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| <a href="_tensor_library_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 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="_tensor_library_8h.xhtml">TensorLibrary.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="_type_printer_8h.xhtml">TypePrinter.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="preprocessor">#include "<a class="code" href="_user_configuration_8h.xhtml">UserConfiguration.h</a>"</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#include "<a class="code" href="tests_2_utils_8h.xhtml">Utils.h</a>"</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#include "<a class="code" href="_i_tensor_8h.xhtml">arm_compute/core/ITensor.h</a>"</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include <cctype></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include <fstream></span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include <limits></span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include <map></span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#include <mutex></span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="preprocessor">#include <sstream></span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#include <stdexcept></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="preprocessor">#include <tuple></span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="preprocessor">#include <unordered_map></span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="preprocessor">#include <utility></span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> {</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="keyword">namespace </span>test</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> {</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> {</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="keywordtype">void</span> convert_rgb_to_u8(<span class="keyword">const</span> RawTensor &src, RawTensor &dst)</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> {</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> min_size = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(src.size(), dst.size());</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0, j = 0; i < min_size; i += 3, ++j)</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>  dst.data()[j] = 0.2126f * src.data()[i + 0] + 0.7152f * src.data()[i + 1] + 0.0722f * src.data()[i + 2];</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  }</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> }</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="keywordtype">void</span> convert_rgb_to_u16(<span class="keyword">const</span> RawTensor &src, RawTensor &dst)</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> {</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> min_size = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(src.size(), dst.size());</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>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0, j = 0; i < min_size; i += 3, ++j)</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  {</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keyword">reinterpret_cast<</span>uint16_t *<span class="keyword">></span>(dst.data())[j] = 0.2126f * src.data()[i + 0] + 0.7152f * src.data()[i + 1] + 0.0722f * src.data()[i + 2];</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  }</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> }</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="keywordtype">void</span> convert_rgb_to_s16(<span class="keyword">const</span> RawTensor &src, RawTensor &dst)</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> {</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> min_size = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(src.size(), dst.size());</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 0, j = 0; i < min_size; i += 3, ++j)</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  {</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keyword">reinterpret_cast<</span>int16_t *<span class="keyword">></span>(dst.data())[j] = 0.2126f * src.data()[i + 0] + 0.7152f * src.data()[i + 1] + 0.0722f * src.data()[i + 2];</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  }</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> <span class="keywordtype">void</span> extract_r_from_rgb(<span class="keyword">const</span> RawTensor &src, RawTensor &dst)</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> {</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> min_size = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(src.size(), dst.size());</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">for</span>(<span class="keywordtype">size_t</span> i = 0, j = 0; i < min_size; i += 3, ++j)</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>  dst.data()[j] = src.data()[i];</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"> 89</span> <span class="keywordtype">void</span> extract_g_from_rgb(<span class="keyword">const</span> RawTensor &src, RawTensor &dst)</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> {</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> min_size = <a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">std::min</a>(src.size(), dst.size());</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> i = 1, j = 0; i < min_size; i += 3, ++j)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  dst.data()[j] = src.data()[i];</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  }</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> }</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> <span class="keywordtype">void</span> discard_comments(std::ifstream &fs)</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>  <span class="keywordflow">while</span>(fs.peek() == <span class="charliteral">'#'</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"> 103</span>  fs.ignore(<a class="code" href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">std::numeric_limits<std::streamsize>::max</a>(), <span class="charliteral">'\n'</span>);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</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> <span class="keywordtype">void</span> discard_comments_and_spaces(std::ifstream &fs)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> {</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">while</span>(<span class="keyword">true</span>)</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  {</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  discard_comments(fs);</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>  <span class="keywordflow">if</span>(isspace(fs.peek()) == 0)</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>  <span class="keywordflow">break</span>;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> </div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  fs.ignore(1);</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> }</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> std::tuple<unsigned int, unsigned int, int> <a class="code" href="namespacearm__compute_1_1utils.xhtml#a3aa8f5f1b94f88fdf5b43a53e29379cf">parse_ppm_header</a>(std::ifstream &fs)</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> {</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="comment">// Check the PPM magic number is valid</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  std::array<char, 2> magic_number{ { 0 } };</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  fs >> magic_number[0] >> magic_number[1];</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span>(magic_number[0] != <span class="charliteral">'P'</span> || magic_number[1] != <span class="charliteral">'6'</span>)</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>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Only raw PPM format is suported"</span>);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  }</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  discard_comments_and_spaces(fs);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 0;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  fs >> width;</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>  discard_comments_and_spaces(fs);</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 0;</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  fs >> height;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  discard_comments_and_spaces(fs);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">int</span> max_value = 0;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  fs >> max_value;</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>  <span class="keywordflow">if</span>(!fs.good())</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  {</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Cannot read image dimensions"</span>);</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> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keywordflow">if</span>(max_value != 255)</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  {</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"RawTensor doesn't have 8-bit values"</span>);</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  }</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> </div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  discard_comments(fs);</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keywordflow">if</span>(isspace(fs.peek()) == 0)</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>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Invalid PPM header"</span>);</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"> 165</span>  fs.ignore(1);</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>  <span class="keywordflow">return</span> std::make_tuple(width, height, max_value);</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> }</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> RawTensor load_ppm(<span class="keyword">const</span> std::string &path)</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>  std::ifstream file(path, std::ios::in | std::ios::binary);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordflow">if</span>(!file.good())</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  {</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Could not load PPM image: "</span> + path);</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> width = 0;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> height = 0;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  std::tie(width, height, std::ignore) = <a class="code" href="namespacearm__compute_1_1utils.xhtml#a3aa8f5f1b94f88fdf5b43a53e29379cf">parse_ppm_header</a>(file);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> </div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  RawTensor raw(TensorShape(width, height), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>);</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>  <span class="comment">// Check if the file is large enough to fill the image</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> current_position = file.tellg();</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  file.seekg(0, std::ios_base::end);</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="keyword">const</span> <span class="keywordtype">size_t</span> end_position = file.tellg();</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  file.seekg(current_position, std::ios_base::beg);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> </div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">if</span>((end_position - current_position) < raw.size())</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="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Not enough data in file"</span>);</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  }</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>  file.read(reinterpret_cast<std::fstream::char_type *>(raw.data()), raw.size());</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordflow">if</span>(!file.good())</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  {</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">"Failure while reading image buffer"</span>);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  }</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>  <span class="keywordflow">return</span> raw;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> }</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> } <span class="comment">// namespace</span></div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> </div><div class="line"><a name="l00208"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316"> 208</a></span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316">TensorLibrary::TensorLibrary</a>(std::string path)</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  : _library_path(std::move(path)), _seed{ std::random_device()() }</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> }</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span> </div><div class="line"><a name="l00213"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a467809227882867d26c5b5eea969497d"> 213</a></span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316">TensorLibrary::TensorLibrary</a>(std::string path, std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">seed</a>)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  : _library_path(std::move(path)), _seed{ seed }</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"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a"> 218</a></span> std::random_device::result_type <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">TensorLibrary::seed</a>()<span class="keyword"> const</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="keyword"></span>{</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">return</span> _seed;</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> }</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> </div><div class="line"><a name="l00223"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a56f49b809537f3564de1eb7703c4dfab"> 223</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)<span class="keyword"> const</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="keyword"></span>{</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &src = <span class="keyword">get</span>(name, format);</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  std::copy_n(src.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>(), raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">size</a>(), raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>());</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> }</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> </div><div class="line"><a name="l00229"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a8fca830911339dca1cefcd78763063cf"> 229</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="keyword"></span>{</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">fill</a>(raw, name, <a class="code" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a>(channel), channel);</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> }</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> </div><div class="line"><a name="l00234"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a32621ba3c7498c558f27e61606af85f4"> 234</a></span> <span class="keywordtype">void</span> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">TensorLibrary::fill</a>(<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw, <span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="keyword"></span>{</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &src = <span class="keyword">get</span>(name, format, channel);</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  std::copy_n(src.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>(), raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">size</a>(), raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">data</a>());</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> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="keyword">const</span> TensorLibrary::Loader &TensorLibrary::get_loader(<span class="keyword">const</span> std::string &extension)<span class="keyword"> const</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="keyword"></span>{</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="keyword">static</span> std::unordered_map<std::string, Loader> loaders =</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  {</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  { <span class="stringliteral">"ppm"</span>, load_ppm }</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> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="keyword">const</span> <span class="keyword">auto</span> it = loaders.find(extension);</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>  <span class="keywordflow">if</span>(it != loaders.end())</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  {</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">return</span> it->second;</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  {</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">"Cannot load image with extension '"</span> + extension + <span class="stringliteral">"'"</span>);</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  }</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> </div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="keyword">const</span> TensorLibrary::Converter &TensorLibrary::get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> dst)<span class="keyword"> const</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="keyword"></span>{</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  <span class="keyword">static</span> std::map<std::pair<Format, Format>, Converter> converters =</div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  {</div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span>  { std::make_pair(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6669348b484e3008dca2bfa8e85e40b5">Format::U8</a>), convert_rgb_to_u8 },</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  { std::make_pair(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58aef9ef3ebca4d2b64b6ec83808bafa5f2">Format::U16</a>), convert_rgb_to_u16 },</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  { std::make_pair(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a6e0b0886efb94aec797f6b830329b72c">Format::S16</a>), convert_rgb_to_s16 }</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> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  <span class="keyword">const</span> <span class="keyword">auto</span> it = converters.find(std::make_pair(src, dst));</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> </div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <span class="keywordflow">if</span>(it != converters.end())</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="keywordflow">return</span> it->second;</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  }</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  {</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  std::stringstream msg;</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  msg << <span class="stringliteral">"Cannot convert from format '"</span> << src << <span class="stringliteral">"' to format '"</span> << dst << <span class="stringliteral">"'\n"</span>;</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordflow">throw</span> std::invalid_argument(msg.str());</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> }</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> </div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> <span class="keyword">const</span> TensorLibrary::Converter &TensorLibrary::get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> dst)<span class="keyword"> const</span></div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> <span class="keyword"></span>{</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">static</span> std::map<std::pair<DataType, Format>, Converter> converters = {};</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <span class="keyword">auto</span> it = converters.find(std::make_pair(src, dst));</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> </div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordflow">if</span>(it != converters.end())</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>  <span class="keywordflow">return</span> it->second;</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>  <span class="keywordflow">else</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>  std::stringstream msg;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  msg << <span class="stringliteral">"Cannot convert from data type '"</span> << src << <span class="stringliteral">"' to format '"</span> << dst << <span class="stringliteral">"'\n"</span>;</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keywordflow">throw</span> std::invalid_argument(msg.str());</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  }</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> }</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> </div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="keyword">const</span> TensorLibrary::Converter &TensorLibrary::get_converter(<a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst)<span class="keyword"> const</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> <span class="keyword"></span>{</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keyword">static</span> std::map<std::pair<DataType, DataType>, Converter> converters = {};</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> </div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  <span class="keyword">const</span> <span class="keyword">auto</span> it = converters.find(std::make_pair(src, dst));</div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  <span class="keywordflow">if</span>(it != converters.end())</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  {</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <span class="keywordflow">return</span> it->second;</div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span>  }</div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  {</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  std::stringstream msg;</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  msg << <span class="stringliteral">"Cannot convert from data type '"</span> << src << <span class="stringliteral">"' to data type '"</span> << dst << <span class="stringliteral">"'\n"</span>;</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="keywordflow">throw</span> std::invalid_argument(msg.str());</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  }</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> }</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> <span class="keyword">const</span> TensorLibrary::Converter &TensorLibrary::get_converter(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> src, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> dst)<span class="keyword"> const</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="keyword"></span>{</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <span class="keyword">static</span> std::map<std::pair<Format, DataType>, Converter> converters = {};</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>  <span class="keyword">const</span> <span class="keyword">auto</span> it = converters.find(std::make_pair(src, dst));</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> </div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <span class="keywordflow">if</span>(it != converters.end())</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>  <span class="keywordflow">return</span> it->second;</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  }</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  {</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  std::stringstream msg;</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  msg << <span class="stringliteral">"Cannot convert from format '"</span> << src << <span class="stringliteral">"' to data type '"</span> << dst << <span class="stringliteral">"'\n"</span>;</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <span class="keywordflow">throw</span> std::invalid_argument(msg.str());</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  }</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> }</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> </div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span> <span class="keyword">const</span> TensorLibrary::Extractor &TensorLibrary::get_extractor(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> <span class="keyword"></span>{</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keyword">static</span> std::map<std::pair<Format, Channel>, Extractor> extractors =</div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  {</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span>  { std::make_pair(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aae1e1d3d40573127e9ee0480caf1283d6">Channel::R</a>), extract_r_from_rgb },</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  { std::make_pair(<a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aadfcf28d0734569a6a693bc8194de62bf">Channel::G</a>), extract_g_from_rgb }</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  };</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span> </div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  <span class="keyword">const</span> <span class="keyword">auto</span> it = extractors.find(std::make_pair(format, channel));</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span> </div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span>  <span class="keywordflow">if</span>(it != extractors.end())</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  {</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keywordflow">return</span> it->second;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  }</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span>  {</div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  std::stringstream msg;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span>  msg << <span class="stringliteral">"Cannot extract channel '"</span> << channel << <span class="stringliteral">"' from format '"</span> << format << <span class="stringliteral">"'\n"</span>;</div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  <span class="keywordflow">throw</span> std::invalid_argument(msg.str());</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  }</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> }</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> TensorLibrary::load_image(<span class="keyword">const</span> std::string &name)<span class="keyword"> const</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="keyword"></span>{</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="preprocessor">#ifdef _WIN32</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  <span class="keyword">const</span> std::string image_path = (<span class="stringliteral">"\\images\\"</span>);</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span> <span class="preprocessor">#else</span></div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  <span class="keyword">const</span> std::string image_path = (<span class="stringliteral">"/images/"</span>);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> <span class="preprocessor">#endif</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="keyword">const</span> std::string path = _library_path + image_path + name;</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <span class="keyword">const</span> std::string extension = path.substr(path.find_last_of(<span class="charliteral">'.'</span>) + 1);</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">return</span> (*get_loader(extension))(path);</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> }</div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span> </div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &TensorLibrary::find_or_create_raw_tensor(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)<span class="keyword"> const</span></div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> <span class="keyword"></span>{</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  std::lock_guard<std::mutex> guard(_format_lock);</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>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> *ptr = _cache.<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">find</a>(std::make_tuple(name, format));</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="keywordflow">if</span>(ptr != <span class="keyword">nullptr</span>)</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="keywordflow">return</span> *ptr;</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  }</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> </div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw = load_image(name);</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> </div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <span class="keywordflow">if</span>(raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a0c875a3203d902e2ad6bc3045355e69e">format</a>() != format)</div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span>  {</div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> dst(raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">shape</a>(), format);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  (*get_converter(raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a0c875a3203d902e2ad6bc3045355e69e">format</a>(), format))(raw, dst);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  raw = std::move(dst);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  }</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> </div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keywordflow">return</span> _cache.<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">add</a>(std::make_tuple(name, format), std::move(raw));</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span> }</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> <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &TensorLibrary::find_or_create_raw_tensor(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> <span class="keyword"></span>{</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  std::lock_guard<std::mutex> guard(_channel_lock);</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="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> *ptr = _cache.<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">find</a>(std::make_tuple(name, format, channel));</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="keywordflow">if</span>(ptr != <span class="keyword">nullptr</span>)</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> *ptr;</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="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &src = <span class="keyword">get</span>(name, format);</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> dst(src.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">shape</a>(), <a class="code" href="namespacearm__compute_1_1test.xhtml#ac7dbe33793790fc37a5eda11ed6b0273">get_channel_format</a>(channel));</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  (*get_extractor(format, channel))(src, dst);</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>  <span class="keywordflow">return</span> _cache.<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">add</a>(std::make_tuple(name, format, channel), std::move(dst));</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span> }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd"> 413</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type, <span class="keywordtype">int</span> num_channels, <span class="keywordtype">int</span> fixed_point_position)</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> {</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(shape, data_type, num_channels, fixed_point_position);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> }</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> </div><div class="line"><a name="l00418"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a62ee584f91819c3ea097827f7630c1dd"> 418</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> <a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a> &shape, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> {</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(shape, format);</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_1test_1_1_tensor_library.xhtml#a024fbe836c85d10afefc81cd2e51658e"> 423</a></span> <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name)<span class="keyword"> const</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> <span class="keyword"></span>{</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keywordflow">return</span> find_or_create_raw_tensor(name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>);</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> }</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span> </div><div class="line"><a name="l00428"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#aa0cf1a79542c521b9f16d117b085c4d5"> 428</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name)</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">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(find_or_create_raw_tensor(name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">Format::RGB888</a>));</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> </div><div class="line"><a name="l00433"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a3675bd0074fa527b42c6516a37f8f232"> 433</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> data_type, <span class="keywordtype">int</span> num_channels)<span class="keyword"> const</span></div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> <span class="keyword"></span>{</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &raw = <span class="keyword">get</span>(name);</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>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(raw.<a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">shape</a>(), data_type, num_channels);</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"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a0e77935822447adc6cdce586f276f97d"> 440</a></span> <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)<span class="keyword"> const</span></div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span> <span class="keyword"></span>{</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <span class="keywordflow">return</span> find_or_create_raw_tensor(name, format);</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"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#ab760ccaa18b95b99c73eb0e763f39ec2"> 445</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format)</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> {</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(find_or_create_raw_tensor(name, format));</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> </div><div class="line"><a name="l00450"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a11f5f1baaad31d1067564eccf599e90c"> 450</a></span> <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> <span class="keyword"></span>{</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">return</span> <span class="keyword">get</span>(name, <a class="code" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a>(channel), channel);</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> </div><div class="line"><a name="l00455"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#af6f0ca724e534653925306023dbb88e7"> 455</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)</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>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(<span class="keyword">get</span>(name, <a class="code" href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">get_format_for_channel</a>(channel), channel));</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> }</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a8c511f7046a704b8352ea8a8bbf456fa"> 460</a></span> <span class="keyword">const</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &<a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)<span class="keyword"> const</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> <span class="keyword"></span>{</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordflow">return</span> find_or_create_raw_tensor(name, format, channel);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> }</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#ad6182f07b3eda32931598aa4f2bfc11a"> 465</a></span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> <a class="code" href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">TensorLibrary::get</a>(<span class="keyword">const</span> std::string &name, <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">Format</a> format, <a class="code" href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">Channel</a> channel)</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> {</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>(find_or_create_raw_tensor(name, format, channel));</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> }</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> } <span class="comment">// namespace test</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> } <span class="comment">// namespace arm_compute</span></div><div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_aabcf39e3917f842dbc5fbb0d802f24d5"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#aabcf39e3917f842dbc5fbb0d802f24d5">arm_compute::test::fixed_point_arithmetic::detail::min</a></div><div class="ttdeci">fixed_point< T > min(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00875">FixedPoint.h:875</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">arm_compute::test::RawTensor</a></div><div class="ttdoc">Simple tensor object that stores elements in a consecutive chunk of memory. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8h_source.xhtml#l00047">RawTensor.h:47</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#l00038">TensorShape.h:38</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a65b3f12d28af30bbb0d6cf75e7c4c316"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a65b3f12d28af30bbb0d6cf75e7c4c316">arm_compute::test::TensorLibrary::TensorLibrary</a></div><div class="ttdeci">TensorLibrary(std::string path)</div><div class="ttdoc">Initialises the library with a path to the image directory. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8cpp_source.xhtml#l00208">TensorLibrary.cpp:208</a></div></div> |
| <div class="ttc" id="_i_tensor_8h_xhtml"><div class="ttname"><a href="_i_tensor_8h.xhtml">ITensor.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a4035a1140831801ced5dfa1d9fe6988a"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a4035a1140831801ced5dfa1d9fe6988a">arm_compute::test::TensorLibrary::seed</a></div><div class="ttdeci">std::random_device::result_type seed() const </div><div class="ttdoc">Seed that is used to fill tensors with random values. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8cpp_source.xhtml#l00218">TensorLibrary.cpp:218</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_xhtml_aa337ab76176f3c4193642ac6de3a61cf"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#aa337ab76176f3c4193642ac6de3a61cf">arm_compute::test::get_format_for_channel</a></div><div class="ttdeci">Format get_format_for_channel(Channel channel)</div><div class="ttdoc">Look up the format corresponding to a channel. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00381">Utils.h:381</a></div></div> |
| <div class="ttc" id="_tensor_library_8h_xhtml"><div class="ttname"><a href="_tensor_library_8h.xhtml">TensorLibrary.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">Unknown image format. </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 S16 per channel </div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a13761831550669f43f4edee978181c46"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a13761831550669f43f4edee978181c46">arm_compute::test::RawTensor::shape</a></div><div class="ttdeci">TensorShape shape() const </div><div class="ttdoc">Shape of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00086">RawTensor.cpp:86</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aae1e1d3d40573127e9ee0480caf1283d6"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aae1e1d3d40573127e9ee0480caf1283d6">arm_compute::Channel::R</a></div><div class="ttdoc">Red channel. </div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_aac782da1f912bceb5d8ad00c8dc892ac"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#aac782da1f912bceb5d8ad00c8dc892ac">arm_compute::test::RawTensor::size</a></div><div class="ttdeci">size_t size() const </div><div class="ttdoc">Total size of the tensor in bytes. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00101">RawTensor.cpp:101</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a0c875a3203d902e2ad6bc3045355e69e"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a0c875a3203d902e2ad6bc3045355e69e">arm_compute::test::RawTensor::format</a></div><div class="ttdeci">Format format() const </div><div class="ttdoc">Image format of the tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00107">RawTensor.cpp:107</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdef"><b>Definition:</b> <a href="01__library_8dox_source.xhtml#l00001">01_library.dox:1</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a30ff380a3be74628024063a99fba10f0">arm_compute::Format::RGB888</a></div><div class="ttdoc">2 channel, 1 U8 per channel </div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_xhtml_ac7dbe33793790fc37a5eda11ed6b0273"><div class="ttname"><a href="namespacearm__compute_1_1test.xhtml#ac7dbe33793790fc37a5eda11ed6b0273">arm_compute::test::get_channel_format</a></div><div class="ttdeci">Format get_channel_format(Channel channel)</div><div class="ttdoc">Return the format of a channel. </div><div class="ttdef"><b>Definition:</b> <a href="tests_2_utils_8h_source.xhtml#l00400">Utils.h:400</a></div></div> |
| <div class="ttc" id="_type_printer_8h_xhtml"><div class="ttname"><a href="_type_printer_8h.xhtml">TypePrinter.h</a></div></div> |
| <div class="ttc" id="_user_configuration_8h_xhtml"><div class="ttname"><a href="_user_configuration_8h.xhtml">UserConfiguration.h</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455a"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455a">arm_compute::Channel</a></div><div class="ttdeci">Channel</div><div class="ttdoc">Available channels. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00304">Types.h:304</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58">arm_compute::Format</a></div><div class="ttdeci">Format</div><div class="ttdoc">Image colour formats. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00038">Types.h:38</a></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 U8 per channel </div></div> |
| <div class="ttc" id="tests_2_utils_8h_xhtml"><div class="ttname"><a href="tests_2_utils_8h.xhtml">Utils.h</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_cache_xhtml_a00b2f7f657ef8060c64fce93abac54e1"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">arm_compute::test::TensorCache::add</a></div><div class="ttdeci">RawTensor & add(std::tuple< const std::string &, Format > key, RawTensor raw)</div><div class="ttdoc">Add the given tensor to the cache. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_cache_8h_source.xhtml#l00105">TensorCache.h:105</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_cache_xhtml_ab9838ae8ffe3b1f98e1330d3ee260f98"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">arm_compute::test::TensorCache::find</a></div><div class="ttdeci">RawTensor * find(std::tuple< const std::string &, Format > key)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_cache_8h_source.xhtml#l00093">TensorCache.h:93</a></div></div> |
| <div class="ttc" id="namespacearm__compute_xhtml_a1ce9b523fd4f3b5bbcadcd796183455aadfcf28d0734569a6a693bc8194de62bf"><div class="ttname"><a href="namespacearm__compute.xhtml#a1ce9b523fd4f3b5bbcadcd796183455aadfcf28d0734569a6a693bc8194de62bf">arm_compute::Channel::G</a></div><div class="ttdoc">Green channel. </div></div> |
| <div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a3aa8f5f1b94f88fdf5b43a53e29379cf"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a3aa8f5f1b94f88fdf5b43a53e29379cf">arm_compute::utils::parse_ppm_header</a></div><div class="ttdeci">std::tuple< unsigned int, unsigned int, int > parse_ppm_header(std::ifstream &fs)</div><div class="ttdoc">Parse the ppm header from an input file stream. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00140">Utils.cpp:140</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail_xhtml_ad91bb73431b4de1f4946ed949d444849"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1fixed__point__arithmetic_1_1detail.xhtml#ad91bb73431b4de1f4946ed949d444849">arm_compute::test::fixed_point_arithmetic::detail::max</a></div><div class="ttdeci">fixed_point< T > max(fixed_point< T > x, fixed_point< T > y)</div><div class="ttdef"><b>Definition:</b> <a href="tests_2validation_2_fixed_point_8h_source.xhtml#l00880">FixedPoint.h:880</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_raw_tensor_xhtml_a7b53deaf986aa58ffa0090cc241dec64"><div class="ttname"><a href="classarm__compute_1_1test_1_1_raw_tensor.xhtml#a7b53deaf986aa58ffa0090cc241dec64">arm_compute::test::RawTensor::data</a></div><div class="ttdeci">const BufferType * data() const </div><div class="ttdoc">Constant pointer to the underlying buffer. </div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8cpp_source.xhtml#l00148">RawTensor.cpp:148</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#l00060">Types.h:60</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a34e94c998e4527d9556ccc5da82765fd"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a34e94c998e4527d9556ccc5da82765fd">arm_compute::test::TensorLibrary::get</a></div><div class="ttdeci">static RawTensor get(const TensorShape &shape, DataType data_type, int num_channels=1, int fixed_point_position=0)</div><div class="ttdoc">Creates an uninitialised raw tensor with the given shape, data_type and num_channels. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8cpp_source.xhtml#l00413">TensorLibrary.cpp:413</a></div></div> |
| <div class="ttc" id="classarm__compute_1_1test_1_1_tensor_library_xhtml_a80a7b5ae084bf22b91bc5f68a06711c0"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_library.xhtml#a80a7b5ae084bf22b91bc5f68a06711c0">arm_compute::test::TensorLibrary::fill</a></div><div class="ttdeci">void fill(T &&tensor, D &&distribution, std::random_device::result_type seed_offset) const </div><div class="ttdoc">Fills the specified tensor with random values drawn from distribution. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_library_8h_source.xhtml#l00368">TensorLibrary.h:368</a></div></div> |
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