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<div class="title">DefaultAsyncExecuteTest.cpp</div> </div>
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<a href="_default_async_execute_test_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 © 2021 Arm Ltd and Contributors. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">//</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="preprocessor">#include <<a class="code" href="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>></span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_2backends_2_tensor_handle_8hpp.xhtml">armnn/backends/TensorHandle.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="include_2armnn_2backends_2_workload_8hpp.xhtml">armnn/backends/Workload.hpp</a>></span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <doctest/doctest.h></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <thread></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> </div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">namespace</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> {</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> </div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <a class="code" href="namespacearmnn.xhtml#a1621fb2f10314c394c9023d3e090d394">TEST_SUITE</a>(<span class="stringliteral">"WorkloadAsyncExecuteTests"</span>)</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> {</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> </div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="keyword">struct </span>Workload0 : <a class="code" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload</a><ElementwiseUnaryQueueDescriptor></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>  Workload0(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">ElementwiseUnaryQueueDescriptor</a>& descriptor, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  : <a class="code" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload</a>(descriptor, info)</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</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> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  Workload0() : <a class="code" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload</a>(<a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">ElementwiseUnaryQueueDescriptor</a>(), <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>())</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  {</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  }</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> </div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="keywordtype">void</span> Execute()<span class="keyword"> const</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordtype">int</span>* inVals = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(m_Data.m_Inputs[0][0].Map());</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordtype">int</span>* outVals = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(m_Data.m_Outputs[0][0].Map());</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  i < m_Data.m_Inputs[0][0].GetShape().GetNumElements();</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  ++i)</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  {</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  outVals[i] = inVals[i] * outVals[i];</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  inVals[i] = outVals[i];</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>  }</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> ExecuteAsync(<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a>& desc)</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="keywordtype">int</span>* inVals = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(desc.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0][0].Map());</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">int</span>* outVals = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(desc.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0][0].Map());</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  i < desc.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0][0].GetShape().GetNumElements();</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  ++i)</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>  outVals[i] = inVals[i] + outVals[i];</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  inVals[i] = outVals[i];</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>  }</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>  <a class="code" href="structarmnn_1_1_queue_descriptor.xhtml">QueueDescriptor</a>* GetQueueDescriptor()</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="keywordflow">return</span> &m_Data;</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="keyword">struct </span>Workload1 : <a class="code" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload</a><ElementwiseUnaryQueueDescriptor></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>  Workload1(<span class="keyword">const</span> <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">ElementwiseUnaryQueueDescriptor</a>& descriptor, <span class="keyword">const</span> <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a>& <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>)</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  : <a class="code" href="classarmnn_1_1_base_workload.xhtml">BaseWorkload</a>(descriptor, info)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordtype">void</span> Execute()<span class="keyword"> const</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="keyword"> </span>{</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">int</span>* inVals = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(m_Data.m_Inputs[0][0].Map());</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">int</span>* outVals = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(m_Data.m_Outputs[0][0].Map());</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  i < m_Data.m_Inputs[0][0].GetShape().GetNumElements();</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  ++i)</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>  outVals[i] = inVals[i] * outVals[i];</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  inVals[i] = outVals[i];</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> };</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="keywordtype">void</span> ValidateTensor(<a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* tensorHandle, <span class="keywordtype">int</span> expectedValue)</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="keywordtype">int</span>* actualOutput = <span class="keyword">static_cast<</span><span class="keywordtype">int</span>*<span class="keyword">></span>(tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">Map</a>());</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>  <span class="keywordtype">bool</span> allValuesCorrect = <span class="keyword">true</span>;</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  i < tensorHandle-><a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml#affd5aae75cad90f472f96cfd25a13f29">GetShape</a>().<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>();</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  ++i)</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  {</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="keywordflow">if</span> (actualOutput[i] != expectedValue)</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  allValuesCorrect = <span class="keyword">false</span>;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  }</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</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>  CHECK(allValuesCorrect);</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> }</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="keyword">template</span><<span class="keyword">typename</span> Workload></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> std::unique_ptr<Workload> <a class="code" href="_elementwise_test_impl_8hpp.xhtml#ab6921db5d86507f5b126af1cc516adb9">CreateWorkload</a>(<a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* inputTensor, <a class="code" href="classarmnn_1_1_i_tensor_handle.xhtml">ITensorHandle</a>* outputTensor)</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> {</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">m_InputTensorInfos</a> = std::vector<TensorInfo>{info};</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  workloadInfo.<a class="code" href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">m_OutputTensorInfos</a> = std::vector<TensorInfo>{info};</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <a class="code" href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">ElementwiseUnaryQueueDescriptor</a> elementwiseUnaryQueueDescriptor;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  elementwiseUnaryQueueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a> = std::vector<ITensorHandle*>{inputTensor};</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  elementwiseUnaryQueueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a> = std::vector<ITensorHandle*>{outputTensor};</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">return</span> std::make_unique<Workload>(elementwiseUnaryQueueDescriptor, workloadInfo);</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> }</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> </div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> TEST_CASE(<span class="stringliteral">"TestAsyncExecute"</span>)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> {</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>, 0.0, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> </div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordtype">int</span> inVals[5]{2, 2, 2, 2, 2};</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">int</span> outVals[5]{1, 1, 1, 1, 1};</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="keywordtype">int</span> expectedExecuteval = 2;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">int</span> expectedExecuteAsyncval = 3;</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>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constInputTensor(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, inVals);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constOutputTensor(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, outVals);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> syncInput0(constInputTensor);</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> syncOutput0(constOutputTensor);</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> </div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  std::unique_ptr<Workload0> workload0 = CreateWorkload<Workload0>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, &syncInput0, &syncOutput0);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  workload0.get()->Execute();</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>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncInput0(constInputTensor);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncOutput0(constOutputTensor);</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> </div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> workingMemDescriptor0;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  workingMemDescriptor0.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a> = std::vector<ITensorHandle*>{&asyncInput0};</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  workingMemDescriptor0.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a> = std::vector<ITensorHandle*>{&asyncOutput0};</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>  workload0.get()->ExecuteAsync(workingMemDescriptor0);</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>  <span class="comment">// Inputs are also changed by the execute/executeAsync calls to make sure there is no interference with them</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  ValidateTensor(workingMemDescriptor0.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0], expectedExecuteAsyncval);</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  ValidateTensor(workingMemDescriptor0.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0], expectedExecuteAsyncval);</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  ValidateTensor(&workload0.get()->GetQueueDescriptor()->m_Outputs[0][0], expectedExecuteval);</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  ValidateTensor(&workload0.get()->GetQueueDescriptor()->m_Inputs[0][0], expectedExecuteval);</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> }</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> TEST_CASE(<span class="stringliteral">"TestDefaultAsyncExecute"</span>)</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({5}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>, 0.0f, 0, <span class="keyword">true</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>  std::vector<int> inVals{2, 2, 2, 2, 2};</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  std::vector<int> outVals{1, 1, 1, 1, 1};</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  std::vector<int> defaultVals{0, 0, 0, 0, 0};</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keywordtype">int</span> expectedExecuteval = 2;</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>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constInputTensor(info, inVals);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constOutputTensor(info, outVals);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> defaultTensor(info, &defaultVals);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> defaultInput = <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a>(defaultTensor);</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> defaultOutput = <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a>(defaultTensor);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  std::unique_ptr<Workload1> workload1 = CreateWorkload<Workload1>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, &defaultInput, &defaultOutput);</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>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncInput(constInputTensor);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncOutput(constOutputTensor);</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>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> workingMemDescriptor;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a> = std::vector<ITensorHandle*>{&asyncInput};</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a> = std::vector<ITensorHandle*>{&asyncOutput};</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>  workload1.get()->ExecuteAsync(workingMemDescriptor);</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> </div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="comment">// workload1 has no AsyncExecute implementation and so should use the default workload AsyncExecute</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// implementation which will call workload1.Execute() in a thread safe manner</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  ValidateTensor(workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0], expectedExecuteval);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  ValidateTensor(workingMemDescriptor.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0], expectedExecuteval);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> TEST_CASE(<span class="stringliteral">"TestDefaultAsyncExeuteWithThreads"</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>  <span class="comment">// Use a large vector so the threads have a chance to interact</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> vecSize = 1000;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> <a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>({vecSize}, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>, 0.0f, 0, <span class="keyword">true</span>);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  std::vector<int> inVals1(vecSize, 2);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  std::vector<int> outVals1(vecSize, 1);</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  std::vector<int> inVals2(vecSize, 5);</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  std::vector<int> outVals2(vecSize, -1);</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> </div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  std::vector<int> defaultVals(vecSize, 0);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="keywordtype">int</span> expectedExecuteval1 = 4;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="keywordtype">int</span> expectedExecuteval2 = 25;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constInputTensor1(info, inVals1);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constOutputTensor1(info, outVals1);</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>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constInputTensor2(info, inVals2);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> constOutputTensor2(info, outVals2);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <a class="code" href="classarmnn_1_1_const_tensor.xhtml">ConstTensor</a> defaultTensor(info, defaultVals.data());</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>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> defaultInput = <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a>(defaultTensor);</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> defaultOutput = <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a>(defaultTensor);</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  std::unique_ptr<Workload1> workload = CreateWorkload<Workload1>(<a class="code" href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">info</a>, &defaultInput, &defaultOutput);</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> </div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncInput1(constInputTensor1);</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncOutput1(constOutputTensor1);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> workingMemDescriptor1;</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  workingMemDescriptor1.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a> = std::vector<ITensorHandle*>{&asyncInput1};</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  workingMemDescriptor1.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a> = std::vector<ITensorHandle*>{&asyncOutput1};</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"> 229</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncInput2(constInputTensor2);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  <a class="code" href="classarmnn_1_1_scoped_tensor_handle.xhtml">ScopedTensorHandle</a> asyncOutput2(constOutputTensor2);</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> </div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">WorkingMemDescriptor</a> workingMemDescriptor2;</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  workingMemDescriptor2.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a> = std::vector<ITensorHandle*>{&asyncInput2};</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  workingMemDescriptor2.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a> = std::vector<ITensorHandle*>{&asyncOutput2};</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  std::thread thread1 = std::thread([&]()</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  workload.get()->ExecuteAsync(workingMemDescriptor1);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  workload.get()->ExecuteAsync(workingMemDescriptor1);</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  });</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> </div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  std::thread thread2 = std::thread([&]()</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>  workload.get()->ExecuteAsync(workingMemDescriptor2);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  workload.get()->ExecuteAsync(workingMemDescriptor2);</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> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  thread1.join();</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  thread2.join();</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>  ValidateTensor(workingMemDescriptor1.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0], expectedExecuteval1);</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  ValidateTensor(workingMemDescriptor1.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0], expectedExecuteval1);</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> </div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  ValidateTensor(workingMemDescriptor2.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">m_Outputs</a>[0], expectedExecuteval2);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  ValidateTensor(workingMemDescriptor2.<a class="code" href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">m_Inputs</a>[0], expectedExecuteval2);</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> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> }</div><div class="ttc" id="namespacearmnn_xhtml_a1621fb2f10314c394c9023d3e090d394"><div class="ttname"><a href="namespacearmnn.xhtml#a1621fb2f10314c394c9023d3e090d394">armnn::TEST_SUITE</a></div><div class="ttdeci">TEST_SUITE("TestConstTensorLayerVisitor")</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00110">ConstTensorLayerVisitor.cpp:110</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorShape::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdoc">Function that calculates the tensor elements by multiplying all dimension size which are Specified...</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00181">Tensor.cpp:181</a></div></div>
<div class="ttc" id="_elementwise_test_impl_8hpp_xhtml_ab6921db5d86507f5b126af1cc516adb9"><div class="ttname"><a href="_elementwise_test_impl_8hpp.xhtml#ab6921db5d86507f5b126af1cc516adb9">CreateWorkload</a></div><div class="ttdeci">std::unique_ptr< armnn::IWorkload > CreateWorkload(const armnn::IWorkloadFactory &workloadFactory, const armnn::WorkloadInfo &info, const DescriptorType &descriptor)</div><div class="ttdef"><b>Definition:</b> <a href="_elementwise_test_impl_8hpp_source.xhtml#l00027">ElementwiseTestImpl.hpp:27</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00152">Tensor.hpp:152</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml">armnn::QueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00024">WorkloadData.hpp:24</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml"><div class="ttname"><a href="namespacearmnn.xhtml">armnn</a></div><div class="ttdoc">Copyright (c) 2021 ARM Limited and Contributors. </div><div class="ttdef"><b>Definition:</b> <a href="01__00__quick__start_8dox_source.xhtml#l00006">01_00_quick_start.dox:6</a></div></div>
<div class="ttc" id="structarmnn_1_1experimental_1_1_working_mem_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml">armnn::experimental::WorkingMemDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.xhtml#l00018">WorkingMemDescriptor.hpp:18</a></div></div>
<div class="ttc" id="structarmnn_1_1experimental_1_1_working_mem_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::experimental::WorkingMemDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.xhtml#l00020">WorkingMemDescriptor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_ac97905bfa0daab357b91df1347600309"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#ac97905bfa0daab357b91df1347600309">armnn::WorkloadInfo::m_InputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_InputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00018">WorkloadInfo.hpp:18</a></div></div>
<div class="ttc" id="classarmnn_1_1_base_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_base_workload.xhtml">armnn::BaseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_8hpp_source.xhtml#l00032">Workload.hpp:32</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml">armnn::ITensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_8hpp_source.xhtml#l00015">ITensorHandle.hpp:15</a></div></div>
<div class="ttc" id="classarmnn_1_1_const_tensor_xhtml"><div class="ttname"><a href="classarmnn_1_1_const_tensor.xhtml">armnn::ConstTensor</a></div><div class="ttdoc">A tensor defined by a TensorInfo (shape and data type) and an immutable backing store. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00327">Tensor.hpp:327</a></div></div>
<div class="ttc" id="classarmnn_1_1_scoped_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_tensor_handle.xhtml">armnn::ScopedTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_tensor_handle_8hpp_source.xhtml#l00115">TensorHandle.hpp:115</a></div></div>
<div class="ttc" id="include_2armnn_2backends_2_workload_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_workload_8hpp.xhtml">Workload.hpp</a></div></div>
<div class="ttc" id="include_2armnn_2backends_2_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="include_2armnn_2backends_2_tensor_handle_8hpp.xhtml">TensorHandle.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml_a67b178f8a836bc1e52b8de109760adfd"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml#a67b178f8a836bc1e52b8de109760adfd">armnn::WorkloadInfo::m_OutputTensorInfos</a></div><div class="ttdeci">std::vector< TensorInfo > m_OutputTensorInfos</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00019">WorkloadInfo.hpp:19</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_affd5aae75cad90f472f96cfd25a13f29"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#affd5aae75cad90f472f96cfd25a13f29">armnn::ITensorHandle::GetShape</a></div><div class="ttdeci">virtual TensorShape GetShape() const =0</div><div class="ttdoc">Get the number of elements for each dimension ordered from slowest iterating dimension to fastest ite...</div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_xhtml_a9afbc055a017adf1bc38ee137bca6e90"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle.xhtml#a9afbc055a017adf1bc38ee137bca6e90">armnn::ITensorHandle::Map</a></div><div class="ttdeci">virtual const void * Map(bool blocking=true) const =0</div><div class="ttdoc">Map the tensor data for access. </div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::QueueDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00027">WorkloadData.hpp:27</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1experimental_1_1_working_mem_descriptor_xhtml_a6abd491bb99ffe88bd472c1ae5a1ed1a"><div class="ttname"><a href="structarmnn_1_1experimental_1_1_working_mem_descriptor.xhtml#a6abd491bb99ffe88bd472c1ae5a1ed1a">armnn::experimental::WorkingMemDescriptor::m_Outputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Outputs</div><div class="ttdef"><b>Definition:</b> <a href="_working_mem_descriptor_8hpp_source.xhtml#l00021">WorkingMemDescriptor.hpp:21</a></div></div>
<div class="ttc" id="structarmnn_1_1_workload_info_xhtml"><div class="ttname"><a href="structarmnn_1_1_workload_info.xhtml">armnn::WorkloadInfo</a></div><div class="ttdoc">Contains information about TensorInfos of a layer. </div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_info_8hpp_source.xhtml#l00016">WorkloadInfo.hpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_xhtml_a4b50e46a6810018f3edecfb68b2a76b3"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor.xhtml#a4b50e46a6810018f3edecfb68b2a76b3">armnn::QueueDescriptor::m_Inputs</a></div><div class="ttdeci">std::vector< ITensorHandle * > m_Inputs</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00026">WorkloadData.hpp:26</a></div></div>
<div class="ttc" id="structarmnn_1_1_elementwise_unary_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_elementwise_unary_queue_descriptor.xhtml">armnn::ElementwiseUnaryQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_workload_data_8hpp_source.xhtml#l00711">WorkloadData.hpp:711</a></div></div>
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