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| <a href="_graph_utils_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) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="preprocessor">#include "<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>"</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="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="arm__compute_2core_2_types_8h.xhtml">arm_compute/core/Types.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#include "<a class="code" href="graph_2_logger_8h.xhtml">arm_compute/graph/Logger.h</a>"</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_sub_tensor_8h.xhtml">arm_compute/runtime/SubTensor.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="_image_loader_8h.xhtml">utils/ImageLoader.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <iomanip></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> </div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> {</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> std::pair<arm_compute::TensorShape, arm_compute::PermutationVector> compute_permutation_parameters(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a> &<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a> data_layout)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="comment">// Set permutation parameters if needed</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a> permuted_shape = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>;</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">arm_compute::PermutationVector</a> perm;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Permute only if num_dimensions greater than 2</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>.num_dimensions() > 2)</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>  perm = (data_layout == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0ad066db54b89b0912e7e7c6da51e2da51">arm_compute::DataLayout::NHWC</a>) ? <a class="code" href="classarm__compute_1_1_strides.xhtml">arm_compute::PermutationVector</a>(2U, 0U, 1U) : <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a>(1U, 2U, 0U);</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_strides.xhtml">arm_compute::PermutationVector</a> perm_shape = (data_layout == <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a>) ? <a class="code" href="classarm__compute_1_1_strides.xhtml">arm_compute::PermutationVector</a>(2U, 0U, 1U) : <a class="code" href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a>(1U, 2U, 0U);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a>(permuted_shape, perm_shape);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">return</span> std::make_pair(permuted_shape, perm);</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> } <span class="comment">// namespace</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#ab58008704726cd07353605b1d9e13d86"> 60</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#ab58008704726cd07353605b1d9e13d86">TFPreproccessor::TFPreproccessor</a>(<span class="keywordtype">float</span> min_range, <span class="keywordtype">float</span> max_range)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  : _min_range(min_range), _max_range(max_range)</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> }</div><div class="line"><a name="l00064"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666"> 64</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">TFPreproccessor::preprocess</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</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="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a87f09c74765be18a99038478f96daf9b">range</a> = _max_range - _min_range;</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> value = *reinterpret_cast<float *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<span class="keywordtype">id</span>));</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordtype">float</span> res = value / 255.f; <span class="comment">// Normalize to [0, 1]</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  res = res * <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a87f09c74765be18a99038478f96daf9b">range</a> + _min_range; <span class="comment">// Map to [min_range, max_range]</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  *reinterpret_cast<float *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<span class="keywordtype">id</span>)) = res;</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> }</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00080"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a5cb89f99531ca5931b461835039fd655"> 80</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a5cb89f99531ca5931b461835039fd655">CaffePreproccessor::CaffePreproccessor</a>(std::array<float, 3> mean, <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>, <span class="keywordtype">bool</span> bgr)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  : _mean(mean), _scale(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>), _bgr(bgr)</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> {</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">if</span>(_bgr)</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>  <a class="code" href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">std::swap</a>(_mean[0], _mean[2]);</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> }</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666"> 89</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">CaffePreproccessor::preprocess</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</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>  <a class="code" href="classarm__compute_1_1_window.xhtml">Window</a> window;</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  window.<a class="code" href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">use_tensor_dimensions</a>(tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel_idx = <a class="code" href="namespacearm__compute.xhtml#a46e938020a3ac8c926d0590b7fe957db">get_data_layout_dimension_index</a>(tensor.<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#a74ce3f7420453d3446218ff3b7453e02af52e9c50a060add65a035429b2a22229">DataLayoutDimension::CHANNEL</a>);</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(window, [&](<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_coordinates.xhtml">Coordinates</a> & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  {</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> value = *reinterpret_cast<float *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<span class="keywordtype">id</span>)) - _mean[<span class="keywordtype">id</span>[channel_idx]];</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  *reinterpret_cast<float *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<span class="keywordtype">id</span>)) = value * _scale;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  });</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> }</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#a9ea94bf5b7d00ddb836df1b8dcedb93a"> 103</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#a9ea94bf5b7d00ddb836df1b8dcedb93a">PPMWriter::PPMWriter</a>(std::string name, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maximum)</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  : _name(std::move(name)), _iterator(0), _maximum(maximum)</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 108</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#ab469d593b4bc92e1d1132a03de0aedca">PPMWriter::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::stringstream ss;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  ss << _name << _iterator << <span class="stringliteral">".ppm"</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="namespacearm__compute_1_1utils.xhtml#a301d0b7bfd70f73fc1924f4281938d08">arm_compute::utils::save_to_ppm</a>(tensor, ss.str());</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  _iterator++;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordflow">if</span>(_maximum == 0)</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>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">return</span> _iterator < _maximum;</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"><a class="line" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ac5ae9597ba20e5581726743fe7c154b5"> 123</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ac5ae9597ba20e5581726743fe7c154b5">DummyAccessor::DummyAccessor</a>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> maximum)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  : _iterator(0), _maximum(maximum)</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> }</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 128</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">DummyAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="_error_8h.xhtml#a6dc630a6ae9cc063b3924bcea8dee9d6">ARM_COMPUTE_UNUSED</a>(tensor);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">bool</span> ret = _maximum == 0 || _iterator < _maximum;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">if</span>(_iterator == _maximum)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  _iterator = 0;</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>  <span class="keywordflow">else</span></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>  _iterator++;</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  }</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <span class="keywordflow">return</span> ret;</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> </div><div class="line"><a name="l00143"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_num_py_accessor.xhtml#ac3bd9a902b0bb7e28e1bed21318bc562"> 143</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_accessor.xhtml#ac3bd9a902b0bb7e28e1bed21318bc562">NumPyAccessor::NumPyAccessor</a>(std::string npy_path, <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>, std::ostream &output_stream)</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  : _npy_tensor(), _filename(std::move(npy_path)), _output_stream(output_stream)</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> {</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml">NumPyBinLoader</a> loader(_filename);</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">shape</a>, 1, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">data_type</a>);</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  _npy_tensor.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">init</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a4f4125dba5283887b34f889b1c615c0c">info</a>);</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  _npy_tensor.<a class="code" href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">allocator</a>()-><a class="code" href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">allocate</a>();</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> </div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  loader.<a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca">access_tensor</a>(_npy_tensor);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="keywordtype">void</span> NumPyAccessor::access_numpy_tensor(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> num_elements = tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>().<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml#a0fdcb923dfd4c74858cc2bc326321efb">total_size</a>();</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  <span class="keywordtype">int</span> num_mismatches = utils::compare_tensor<T>(tensor, _npy_tensor);</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordtype">float</span> percentage_mismatches = static_cast<float>(num_mismatches) / num_elements;</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>  _output_stream << <span class="stringliteral">"Results: "</span> << 100.f - (percentage_mismatches * 100) << <span class="stringliteral">" % matches with the provided output["</span> << _filename << <span class="stringliteral">"]."</span> << std::endl;</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> </div><div class="line"><a name="l00165"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_num_py_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 165</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">NumPyAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> {</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(&tensor, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_npy_tensor.<a class="code" href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">info</a>()-><a class="code" href="classarm__compute_1_1_i_tensor_info.xhtml#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0) != tensor.<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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="keywordflow">switch</span>(tensor.<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="l00171"></a><span class="lineno"> 171</span>  {</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  access_numpy_tensor<float>(tensor);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  }</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> }</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_image_accessor.xhtml#a872e7ef3563a74e35a6912d12706c012"> 182</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_image_accessor.xhtml#a872e7ef3563a74e35a6912d12706c012">ImageAccessor::ImageAccessor</a>(std::string filename, <span class="keywordtype">bool</span> bgr, std::unique_ptr<IPreprocessor> preprocessor)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  : _already_loaded(false), _filename(std::move(filename)), _bgr(bgr), _preprocessor(std::move(preprocessor))</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> }</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_image_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 187</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_image_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">ImageAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> {</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keywordflow">if</span>(!_already_loaded)</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">auto</span> image_loader = <a class="code" href="classarm__compute_1_1utils_1_1_image_loader_factory.xhtml#ada8c92fe057e34525e7f8c4e8e422179">utils::ImageLoaderFactory::create</a>(_filename);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(image_loader == <span class="keyword">nullptr</span>, <span class="stringliteral">"Unsupported image type"</span>);</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>  <span class="comment">// Open image file</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  image_loader->open(_filename);</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="comment">// Get permutated shape and permutation parameters</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> permuted_shape = tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">arm_compute::PermutationVector</a> perm;</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keywordflow">if</span>(tensor.<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#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  {</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  std::tie(permuted_shape, perm) = compute_permutation_parameters(tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(), tensor.<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>());</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  }</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(image_loader->width() != permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>() || image_loader->height() != permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>(),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="stringliteral">"Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d]."</span>,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  image_loader->width(), image_loader->height(), permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>(), permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>());</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// Fill the tensor with the PPM content (BGR)</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  image_loader->fill_planar_tensor(tensor, _bgr);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span> </div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  <span class="comment">// Preprocess tensor</span></div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordflow">if</span>(_preprocessor)</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  _preprocessor->preprocess(tensor);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  }</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  }</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> </div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  _already_loaded = !_already_loaded;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">return</span> _already_loaded;</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_validation_input_accessor.xhtml#a458082188f9eec9fdf459f508d64d9be"> 222</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_validation_input_accessor.xhtml#a458082188f9eec9fdf459f508d64d9be">ValidationInputAccessor::ValidationInputAccessor</a>(<span class="keyword">const</span> std::string &image_list,</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  std::string images_path,</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  std::unique_ptr<IPreprocessor> preprocessor,</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keywordtype">bool</span> bgr,</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start,</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> end,</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  std::ostream &output_stream)</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  : _path(std::move(images_path)), _images(), _preprocessor(std::move(preprocessor)), _bgr(bgr), _offset(0), _output_stream(output_stream)</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> {</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(start > end, <span class="stringliteral">"Invalid validation range!"</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>  std::ifstream ifs;</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  {</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  ifs.exceptions(std::ifstream::badbit);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  ifs.open(image_list, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> </div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="comment">// Parse image names</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> counter = 0;</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  <span class="keywordflow">for</span>(std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter)</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  {</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// Add image to process if withing range</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordflow">if</span>(counter >= start)</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>  std::stringstream linestream(line);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  std::string image_name;</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  linestream >> image_name;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  _images.emplace_back(std::move(image_name));</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  }</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  }</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  }</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::ifstream::failure &e)</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  {</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Accessing %s: %s"</span>, image_list.c_str(), e.what());</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_validation_input_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 260</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_validation_input_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">ValidationInputAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a> &tensor)</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> {</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  <span class="keywordtype">bool</span> ret = _offset < _images.size();</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  <span class="keywordflow">if</span>(ret)</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  {</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  <a class="code" href="classarm__compute_1_1utils_1_1_j_p_e_g_loader.xhtml">utils::JPEGLoader</a> jpeg;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> </div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="comment">// Open JPEG file</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  std::string image_name = _path + _images[_offset++];</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  jpeg.<a class="code" href="classarm__compute_1_1utils_1_1_j_p_e_g_loader.xhtml#ab23b23a466d459ecbad7d046bb085324">open</a>(image_name);</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  _output_stream << <span class="stringliteral">"["</span> << _offset << <span class="stringliteral">"/"</span> << _images.size() << <span class="stringliteral">"] Validating "</span> << image_name << std::endl;</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>  <span class="comment">// Get permutated shape and permutation parameters</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> permuted_shape = tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>();</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="classarm__compute_1_1_strides.xhtml">arm_compute::PermutationVector</a> perm;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keywordflow">if</span>(tensor.<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#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">DataLayout::NCHW</a>)</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>  std::tie(permuted_shape, perm) = compute_permutation_parameters(tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>(),</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  tensor.<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>());</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  }</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(jpeg.<a class="code" href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#a7f0f3e5dd09a150b2cc221c01804d1a7">width</a>() != permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>() || jpeg.<a class="code" href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#adc7679009b582b99d859c0edfc35aa4a">height</a>() != permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>(),</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="stringliteral">"Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d]."</span>,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  jpeg.<a class="code" href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#a7f0f3e5dd09a150b2cc221c01804d1a7">width</a>(), jpeg.<a class="code" href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#adc7679009b582b99d859c0edfc35aa4a">height</a>(), permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">x</a>(), permuted_shape.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>());</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> </div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// Fill the tensor with the JPEG content (BGR)</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  jpeg.<a class="code" href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#a7c3f70e1caee95bb95c62346e130e5ab">fill_planar_tensor</a>(tensor, _bgr);</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// Preprocess tensor</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordflow">if</span>(_preprocessor)</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  {</div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  _preprocessor->preprocess(tensor);</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>  }</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>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> </div><div class="line"><a name="l00297"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ac3ae33bed176ca84786a8c910c3072c9"> 297</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ac3ae33bed176ca84786a8c910c3072c9">ValidationOutputAccessor::ValidationOutputAccessor</a>(<span class="keyword">const</span> std::string &image_list,</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  std::ostream &output_stream,</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> start,</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> end)</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  : _results(), _output_stream(output_stream), _offset(0), _positive_samples_top1(0), _positive_samples_top5(0)</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>  <a class="code" href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a>(start > end, <span class="stringliteral">"Invalid validation range!"</span>);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  std::ifstream ifs;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">try</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>  ifs.exceptions(std::ifstream::badbit);</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  ifs.open(image_list, std::ios::in | std::ios::binary);</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">// Parse image correctly classified labels</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> counter = 0;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="keywordflow">for</span>(std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter)</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  {</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">// Add label if within range</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  <span class="keywordflow">if</span>(counter >= start)</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  {</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  std::stringstream linestream(line);</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  std::string image_name;</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keywordtype">int</span> result;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  linestream >> image_name >> result;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  _results.emplace_back(result);</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  }</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  }</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>  <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::ifstream::failure &e)</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>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Accessing %s: %s"</span>, image_list.c_str(), e.what());</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  }</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> }</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> </div><div class="line"><a name="l00333"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ad20897c5c8bd47f5d4005989bead0e55"> 333</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ad20897c5c8bd47f5d4005989bead0e55">ValidationOutputAccessor::reset</a>()</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>  _offset = 0;</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  _positive_samples_top1 = 0;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  _positive_samples_top5 = 0;</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> }</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> </div><div class="line"><a name="l00340"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 340</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">ValidationOutputAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a> &tensor)</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span> {</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <span class="keywordtype">bool</span> ret = _offset < _results.size();</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <span class="keywordflow">if</span>(ret)</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  {</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  <span class="comment">// Get results</span></div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  std::vector<size_t> tensor_results;</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="keywordflow">switch</span>(tensor.<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="l00348"></a><span class="lineno"> 348</span>  {</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  tensor_results = access_predictions_tensor<uint8_t>(tensor);</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  tensor_results = access_predictions_tensor<float>(tensor);</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  }</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">// Check if tensor results are within top-n accuracy</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span>  <span class="keywordtype">size_t</span> correct_label = _results[_offset++];</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>  aggregate_sample(tensor_results, _positive_samples_top1, 1, correct_label);</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  aggregate_sample(tensor_results, _positive_samples_top5, 5, correct_label);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  }</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">// Report top_n accuracy</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keywordflow">if</span>(_offset >= _results.size())</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>  report_top_n(1, _results.size(), _positive_samples_top1);</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  report_top_n(5, _results.size(), _positive_samples_top5);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  }</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="keywordflow">return</span> ret;</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> </div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> std::vector<size_t> ValidationOutputAccessor::access_predictions_tensor(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">arm_compute::ITensor</a> &tensor)</div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> {</div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span>  <span class="comment">// Get the predicted class</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  std::vector<size_t> index;</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <span class="keyword">const</span> <span class="keyword">auto</span> output_net = reinterpret_cast<T *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + tensor.<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#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>());</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_classes = tensor.<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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> </div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  index.resize(num_classes);</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> </div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  <span class="comment">// Sort results</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  std::sort(std::begin(index), std::end(index),</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  [&](<span class="keywordtype">size_t</span> a, <span class="keywordtype">size_t</span> b)</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="keywordflow">return</span> output_net[a] > output_net[b];</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  });</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="keywordflow">return</span> index;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span> }</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> <span class="keywordtype">void</span> ValidationOutputAccessor::aggregate_sample(<span class="keyword">const</span> std::vector<size_t> &res, <span class="keywordtype">size_t</span> &positive_samples, <span class="keywordtype">size_t</span> top_n, <span class="keywordtype">size_t</span> correct_label)</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span> {</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  <span class="keyword">auto</span> is_valid_label = [correct_label](<span class="keywordtype">size_t</span> label)</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  {</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keywordflow">return</span> label == correct_label;</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>  <span class="keywordflow">if</span>(std::any_of(std::begin(res), std::begin(res) + top_n, is_valid_label))</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  {</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  ++positive_samples;</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"> 411</span> <span class="keywordtype">void</span> ValidationOutputAccessor::report_top_n(<span class="keywordtype">size_t</span> top_n, <span class="keywordtype">size_t</span> total_samples, <span class="keywordtype">size_t</span> positive_samples)</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> {</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keywordtype">size_t</span> negative_samples = total_samples - positive_samples;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordtype">float</span> accuracy = positive_samples / static_cast<float>(total_samples);</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>  _output_stream << <span class="stringliteral">"----------Top "</span> << top_n << <span class="stringliteral">" accuracy ----------"</span> << std::endl</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  << std::endl;</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  _output_stream << <span class="stringliteral">"Positive samples : "</span> << positive_samples << std::endl;</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  _output_stream << <span class="stringliteral">"Negative samples : "</span> << negative_samples << std::endl;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  _output_stream << <span class="stringliteral">"Accuracy : "</span> << accuracy << std::endl;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> }</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> </div><div class="line"><a name="l00423"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_detection_output_accessor.xhtml#a27dfee7cd6e032a9d766786a8e1e3c8f"> 423</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_detection_output_accessor.xhtml#a27dfee7cd6e032a9d766786a8e1e3c8f">DetectionOutputAccessor::DetectionOutputAccessor</a>(<span class="keyword">const</span> std::string &labels_path, std::vector<TensorShape> &imgs_tensor_shapes, std::ostream &output_stream)</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  : _labels(), _tensor_shapes(std::move(imgs_tensor_shapes)), _output_stream(output_stream)</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> {</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  _labels.clear();</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span>  std::ifstream ifs;</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span> </div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  ifs.exceptions(std::ifstream::badbit);</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  ifs.open(labels_path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keywordflow">for</span>(std::string line; !std::getline(ifs, line).fail();)</div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  {</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  _labels.emplace_back(line);</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>  }</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::ifstream::failure &e)</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>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Accessing %s: %s"</span>, labels_path.c_str(), e.what());</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> }</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> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> <span class="keywordtype">void</span> DetectionOutputAccessor::access_predictions_tensor(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</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="keyword">const</span> <span class="keywordtype">size_t</span> num_detection = tensor.<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#a74dfd07380a290c34fe7c8e065029b95">valid_region</a>().<a class="code" href="structarm__compute_1_1_valid_region.xhtml#a1fcd64682b37ed3c2098d0094ce788d8">shape</a>.<a class="code" href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">y</a>();</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <span class="keyword">const</span> <span class="keyword">auto</span> output_prt = reinterpret_cast<T *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + tensor.<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#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>());</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">if</span>(num_detection > 0)</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  {</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  _output_stream << <span class="stringliteral">"---------------------- Detections ----------------------"</span> << std::endl</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  << std::endl;</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> </div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  _output_stream << std::left << std::setprecision(4) << std::setw(8) << <span class="stringliteral">"Image | "</span> << std::setw(8) << <span class="stringliteral">"Label | "</span> << std::setw(12) << <span class="stringliteral">"Confidence | "</span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  << <span class="stringliteral">"[ xmin, ymin, xmax, ymax ]"</span> << std::endl;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < num_detection; ++i)</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  {</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">auto</span> im = static_cast<const int>(output_prt[i * 7]);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  _output_stream << std::setw(8) << im << std::setw(8)</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  << _labels[output_prt[i * 7 + 1]] << std::setw(12) << output_prt[i * 7 + 2]</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  << <span class="stringliteral">" ["</span> << (output_prt[i * 7 + 3] * _tensor_shapes[im].x())</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  << <span class="stringliteral">", "</span> << (output_prt[i * 7 + 4] * _tensor_shapes[im].y())</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  << <span class="stringliteral">", "</span> << (output_prt[i * 7 + 5] * _tensor_shapes[im].x())</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  << <span class="stringliteral">", "</span> << (output_prt[i * 7 + 6] * _tensor_shapes[im].y())</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  << <span class="stringliteral">"]"</span> << std::endl;</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>  }</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  {</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  _output_stream << <span class="stringliteral">"No detection found."</span> << std::endl;</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> }</div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_detection_output_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 478</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_detection_output_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">DetectionOutputAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> {</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(&tensor, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordflow">switch</span>(tensor.<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="l00483"></a><span class="lineno"> 483</span>  {</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  access_predictions_tensor<float>(tensor);</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  }</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> }</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ad12f4e3c945ec4fad9ab6386954a3550"> 494</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ad12f4e3c945ec4fad9ab6386954a3550">TopNPredictionsAccessor::TopNPredictionsAccessor</a>(<span class="keyword">const</span> std::string &labels_path, <span class="keywordtype">size_t</span> top_n, std::ostream &output_stream)</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  : _labels(), _output_stream(output_stream), _top_n(top_n)</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> {</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  _labels.clear();</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> </div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  std::ifstream ifs;</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <span class="keywordflow">try</span></div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  {</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  ifs.exceptions(std::ifstream::badbit);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  ifs.open(labels_path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <span class="keywordflow">for</span>(std::string line; !std::getline(ifs, line).fail();)</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  {</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  _labels.emplace_back(line);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  }</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  }</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keywordflow">catch</span>(<span class="keyword">const</span> std::ifstream::failure &e)</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  {</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"Accessing %s: %s"</span>, labels_path.c_str(), e.what());</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  }</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> }</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> <span class="keywordtype">void</span> TopNPredictionsAccessor::access_predictions_tensor(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> {</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="comment">// Get the predicted class</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  std::vector<T> classes_prob;</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  std::vector<size_t> index;</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <span class="keyword">const</span> <span class="keyword">auto</span> output_net = reinterpret_cast<T *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + tensor.<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#ad0bd5cc32e7e4c0699eccba91e5de397">offset_first_element_in_bytes</a>());</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> num_classes = tensor.<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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  classes_prob.resize(num_classes);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  index.resize(num_classes);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">std::copy</a>(output_net, output_net + num_classes, classes_prob.begin());</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> </div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="comment">// Sort results</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  std::iota(std::begin(index), std::end(index), static_cast<size_t>(0));</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  std::sort(std::begin(index), std::end(index),</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  [&](<span class="keywordtype">size_t</span> a, <span class="keywordtype">size_t</span> b)</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <span class="keywordflow">return</span> classes_prob[a] > classes_prob[b];</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  });</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  _output_stream << <span class="stringliteral">"---------- Top "</span> << _top_n << <span class="stringliteral">" predictions ----------"</span> << std::endl</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  << std::endl;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0; i < _top_n; ++i)</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  {</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  _output_stream << std::fixed << std::setprecision(4)</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  << +classes_prob[index.at(i)]</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  << <span class="stringliteral">" - [id = "</span> << index.at(i) << <span class="stringliteral">"]"</span></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  << <span class="stringliteral">", "</span> << _labels[index.at(i)] << std::endl;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  }</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> }</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> </div><div class="line"><a name="l00551"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 551</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">TopNPredictionsAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> {</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <a class="code" href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a>(&tensor, 1, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>);</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  <a class="code" href="_error_8h.xhtml#a54a6080c9f4df1f908e57a9bbb46f5da">ARM_COMPUTE_ERROR_ON</a>(_labels.size() != tensor.<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#a178f0d3d87f959e00a743328d95359d2">dimension</a>(0));</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> </div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  <span class="keywordflow">switch</span>(tensor.<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="l00557"></a><span class="lineno"> 557</span>  {</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">DataType::QASYMM8</a>:</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  access_predictions_tensor<uint8_t>(tensor);</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span>  access_predictions_tensor<float>(tensor);</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  }</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> }</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#a47e2e3f731e842dde7baaf69634a9530"> 571</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#a47e2e3f731e842dde7baaf69634a9530">RandomAccessor::RandomAccessor</a>(<a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> lower, <a class="code" href="classarm__compute_1_1_pixel_value.xhtml">PixelValue</a> upper, std::random_device::result_type seed)</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  : _lower(lower), _upper(upper), _seed(seed)</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> {</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span> }</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> </div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keyword">typename</span> D></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> <span class="keywordtype">void</span> RandomAccessor::fill(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor, D &&distribution)</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> {</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  std::mt19937 gen(_seed);</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span>  <span class="keywordflow">if</span>(tensor.<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#a07b929c34ad1dc823d8315876aa403ce">padding</a>().<a class="code" href="structarm__compute_1_1_border_size.xhtml#afaafdfc441c2433c70959e3dfe46fd97">empty</a>() && (dynamic_cast<SubTensor *>(&tensor) == <span class="keyword">nullptr</span>))</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  {</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0; <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> < tensor.<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#a18064e0011c3869d884653e9e7c47b66">total_size</a>(); <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += tensor.<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#aa459796b5489eca8a9160cb5dcf1a103">element_size</a>())</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  {</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">const</span> <span class="keyword">auto</span> value = static_cast<T>(distribution(gen));</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  *reinterpret_cast<T *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">buffer</a>() + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>) = value;</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  }</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  }</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  {</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="comment">// If tensor has padding accessing tensor elements through execution window.</span></div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  Window window;</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  window.use_tensor_dimensions(tensor.<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#a7c66505457d00ece3aa4b34cab80757d">tensor_shape</a>());</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <a class="code" href="namespacearm__compute.xhtml#a5002bf7ec46d52971f9526e94172cfee">execute_window_loop</a>(window, [&](<span class="keyword">const</span> Coordinates & <span class="keywordtype">id</span>)</div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  {</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  <span class="keyword">const</span> <span class="keyword">auto</span> value = static_cast<T>(distribution(gen));</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  *reinterpret_cast<T *>(tensor.<a class="code" href="classarm__compute_1_1_i_tensor.xhtml#adbd73147d41e8a640bc299d12613c31e">ptr_to_element</a>(<span class="keywordtype">id</span>)) = value;</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span>  });</div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  }</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> }</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span> </div><div class="line"><a name="l00603"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 603</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">RandomAccessor::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> {</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="keywordflow">switch</span>(tensor.<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="l00606"></a><span class="lineno"> 606</span>  {</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">DataType::U8</a>:</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  {</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  std::uniform_int_distribution<uint8_t> distribution_u8(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint8_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint8_t>());</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  fill<uint8_t>(tensor, distribution_u8);</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  }</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">DataType::S8</a>:</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  {</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  std::uniform_int_distribution<int8_t> distribution_s8(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int8_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int8_t>());</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  fill<int8_t>(tensor, distribution_s8);</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  }</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">DataType::U16</a>:</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  {</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  std::uniform_int_distribution<uint16_t> distribution_u16(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint16_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint16_t>());</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  fill<uint16_t>(tensor, distribution_u16);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  }</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">DataType::S16</a>:</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  {</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  std::uniform_int_distribution<int16_t> distribution_s16(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int16_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int16_t>());</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  fill<int16_t>(tensor, distribution_s16);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  }</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">DataType::U32</a>:</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  {</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  std::uniform_int_distribution<uint32_t> distribution_u32(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint32_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint32_t>());</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  fill<uint32_t>(tensor, distribution_u32);</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  }</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">DataType::S32</a>:</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  {</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  std::uniform_int_distribution<int32_t> distribution_s32(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int32_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int32_t>());</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  fill<int32_t>(tensor, distribution_s32);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  }</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">DataType::U64</a>:</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  {</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  std::uniform_int_distribution<uint64_t> distribution_u64(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint64_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><uint64_t>());</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  fill<uint64_t>(tensor, distribution_u64);</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  }</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">DataType::S64</a>:</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  {</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  std::uniform_int_distribution<int64_t> distribution_s64(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int64_t>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><int64_t>());</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  fill<int64_t>(tensor, distribution_s64);</div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  }</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a56d8353718e6fdc78b8d69078a2cdb94">DataType::F16</a>:</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  {</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  std::uniform_real_distribution<float> distribution_f16(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><<span class="keywordtype">float</span>>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><<span class="keywordtype">float</span>>());</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  fill<half>(tensor, distribution_f16);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  }</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>:</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  {</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  std::uniform_real_distribution<float> distribution_f32(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><<span class="keywordtype">float</span>>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><<span class="keywordtype">float</span>>());</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  fill<float>(tensor, distribution_f32);</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  }</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <span class="keywordflow">case</span> <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">DataType::F64</a>:</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  {</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  std::uniform_real_distribution<double> distribution_f64(_lower.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><<span class="keywordtype">double</span>>(), _upper.<a class="code" href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">get</a><<span class="keywordtype">double</span>>());</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  fill<double>(tensor, distribution_f64);</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  }</div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keywordflow">default</span>:</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <a class="code" href="_error_8h.xhtml#a05b19c75afe9c24200a62b9724734bbd">ARM_COMPUTE_ERROR</a>(<span class="stringliteral">"NOT SUPPORTED!"</span>);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  }</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <span class="keywordflow">return</span> <span class="keyword">true</span>;</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span> }</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div><div class="line"><a name="l00679"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#abf92dad7656a4a25c3aa3d1112ef06e5"> 679</a></span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#abf92dad7656a4a25c3aa3d1112ef06e5">NumPyBinLoader::NumPyBinLoader</a>(std::string filename, <a class="code" href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">DataLayout</a> file_layout)</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  : _already_loaded(false), _filename(std::move(filename)), _file_layout(file_layout)</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> {</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span> }</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> </div><div class="line"><a name="l00684"></a><span class="lineno"><a class="line" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca"> 684</a></span> <span class="keywordtype">bool</span> <a class="code" href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca">NumPyBinLoader::access_tensor</a>(<a class="code" href="classarm__compute_1_1_i_tensor.xhtml">ITensor</a> &tensor)</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> {</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="keywordflow">if</span>(!_already_loaded)</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  {</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  <a class="code" href="classarm__compute_1_1utils_1_1_n_p_y_loader.xhtml">utils::NPYLoader</a> loader;</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  loader.<a class="code" href="classarm__compute_1_1utils_1_1_n_p_y_loader.xhtml#a739fd000f9e30bbc875cb48a9c6edab1">open</a>(_filename, _file_layout);</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  loader.<a class="code" href="classarm__compute_1_1utils_1_1_n_p_y_loader.xhtml#a055a51a536088065021a54e13968521d">fill_tensor</a>(tensor);</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  }</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> </div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  _already_loaded = !_already_loaded;</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="keywordflow">return</span> _already_loaded;</div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> }</div><div class="ttc" id="classarm__compute_1_1graph__utils_1_1_image_accessor_xhtml_a872e7ef3563a74e35a6912d12706c012"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_image_accessor.xhtml#a872e7ef3563a74e35a6912d12706c012">arm_compute::graph_utils::ImageAccessor::ImageAccessor</a></div><div class="ttdeci">ImageAccessor(std::string filename, bool bgr=true, std::unique_ptr< IPreprocessor > preprocessor=nullptr)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00182">GraphUtils.cpp:182</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00312">helpers.h:312</a></div></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_1_1utils_xhtml_a301d0b7bfd70f73fc1924f4281938d08"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a301d0b7bfd70f73fc1924f4281938d08">arm_compute::utils::save_to_ppm</a></div><div class="ttdeci">void save_to_ppm(T &tensor, const std::string &ppm_filename)</div><div class="ttdoc">Template helper function to save a tensor image to a PPM file.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00537">Utils.h:537</a></div></div> |
| <div class="ttc" id="_sub_tensor_8h_xhtml"><div class="ttname"><a href="_sub_tensor_8h.xhtml">SubTensor.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml">arm_compute::PixelValue</a></div><div class="ttdoc">Class describing the value of a pixel for any image format.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00034">PixelValue.h:34</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a45cde9abb508c62d67c3bb2b9bf566a5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a45cde9abb508c62d67c3bb2b9bf566a5">arm_compute::test::validation::shape</a></div><div class="ttdeci">shape</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00097">AbsoluteDifference.cpp:97</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_j_p_e_g_loader_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_j_p_e_g_loader.xhtml">arm_compute::utils::JPEGLoader</a></div><div class="ttdoc">Class to load the content of a JPEG file into an Image.</div><div class="ttdef"><b>Definition:</b> <a href="_image_loader_8h_source.xhtml#l00422">ImageLoader.h:422</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::TopNPredictionsAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00551">GraphUtils.cpp:551</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_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00039">TensorShape.h:39</a></div></div> |
| <div class="ttc" id="structarm__compute_1_1_valid_region_xhtml_a1fcd64682b37ed3c2098d0094ce788d8"><div class="ttname"><a href="structarm__compute_1_1_valid_region.xhtml#a1fcd64682b37ed3c2098d0094ce788d8">arm_compute::ValidRegion::shape</a></div><div class="ttdeci">TensorShape shape</div><div class="ttdoc">Shape of the valid region.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00301">Types.h:301</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="classarm__compute_1_1graph__utils_1_1_num_py_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::NumPyAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00165">GraphUtils.cpp:165</a></div></div> |
| <div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">arm_compute::Format::U8</a></div><div class="ttdoc">1 channel, 1 U8 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_pixel_value_xhtml_a66c4c1f8b1962d71162d7ac0b3ef65bc"><div class="ttname"><a href="classarm__compute_1_1_pixel_value.xhtml#a66c4c1f8b1962d71162d7ac0b3ef65bc">arm_compute::PixelValue::get</a></div><div class="ttdeci">void get(uint8_t &v) const</div><div class="ttdoc">Interpret the pixel value as a U8.</div><div class="ttdef"><b>Definition:</b> <a href="_pixel_value_8h_source.xhtml#l00209">PixelValue.h:209</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_1utils_1_1_n_p_y_loader_xhtml_a055a51a536088065021a54e13968521d"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_n_p_y_loader.xhtml#a055a51a536088065021a54e13968521d">arm_compute::utils::NPYLoader::fill_tensor</a></div><div class="ttdeci">void fill_tensor(T &tensor)</div><div class="ttdoc">Fill a tensor with the content of the currently open NPY file.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00396">Utils.h:396</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="namespacearm__compute_xhtml_a33e65be485104e2e9e69fca551d6f492"><div class="ttname"><a href="namespacearm__compute.xhtml#a33e65be485104e2e9e69fca551d6f492">arm_compute::PermutationVector</a></div><div class="ttdeci">Strides PermutationVector</div><div class="ttdoc">Permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00047">Types.h:47</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::NumPyBinLoader::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00684">GraphUtils.cpp:684</a></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_1graph__utils_1_1_validation_output_accessor_xhtml_ac3ae33bed176ca84786a8c910c3072c9"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ac3ae33bed176ca84786a8c910c3072c9">arm_compute::graph_utils::ValidationOutputAccessor::ValidationOutputAccessor</a></div><div class="ttdeci">ValidationOutputAccessor(const std::string &image_list, std::ostream &output_stream=std::cout, unsigned int start=0, unsigned int end=0)</div><div class="ttdoc">Default Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00297">GraphUtils.cpp:297</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_xhtml_a28edc8880596d14c099f3c2509efc8b3"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#a28edc8880596d14c099f3c2509efc8b3">arm_compute::test::swap</a></div><div class="ttdeci">void swap(SimpleTensor< U > &tensor1, SimpleTensor< U > &tensor2)</div><div class="ttdef"><b>Definition:</b> <a href="_simple_tensor_8h_source.xhtml#l00443">SimpleTensor.h:443</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_image_loader_factory_xhtml_ada8c92fe057e34525e7f8c4e8e422179"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_image_loader_factory.xhtml#ada8c92fe057e34525e7f8c4e8e422179">arm_compute::utils::ImageLoaderFactory::create</a></div><div class="ttdeci">static std::unique_ptr< IImageLoader > create(const std::string &filename)</div><div class="ttdoc">Create an image loader depending on the image type.</div><div class="ttdef"><b>Definition:</b> <a href="_image_loader_8h_source.xhtml#l00499">ImageLoader.h:499</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">arm_compute::Format::U16</a></div><div class="ttdoc">1 channel, 1 U16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor_xhtml_a1545d087d050b8e9733e7e212df73666"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">arm_compute::graph_utils::CaffePreproccessor::preprocess</a></div><div class="ttdeci">void preprocess(ITensor &tensor) override</div><div class="ttdoc">Preprocess the given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00089">GraphUtils.cpp:89</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_random_accessor_xhtml_a47e2e3f731e842dde7baaf69634a9530"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#a47e2e3f731e842dde7baaf69634a9530">arm_compute::graph_utils::RandomAccessor::RandomAccessor</a></div><div class="ttdeci">RandomAccessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed=0)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00571">GraphUtils.cpp:571</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_ad9000ce99b9ffcec5722cade36d7e757"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#ad9000ce99b9ffcec5722cade36d7e757">arm_compute::test::validation::reference::copy</a></div><div class="ttdeci">SimpleTensor< T > copy(const SimpleTensor< T > &src, const TensorShape &output_shape)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_copy_8cpp_source.xhtml#l00037">Copy.cpp:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_i_image_loader_xhtml_a7c3f70e1caee95bb95c62346e130e5ab"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#a7c3f70e1caee95bb95c62346e130e5ab">arm_compute::utils::IImageLoader::fill_planar_tensor</a></div><div class="ttdeci">void fill_planar_tensor(T &tensor, bool bgr=false)</div><div class="ttdoc">Fill a tensor with 3 planes (one for each channel) with the content of the currently open image file.</div><div class="ttdef"><b>Definition:</b> <a href="_image_loader_8h_source.xhtml#l00256">ImageLoader.h:256</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="classarm__compute_1_1_window_xhtml_a14470b4cb59140a1b6ff3b8f16c89ab6"><div class="ttname"><a href="classarm__compute_1_1_window.xhtml#a14470b4cb59140a1b6ff3b8f16c89ab6">arm_compute::Window::use_tensor_dimensions</a></div><div class="ttdeci">void use_tensor_dimensions(const TensorShape &shape, size_t first_dimension=Window::DimX)</div><div class="ttdoc">Use the tensor's dimensions to fill the window dimensions.</div><div class="ttdef"><b>Definition:</b> <a href="_window_8inl_source.xhtml#l00243">Window.inl:243</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_tensor_xhtml_adbd0cf83a8e1b335a9bf405a8e5019fa"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#adbd0cf83a8e1b335a9bf405a8e5019fa">arm_compute::Tensor::allocator</a></div><div class="ttdeci">TensorAllocator * allocator()</div><div class="ttdoc">Return a pointer to the tensor's allocator.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00048">Tensor.cpp:48</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_xhtml_a47d74e4e51f9b1a636c4831bd747a97c"><div class="ttname"><a href="classarm__compute_1_1_tensor.xhtml#a47d74e4e51f9b1a636c4831bd747a97c">arm_compute::Tensor::info</a></div><div class="ttdeci">ITensorInfo * info() const override</div><div class="ttdoc">Interface to be implemented by the child class to return the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="runtime_2_tensor_8cpp_source.xhtml#l00033">Tensor.cpp:33</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a21c3e11887f3acf9284ca763372c7da0"><div class="ttname"><a href="namespacearm__compute.xhtml#a21c3e11887f3acf9284ca763372c7da0">arm_compute::permute</a></div><div class="ttdeci">void permute(Dimensions< T > &dimensions, const PermutationVector &perm)</div><div class="ttdoc">Permutes given Dimensions according to a permutation vector.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_helpers_8h_source.xhtml#l00570">Helpers.h:570</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_validation_input_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_validation_input_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::ValidationInputAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00260">GraphUtils.cpp:260</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aa1e28eee0339658d39a8b4d325b56e9c">arm_compute::Format::S32</a></div><div class="ttdoc">1 channel, 1 S32 per channel</div></div> |
| <div class="ttc" id="_error_8h_xhtml_ad39a3601153da57978bb5124ace35d36"><div class="ttname"><a href="_error_8h.xhtml#ad39a3601153da57978bb5124ace35d36">ARM_COMPUTE_EXIT_ON_MSG</a></div><div class="ttdeci">#define ARM_COMPUTE_EXIT_ON_MSG(cond,...)</div><div class="ttdoc">If the condition is true, the given message is printed and program exits.</div><div class="ttdef"><b>Definition:</b> <a href="_error_8h_source.xhtml#l00277">Error.h:277</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_dimensions_xhtml_aa87f8fc26981b0f3228a78c83b95b802"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#aa87f8fc26981b0f3228a78c83b95b802">arm_compute::Dimensions::x</a></div><div class="ttdeci">T x() const</div><div class="ttdoc">Alias to access the size of the first dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00081">Dimensions.h:81</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a115dca124dc6423c7a400b8a8a0270cc">arm_compute::DataType::S64</a></div><div class="ttdoc">signed 64-bit number</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_ac2ad7f431e3446fddcd9b6b9f93c4c14"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#ac2ad7f431e3446fddcd9b6b9f93c4c14">arm_compute::test::validation::data_type</a></div><div class="ttdeci">data_type</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_1graph__utils_1_1_num_py_bin_loader_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml">arm_compute::graph_utils::NumPyBinLoader</a></div><div class="ttdoc">Numpy Binary loader class.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00372">GraphUtils.h:372</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="classarm__compute_1_1graph__utils_1_1_detection_output_accessor_xhtml_a27dfee7cd6e032a9d766786a8e1e3c8f"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_detection_output_accessor.xhtml#a27dfee7cd6e032a9d766786a8e1e3c8f">arm_compute::graph_utils::DetectionOutputAccessor::DetectionOutputAccessor</a></div><div class="ttdeci">DetectionOutputAccessor(const std::string &labels_path, std::vector< TensorShape > &imgs_tensor_shapes, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00423">GraphUtils.cpp:423</a></div></div> |
| <div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_random_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_random_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::RandomAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00603">GraphUtils.cpp:603</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58ac8bd5bedff8ef192d39a962afc0e19ee">arm_compute::Format::U32</a></div><div class="ttdoc">1 channel, 1 U32 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a7c66505457d00ece3aa4b34cab80757d"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a7c66505457d00ece3aa4b34cab80757d">arm_compute::ITensorInfo::tensor_shape</a></div><div class="ttdeci">virtual const TensorShape & tensor_shape() const =0</div><div class="ttdoc">Size for each dimension of the tensor.</div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_1_1reference_xhtml_a87f09c74765be18a99038478f96daf9b"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation_1_1reference.xhtml#a87f09c74765be18a99038478f96daf9b">arm_compute::test::validation::reference::range</a></div><div class="ttdeci">SimpleTensor< T > range(SimpleTensor< T > &dst, float start, const size_t num_of_elements, float step)</div><div class="ttdef"><b>Definition:</b> <a href="reference_2_range_8cpp_source.xhtml#l00050">Range.cpp:50</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_n_p_y_loader_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_n_p_y_loader.xhtml">arm_compute::utils::NPYLoader</a></div><div class="ttdoc">Numpy data loader.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00318">Utils.h:318</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6af14462d71aa842202c3e4b272c7ec924">arm_compute::DataType::QASYMM8</a></div><div class="ttdoc">quantized, asymmetric fixed-point 8-bit number</div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_i_image_loader_xhtml_a7f0f3e5dd09a150b2cc221c01804d1a7"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#a7f0f3e5dd09a150b2cc221c01804d1a7">arm_compute::utils::IImageLoader::width</a></div><div class="ttdeci">unsigned int width() const</div><div class="ttdoc">Return the width of the currently open image file.</div><div class="ttdef"><b>Definition:</b> <a href="_image_loader_8h_source.xhtml#l00136">ImageLoader.h:136</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_i_tensor_xhtml_ab988210662dbd3bf32fd563c7dd1bdbf"><div class="ttname"><a href="classarm__compute_1_1_i_tensor.xhtml#ab988210662dbd3bf32fd563c7dd1bdbf">arm_compute::ITensor::buffer</a></div><div class="ttdeci">virtual uint8_t * buffer() const =0</div><div class="ttdoc">Interface to be implemented by the child class to return a pointer to CPU memory.</div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a6e509c2a177b0b29e9e2369535094dee"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a6e509c2a177b0b29e9e2369535094dee">arm_compute::TensorAllocator::allocate</a></div><div class="ttdeci">void allocate() override</div><div class="ttdoc">Allocate size specified by TensorInfo of CPU memory.</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00133">TensorAllocator.cpp:133</a></div></div> |
| <div class="ttc" id="_image_loader_8h_xhtml"><div class="ttname"><a href="_image_loader_8h.xhtml">ImageLoader.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml_a0fdcb923dfd4c74858cc2bc326321efb"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml#a0fdcb923dfd4c74858cc2bc326321efb">arm_compute::TensorShape::total_size</a></div><div class="ttdeci">size_t total_size() const</div><div class="ttdoc">Collapses all dimensions to a single linear total size.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00171">TensorShape.h:171</a></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="graph_2_logger_8h_xhtml"><div class="ttname"><a href="graph_2_logger_8h.xhtml">Logger.h</a></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="classarm__compute_1_1_i_tensor_info_xhtml_a07b929c34ad1dc823d8315876aa403ce"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a07b929c34ad1dc823d8315876aa403ce">arm_compute::ITensorInfo::padding</a></div><div class="ttdeci">virtual PaddingSize padding() const =0</div><div class="ttdoc">Padding of tensor.</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_ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">arm_compute::Format::S16</a></div><div class="ttdoc">1 channel, 1 S16 per channel</div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor_xhtml_ad12f4e3c945ec4fad9ab6386954a3550"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_top_n_predictions_accessor.xhtml#ad12f4e3c945ec4fad9ab6386954a3550">arm_compute::graph_utils::TopNPredictionsAccessor::TopNPredictionsAccessor</a></div><div class="ttdeci">TopNPredictionsAccessor(const std::string &labels_path, size_t top_n=5, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00494">GraphUtils.cpp:494</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0a6b99f356fe3b30a2a850b5ea897c289f">arm_compute::DataLayout::NCHW</a></div><div class="ttdoc">Num samples, channels, height, width.</div></div> |
| <div class="ttc" id="_validate_8h_xhtml_aadf5c9cff86327b96d88d04649d9715e"><div class="ttname"><a href="_validate_8h.xhtml#aadf5c9cff86327b96d88d04649d9715e">ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN</a></div><div class="ttdeci">#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)</div><div class="ttdef"><b>Definition:</b> <a href="_validate_8h_source.xhtml#l00789">Validate.h:789</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_num_py_accessor_xhtml_ac3bd9a902b0bb7e28e1bed21318bc562"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_accessor.xhtml#ac3bd9a902b0bb7e28e1bed21318bc562">arm_compute::graph_utils::NumPyAccessor::NumPyAccessor</a></div><div class="ttdeci">NumPyAccessor(std::string npy_path, TensorShape shape, DataType data_type, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00143">GraphUtils.cpp:143</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_strides_xhtml"><div class="ttname"><a href="classarm__compute_1_1_strides.xhtml">arm_compute::Strides</a></div><div class="ttdoc">Strides of an item in bytes.</div><div class="ttdef"><b>Definition:</b> <a href="_strides_8h_source.xhtml#l00037">Strides.h:37</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_ad0bd5cc32e7e4c0699eccba91e5de397"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#ad0bd5cc32e7e4c0699eccba91e5de397">arm_compute::ITensorInfo::offset_first_element_in_bytes</a></div><div class="ttdeci">virtual size_t offset_first_element_in_bytes() const =0</div><div class="ttdoc">The offset from the beginning of the memory allocation to the first element of the tensor.</div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_image_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_image_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::ImageAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00187">GraphUtils.cpp:187</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acec6d8ad52a28972fa74e071c1a63b6a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">arm_compute::test::validation::scale</a></div><div class="ttdeci">scale</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_pixel_wise_multiplication_8cpp_source.xhtml#l00168">PixelWiseMultiplication.cpp:168</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor_xhtml_a5cb89f99531ca5931b461835039fd655"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_caffe_preproccessor.xhtml#a5cb89f99531ca5931b461835039fd655">arm_compute::graph_utils::CaffePreproccessor::CaffePreproccessor</a></div><div class="ttdeci">CaffePreproccessor(std::array< float, 3 > mean=std::array< float, 3 > { { 0, 0, 0 } }, float scale=1.f, bool bgr=true)</div><div class="ttdoc">Default Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00080">GraphUtils.cpp:80</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_validation_output_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::ValidationOutputAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00340">GraphUtils.cpp:340</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor_xhtml_a1545d087d050b8e9733e7e212df73666"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#a1545d087d050b8e9733e7e212df73666">arm_compute::graph_utils::TFPreproccessor::preprocess</a></div><div class="ttdeci">void preprocess(ITensor &tensor) override</div><div class="ttdoc">Preprocess the given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00064">GraphUtils.cpp:64</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00044">GraphUtils.h:44</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_i_tensor_info_xhtml_a18064e0011c3869d884653e9e7c47b66"><div class="ttname"><a href="classarm__compute_1_1_i_tensor_info.xhtml#a18064e0011c3869d884653e9e7c47b66">arm_compute::ITensorInfo::total_size</a></div><div class="ttdeci">virtual size_t total_size() const =0</div><div class="ttdoc">Returns the total size of the tensor in bytes.</div></div> |
| <div class="ttc" id="structarm__compute_1_1_border_size_xhtml_afaafdfc441c2433c70959e3dfe46fd97"><div class="ttname"><a href="structarm__compute_1_1_border_size.xhtml#afaafdfc441c2433c70959e3dfe46fd97">arm_compute::BorderSize::empty</a></div><div class="ttdeci">constexpr bool empty() const</div><div class="ttdoc">Check if the entire border is zero.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00340">Types.h:340</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_p_p_m_writer_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::PPMWriter::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00108">GraphUtils.cpp:108</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="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor_xhtml_ab58008704726cd07353605b1d9e13d86"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_t_f_preproccessor.xhtml#ab58008704726cd07353605b1d9e13d86">arm_compute::graph_utils::TFPreproccessor::TFPreproccessor</a></div><div class="ttdeci">TFPreproccessor(float min_range=-1.f, float max_range=1.f)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00060">GraphUtils.cpp:60</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_n_p_y_loader_xhtml_a739fd000f9e30bbc875cb48a9c6edab1"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_n_p_y_loader.xhtml#a739fd000f9e30bbc875cb48a9c6edab1">arm_compute::utils::NPYLoader::open</a></div><div class="ttdeci">void open(const std::string &npy_filename, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Open a NPY file and reads its metadata.</div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00332">Utils.h:332</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_detection_output_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_detection_output_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::DetectionOutputAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00478">GraphUtils.cpp:478</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_validation_output_accessor_xhtml_ad20897c5c8bd47f5d4005989bead0e55"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_validation_output_accessor.xhtml#ad20897c5c8bd47f5d4005989bead0e55">arm_compute::graph_utils::ValidationOutputAccessor::reset</a></div><div class="ttdeci">void reset()</div><div class="ttdoc">Reset accessor state.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00333">GraphUtils.cpp:333</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_allocator_xhtml_a3014ce2f4215e8a44331aa5daf3ba0d4"><div class="ttname"><a href="classarm__compute_1_1_tensor_allocator.xhtml#a3014ce2f4215e8a44331aa5daf3ba0d4">arm_compute::TensorAllocator::init</a></div><div class="ttdeci">void init(const TensorAllocator &allocator, const Coordinates &coords, TensorInfo sub_info)</div><div class="ttdoc">Shares the same backing memory with another tensor allocator, while the tensor info might be differen...</div><div class="ttdef"><b>Definition:</b> <a href="src_2runtime_2_tensor_allocator_8cpp_source.xhtml#l00108">TensorAllocator.cpp:108</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor's metadata.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00045">TensorInfo.h:45</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="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml_ac5ae9597ba20e5581726743fe7c154b5"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ac5ae9597ba20e5581726743fe7c154b5">arm_compute::graph_utils::DummyAccessor::DummyAccessor</a></div><div class="ttdeci">DummyAccessor(unsigned int maximum=1)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00123">GraphUtils.cpp:123</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="classarm__compute_1_1_dimensions_xhtml_ac4a1050be02b20b3f791b9a483f3abe2"><div class="ttname"><a href="classarm__compute_1_1_dimensions.xhtml#ac4a1050be02b20b3f791b9a483f3abe2">arm_compute::Dimensions::y</a></div><div class="ttdeci">T y() const</div><div class="ttdoc">Alias to access the size of the second dimension.</div><div class="ttdef"><b>Definition:</b> <a href="_dimensions_8h_source.xhtml#l00086">Dimensions.h:86</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_p_p_m_writer_xhtml_a9ea94bf5b7d00ddb836df1b8dcedb93a"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_p_p_m_writer.xhtml#a9ea94bf5b7d00ddb836df1b8dcedb93a">arm_compute::graph_utils::PPMWriter::PPMWriter</a></div><div class="ttdeci">PPMWriter(std::string name, unsigned int maximum=1)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00103">GraphUtils.cpp:103</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml_ab469d593b4bc92e1d1132a03de0aedca"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml#ab469d593b4bc92e1d1132a03de0aedca">arm_compute::graph_utils::DummyAccessor::access_tensor</a></div><div class="ttdeci">bool access_tensor(ITensor &tensor) override</div><div class="ttdoc">Interface to be implemented to access a given tensor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00128">GraphUtils.cpp:128</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a1ad5f6f3069070ec4cbbdc94d5e61e0e">arm_compute::DataType::F64</a></div><div class="ttdoc">64-bit floating-point number</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="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a31d65cccd6593e4101db93fb878abcaa">arm_compute::DataType::U64</a></div><div class="ttdoc">unsigned 64-bit number</div></div> |
| <div class="ttc" id="arm__compute_2core_2_types_8h_xhtml"><div class="ttname"><a href="arm__compute_2core_2_types_8h.xhtml">Types.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">arm_compute::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdoc">Available data types.</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00074">Types.h:74</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader_xhtml_abf92dad7656a4a25c3aa3d1112ef06e5"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_num_py_bin_loader.xhtml#abf92dad7656a4a25c3aa3d1112ef06e5">arm_compute::graph_utils::NumPyBinLoader::NumPyBinLoader</a></div><div class="ttdeci">NumPyBinLoader(std::string filename, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Default Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00679">GraphUtils.cpp:679</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_i_image_loader_xhtml_adc7679009b582b99d859c0edfc35aa4a"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_i_image_loader.xhtml#adc7679009b582b99d859c0edfc35aa4a">arm_compute::utils::IImageLoader::height</a></div><div class="ttdeci">unsigned int height() const</div><div class="ttdoc">Return the height of the currently open image file.</div><div class="ttdef"><b>Definition:</b> <a href="_image_loader_8h_source.xhtml#l00141">ImageLoader.h:141</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad1d5cce2d9e9a5d61c243e5c989112e0"><div class="ttname"><a href="namespacearm__compute.xhtml#ad1d5cce2d9e9a5d61c243e5c989112e0">arm_compute::DataLayout</a></div><div class="ttdeci">DataLayout</div><div class="ttdoc">[DataLayout enum definition]</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00111">Types.h:111</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975"><div class="ttname"><a href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6aafb0fced528eaac5fe170b763cda5975">arm_compute::DataType::S8</a></div><div class="ttdoc">signed 8-bit number</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="classarm__compute_1_1graph__utils_1_1_validation_input_accessor_xhtml_a458082188f9eec9fdf459f508d64d9be"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_validation_input_accessor.xhtml#a458082188f9eec9fdf459f508d64d9be">arm_compute::graph_utils::ValidationInputAccessor::ValidationInputAccessor</a></div><div class="ttdeci">ValidationInputAccessor(const std::string &image_list, std::string images_path, std::unique_ptr< IPreprocessor > preprocessor=nullptr, bool bgr=true, unsigned int start=0, unsigned int end=0, std::ostream &output_stream=std::cout)</div><div class="ttdoc">Constructor.</div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8cpp_source.xhtml#l00222">GraphUtils.cpp:222</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1utils_1_1_j_p_e_g_loader_xhtml_ab23b23a466d459ecbad7d046bb085324"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_j_p_e_g_loader.xhtml#ab23b23a466d459ecbad7d046bb085324">arm_compute::utils::JPEGLoader::open</a></div><div class="ttdeci">void open(const std::string &filename) override</div><div class="ttdoc">Open an image file and reads its metadata (Width, height)</div><div class="ttdef"><b>Definition:</b> <a href="_image_loader_8h_source.xhtml#l00446">ImageLoader.h:446</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> |
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