detail::lubrelu<float16_t, 8>> }</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  };</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> <span class="preprocessor">#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keywordflow">switch</span>(_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">data_type</a>())</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  {</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> <span class="preprocessor">#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  _func = (_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>) ? bn_fused_map_f16_nhwc[_act_info.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a9e0fb1d1462557f28966ae19988532c2">activation</a>()] : bn_fused_map_f16_nchw[_act_info.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a9e0fb1d1462557f28966ae19988532c2">activation</a>()];</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> <span class="preprocessor">#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC</span></div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  _func = (_input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">data_layout</a>() == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">DataLayout::NHWC</a>) ? bn_fused_map_f32_nhwc[_act_info.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a9e0fb1d1462557f28966ae19988532c2">activation</a>()] : bn_fused_map_f32_nchw[_act_info.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a9e0fb1d1462557f28966ae19988532c2">activation</a>()];</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Element size not supported"</span>);</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  }</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span> }</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> </div><div class="line"><a name="l00406"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a919d4b5219114921ee77fb603f27293d"> 406</a></span> <a class="code" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a919d4b5219114921ee77fb603f27293d">NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel</a>()</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  : _func(nullptr), _input(nullptr), _output(nullptr), _mean(nullptr), _var(nullptr), _gamma(nullptr), _beta(nullptr), _epsilon(), _act_info()</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> {</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> }</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span> </div><div class="line"><a name="l00411"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a8f98f383f8998195408b570534483536"> 411</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a8f98f383f8998195408b570534483536">NEBatchNormalizationLayerKernel::configure</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *input, <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *output,</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *mean, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *var,</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *beta, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> *gamma,</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordtype">float</span> <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> {</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a>(input, mean, var);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(validate_arguments(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), (output != <span class="keyword">nullptr</span>) ? output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  mean-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), var-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(),</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  (beta != <span class="keyword">nullptr</span>) ? beta-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  (gamma != <span class="keyword">nullptr</span>) ? gamma-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>));</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  _input = input;</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  _output = input;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  _mean = mean;</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  _var = var;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  _gamma = gamma;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  _beta = beta;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  _epsilon = <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>;</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  _act_info = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>;</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span> </div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> run_in_place = (output == <span class="keyword">nullptr</span>) || (output == input);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  <span class="keywordflow">if</span>(!run_in_place)</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  {</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  _output = output;</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  }</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span> </div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span>  <span class="comment">// Configure activation function to run</span></div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordflow">if</span>(_act_info.<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#af5a8385102f8f8dd6c5957eac08b04c2">enabled</a>())</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  {</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  configure_fused();</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  }</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  {</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  configure_non_fused();</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  }</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <span class="comment">// Configure kernel window</span></div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keyword">auto</span> win_config = <a class="code" href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">validate_and_configure_window</a>(input-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), (run_in_place) ? <span class="keyword">nullptr</span> : output-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), mean-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), var-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>(), (gamma != <span class="keyword">nullptr</span>) ? gamma-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>,</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  (beta != <span class="keyword">nullptr</span>) ? beta-><a class="code" href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">info</a>() : <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <a class="code" href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a>(win_config.first);</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  INEKernel::configure(win_config.second);</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> }</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a7b183c349117a66b3f74138d5d86d634"> 456</a></span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a> <a class="code" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a7b183c349117a66b3f74138d5d86d634">NEBatchNormalizationLayerKernel::validate</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *input, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *output,</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *mean, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *var,</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *beta, <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml">ITensorInfo</a> *gamma,</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span>  <span class="keywordtype">float</span> <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>)</div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> {</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(validate_arguments(input, output, mean, var, beta, gamma, <a class="code" href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a>, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">act_info</a>));</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <a class="code" href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a>(<a class="code" href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">validate_and_configure_window</a>(input-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get(), output ? output-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get() : <span class="keyword">nullptr</span>, mean-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get(), var-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get(),</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  (gamma != <span class="keyword">nullptr</span>) ? gamma-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get() : <span class="keyword">nullptr</span>, (beta != <span class="keyword">nullptr</span>) ? beta-><a class="code" href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">clone</a>().get() : <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  .first);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> </div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1_status.xhtml">Status</a>{};</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> }</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"><a class="line" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a112b35dd205c62ea6ed1447ef226da82"> 469</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a112b35dd205c62ea6ed1447ef226da82">NEBatchNormalizationLayerKernel::run</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> &window, <span class="keyword">const</span> <a class="code" href="structarm__compute_1_1_thread_info.xhtml">ThreadInfo</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>)</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> {</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a>(<span class="keyword">this</span>);</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <a class="code" href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a>(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">INEKernel::window</a>(), <a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_func == <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  (this->*_func)(<a class="code" href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">window</a>);</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> }</div><div class="ttc" id="_error_8h_xhtml_a05b19c75afe9c24200a62b9724734bbd"><div class="ttname"><a href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR(...)</div><div class="ttdoc">Print the given message then throw an std::runtime_error.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00261">Error.h:261</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab8970d7aed07d8649f5e3088455948b8"><div class="ttname"><a href="namespacearm__compute.xhtml#ab8970d7aed07d8649f5e3088455948b8">arm_compute::vinvsqrtq_f32</a></div><div class="ttdeci">float32x4_t vinvsqrtq_f32(float32x4_t x)</div><div class="ttdoc">Calculate inverse square root.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_kernel_xhtml_ad34a46f53686c12a5c5e717cc9617fb6"><div class="ttname"><a href="classarm__compute_1_1_i_kernel.xhtml#ad34a46f53686c12a5c5e717cc9617fb6">arm_compute::IKernel::window</a></div><div class="ttdeci">const Window & window() const</div><div class="ttdoc">The maximum window the kernel can be executed on.</div><div class="ttdef"><b>Definition:</b> <a href="_i_kernel_8cpp_source.xhtml#l00028">IKernel.cpp:28</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_adbd73147d41e8a640bc299d12613c31e"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">arm_compute::ITensor::ptr_to_element</a></div><div class="ttdeci">uint8_t * ptr_to_element(const Coordinates &id) const</div><div class="ttdoc">Return a pointer to the element at the passed coordinates.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00063">ITensor.h:63</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_af5a8385102f8f8dd6c5957eac08b04c2"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#af5a8385102f8f8dd6c5957eac08b04c2">arm_compute::ActivationLayerInfo::enabled</a></div><div class="ttdeci">bool enabled() const</div><div class="ttdoc">Check if initialised.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01565">Types.h:1565</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a1f8aca235c095df227e7444f6b237eb1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a1f8aca235c095df227e7444f6b237eb1">arm_compute::test::validation::act_info</a></div><div class="ttdeci">act_info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00175">ConvolutionLayer.cpp:175</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a178f0d3d87f959e00a743328d95359d2"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">arm_compute::ITensorInfo::dimension</a></div><div class="ttdeci">virtual size_t dimension(size_t index) const =0</div><div class="ttdoc">Return the size of the requested dimension.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_abdb9168800c70e5e2c4c020a3b905738"><div class="ttname"><a href="_validate_8h.xhtml#abdb9168800c70e5e2c4c020a3b905738">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00494">Validate.h:494</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a8f3ff7da485ff7e75dab07baadf5b4bd"><div class="ttname"><a href="_validate_8h.xhtml#a8f3ff7da485ff7e75dab07baadf5b4bd">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00545">Validate.h:545</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="namespacearm__compute_xhtml_ad6630777dc2d315531f1e0b02491051f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad6630777dc2d315531f1e0b02491051f">arm_compute::validate_and_configure_window</a></div><div class="ttdeci">std::pair< Status, Window > validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation)</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_depthwise_convolution_layer_native_kernel_8cpp_source.xhtml#l00221">NEDepthwiseConvolutionLayerNativeKernel.cpp:221</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a8a1e1c105f0bdaf37db408c7cfcb77a4"><div class="ttname"><a href="_error_8h.xhtml#a8a1e1c105f0bdaf37db408c7cfcb77a4">ARM_COMPUTE_RETURN_ON_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ON_ERROR(status)</div><div class="ttdoc">Checks if a status contains an error and returns it.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00193">Error.h:193</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7cfb31af63202568efef5214acfbf3ba"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7cfb31af63202568efef5214acfbf3ba">arm_compute::ITensorInfo::data_type</a></div><div class="ttdeci">virtual DataType data_type() const =0</div><div class="ttdoc">Data type used for each element of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_batch_normalization_layer_kernel_xhtml_a7b183c349117a66b3f74138d5d86d634"><div class="ttname"><a href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a7b183c349117a66b3f74138d5d86d634">arm_compute::NEBatchNormalizationLayerKernel::validate</a></div><div class="ttdeci">static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, const ITensorInfo *beta=nullptr, const ITensorInfo *gamma=nullptr, float epsilon=0.001f, ActivationLayerInfo act_info=ActivationLayerInfo())</div><div class="ttdoc">Static function to check if given info will lead to a valid configuration of NEBatchNormalizationLaye...</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_batch_normalization_layer_kernel_8cpp_source.xhtml#l00456">NEBatchNormalizationLayerKernel.cpp:456</a></div></div> |
| <div class="ttc" id="_asymm_helpers_8cpp_xhtml_a552dc3787d7ea1675f3e4e8993501d58"><div class="ttname"><a href="_asymm_helpers_8cpp.xhtml#a552dc3787d7ea1675f3e4e8993501d58">epsilon</a></div><div class="ttdeci">constexpr float epsilon</div><div class="ttdef"><b>Definition:</b> <a href="_asymm_helpers_8cpp_source.xhtml#l00033">AsymmHelpers.cpp:33</a></div></div> |
| <div class="ttc" id="_window_8h_xhtml"><div class="ttname"><a href="_window_8h.xhtml">Window.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_ae7eed178dac535c6e727061b1f5bc6eb"><div class="ttname"><a href="_validate_8h.xhtml#ae7eed178dac535c6e727061b1f5bc6eb">ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00791">Validate.h:791</a></div></div> |
| <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> |
| <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#l00337">Error.h:337</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml">arm_compute::ITensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_info_8h_source.xhtml#l00040">ITensorInfo.h:40</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a938dcd406ce611ef5345ad2531cdb948"><div class="ttname"><a href="_error_8h.xhtml#a938dcd406ce611ef5345ad2531cdb948">ARM_COMPUTE_ERROR_THROW_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_THROW_ON(status)</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00327">Error.h:327</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_status_xhtml"><div class="ttname"><a href="classarm__compute_1_1_status.xhtml">arm_compute::Status</a></div><div class="ttdoc">Status class.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00052">Error.h:52</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a046fbca6a9505ce038bc02830c739fed"><div class="ttname"><a href="_error_8h.xhtml#a046fbca6a9505ce038bc02830c739fed">ARM_COMPUTE_CREATE_ERROR</a></div><div class="ttdeci">#define ARM_COMPUTE_CREATE_ERROR(error_code,...)</div><div class="ttdoc">Creates an error with a given message.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00167">Error.h:167</a></div></div> |
| <div class="ttc" id="_tensor_info_8h_xhtml"><div class="ttname"><a href="_tensor_info_8h.xhtml">TensorInfo.h</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a206d6e247e0957ac3dee45d27756fc25"><div class="ttname"><a href="_error_8h.xhtml#a206d6e247e0957ac3dee45d27756fc25">ARM_COMPUTE_RETURN_ERROR_ON</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON(cond)</div><div class="ttdoc">If the condition is true, an error is returned.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00244">Error.h:244</a></div></div> |
| <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#l01517">Types.h:1517</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a></div><div class="ttdoc">Interface for NEON tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_i_tensor_8h_source.xhtml#l00036">ITensor.h:36</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab7980fa5ee693e3282a76da047a1c3b5"><div class="ttname"><a href="namespacearm__compute.xhtml#ab7980fa5ee693e3282a76da047a1c3b5">arm_compute::calculate_max_window</a></div><div class="ttdeci">Window calculate_max_window(const ValidRegion &valid_region, const Steps &steps=Steps(), bool skip_border=false, BorderSize border_size=BorderSize())</div><div class="ttdoc">Calculate the maximum window for a given tensor shape and border setting.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_helpers_8cpp_source.xhtml#l00028">Helpers.cpp:28</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2018 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a47be6fa38308d0003c25b60b7dbc45ce"><div class="ttname"><a href="namespacearm__compute.xhtml#a47be6fa38308d0003c25b60b7dbc45ce">arm_compute::auto_init_if_empty</a></div><div class="ttdeci">bool auto_init_if_empty(ITensorInfo &info, const TensorShape &shape, int num_channels, DataType data_type, QuantizationInfo quantization_info=QuantizationInfo())</div><div class="ttdoc">Auto initialize the tensor info (shape, number of channels and data type) if the current assignment i...</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00201">Helpers.inl:201</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9ea"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9ea">arm_compute::ActivationLayerInfo::ActivationFunction</a></div><div class="ttdeci">ActivationFunction</div><div class="ttdoc">Available activation functions.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01521">Types.h:1521</a></div></div> |
| <div class="ttc" id="_c_p_p_2_validate_8h_xhtml_ad2633f3560322e1f8d926949dec1b730"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml#ad2633f3560322e1f8d926949dec1b730">ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(tensor)</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_2_validate_8h_source.xhtml#l00071">Validate.h:71</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">arm_compute::Format::F16</a></div><div class="ttdoc">1 channel, 1 F16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a74dfd07380a290c34fe7c8e065029b95"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a74dfd07380a290c34fe7c8e065029b95">arm_compute::ITensorInfo::valid_region</a></div><div class="ttdeci">virtual ValidRegion valid_region() const =0</div><div class="ttdoc">Valid region of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_batch_normalization_layer_kernel_xhtml_a919d4b5219114921ee77fb603f27293d"><div class="ttname"><a href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a919d4b5219114921ee77fb603f27293d">arm_compute::NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel</a></div><div class="ttdeci">NEBatchNormalizationLayerKernel()</div><div class="ttdoc">Default constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_batch_normalization_layer_kernel_8cpp_source.xhtml#l00406">NEBatchNormalizationLayerKernel.cpp:406</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_batch_normalization_layer_kernel_xhtml_a112b35dd205c62ea6ed1447ef226da82"><div class="ttname"><a href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a112b35dd205c62ea6ed1447ef226da82">arm_compute::NEBatchNormalizationLayerKernel::run</a></div><div class="ttdeci">void run(const Window &window, const ThreadInfo &info) override</div><div class="ttdoc">Execute the kernel on the passed window.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_batch_normalization_layer_kernel_8cpp_source.xhtml#l00469">NEBatchNormalizationLayerKernel.cpp:469</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_utils_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_afc4bd8e872567d9c4c57d89eb0bb3da1"><div class="ttname"><a href="namespacearm__compute.xhtml#afc4bd8e872567d9c4c57d89eb0bb3da1">arm_compute::update_window_and_padding</a></div><div class="ttdeci">bool update_window_and_padding(Window &win, Ts &&... patterns)</div><div class="ttdoc">Update window and padding size for each of the access patterns.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00402">Helpers.h:402</a></div></div> |
| <div class="ttc" id="_error_8h_xhtml_a6dc630a6ae9cc063b3924bcea8dee9d6"><div class="ttname"><a href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a></div><div class="ttdeci">#define ARM_COMPUTE_UNUSED(...)</div><div class="ttdoc">To avoid unused variables warnings.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00160">Error.h:160</a></div></div> |
| <div class="ttc" id="_c_p_p_2_validate_8h_xhtml"><div class="ttname"><a href="_c_p_p_2_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a27e4638546c88b8916f967e6e54480a9"><div class="ttname"><a href="_validate_8h.xhtml#a27e4638546c88b8916f967e6e54480a9">ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES</a></div><div class="ttdeci">#define ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00443">Validate.h:443</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_steps_xhtml"><div class="ttname"><a href="classarm__compute_1_1_steps.xhtml">arm_compute::Steps</a></div><div class="ttdoc">Class to describe a number of elements in each dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_steps_8h_source.xhtml#l00040">Steps.h:40</a></div></div> |
| <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> |
| <div class="ttc" id="classarm__compute_1_1_access_window_horizontal_xhtml"><div class="ttname"><a href="classarm__compute_1_1_access_window_horizontal.xhtml">arm_compute::AccessWindowHorizontal</a></div><div class="ttdoc">Implementation of a row access pattern.</div><div class="ttdef"><b>Definition:</b> <a href="_i_access_window_8h_source.xhtml#l00231">IAccessWindow.h:231</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1misc_1_1_i_cloneable_xhtml_a4d10e5012a872e7f78f2b539b673049d"><div class="ttname"><a href="classarm__compute_1_1misc_1_1_i_cloneable.xhtml#a4d10e5012a872e7f78f2b539b673049d">arm_compute::misc::ICloneable::clone</a></div><div class="ttdeci">virtual std::unique_ptr< T > clone() const =0</div><div class="ttdoc">Provide a clone of the current object of class T.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_xhtml_a0e95dc1e53c361348314873b168ae237"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#a0e95dc1e53c361348314873b168ae237">arm_compute::ITensor::info</a></div><div class="ttdeci">virtual ITensorInfo * info() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_aa459796b5489eca8a9160cb5dcf1a103"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#aa459796b5489eca8a9160cb5dcf1a103">arm_compute::ITensorInfo::element_size</a></div><div class="ttdeci">virtual size_t element_size() const =0</div><div class="ttdoc">Element size in bytes calculated as data_size() * num_channels()</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229"><div class="ttname"><a href="namespacearm__compute.xhtml#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">arm_compute::DataLayoutDimension::CHANNEL</a></div><div class="ttdoc">channel</div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a59e56af19e754a6aa26a612ebf91d05fa62be47fdd89da032cf78dfce82239579"><div class="ttname"><a href="namespacearm__compute.xhtml#a59e56af19e754a6aa26a612ebf91d05fa62be47fdd89da032cf78dfce82239579">arm_compute::ErrorCode::RUNTIME_ERROR</a></div><div class="ttdoc">Generic runtime error.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaaab1d4411a9e7f5e82002512cddfdc33a">arm_compute::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU</a></div><div class="ttdoc">Lower and Upper Bounded Rectifier ( )</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a921b705e9e3e0fe928928447869e62a5"><div class="ttname"><a href="_validate_8h.xhtml#a921b705e9e3e0fe928928447869e62a5">ARM_COMPUTE_ERROR_ON_NULLPTR</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_NULLPTR(...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00161">Validate.h:161</a></div></div> |
| <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> |
| <div class="ttc" id="structarm__compute_1_1_thread_info_xhtml"><div class="ttname"><a href="structarm__compute_1_1_thread_info.xhtml">arm_compute::ThreadInfo</a></div><div class="ttdoc">Information about executing thread and CPU.</div><div class="ttdef"><b>Definition:</b> <a href="_c_p_p_types_8h_source.xhtml#l00225">CPPTypes.h:225</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a></div><div class="ttdoc">Num samples, height, width, channels.</div></div> |
| <div class="ttc" id="_n_e_math_8h_xhtml"><div class="ttname"><a href="_n_e_math_8h.xhtml">NEMath.h</a></div></div> |
| <div class="ttc" id="arm__compute_2core_2_helpers_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_helpers_8h.xhtml">Helpers.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a5002bf7ec46d52971f9526e94172cfee"><div class="ttname"><a href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">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> |
| <div class="ttc" id="_n_e_activation_function_detail_8h_xhtml"><div class="ttname"><a href="_n_e_activation_function_detail_8h.xhtml">NEActivationFunctionDetail.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_n_e_batch_normalization_layer_kernel_xhtml_a8f98f383f8998195408b570534483536"><div class="ttname"><a href="classarm__compute_1_1_n_e_batch_normalization_layer_kernel.xhtml#a8f98f383f8998195408b570534483536">arm_compute::NEBatchNormalizationLayerKernel::configure</a></div><div class="ttdeci">void configure(ITensor *input, ITensor *output, const ITensor *mean, const ITensor *var, const ITensor *beta=nullptr, const ITensor *gamma=nullptr, float epsilon=0.001f, ActivationLayerInfo act_info=ActivationLayerInfo())</div><div class="ttdoc">Set the input and output tensors.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_batch_normalization_layer_kernel_8cpp_source.xhtml#l00411">NEBatchNormalizationLayerKernel.cpp:411</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a9e0fb1d1462557f28966ae19988532c2"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a9e0fb1d1462557f28966ae19988532c2">arm_compute::ActivationLayerInfo::activation</a></div><div class="ttdeci">ActivationFunction activation() const</div><div class="ttdoc">Get the type of activation function.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l01550">Types.h:1550</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a46e938020a3ac8c926d0590b7fe957db"><div class="ttname"><a href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">arm_compute::get_data_layout_dimension_index</a></div><div class="ttdeci">size_t get_data_layout_dimension_index(const DataLayout data_layout, const DataLayoutDimension data_layout_dimension)</div><div class="ttdoc">Get the index of the given dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_helpers_8inl_source.xhtml#l00326">Helpers.inl:326</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a4f4125dba5283887b34f889b1c615c0c"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">arm_compute::test::validation::info</a></div><div class="ttdeci">info</div><div class="ttdef"><b>Definition:</b> <a href="validation_2_c_l_2_convolution_layer_8cpp_source.xhtml#l00174">ConvolutionLayer.cpp:174</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_iterator_xhtml"><div class="ttname"><a href="classarm__compute_1_1_iterator.xhtml">arm_compute::Iterator</a></div><div class="ttdoc">Iterator updated by execute_window_loop for each window element.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00318">Helpers.h:318</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml_a6eb9ce82815fe429250189da7592ba75"><div class="ttname"><a href="_validate_8h.xhtml#a6eb9ce82815fe429250189da7592ba75">ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(f, s)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00205">Validate.h:205</a></div></div> |
| <div class="ttc" id="_n_e_fixed_point_8h_xhtml"><div class="ttname"><a href="_n_e_fixed_point_8h.xhtml">NEFixedPoint.h</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1detail_1_1dummy_xhtml"><div class="ttname"><a href="structarm__compute_1_1detail_1_1dummy.xhtml">arm_compute::detail::dummy</a></div><div class="ttdoc">Dummy activation object.</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_activation_function_detail_8h_source.xhtml#l00035">NEActivationFunctionDetail.h:35</a></div></div> |
| <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> |
| <div class="ttc" id="_validate_8h_xhtml_a1b35b0d258183cf9ef36adf684d0b88c"><div class="ttname"><a href="_validate_8h.xhtml#a1b35b0d258183cf9ef36adf684d0b88c">ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(k)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00940">Validate.h:940</a></div></div> |
| <div class="ttc" id="_validate_8h_xhtml"><div class="ttname"><a href="_validate_8h.xhtml">Validate.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a548131b3d37da47a2e9d32111c88dfe1"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a548131b3d37da47a2e9d32111c88dfe1">arm_compute::test::validation::reference::slice</a></div><div class="ttdeci">SimpleTensor< T > slice(const SimpleTensor< T > &src, Coordinates starts, Coordinates ends)</div><div class="ttdef"><b>Definition:</b> <a href="_slice_operations_8cpp_source.xhtml#l00038">SliceOperations.cpp:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a367b5090ab432bc7de2c32369e087ab1"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a367b5090ab432bc7de2c32369e087ab1">arm_compute::ITensorInfo::data_layout</a></div><div class="ttdeci">virtual DataLayout data_layout() const =0</div><div class="ttdoc">Get the data layout of the tensor.</div></div> |
| <div class="ttc" id="_n_e_batch_normalization_layer_kernel_8h_xhtml"><div class="ttname"><a href="_n_e_batch_normalization_layer_kernel_8h.xhtml">NEBatchNormalizationLayerKernel.h</a></div></div> |
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