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<div class="title">TensorCache.h</div> </div>
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<a href="_tensor_cache_8h.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;<span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;<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>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;<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>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<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>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;<span class="preprocessor">#ifndef ARM_COMPUTE_TEST_TENSOR_CACHE_H</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;<span class="preprocessor">#define ARM_COMPUTE_TEST_TENSOR_CACHE_H</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_raw_tensor_8h.xhtml">RawTensor.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_mutex_8h.xhtml">support/Mutex.h</a>&quot;</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;<span class="preprocessor">#include &lt;map&gt;</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;<span class="preprocessor">#include &lt;mutex&gt;</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;<span class="preprocessor">#include &lt;utility&gt;</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacearm__compute.xhtml">arm_compute</a></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;<span class="keyword">namespace </span>test</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;{<span class="comment"></span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;<span class="comment">/** Stores @ref RawTensor categorised by the image they are created from</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;<span class="comment"> * including name, format and channel.</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml"> 42</a></span>&#160;<span class="keyword">class </span><a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">TensorCache</a></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;{</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;<span class="keyword">public</span>:<span class="comment"></span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;<span class="comment"> /** Search the cache for a tensor of created from the specified image and</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;<span class="comment"> * format.</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;<span class="comment"> * @param[in] key Key to look up the tensor. Consists of image name and format.</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;<span class="comment"> * @return The cached tensor matching the image name and format if found. A</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;<span class="comment"> * nullptr otherwise.</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160; <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_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">find</a>(std::tuple&lt;const std::string &amp;, Format&gt; key);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;<span class="comment"> /** Search the cache for a tensor of created from the specified image,</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;<span class="comment"> * format and channel.</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;<span class="comment"> * @param[in] key Key to look up the tensor. Consists of image name, format and channel.</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;<span class="comment"> * @return The cached tensor matching the image name and format if found. A</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;<span class="comment"> * nullptr otherwise.</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160; <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_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">find</a>(std::tuple&lt;const std::string &amp;, Format, Channel&gt; key);</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;<span class="comment"> /** Add the given tensor to the cache. Can later be found under the given</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;<span class="comment"> * image name and format.</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;<span class="comment"> * @param[in] key Key under which to store the tensor. Consists of image name and format.</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;<span class="comment"> * @param[in] raw Raw tensor to be stored.</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;<span class="comment"> * @return A reference to the cached tensor.</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">add</a>(std::tuple&lt;const std::string &amp;, Format&gt; key, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;<span class="comment"></span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;<span class="comment"> /** Add the given tensor to the cache. Can later be found under the given</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160;<span class="comment"> * image name, format and channel.</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160;<span class="comment"> * @param[in] key Key under which to store the tensor. Consists of image name, format and channel.</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;<span class="comment"> * @param[in] raw Raw tensor to be stored.</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;<span class="comment"> * @return A reference to the cached tensor.</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160; <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">add</a>(std::tuple&lt;const std::string &amp;, Format, Channel&gt; key, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw);</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160; <span class="keyword">using</span> FormatMap = std::map&lt;std::tuple&lt;std::string, Format&gt;, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>&gt;;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160; <span class="keyword">using</span> ChannelMap = std::map&lt;std::tuple&lt;std::string, Format, Channel&gt;, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a>&gt;;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160; FormatMap _raw_tensor_cache{};</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160; ChannelMap _raw_tensor_channel_cache{};</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160; <a class="code" href="namespacearm__compute.xhtml#acded863dbfdd730829d4188d67eefcf0">arm_compute::Mutex</a> _raw_tensor_cache_mutex{};</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160; <a class="code" href="namespacearm__compute.xhtml#acded863dbfdd730829d4188d67eefcf0">arm_compute::Mutex</a> _raw_tensor_channel_cache_mutex{};</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;};</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98"> 95</a></span>&#160;<span class="keyword">inline</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_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">TensorCache::find</a>(std::tuple&lt;const std::string &amp;, Format&gt; key)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;{</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160; <span class="keyword">const</span> <span class="keyword">auto</span> it = _raw_tensor_cache.find(key);</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160; <span class="keywordflow">return</span> it == _raw_tensor_cache.end() ? nullptr : &amp;it-&gt;second;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;}</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a77d062d1fc4b239c215c4534deee8a2f"> 101</a></span>&#160;<span class="keyword">inline</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_cache.xhtml#ab9838ae8ffe3b1f98e1330d3ee260f98">TensorCache::find</a>(std::tuple&lt;const std::string &amp;, Format, Channel&gt; key)</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160;{</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160; <span class="keyword">const</span> <span class="keyword">auto</span> it = _raw_tensor_channel_cache.find(key);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160; <span class="keywordflow">return</span> it == _raw_tensor_channel_cache.end() ? nullptr : &amp;it-&gt;second;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;}</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160;</div><div class="line"><a name="l00107"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1"> 107</a></span>&#160;<span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">TensorCache::add</a>(std::tuple&lt;const std::string &amp;, Format&gt; key, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw)</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;{</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160; std::lock_guard&lt;arm_compute::Mutex&gt; lock(_raw_tensor_cache_mutex);</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160; <span class="keywordflow">return</span> std::get&lt;0&gt;(_raw_tensor_cache.emplace(std::move(key), std::move(raw)))-&gt;second;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160;}</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno"><a class="line" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a8a342abc82aa86a51f668dd6a7f89934"> 113</a></span>&#160;<span class="keyword">inline</span> <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> &amp;<a class="code" href="classarm__compute_1_1test_1_1_tensor_cache.xhtml#a00b2f7f657ef8060c64fce93abac54e1">TensorCache::add</a>(std::tuple&lt;const std::string &amp;, Format, Channel&gt; key, <a class="code" href="classarm__compute_1_1test_1_1_raw_tensor.xhtml">RawTensor</a> raw)</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160;{</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160; std::lock_guard&lt;arm_compute::Mutex&gt; lock(_raw_tensor_channel_cache_mutex);</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160; <span class="keywordflow">return</span> std::get&lt;0&gt;(_raw_tensor_channel_cache.emplace(std::move(key), std::move(raw)))-&gt;second;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;}</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;} <span class="comment">// namespace test</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;} <span class="comment">// namespace arm_compute</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160;<span class="preprocessor">#endif </span><span class="comment">/* ARM_COMPUTE_TEST_TENSOR_CACHE_H */</span><span class="preprocessor"></span></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">Subclass of SimpleTensor using uint8_t as value type.</div><div class="ttdef"><b>Definition:</b> <a href="_raw_tensor_8h_source.xhtml#l00038">RawTensor.h:38</a></div></div>
<div class="ttc" id="classarm__compute_1_1test_1_1_tensor_cache_xhtml"><div class="ttname"><a href="classarm__compute_1_1test_1_1_tensor_cache.xhtml">arm_compute::test::TensorCache</a></div><div class="ttdoc">Stores RawTensor categorised by the image they are created from including name, format and channel.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_cache_8h_source.xhtml#l00042">TensorCache.h:42</a></div></div>
<div class="ttc" id="_mutex_8h_xhtml"><div class="ttname"><a href="_mutex_8h.xhtml">Mutex.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_acded863dbfdd730829d4188d67eefcf0"><div class="ttname"><a href="namespacearm__compute.xhtml#acded863dbfdd730829d4188d67eefcf0">arm_compute::Mutex</a></div><div class="ttdeci">std::mutex Mutex</div><div class="ttdoc">Wrapper of Mutex data-object.</div><div class="ttdef"><b>Definition:</b> <a href="_mutex_8h_source.xhtml#l00033">Mutex.h:33</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml"><div class="ttname"><a href="namespacearm__compute.xhtml">arm_compute</a></div><div class="ttdoc">Copyright (c) 2017-2020 ARM Limited.</div><div class="ttdef"><b>Definition:</b> <a href="00__introduction_8dox_source.xhtml#l00024">00_introduction.dox:24</a></div></div>
<div class="ttc" id="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 &amp; add(std::tuple&lt; const std::string &amp;, Format &gt; 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#l00107">TensorCache.h:107</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&lt; const std::string &amp;, Format &gt; key)</div><div class="ttdoc">Search the cache for a tensor of created from the specified image and format.</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_cache_8h_source.xhtml#l00095">TensorCache.h:95</a></div></div>
<div class="ttc" id="_raw_tensor_8h_xhtml"><div class="ttname"><a href="_raw_tensor_8h.xhtml">RawTensor.h</a></div></div>
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