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<a href="_concat_test_impl_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 © 2017 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="_concat_test_impl_8hpp.xhtml">ConcatTestImpl.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="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include <<a class="code" href="_resolve_type_8hpp.xhtml">ResolveType.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> </div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include <<a class="code" href="_permute_8hpp.xhtml">armnnUtils/Permute.hpp</a>></span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_tensor_copy_utils_8hpp.xhtml">backendsCommon/test/TensorCopyUtils.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#include <<a class="code" href="_workload_test_utils_8hpp.xhtml">backendsCommon/test/WorkloadTestUtils.hpp</a>></span></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> <span class="preprocessor">#include <<a class="code" href="_tensor_helpers_8hpp.xhtml">test/TensorHelpers.hpp</a>></span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> </div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn_utils.xhtml">armnnUtils</a>;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">//</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// Helper functions and templates</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment">//</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"><a class="line" href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147"> 26</a></span> <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147">CreateDescriptorForConcat</a>(</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <span class="keyword">const</span> std::vector<TensorInfo> & inputTensorInfos,</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDim)</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>  std::vector<TensorShape> shapes;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  shapes.reserve(inputTensorInfos.size());</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="keywordflow">for</span> (<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& it: inputTensorInfos)</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>  shapes.push_back(it.GetShape());</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  }</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="keywordflow">return</span> <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">CreateDescriptorForConcatenation</a>(shapes.begin(), shapes.end(), concatDim);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> }</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> </div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment">//</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment">// Concat is only supported for N and C dimensions for NCHW and the inner most dimension</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment">// In case of <4 dimensions we need to make sure that the concat dimensions are at least</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment">// the 3rd slowest iterating one or the inner most dimension.</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment">//</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div><div class="line"><a name="l00046"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596"> 46</a></span> <span class="keywordtype">bool</span> <a class="code" href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596">NeedPermuteForConcat</a>(</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keyword">const</span> std::vector<TensorInfo> & inputTensorInfos,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDim)</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// See note above. Additionally we expect the input shapes to have the</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="comment">// same number of dimensions.</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nDimensions = 0;</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="comment">// Determine the number of dimensions as well as sanity check them</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// agains test implementation issues.</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> && tensorInfo : inputTensorInfos)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  <span class="keywordflow">if</span> (!nDimensions)</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  {</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  nDimensions = tensorInfo.GetShape().GetNumDimensions();</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>  <span class="keywordflow">else</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(nDimensions == tensorInfo.GetShape().GetNumDimensions(),</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="stringliteral">"Input shapes must have the same number of dimensions"</span>);</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="keywordflow">return</span> (nDimensions < 3 || (nDimensions == 3 && (nDimensions-concatDim) < 3 && (nDimensions-concatDim) != 1));</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> </div><div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d"> 72</a></span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a>(<span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> & inputShape)</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>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = inputShape.<a class="code" href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">GetNumDimensions</a>();</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="keywordflow">if</span> (numDims >= 3)</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>  <span class="comment">// Nothing to do if the inputShape has at least 3 dimensions.</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">return</span> inputShape;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  }</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  std::vector<unsigned int> newDims(<span class="keywordtype">size_t</span>(3), 1u);</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expandedBy = 3 - numDims;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i=0; i<numDims; ++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>  newDims[expandedBy+i] = inputShape[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>  <span class="keywordflow">return</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a>(3u, &newDims[0]);</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"><a class="line" href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8"> 90</a></span> <span class="keywordtype">void</span> <a class="code" href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8">Generate3dPermuteVectorForConcat</a>(</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDimensions,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> & concatDim,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  std::pair<PermutationVector, PermutationVector> & permutations)</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>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(numDimensions <= 3,</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="stringliteral">"Only dimensions 1,2 and 3 are supported by this helper"</span>);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expandedBy = 3 - numDimensions;</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> expandedConcatAxis = concatDim + expandedBy;</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> (expandedConcatAxis == 2)</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>  concatDim = 0;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> forwardPermutation({1, 2, 0});</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> reversePermutation({2, 0, 1});</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  permutations = std::make_pair(forwardPermutation, reversePermutation);</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="keywordflow">else</span> <span class="keywordflow">if</span> (expandedConcatAxis == 1)</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>  concatDim = 0;</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> forwardPermutation({2, 0, 1});</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> reversePermutation({1, 2, 0});</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  permutations = std::make_pair(forwardPermutation, reversePermutation);</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="keywordflow">else</span></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="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a>(expandedConcatAxis == 0);</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  concatDim = 0;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  }</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> }</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> </div><div class="line"><a name="l00121"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a452d79381b887bbbcc645b44025c03ca"> 121</a></span> <span class="keyword">template</span><<span class="keyword">typename</span> T> <span class="keywordtype">void</span> <a class="code" href="_concat_test_impl_8cpp.xhtml#a452d79381b887bbbcc645b44025c03ca">PermuteTensorData</a>(</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a>& mappings,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> & inputTensorInfo,</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keyword">const</span> T * inputData,</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  std::vector<T>& outputData)</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> {</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputData != <span class="keyword">nullptr</span>, <span class="stringliteral">"inputData must not be null"</span>);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  <span class="keywordflow">if</span> (inputData == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  {</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// Nullptr is an error in the test. By returning without doing the concatenation</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="comment">// I expect the caller to fail the test. It still makes sense to report this as</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// an assert for Debug builds.</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="keywordflow">return</span>;</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> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(inputTensorInfo, mappings);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  std::unique_ptr<ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> </div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <a class="code" href="structarmnn_1_1_permute_queue_descriptor.xhtml">PermuteQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = <a class="code" href="structarmnn_1_1_permute_descriptor.xhtml">PermuteDescriptor</a>{mappings};</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get());</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());</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>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a2dcee0bc4bbae1f745324aed0f841a0a">CreatePermute</a>(queueDescriptor, workloadInfo);</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>  inputHandle->Allocate();</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  outputHandle->Allocate();</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputData);</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>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  workload->Execute();</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>  outputData.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&outputData[0], outputHandle.get());</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  inputTensorInfo = outputTensorInfo;</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> <span class="comment">//</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="comment">// Permute the input tensors so we can do a supported concatenation.</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="comment">// Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="comment">// at the front. Finally this function tells what the output shape</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="comment">// of the permuted concatenated tensor is going to be.</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="comment">//</span></div><div class="line"><a name="l00171"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a88cd4482bd3e898692dd42f3dcff60c0"> 171</a></span> <span class="keyword">template</span><<span class="keyword">typename</span> T> <span class="keywordtype">void</span> <a class="code" href="_concat_test_impl_8cpp.xhtml#a88cd4482bd3e898692dd42f3dcff60c0">PermuteInputsForConcat</a>(</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  std::vector<TensorInfo> & inputTensorInfos,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  std::vector<T *> & inputData,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  std::vector<std::vector<T>> & inputDataStorage,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> & permuteVector,</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> & concatDim,</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> & outputTensorInfo)</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="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(inputTensorInfos.size() > 1,</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="stringliteral">"Expecting more than one tensor to be concatenated here"</span>);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> </div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> numDims = 0;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nthInput = 0;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> identity({0, 1, 2});</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> </div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  std::pair<PermutationVector, PermutationVector> permutations =</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  std::make_pair(identity, identity);</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>  inputDataStorage.resize(inputData.size());</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> </div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> && tensorInfo : inputTensorInfos)</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  {</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  <span class="keywordflow">if</span> (numDims == 0)</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>  numDims = tensorInfo.GetShape().GetNumDimensions();</div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <a class="code" href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8">Generate3dPermuteVectorForConcat</a>(numDims, concatDim, permutations);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  <span class="comment">// Store the reverese permutation.</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  permuteVector = permutations.second;</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(!permuteVector.<a class="code" href="classarmnn_1_1_permutation_vector.xhtml#aae44e4154aa80fba7616747450ff69d5">IsEqual</a>(identity),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="stringliteral">"Test logic error, we don't need permutation, so we shouldn't arrive here"</span>);</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="keywordflow">else</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  {</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(numDims == tensorInfo.GetShape().GetNumDimensions(),</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="stringliteral">"All inputs must have the same number of dimensions"</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"> 213</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> newTensorInfo = tensorInfo;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  newTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(<a class="code" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a>(tensorInfo.GetShape()));</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>  PermuteTensorData<T>(workloadFactory,</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  memoryManager,</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  tensorHandleFactory,</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  permutations.first,</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  newTensorInfo,</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  inputData[nthInput],</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  inputDataStorage[nthInput]);</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>  inputData[nthInput] = inputDataStorage[nthInput].data();</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  inputTensorInfos[nthInput] = newTensorInfo;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> </div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span>  ++nthInput;</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> </div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">SetShape</a>(</div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <a class="code" href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a>(</div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <a class="code" href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a>(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>()),</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  permutations.first));</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> }</div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> </div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="comment">//</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span> <span class="comment">// This is the pair of PermuteInputsForConcat(...) which permutes back</span></div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span> <span class="comment">// the output of the concatenation so we can check it against an expected</span></div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> <span class="comment">// output.</span></div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> <span class="comment">//</span></div><div class="line"><a name="l00241"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#af61d63d5fc13eca28851f3fa65289c47"> 241</a></span> <span class="keyword">template</span> <<span class="keyword">typename</span> T> <span class="keywordtype">void</span> <a class="code" href="_concat_test_impl_8cpp.xhtml#af61d63d5fc13eca28851f3fa65289c47">PermuteOutputForConcat</a>(</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> & tensorInfo,</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> & permuteVector,</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  std::unique_ptr<ITensorHandle> && inputDataHandle,</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  T * data)</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> {</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(data != <span class="keyword">nullptr</span>, <span class="stringliteral">"data must not be null"</span>);</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="keywordflow">if</span> (data == <span class="keyword">nullptr</span>)</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  {</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="comment">// Nullptr is an error in the test. By returning without doing the permutation</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  <span class="comment">// I expect the caller to fail the test. It still makes sense to report this as</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  <span class="comment">// an assert for Debug builds.</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="keywordflow">return</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> resultTensorInfo = tensorInfo;</div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  std::vector<T> inputData(tensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  std::vector<T> outputData;</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&inputData[0], inputDataHandle.get());</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> </div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  PermuteTensorData<T>(workloadFactory,</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  memoryManager,</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  tensorHandleFactory,</div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  permuteVector,</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  resultTensorInfo,</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  &inputData[0],</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  outputData);</div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> </div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span>  ::memcpy(data, &outputData[0], <span class="keyword">sizeof</span>(T)*outputData.size());</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</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"><a class="line" href="_concat_test_impl_8cpp.xhtml#a1035b2d9f77d4a2200919945e1502de2"> 276</a></span> <span class="keyword">template</span><<span class="keyword">typename</span> T> <span class="keywordtype">void</span> <a class="code" href="namespacearmnn.xhtml#a1deafe1b2777bcaadefe2371b3ebbb27">Concatenate</a>(</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  std::initializer_list<const TensorInfo> inputTensorInfosOrig,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  std::initializer_list<T *> inputsOrig,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfoOrig,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  T * output,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> concatDim,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> {</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <a class="code" href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a>(output != <span class="keyword">nullptr</span>, <span class="stringliteral">"output must not be null"</span>);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordflow">if</span> (output == <span class="keyword">nullptr</span>)</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="comment">// Nullptr is an error in the test. By returning without doing the permutation</span></div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// I expect the caller to fail the test. It still makes sense to report this as</span></div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// an assert for Debug builds.</span></div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordflow">return</span>;</div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  }</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> </div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="comment">// Saves a copy of the parameters which we might need to change.</span></div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  std::vector<TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end());</div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  std::vector<T *> inputs = inputsOrig;</div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo = outputTensorInfoOrig;</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> </div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  <a class="code" href="classarmnn_1_1_permutation_vector.xhtml">PermutationVector</a> permuteVector{0, 1, 2};</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> </div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  <span class="comment">// Holds and automatically releases memory for the reshaped input data.</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  std::vector<std::vector<T>> tmpInputDataStorage;</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="keyword">const</span> <span class="keywordtype">size_t</span> inputCount = inputTensorInfos.size();</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="keywordtype">bool</span> needPermuteForConcat = <a class="code" href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596">NeedPermuteForConcat</a>(inputTensorInfos, concatDim);</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">if</span> (needPermuteForConcat)</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>  <span class="comment">//</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  <span class="comment">// We need to permute the inputs, because concatenation along</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  <span class="comment">// the requested axis is not supported.</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  <span class="comment">//</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span>  PermuteInputsForConcat<T>(workloadFactory,</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span>  memoryManager,</div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  tensorHandleFactory,</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  inputTensorInfos,</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  inputs,</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  tmpInputDataStorage,</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  permuteVector,</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  concatDim,</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  outputTensorInfo);</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  }</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> </div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  std::vector<std::unique_ptr<ITensorHandle>> inputHandles;</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  inputHandles.reserve(inputCount);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> </div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</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>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> viewsDescriptor = <a class="code" href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147">CreateDescriptorForConcat</a>(inputTensorInfos, concatDim);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = viewsDescriptor;</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span> </div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span>  <span class="keywordflow">if</span> (useSubtensor)</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>  queueDescriptor.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.reserve(viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a35546e7b56e6e972a495b48748478ede">GetNumViews</a>());</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a35546e7b56e6e972a495b48748478ede">GetNumViews</a>(); ++i)</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>  queueDescriptor.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.emplace_back(std::vector<unsigned int>(viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">GetViewOrigin</a>(i),</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">GetViewOrigin</a>(i) + viewsDescriptor.<a class="code" href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">GetNumDimensions</a>()));</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> </div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> </div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keyword">const</span> <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputCount; ++i)</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>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& inputTensorInfo = inputTensorInfos[i];</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  std::unique_ptr<ITensorHandle> inputHandle =</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  subTensorsSupported ?</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle,</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  inputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">GetShape</a>(),</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[i].m_Origin.data()) :</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo);</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  inputHandles.emplace_back(std::move(inputHandle));</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  }</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span> </div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span> </div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  }</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  {</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputCount; ++i)</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>  std::unique_ptr<ITensorHandle> inputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfos[i]);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  inputHandles.emplace_back(std::move(inputHandle));</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  }</div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  }</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="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < inputCount; ++i)</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>  AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].<span class="keyword">get</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> </div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get());</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>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(queueDescriptor, workloadInfo);</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">for</span> (<span class="keyword">auto</span>& inputHandle : inputHandles)</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>  inputHandle->Allocate();</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  }</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> </div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  outputHandle->Allocate();</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="keywordtype">unsigned</span> <span class="keywordtype">int</span> nextInputId = 0;</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span>& inputHandle : inputHandles)</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>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle.get(), inputs[nextInputId]);</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  ++nextInputId;</div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  }</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  workload->Execute();</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> </div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keywordflow">if</span> (needPermuteForConcat)</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  {</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  PermuteOutputForConcat<T>(workloadFactory,</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  memoryManager,</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  tensorHandleFactory,</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  outputTensorInfo,</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  permuteVector,</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  std::move(outputHandle),</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  output);</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  }</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <span class="keywordflow">else</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"> 413</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(output, outputHandle.get());</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> }</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> <span class="comment">//</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> <span class="comment">// Implementation templates</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> <span class="comment">//</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> </div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00422"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a6f4024f1c2bb14620c566ede118e0c4b"> 422</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 1></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a6f4024f1c2bb14620c566ede118e0c4b">Concat1dTestImpl</a>(</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  int32_t qOffset)</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> {</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> </div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 1.0f, 2.0f, 3.0f }, qScale, qOffset));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 4.0f, 5.0f, 6.0f }, qScale, qOffset));</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>({ 7.0f, 8.0f, 9.0f }, qScale, qOffset));</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span> </div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 9 }, ArmnnType, qScale, qOffset);</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>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 1></a> result(outputTensorInfo);</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>  std::vector<T> output;</div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  { inputTensorInfo, inputTensorInfo, inputTensorInfo },</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  outputTensorInfo,</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  output.data(),</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  0,</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span> </div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  result.output = MakeTensor<T, 1>(outputTensorInfo, output);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  {</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f</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>  qScale, qOffset));</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> </div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> }</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> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00460"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a11311cacab78cfb69270c6fba87702e7"> 460</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a11311cacab78cfb69270c6fba87702e7">Concat2dTestImpl</a>(</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension,</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <span class="keyword">const</span> int32_t qOffset)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> </div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  {</div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> </div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  },</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  qScale, qOffset));</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> </div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  {</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> </div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  },</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span>  qScale, qOffset));</div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> </div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  {</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> </div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  },</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  qScale, qOffset));</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span> </div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> </div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  std::vector<T> output;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  output.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  { inputTensorInfo, inputTensorInfo, inputTensorInfo },</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  outputTensorInfo,</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  output.data(),</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  dimension,</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a> = MakeTensor<T, 2>(outputTensorInfo, output);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span> }</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span> </div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00518"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a01c3919ddf83b3f3c527cc8433bbddff"> 518</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a01c3919ddf83b3f3c527cc8433bbddff">Concat2dDim0TestImpl</a>(</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  int32_t qOffset)</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span> {</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result = Concat2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 0, qScale, qOffset);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  {</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span> </div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span> </div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <span class="comment">// Batch 2</span></div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> </div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <span class="comment">// Batch 3</span></div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> </div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <span class="comment">// Batch 4</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  <span class="comment">// Batch 5</span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  },</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  qScale, qOffset));</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> </div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> }</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span> </div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00556"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#ae36ae7043e1761dcfcac538fa29c47d6"> 556</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ae36ae7043e1761dcfcac538fa29c47d6">Concat2dDim1TestImpl</a>(</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  int32_t qOffset)</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> {</div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> </div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result = Concat2dTestImpl<ArmnnType>(</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 1, qScale, qOffset);</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span> </div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span>  {</div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> </div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  },</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  qScale, qOffset));</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> </div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> }</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> </div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00582"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#aa298272bc356453c2814543fd0e2cb5f"> 582</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aa298272bc356453c2814543fd0e2cb5f">Concat2dDim0DiffInputDimsTestImpl</a>(</div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  int32_t qOffset)</div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> {</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  {</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> </div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  },</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  qScale, qOffset));</div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> </div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 3, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  {</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span>  4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span> </div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> </div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  },</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  qScale, qOffset));</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 1, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  {</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  },</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  qScale, qOffset));</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> </div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  std::vector<T> output;</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  outputTensorInfo,</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  output.data(),</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  0,</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span> </div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  result.output = MakeTensor<T, 2>(outputTensorInfo, output);</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  {</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span> </div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span> </div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  <span class="comment">// Batch 2</span></div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="comment">// Batch 3</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span> </div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  <span class="comment">// Batch 4</span></div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  <span class="comment">// Batch 5</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  },</div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  qScale, qOffset));</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span> </div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> }</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span> </div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00662"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#af1042834da8c73c7d8f46160b0b73b45"> 662</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#af1042834da8c73c7d8f46160b0b73b45">Concat2dDim1DiffInputDimsTestImpl</a>(</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  int32_t qOffset)</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> {</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span>  {</div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  },</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  qScale, qOffset));</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> </div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 2, 5 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  {</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span>  4.0f, 5.0f, 6.0f, 7.0f, 8.0f,</div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> </div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span>  13.0f, 14.0f, 15.0f, 16.0f, 17.0f,</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  },</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span>  qScale, qOffset));</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> </div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 2, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  {</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  9.0f,</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> </div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span>  18.0f</div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  },</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span>  qScale, qOffset));</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> </div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 9 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 2></a> result(outputTensorInfo);</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  std::vector<T> output;</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  outputTensorInfo,</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  output.data(),</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  1,</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span> </div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  result.output = MakeTensor<T, 2>(outputTensorInfo, output);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  {</div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <span class="comment">// Batch 0</span></div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  <span class="comment">// Batch 1</span></div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  },</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  qScale, qOffset));</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span> </div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span> }</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span> </div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00730"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#ac8fc5aa4f5e286f98708650f6a8682a6"> 730</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ac8fc5aa4f5e286f98708650f6a8682a6">Concat3dTestImpl</a>(</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension,</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span>  <span class="keywordtype">bool</span> useSubtensor,</div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span>  int32_t qOffset)</div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span> {</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> </div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  {</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  1.0f, 2.0f,</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> </div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  3.0f, 4.0f,</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  5.0f, 6.0f,</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span> </div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  19.0f, 20.0f,</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> </div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  21.0f, 22.0f,</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span> </div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  23.0f, 24.0f</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span>  },</div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span>  qScale, qOffset));</div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> </div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  {</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  7.0f, 8.0f,</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> </div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  9.0f, 10.0f,</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span> </div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  11.0f, 12.0f,</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span> </div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  25.0f, 26.0f,</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span> </div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  27.0f, 28.0f,</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span> </div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  29.0f, 30.0f</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span>  },</div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span>  qScale, qOffset));</div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> </div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  {</div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  13.0f, 14.0f,</div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> </div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span>  15.0f, 16.0f,</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> </div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span>  17.0f, 18.0f,</div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span>  31.0f, 32.0f,</div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> </div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span>  33.0f, 34.0f,</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> </div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  35.0f, 36.0f</div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  },</div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span>  qScale, qOffset));</div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> </div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result(outputTensorInfo);</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span> </div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span>  std::vector<T> output;</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  output.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  { inputTensorInfo, inputTensorInfo, inputTensorInfo },</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span>  outputTensorInfo,</div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  output.data(),</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span>  dimension,</div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  useSubtensor);</div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> </div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a> = MakeTensor<T, 3>(outputTensorInfo, output);</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span> }</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> </div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00825"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a6552cacc8f771f0e210949d56193c8d7"> 825</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a6552cacc8f771f0e210949d56193c8d7">Concat3dDim0TestImpl</a>(</div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  int32_t qOffset)</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span> {</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result = Concat3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 0, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span> </div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  {</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  1.0f, 2.0f,</div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> </div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span>  3.0f, 4.0f,</div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> </div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  5.0f, 6.0f,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span> </div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  19.0f, 20.0f,</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span> </div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  21.0f, 22.0f,</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span>  23.0f, 24.0f,</div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span> </div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  <span class="comment">// Batch 2, Channel 0</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  7.0f, 8.0f,</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  <span class="comment">// Batch 2, Channel 1</span></div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  9.0f, 10.0f,</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span> </div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span>  <span class="comment">// Batch 2, Channel 2</span></div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  11.0f, 12.0f,</div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span> </div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  <span class="comment">// Batch 3, Channel 0</span></div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span>  25.0f, 26.0f,</div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span> </div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <span class="comment">// Batch 3, Channel 1</span></div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  27.0f, 28.0f,</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span> </div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="comment">// Batch 3, Channel 2</span></div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span>  29.0f, 30.0f,</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span> </div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span>  <span class="comment">// Batch 4, Channel 0</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span>  13.0f, 14.0f,</div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> </div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span>  <span class="comment">// Batch 4, Channel 1</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span>  15.0f, 16.0f,</div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span> </div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span>  <span class="comment">// Batch 4, Channel 2</span></div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span>  17.0f, 18.0f,</div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> </div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span>  <span class="comment">// Batch 5, Channel 0</span></div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span>  31.0f, 32.0f,</div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> </div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="comment">// Batch 5, Channel 1</span></div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  33.0f, 34.0f,</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span> </div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span>  <span class="comment">// Batch 5, Channel 2</span></div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span>  35.0f, 36.0f</div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span>  },</div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  qScale, qOffset));</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> </div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span> }</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span> </div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00899"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a4e2a03f3c7e369896b804e963cfca10c"> 899</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a4e2a03f3c7e369896b804e963cfca10c">Concat3dDim1TestImpl</a>(</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span>  int32_t qOffset)</div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span> {</div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 9, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> </div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result = Concat3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 1, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> </div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span>  {</div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  1.0f, 2.0f,</div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> </div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  3.0f, 4.0f,</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span> </div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  5.0f, 6.0f,</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> </div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  <span class="comment">// Batch 0, Channel 3</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span>  7.0f, 8.0f,</div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span> </div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <span class="comment">// Batch 0, Channel 4</span></div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span>  9.0f, 10.0f,</div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span> </div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <span class="comment">// Batch 0, Channel 5</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  11.0f, 12.0f,</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> </div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="comment">// Batch 0, Channel 6</span></div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span>  13.0f, 14.0f,</div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> </div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  <span class="comment">// Batch 0, Channel 7</span></div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  15.0f, 16.0f,</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> </div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  <span class="comment">// Batch 0, Channel 8</span></div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  17.0f, 18.0f,</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  19.0f, 20.0f,</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> </div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span>  21.0f, 22.0f,</div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> </div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  23.0f, 24.0f,</div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span> </div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  <span class="comment">// Batch 1, Channel 3</span></div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  25.0f, 26.0f,</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> </div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span>  <span class="comment">// Batch 1, Channel 4</span></div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  27.0f, 28.0f,</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span> </div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="comment">// Batch 1, Channel 5</span></div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  29.0f, 30.0f,</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> </div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="comment">// Batch 1, Channel 6</span></div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  31.0f, 32.0f,</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> </div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  <span class="comment">// Batch 1, Channel 7</span></div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  33.0f, 34.0f,</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span> </div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <span class="comment">// Batch 1, Channel 8</span></div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  35.0f, 36.0f</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  },</div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  qScale, qOffset));</div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span> </div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span> }</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> </div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l00973"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#af7bc28cdb723e0cea4c0b860f40fa47e"> 973</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#af7bc28cdb723e0cea4c0b860f40fa47e">Concat3dDim2TestImpl</a>(</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span>  <span class="keywordtype">bool</span> useSubtensor,</div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  int32_t qOffset)</div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span> {</div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> </div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result = Concat3dTestImpl<ArmnnType>(</div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 2, useSubtensor, qScale, qOffset);</div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span> </div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span>  {</div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f,</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f,</div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span> </div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span>  5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f,</div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> </div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f,</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> </div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f,</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> </div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f,</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  },</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  qScale, qOffset));</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span> </div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span> }</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> </div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01012"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#aa97b8ca791f9380d2b678ff2ec923985"> 1012</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aa97b8ca791f9380d2b678ff2ec923985">Concat3dDim0DiffInputDimsTestImpl</a>(</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  int32_t qOffset)</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> {</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  {</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span>  1.0f, 2.0f,</div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> </div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  3.0f, 4.0f,</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span> </div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span>  5.0f, 6.0f,</div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> </div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span>  19.0f, 20.0f,</div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> </div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span>  21.0f, 22.0f,</div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> </div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span>  23.0f, 24.0f</div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span>  },</div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span>  qScale, qOffset));</div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> </div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 1, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span>  {</div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span>  7.0f, 8.0f,</div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span> </div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span>  9.0f, 10.0f,</div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> </div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span>  11.0f, 12.0f,</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  },</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  qScale, qOffset));</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> </div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 3, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  {</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span>  25.0f, 26.0f,</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> </div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  27.0f, 28.0f,</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> </div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  29.0f, 30.0f,</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> </div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  13.0f, 14.0f,</div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> </div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  15.0f, 16.0f,</div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> </div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span>  17.0f, 18.0f,</div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> </div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  <span class="comment">// Batch 2, Channel 0</span></div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  31.0f, 32.0f,</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> </div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span>  <span class="comment">// Batch 2, Channel 1</span></div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  33.0f, 34.0f,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> </div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  <span class="comment">// Batch 2, Channel 2</span></div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span>  35.0f, 36.0f</div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  },</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span>  qScale, qOffset));</div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span> </div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 6, 3, 2 }, ArmnnType);</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result(outputTensorInfo);</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> </div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span>  std::vector<T> output;</div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span>  { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span>  outputTensorInfo,</div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span>  output.data(),</div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span>  0,</div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> </div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span>  result.output = MakeTensor<T, 3>(outputTensorInfo, output);</div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span>  result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span>  {</div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span>  1.0f, 2.0f,</div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> </div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span>  3.0f, 4.0f,</div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> </div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span>  5.0f, 6.0f,</div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> </div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span>  19.0f, 20.0f,</div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> </div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span>  21.0f, 22.0f,</div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> </div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span>  23.0f, 24.0f,</div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> </div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span>  <span class="comment">// Batch 2, Channel 0</span></div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span>  7.0f, 8.0f,</div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> </div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span>  <span class="comment">// Batch 2, Channel 1</span></div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span>  9.0f, 10.0f,</div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span> </div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span>  <span class="comment">// Batch 2, Channel 2</span></div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span>  11.0f, 12.0f,</div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> </div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <span class="comment">// Batch 3, Channel 0</span></div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  25.0f, 26.0f,</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span> </div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span>  <span class="comment">// Batch 3, Channel 1</span></div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  27.0f, 28.0f,</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span> </div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span>  <span class="comment">// Batch 3, Channel 2</span></div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span>  29.0f, 30.0f,</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> </div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <span class="comment">// Batch 4, Channel 0</span></div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  13.0f, 14.0f,</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span> </div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span>  <span class="comment">// Batch 4, Channel 1</span></div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  15.0f, 16.0f,</div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span> </div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <span class="comment">// Batch 4, Channel 2</span></div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span>  17.0f, 18.0f,</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span> </div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <span class="comment">// Batch 5, Channel 0</span></div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span>  31.0f, 32.0f,</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span> </div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span>  <span class="comment">// Batch 5, Channel 1</span></div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  33.0f, 34.0f,</div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span> </div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="comment">// Batch 5, Channel 2</span></div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  35.0f, 36.0f</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  },</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  qScale, qOffset));</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span> </div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span> }</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> </div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01164"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a8ce943ec73def7b13345b918d787d7ca"> 1164</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a8ce943ec73def7b13345b918d787d7ca">Concat3dDim1DiffInputDimsTestImpl</a>(</div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  int32_t qOffset)</div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span> {</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  {</div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  1.0f, 2.0f,</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> </div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  3.0f, 4.0f,</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span> </div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  5.0f, 6.0f,</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span> </div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  19.0f, 20.0f,</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span> </div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  21.0f, 22.0f,</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span> </div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  23.0f, 24.0f</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  },</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  qScale, qOffset));</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span> </div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 2, 4, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  {</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  7.0f, 8.0f,</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span> </div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span>  9.0f, 10.0f,</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span> </div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  11.0f, 12.0f,</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> </div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  <span class="comment">// Batch 0, Channel 3</span></div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  25.0f, 26.0f,</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span> </div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  27.0f, 28.0f,</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> </div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  29.0f, 30.0f,</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> </div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span>  13.0f, 14.0f,</div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> </div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span>  <span class="comment">// Batch 1, Channel 3</span></div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span>  15.0f, 16.0f,</div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span>  },</div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span>  qScale, qOffset));</div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> </div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 2, 1, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span>  {</div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span>  17.0f, 18.0f,</div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> </div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span>  31.0f, 32.0f,</div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span>  },</div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span>  qScale, qOffset));</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span> </div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 8, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result(outputTensorInfo);</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span> </div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span>  std::vector<T> output;</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  outputTensorInfo,</div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  output.data(),</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  1,</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> </div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  result.output = MakeTensor<T, 3>(outputTensorInfo, output);</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span>  {</div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  1.0f, 2.0f,</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> </div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  3.0f, 4.0f,</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span> </div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  5.0f, 6.0f,</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span> </div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  <span class="comment">// Batch 0, Channel 3</span></div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  7.0f, 8.0f,</div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> </div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span>  <span class="comment">// Batch 0, Channel 4</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  9.0f, 10.0f,</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span> </div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <span class="comment">// Batch 0, Channel 5</span></div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  11.0f, 12.0f,</div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span> </div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  <span class="comment">// Batch 0, Channel 6</span></div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  25.0f, 26.0f,</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span> </div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span>  <span class="comment">// Batch 0, Channel 7</span></div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  17.0f, 18.0f,</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span> </div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span>  19.0f, 20.0f,</div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> </div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  21.0f, 22.0f,</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span> </div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  23.0f, 24.0f,</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span> </div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span>  <span class="comment">// Batch 1, Channel 3</span></div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span>  27.0f, 28.0f,</div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span> </div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <span class="comment">// Batch 1, Channel 4</span></div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  29.0f, 30.0f,</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span> </div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  <span class="comment">// Batch 1, Channel 5</span></div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span>  13.0f, 14.0f,</div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span> </div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  <span class="comment">// Batch 1, Channel 6</span></div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  15.0f, 16.0f,</div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span> </div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  <span class="comment">// Batch 1, Channel 7</span></div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  31.0f, 32.0f,</div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  },</div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  qScale, qOffset));</div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span> </div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span> }</div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> </div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01304"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a08b924fe6933a0c221cc0194dffb2b7d"> 1304</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a08b924fe6933a0c221cc0194dffb2b7d">Concat3dDim2DiffInputDimsTestImpl</a>(</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  <span class="keywordtype">bool</span> useSubtensor,</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span>  int32_t qOffset)</div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span> {</div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input0TensorInfo({ 2, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  {</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span>  1.0f, 2.0f,</div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span> </div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  3.0f, 4.0f,</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span> </div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  5.0f, 6.0f,</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span> </div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span>  19.0f, 20.0f,</div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span>  21.0f, 22.0f,</div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span> </div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span>  23.0f, 24.0f</div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span>  },</div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span>  qScale, qOffset));</div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> </div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input1TensorInfo({ 2, 3, 1 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span>  {</div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span>  7.0f,</div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span> </div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span>  9.0f,</div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span> </div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span>  11.0f,</div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> </div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span>  25.0f,</div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> </div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span>  27.0f,</div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span> </div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span>  29.0f</div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span>  },</div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span>  qScale, qOffset));</div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span> </div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> input2TensorInfo({ 2, 3, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span>  {</div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span>  13.0f, 14.0f, 50.0f,</div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span> </div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span>  15.0f, 16.0f, 51.0f,</div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span> </div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span>  17.0f, 18.0f, 52.0f,</div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> </div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span>  31.0f, 32.0f, 53.0f,</div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> </div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span>  33.0f, 34.0f, 54.0f,</div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span> </div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span>  35.0f, 36.0f, 55.0f,</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  },</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  qScale, qOffset));</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span> </div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 2, 3, 6 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> result(outputTensorInfo);</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span> </div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span>  std::vector<T> output;</div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span>  Concatenate<T>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  { input0TensorInfo, input1TensorInfo, input2TensorInfo },</div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  { input0.data(), input1.data(), input2.data() },</div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span>  outputTensorInfo,</div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  output.data(),</div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  2,</div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span>  useSubtensor);</div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span> </div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span>  result.output = MakeTensor<T, 3>(outputTensorInfo, output);</div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span>  result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  {</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span>  <span class="comment">// Batch 0, Channel 0</span></div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f,</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span> </div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  <span class="comment">// Batch 0, Channel 1</span></div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span>  3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f,</div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span> </div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  <span class="comment">// Batch 0, Channel 2</span></div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span>  5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f,</div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span> </div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span>  <span class="comment">// Batch 1, Channel 0</span></div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f,</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span> </div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span>  <span class="comment">// Batch 1, Channel 1</span></div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span>  21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f,</div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span> </div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  <span class="comment">// Batch 1, Channel 2</span></div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f,</div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  },</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  qScale, qOffset));</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> </div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span> }</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span> </div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01421"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a597f0b89f3fb6389802de4a51c979b17"> 1421</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a597f0b89f3fb6389802de4a51c979b17">Concat4dTestImpl</a>(</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a>& outputTensorInfo,</div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension,</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  <span class="keywordtype">bool</span> useSubtensor,</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  int32_t qOffset)</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span> {</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span> </div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  {</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  1.0f, 2.0f,</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  3.0f, 4.0f,</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  5.0f, 6.0f,</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span>  7.0f, 8.0f,</div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span>  9.0f, 10.0f,</div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  11.0f, 12.0f</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span>  },</div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  qScale, qOffset));</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span> </div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  {</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  11.0f, 12.0f,</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  13.0f, 14.0f,</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  15.0f, 16.0f,</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  17.0f, 18.0f,</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span>  19.0f, 20.0f,</div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span>  21.0f, 22.0f</div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  },</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span>  qScale, qOffset));</div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> </div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span>  {</div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  21.0f, 22.0f,</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  23.0f, 24.0f,</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span>  25.0f, 26.0f,</div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  27.0f, 28.0f,</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  29.0f, 30.0f,</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span>  31.0f, 32.0f</div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  },</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span>  qScale, qOffset));</div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span> </div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(outputTensorInfo);</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> </div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span>  std::vector<T> output;</div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span>  output.resize(outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">GetNumElements</a>());</div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span> </div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span>  Concatenate<T>(workloadFactory,</div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span>  memoryManager,</div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span>  tensorHandleFactory,</div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span>  {inputTensorInfo, inputTensorInfo, inputTensorInfo},</div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span>  {input0.data(), input1.data(), input2.data()},</div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span>  outputTensorInfo,</div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span>  output.data(),</div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span>  dimension,</div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span>  useSubtensor);</div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span> </div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span>  result.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a> = MakeTensor<T, 4>(outputTensorInfo, output);</div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span> }</div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> </div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01486"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a198a5812a5bf73d54928ec978f8cf7af"> 1486</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a198a5812a5bf73d54928ec978f8cf7af">Concat4dDim0TestImpl</a>(</div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span>  int32_t qOffset)</div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span> {</div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span> </div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result = Concat4dTestImpl<ArmnnType>(</div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 0, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span> </div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span>  {</div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span>  1.0f, 2.0f,</div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span>  3.0f, 4.0f,</div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span>  5.0f, 6.0f,</div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span>  7.0f, 8.0f,</div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span>  9.0f, 10.0f,</div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span>  11.0f, 12.0f,</div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span> </div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span>  11.0f, 12.0f,</div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span>  13.0f, 14.0f,</div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span>  15.0f, 16.0f,</div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span>  17.0f, 18.0f,</div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span>  19.0f, 20.0f,</div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span>  21.0f, 22.0f,</div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span> </div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span>  21.0f, 22.0f,</div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span>  23.0f, 24.0f,</div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span>  25.0f, 26.0f,</div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span>  27.0f, 28.0f,</div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span>  29.0f, 30.0f,</div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span>  31.0f, 32.0f</div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span>  },</div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span>  qScale, qOffset));</div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span> </div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span> }</div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> </div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01527"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a65992de6287260bcb4be7a4852a22c9c"> 1527</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a65992de6287260bcb4be7a4852a22c9c">Concat4dDim1TestImpl</a>(</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span>  int32_t qOffset)</div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span> {</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 9, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span> </div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result = Concat4dTestImpl<ArmnnType>(</div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 1, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span> </div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span>  {</div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  1.0f, 2.0f,</div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span>  3.0f, 4.0f,</div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span>  5.0f, 6.0f,</div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  7.0f, 8.0f,</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span>  9.0f, 10.0f,</div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span>  11.0f, 12.0f,</div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span> </div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span>  11.0f, 12.0f,</div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  13.0f, 14.0f,</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span>  15.0f, 16.0f,</div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span>  17.0f, 18.0f,</div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  19.0f, 20.0f,</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  21.0f, 22.0f,</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span> </div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span>  21.0f, 22.0f,</div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  23.0f, 24.0f,</div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  25.0f, 26.0f,</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span>  27.0f, 28.0f,</div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  29.0f, 30.0f,</div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  31.0f, 32.0f</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  },</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  qScale, qOffset));</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span> </div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span> }</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span> </div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01568"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a0047c671718849c74d3bdbd38385f807"> 1568</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a0047c671718849c74d3bdbd38385f807">Concat4dDim2TestImpl</a>(</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  int32_t qOffset)</div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span> {</div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 6, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span> </div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result = Concat4dTestImpl<ArmnnType>(</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 2, <span class="keyword">true</span>, qScale, qOffset);</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span> </div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  {</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  1.0f, 2.0f,</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span>  3.0f, 4.0f,</div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  11.0f, 12.0f,</div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  13.0f, 14.0f,</div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  21.0f, 22.0f,</div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  23.0f, 24.0f,</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span> </div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  5.0f, 6.0f,</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  7.0f, 8.0f,</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  15.0f, 16.0f,</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span>  17.0f, 18.0f,</div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  25.0f, 26.0f,</div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  27.0f, 28.0f,</div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span> </div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  9.0f, 10.0f,</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  11.0f, 12.0f,</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  19.0f, 20.0f,</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  21.0f, 22.0f,</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  29.0f, 30.0f,</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span>  31.0f, 32.0f</div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  },</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  qScale, qOffset));</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span> </div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span> }</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span> </div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01609"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a9b4987fbd28822cfde9a5fcbf50793a5"> 1609</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a9b4987fbd28822cfde9a5fcbf50793a5">Concat4dDim3TestImpl</a>(</div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span>  int32_t qOffset,</div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span> {</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 2, 6 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span> </div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result = Concat4dTestImpl<ArmnnType>(</div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span>  workloadFactory, memoryManager, tensorHandleFactory, outputTensorInfo, 3, useSubtensor, qScale, qOffset);</div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span> </div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span>  result.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  {</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span>  1.0f, 2.0f,</div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span>  11.0f, 12.0f,</div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  21.0f, 22.0f,</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  3.0f, 4.0f,</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span>  13.0f, 14.0f,</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span>  23.0f, 24.0f,</div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span> </div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span>  5.0f, 6.0f,</div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span>  15.0f, 16.0f,</div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span>  25.0f, 26.0f,</div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span>  7.0f, 8.0f,</div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span>  17.0f, 18.0f,</div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span>  27.0f, 28.0f,</div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span> </div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span>  9.0f, 10.0f,</div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span>  19.0f, 20.0f,</div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span>  29.0f, 30.0f,</div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span>  11.0f, 12.0f,</div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span>  21.0f, 22.0f,</div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span>  31.0f, 32.0f</div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span>  },</div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span>  qScale, qOffset));</div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> </div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span> }</div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span> </div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01651"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a4b3d058400cdae1f9f42bca0bd4cdd78"> 1651</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a4b3d058400cdae1f9f42bca0bd4cdd78">Concat4dDiffShapeDim0TestImpl</a>(</div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span>  int32_t qOffset)</div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span> {</div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 0u;</div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span> </div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(</div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span>  {</div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span>  1.0f, 2.0f,</div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span>  3.0f, 4.0f,</div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span>  5.0f, 6.0f,</div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span>  7.0f, 8.0f,</div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span>  9.0f, 10.0f,</div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span>  11.0f, 12.0f</div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span>  },</div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span>  qScale, qOffset));</div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span> </div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 2, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span> </div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(</div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span>  {</div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span>  11.0f, 12.0f,</div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span>  13.0f, 14.0f,</div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span>  15.0f, 16.0f,</div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span>  17.0f, 18.0f,</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  19.0f, 20.0f,</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  21.0f, 22.0f,</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span> </div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span>  21.0f, 22.0f,</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  23.0f, 24.0f,</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span>  25.0f, 26.0f,</div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  27.0f, 28.0f,</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span>  29.0f, 30.0f,</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  31.0f, 32.0f</div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  },</div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span>  qScale, qOffset));</div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span> </div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 3, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> </div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(outputTensorInfo);</div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span> </div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span>  std::vector<T> output;</div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span>  Concatenate<T>(workloadFactory,</div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span>  memoryManager,</div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  tensorHandleFactory,</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span>  {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  {input0.data(), input1.data()},</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span>  outputTensorInfo,</div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span>  output.data(),</div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  dimension,</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span> </div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span>  result.output = MakeTensor<T, 4>(outputTensorInfo, output);</div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  {</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  1.0f, 2.0f,</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  3.0f, 4.0f,</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  5.0f, 6.0f,</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span>  7.0f, 8.0f,</div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  9.0f, 10.0f,</div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  11.0f, 12.0f,</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span> </div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  11.0f, 12.0f,</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  13.0f, 14.0f,</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  15.0f, 16.0f,</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  17.0f, 18.0f,</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  19.0f, 20.0f,</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  21.0f, 22.0f,</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span> </div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span>  21.0f, 22.0f,</div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  23.0f, 24.0f,</div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  25.0f, 26.0f,</div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  27.0f, 28.0f,</div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  29.0f, 30.0f,</div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  31.0f, 32.0f</div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  },</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  qScale, qOffset));</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span> </div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span> }</div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span> </div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01738"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a8f747abd7bf17b57c3178c077a8e1949"> 1738</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a8f747abd7bf17b57c3178c077a8e1949">Concat4dDiffShapeDim1TestImpl</a>(</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  int32_t qOffset)</div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span> {</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 1u;</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span> </div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  {</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span>  1.0f, 2.0f,</div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  3.0f, 4.0f,</div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  5.0f, 6.0f,</div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  7.0f, 8.0f,</div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  9.0f, 10.0f,</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  11.0f, 12.0f</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  },</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  qScale, qOffset));</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span> </div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 1, 2, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span> </div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(</div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  {</div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  11.0f, 12.0f,</div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  13.0f, 14.0f,</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  15.0f, 16.0f,</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  17.0f, 18.0f,</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  },</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  qScale, qOffset));</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span> </div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 5, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span> </div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(outputTensorInfo);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span> </div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  std::vector<T> output;</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  Concatenate<T>(workloadFactory,</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span>  memoryManager,</div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  tensorHandleFactory,</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  {input0.data(), input1.data()},</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  outputTensorInfo,</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  output.data(),</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  dimension,</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span> </div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  result.output = MakeTensor<T, 4>(outputTensorInfo, output);</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  {</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  1.0f, 2.0f,</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  3.0f, 4.0f,</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span>  5.0f, 6.0f,</div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  7.0f, 8.0f,</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  9.0f, 10.0f,</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  11.0f, 12.0f,</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  11.0f, 12.0f,</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  13.0f, 14.0f,</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  15.0f, 16.0f,</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span>  17.0f, 18.0f</div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  },</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  qScale, qOffset));</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span> </div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span> }</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span> </div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01806"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a05eed8a9797d93f01639ca03025058f0"> 1806</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a05eed8a9797d93f01639ca03025058f0">Concat4dDiffShapeDim2TestImpl</a>(</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  int32_t qOffset)</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span> {</div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 2u;</div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span> </div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span>  {</div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  1.0f, 2.0f,</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  3.0f, 4.0f,</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  5.0f, 6.0f,</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  7.0f, 8.0f,</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span>  9.0f, 10.0f,</div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span>  11.0f, 12.0f</div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span>  },</div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  qScale, qOffset));</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span> </div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 1, 3, 3, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(</div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span>  {</div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  11.0f, 12.0f,</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  13.0f, 14.0f,</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  15.0f, 16.0f,</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  17.0f, 18.0f,</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span>  19.0f, 20.0f,</div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span>  21.0f, 22.0f,</div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  23.0f, 24.0f,</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span>  25.0f, 26.0f,</div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  27.0f, 28.0f</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  },</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  qScale, qOffset));</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span> </div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 5, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(outputTensorInfo);</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span> </div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span>  std::vector<T> output;</div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span>  Concatenate<T>(workloadFactory,</div><div class="line"><a name="l01848"></a><span class="lineno"> 1848</span>  memoryManager,</div><div class="line"><a name="l01849"></a><span class="lineno"> 1849</span>  tensorHandleFactory,</div><div class="line"><a name="l01850"></a><span class="lineno"> 1850</span>  {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01851"></a><span class="lineno"> 1851</span>  {input0.data(), input1.data()},</div><div class="line"><a name="l01852"></a><span class="lineno"> 1852</span>  outputTensorInfo,</div><div class="line"><a name="l01853"></a><span class="lineno"> 1853</span>  output.data(),</div><div class="line"><a name="l01854"></a><span class="lineno"> 1854</span>  dimension,</div><div class="line"><a name="l01855"></a><span class="lineno"> 1855</span>  <span class="keyword">true</span>);</div><div class="line"><a name="l01856"></a><span class="lineno"> 1856</span> </div><div class="line"><a name="l01857"></a><span class="lineno"> 1857</span>  result.output = MakeTensor<T, 4>(outputTensorInfo, output);</div><div class="line"><a name="l01858"></a><span class="lineno"> 1858</span>  result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01859"></a><span class="lineno"> 1859</span>  {</div><div class="line"><a name="l01860"></a><span class="lineno"> 1860</span>  1.0f, 2.0f,</div><div class="line"><a name="l01861"></a><span class="lineno"> 1861</span>  3.0f, 4.0f,</div><div class="line"><a name="l01862"></a><span class="lineno"> 1862</span>  11.0f, 12.0f,</div><div class="line"><a name="l01863"></a><span class="lineno"> 1863</span>  13.0f, 14.0f,</div><div class="line"><a name="l01864"></a><span class="lineno"> 1864</span>  15.0f, 16.0f,</div><div class="line"><a name="l01865"></a><span class="lineno"> 1865</span> </div><div class="line"><a name="l01866"></a><span class="lineno"> 1866</span>  5.0f, 6.0f,</div><div class="line"><a name="l01867"></a><span class="lineno"> 1867</span>  7.0f, 8.0f,</div><div class="line"><a name="l01868"></a><span class="lineno"> 1868</span>  17.0f, 18.0f,</div><div class="line"><a name="l01869"></a><span class="lineno"> 1869</span>  19.0f, 20.0f,</div><div class="line"><a name="l01870"></a><span class="lineno"> 1870</span>  21.0f, 22.0f,</div><div class="line"><a name="l01871"></a><span class="lineno"> 1871</span> </div><div class="line"><a name="l01872"></a><span class="lineno"> 1872</span>  9.0f, 10.0f,</div><div class="line"><a name="l01873"></a><span class="lineno"> 1873</span>  11.0f, 12.0f,</div><div class="line"><a name="l01874"></a><span class="lineno"> 1874</span>  23.0f, 24.0f,</div><div class="line"><a name="l01875"></a><span class="lineno"> 1875</span>  25.0f, 26.0f,</div><div class="line"><a name="l01876"></a><span class="lineno"> 1876</span>  27.0f, 28.0f</div><div class="line"><a name="l01877"></a><span class="lineno"> 1877</span>  },</div><div class="line"><a name="l01878"></a><span class="lineno"> 1878</span>  qScale, qOffset));</div><div class="line"><a name="l01879"></a><span class="lineno"> 1879</span> </div><div class="line"><a name="l01880"></a><span class="lineno"> 1880</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01881"></a><span class="lineno"> 1881</span> }</div><div class="line"><a name="l01882"></a><span class="lineno"> 1882</span> </div><div class="line"><a name="l01883"></a><span class="lineno"> 1883</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T = ResolveType<ArmnnType>></div><div class="line"><a name="l01884"></a><span class="lineno"><a class="line" href="_concat_test_impl_8cpp.xhtml#a4a9959df06e443a3e38d234564ad7736"> 1884</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a4a9959df06e443a3e38d234564ad7736">Concat4dDiffShapeDim3TestImpl</a>(</div><div class="line"><a name="l01885"></a><span class="lineno"> 1885</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01886"></a><span class="lineno"> 1886</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01887"></a><span class="lineno"> 1887</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01888"></a><span class="lineno"> 1888</span>  <span class="keywordtype">float</span> qScale,</div><div class="line"><a name="l01889"></a><span class="lineno"> 1889</span>  int32_t qOffset,</div><div class="line"><a name="l01890"></a><span class="lineno"> 1890</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l01891"></a><span class="lineno"> 1891</span> {</div><div class="line"><a name="l01892"></a><span class="lineno"> 1892</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimension = 3u;</div><div class="line"><a name="l01893"></a><span class="lineno"> 1893</span> </div><div class="line"><a name="l01894"></a><span class="lineno"> 1894</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo0({ 1, 3, 2, 2 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01895"></a><span class="lineno"> 1895</span>  <span class="keyword">auto</span> input0 = MakeTensor<T, 4>(inputTensorInfo0, QuantizedVector<T>(</div><div class="line"><a name="l01896"></a><span class="lineno"> 1896</span>  {</div><div class="line"><a name="l01897"></a><span class="lineno"> 1897</span>  1.0f, 2.0f,</div><div class="line"><a name="l01898"></a><span class="lineno"> 1898</span>  3.0f, 4.0f,</div><div class="line"><a name="l01899"></a><span class="lineno"> 1899</span>  5.0f, 6.0f,</div><div class="line"><a name="l01900"></a><span class="lineno"> 1900</span>  7.0f, 8.0f,</div><div class="line"><a name="l01901"></a><span class="lineno"> 1901</span>  9.0f, 10.0f,</div><div class="line"><a name="l01902"></a><span class="lineno"> 1902</span>  11.0f, 12.0f</div><div class="line"><a name="l01903"></a><span class="lineno"> 1903</span>  },</div><div class="line"><a name="l01904"></a><span class="lineno"> 1904</span>  qScale, qOffset));</div><div class="line"><a name="l01905"></a><span class="lineno"> 1905</span> </div><div class="line"><a name="l01906"></a><span class="lineno"> 1906</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 1, 3, 2, 3 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01907"></a><span class="lineno"> 1907</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(</div><div class="line"><a name="l01908"></a><span class="lineno"> 1908</span>  {</div><div class="line"><a name="l01909"></a><span class="lineno"> 1909</span>  11.0f, 12.0f, 13.0f,</div><div class="line"><a name="l01910"></a><span class="lineno"> 1910</span>  14.0f, 15.0f, 16.0f,</div><div class="line"><a name="l01911"></a><span class="lineno"> 1911</span> </div><div class="line"><a name="l01912"></a><span class="lineno"> 1912</span>  17.0f, 18.0f, 19.0f,</div><div class="line"><a name="l01913"></a><span class="lineno"> 1913</span>  20.0f, 21.0f, 22.0f,</div><div class="line"><a name="l01914"></a><span class="lineno"> 1914</span> </div><div class="line"><a name="l01915"></a><span class="lineno"> 1915</span>  23.0f, 24.0f, 25.0f,</div><div class="line"><a name="l01916"></a><span class="lineno"> 1916</span>  26.0f, 27.0f, 28.0f</div><div class="line"><a name="l01917"></a><span class="lineno"> 1917</span>  },</div><div class="line"><a name="l01918"></a><span class="lineno"> 1918</span>  qScale, qOffset));</div><div class="line"><a name="l01919"></a><span class="lineno"> 1919</span> </div><div class="line"><a name="l01920"></a><span class="lineno"> 1920</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 1, 3, 2, 5 }, ArmnnType, qScale, qOffset);</div><div class="line"><a name="l01921"></a><span class="lineno"> 1921</span> </div><div class="line"><a name="l01922"></a><span class="lineno"> 1922</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 4></a> result(outputTensorInfo);</div><div class="line"><a name="l01923"></a><span class="lineno"> 1923</span> </div><div class="line"><a name="l01924"></a><span class="lineno"> 1924</span>  std::vector<T> output;</div><div class="line"><a name="l01925"></a><span class="lineno"> 1925</span>  output.resize(outputTensorInfo.GetNumElements());</div><div class="line"><a name="l01926"></a><span class="lineno"> 1926</span>  Concatenate<T>(workloadFactory,</div><div class="line"><a name="l01927"></a><span class="lineno"> 1927</span>  memoryManager,</div><div class="line"><a name="l01928"></a><span class="lineno"> 1928</span>  tensorHandleFactory,</div><div class="line"><a name="l01929"></a><span class="lineno"> 1929</span>  {inputTensorInfo0, inputTensorInfo1},</div><div class="line"><a name="l01930"></a><span class="lineno"> 1930</span>  {input0.data(), input1.data()},</div><div class="line"><a name="l01931"></a><span class="lineno"> 1931</span>  outputTensorInfo,</div><div class="line"><a name="l01932"></a><span class="lineno"> 1932</span>  output.data(),</div><div class="line"><a name="l01933"></a><span class="lineno"> 1933</span>  dimension,</div><div class="line"><a name="l01934"></a><span class="lineno"> 1934</span>  useSubtensor);</div><div class="line"><a name="l01935"></a><span class="lineno"> 1935</span> </div><div class="line"><a name="l01936"></a><span class="lineno"> 1936</span>  result.output = MakeTensor<T, 4>(outputTensorInfo, output);</div><div class="line"><a name="l01937"></a><span class="lineno"> 1937</span>  result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(</div><div class="line"><a name="l01938"></a><span class="lineno"> 1938</span>  {</div><div class="line"><a name="l01939"></a><span class="lineno"> 1939</span>  1.0f, 2.0f, 11.0f, 12.0f, 13.0f,</div><div class="line"><a name="l01940"></a><span class="lineno"> 1940</span>  3.0f, 4.0f, 14.0f, 15.0f, 16.0f,</div><div class="line"><a name="l01941"></a><span class="lineno"> 1941</span>  5.0f, 6.0f, 17.0f, 18.0f, 19.0f,</div><div class="line"><a name="l01942"></a><span class="lineno"> 1942</span>  7.0f, 8.0f, 20.0f, 21.0f, 22.0f,</div><div class="line"><a name="l01943"></a><span class="lineno"> 1943</span>  9.0f, 10.0f, 23.0f, 24.0f, 25.0f,</div><div class="line"><a name="l01944"></a><span class="lineno"> 1944</span>  11.0f, 12.0f, 26.0f, 27.0f, 28.0f</div><div class="line"><a name="l01945"></a><span class="lineno"> 1945</span>  },</div><div class="line"><a name="l01946"></a><span class="lineno"> 1946</span>  qScale, qOffset));</div><div class="line"><a name="l01947"></a><span class="lineno"> 1947</span> </div><div class="line"><a name="l01948"></a><span class="lineno"> 1948</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l01949"></a><span class="lineno"> 1949</span> }</div><div class="line"><a name="l01950"></a><span class="lineno"> 1950</span> </div><div class="line"><a name="l01951"></a><span class="lineno"> 1951</span> <span class="keyword">template</span><DataType ArmnnType, <span class="keyword">typename</span> T></div><div class="line"><a name="l01952"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a9c875c9398293adbbab5ba306375958f"> 1952</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a460c8d79a829f1ed16f088c12acf41f3">ConcatDifferentInputOutputQParamTest</a>(</div><div class="line"><a name="l01953"></a><span class="lineno"> 1953</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l01954"></a><span class="lineno"> 1954</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l01955"></a><span class="lineno"> 1955</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l01956"></a><span class="lineno"> 1956</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l01957"></a><span class="lineno"> 1957</span> {</div><div class="line"><a name="l01958"></a><span class="lineno"> 1958</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l01959"></a><span class="lineno"> 1959</span> </div><div class="line"><a name="l01960"></a><span class="lineno"> 1960</span>  <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l01961"></a><span class="lineno"> 1961</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ 3, 6, 3 }, ArmnnType);</div><div class="line"><a name="l01962"></a><span class="lineno"> 1962</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ 3, 6, 2 }, ArmnnType);</div><div class="line"><a name="l01963"></a><span class="lineno"> 1963</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ 3, 6, 1 }, ArmnnType);</div><div class="line"><a name="l01964"></a><span class="lineno"> 1964</span> </div><div class="line"><a name="l01965"></a><span class="lineno"> 1965</span>  std::vector<TensorShape> inputTensorShapes({inputTensorInfo1.GetShape(), inputTensorInfo2.GetShape()});</div><div class="line"><a name="l01966"></a><span class="lineno"> 1966</span> </div><div class="line"><a name="l01967"></a><span class="lineno"> 1967</span>  <span class="comment">// Quantized input1 tensor.</span></div><div class="line"><a name="l01968"></a><span class="lineno"> 1968</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale1 = 0.5f;</div><div class="line"><a name="l01969"></a><span class="lineno"> 1969</span>  <span class="keyword">const</span> int32_t inputOffset1 = 5;</div><div class="line"><a name="l01970"></a><span class="lineno"> 1970</span> </div><div class="line"><a name="l01971"></a><span class="lineno"> 1971</span>  <span class="keyword">auto</span> input1 = MakeTensor<T, 3>(inputTensorInfo1, std::vector<T>(</div><div class="line"><a name="l01972"></a><span class="lineno"> 1972</span>  {</div><div class="line"><a name="l01973"></a><span class="lineno"> 1973</span>  1, 2, 3,</div><div class="line"><a name="l01974"></a><span class="lineno"> 1974</span>  4, 5, 6,</div><div class="line"><a name="l01975"></a><span class="lineno"> 1975</span>  7, 8, 9,</div><div class="line"><a name="l01976"></a><span class="lineno"> 1976</span>  10, 11, 12,</div><div class="line"><a name="l01977"></a><span class="lineno"> 1977</span>  13, 14, 15,</div><div class="line"><a name="l01978"></a><span class="lineno"> 1978</span>  16, 17, 18,</div><div class="line"><a name="l01979"></a><span class="lineno"> 1979</span> </div><div class="line"><a name="l01980"></a><span class="lineno"> 1980</span>  19, 20, 21,</div><div class="line"><a name="l01981"></a><span class="lineno"> 1981</span>  22, 23, 24,</div><div class="line"><a name="l01982"></a><span class="lineno"> 1982</span>  25, 26, 27,</div><div class="line"><a name="l01983"></a><span class="lineno"> 1983</span>  28, 29, 30,</div><div class="line"><a name="l01984"></a><span class="lineno"> 1984</span>  31, 32, 33,</div><div class="line"><a name="l01985"></a><span class="lineno"> 1985</span>  34, 35, 36</div><div class="line"><a name="l01986"></a><span class="lineno"> 1986</span>  }));</div><div class="line"><a name="l01987"></a><span class="lineno"> 1987</span> </div><div class="line"><a name="l01988"></a><span class="lineno"> 1988</span>  <span class="comment">// Quatized input2 tensor.</span></div><div class="line"><a name="l01989"></a><span class="lineno"> 1989</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale2 = 0.2f;</div><div class="line"><a name="l01990"></a><span class="lineno"> 1990</span>  <span class="keyword">const</span> int32_t inputOffset2 = 10;</div><div class="line"><a name="l01991"></a><span class="lineno"> 1991</span> </div><div class="line"><a name="l01992"></a><span class="lineno"> 1992</span>  <span class="keyword">auto</span> input2 = MakeTensor<T, 3>(inputTensorInfo2, std::vector<T>(</div><div class="line"><a name="l01993"></a><span class="lineno"> 1993</span>  {</div><div class="line"><a name="l01994"></a><span class="lineno"> 1994</span>  37, 38, 39,</div><div class="line"><a name="l01995"></a><span class="lineno"> 1995</span>  40, 41, 42,</div><div class="line"><a name="l01996"></a><span class="lineno"> 1996</span>  43, 44, 45,</div><div class="line"><a name="l01997"></a><span class="lineno"> 1997</span>  46, 47, 48,</div><div class="line"><a name="l01998"></a><span class="lineno"> 1998</span>  49, 50, 51,</div><div class="line"><a name="l01999"></a><span class="lineno"> 1999</span>  52, 53, 54</div><div class="line"><a name="l02000"></a><span class="lineno"> 2000</span>  }));</div><div class="line"><a name="l02001"></a><span class="lineno"> 2001</span> </div><div class="line"><a name="l02002"></a><span class="lineno"> 2002</span>  <span class="comment">// Quantized output tensor.</span></div><div class="line"><a name="l02003"></a><span class="lineno"> 2003</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> outputScale = 0.1f;</div><div class="line"><a name="l02004"></a><span class="lineno"> 2004</span>  <span class="keyword">const</span> int32_t outputOffset = 20;</div><div class="line"><a name="l02005"></a><span class="lineno"> 2005</span> </div><div class="line"><a name="l02006"></a><span class="lineno"> 2006</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<T, 3></a> ret(outputTensorInfo);</div><div class="line"><a name="l02007"></a><span class="lineno"> 2007</span> </div><div class="line"><a name="l02008"></a><span class="lineno"> 2008</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>(</div><div class="line"><a name="l02009"></a><span class="lineno"> 2009</span>  {</div><div class="line"><a name="l02010"></a><span class="lineno"> 2010</span>  0, 5, 74,</div><div class="line"><a name="l02011"></a><span class="lineno"> 2011</span>  10, 15, 76,</div><div class="line"><a name="l02012"></a><span class="lineno"> 2012</span>  20, 25, 78,</div><div class="line"><a name="l02013"></a><span class="lineno"> 2013</span>  30, 35, 80,</div><div class="line"><a name="l02014"></a><span class="lineno"> 2014</span>  40, 45, 82,</div><div class="line"><a name="l02015"></a><span class="lineno"> 2015</span>  50, 55, 84,</div><div class="line"><a name="l02016"></a><span class="lineno"> 2016</span> </div><div class="line"><a name="l02017"></a><span class="lineno"> 2017</span>  60, 65, 86,</div><div class="line"><a name="l02018"></a><span class="lineno"> 2018</span>  70, 75, 88,</div><div class="line"><a name="l02019"></a><span class="lineno"> 2019</span>  80, 85, 90,</div><div class="line"><a name="l02020"></a><span class="lineno"> 2020</span>  90, 95, 92,</div><div class="line"><a name="l02021"></a><span class="lineno"> 2021</span>  100, 105, 94,</div><div class="line"><a name="l02022"></a><span class="lineno"> 2022</span>  110, 115, 96,</div><div class="line"><a name="l02023"></a><span class="lineno"> 2023</span> </div><div class="line"><a name="l02024"></a><span class="lineno"> 2024</span>  120, 125, 98,</div><div class="line"><a name="l02025"></a><span class="lineno"> 2025</span>  130, 135, 100,</div><div class="line"><a name="l02026"></a><span class="lineno"> 2026</span>  140, 145, 102,</div><div class="line"><a name="l02027"></a><span class="lineno"> 2027</span>  150, 155, 104,</div><div class="line"><a name="l02028"></a><span class="lineno"> 2028</span>  160, 165, 106,</div><div class="line"><a name="l02029"></a><span class="lineno"> 2029</span>  170, 175, 108</div><div class="line"><a name="l02030"></a><span class="lineno"> 2030</span>  }));</div><div class="line"><a name="l02031"></a><span class="lineno"> 2031</span> </div><div class="line"><a name="l02032"></a><span class="lineno"> 2032</span>  outputTensorInfo.SetQuantizationScale(outputScale);</div><div class="line"><a name="l02033"></a><span class="lineno"> 2033</span>  outputTensorInfo.SetQuantizationOffset(outputOffset);</div><div class="line"><a name="l02034"></a><span class="lineno"> 2034</span>  inputTensorInfo1.SetQuantizationScale(inputScale1);</div><div class="line"><a name="l02035"></a><span class="lineno"> 2035</span>  inputTensorInfo1.SetQuantizationOffset(inputOffset1);</div><div class="line"><a name="l02036"></a><span class="lineno"> 2036</span>  inputTensorInfo2.SetQuantizationScale(inputScale2);</div><div class="line"><a name="l02037"></a><span class="lineno"> 2037</span>  inputTensorInfo2.SetQuantizationOffset(inputOffset2);</div><div class="line"><a name="l02038"></a><span class="lineno"> 2038</span> </div><div class="line"><a name="l02039"></a><span class="lineno"> 2039</span>  std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02040"></a><span class="lineno"> 2040</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02041"></a><span class="lineno"> 2041</span> </div><div class="line"><a name="l02042"></a><span class="lineno"> 2042</span>  std::vector<unsigned int> wOrigin2 = { 0, 0, 2 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02043"></a><span class="lineno"> 2043</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02044"></a><span class="lineno"> 2044</span> </div><div class="line"><a name="l02045"></a><span class="lineno"> 2045</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02046"></a><span class="lineno"> 2046</span> </div><div class="line"><a name="l02047"></a><span class="lineno"> 2047</span>  <span class="keywordtype">bool</span> subTensorsSupported = useSubtensor && workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02048"></a><span class="lineno"> 2048</span> </div><div class="line"><a name="l02049"></a><span class="lineno"> 2049</span>  std::unique_ptr<ITensorHandle> inputHandle1 =</div><div class="line"><a name="l02050"></a><span class="lineno"> 2050</span>  subTensorsSupported ?</div><div class="line"><a name="l02051"></a><span class="lineno"> 2051</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02052"></a><span class="lineno"> 2052</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02053"></a><span class="lineno"> 2053</span> </div><div class="line"><a name="l02054"></a><span class="lineno"> 2054</span>  std::unique_ptr<ITensorHandle> inputHandle2 =</div><div class="line"><a name="l02055"></a><span class="lineno"> 2055</span>  subTensorsSupported ?</div><div class="line"><a name="l02056"></a><span class="lineno"> 2056</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02057"></a><span class="lineno"> 2057</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02058"></a><span class="lineno"> 2058</span> </div><div class="line"><a name="l02059"></a><span class="lineno"> 2059</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02060"></a><span class="lineno"> 2060</span>  <a class="code" href="structarmnn_1_1_origins_descriptor.xhtml">OriginsDescriptor</a> desc = <a class="code" href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">CreateDescriptorForConcatenation</a>(</div><div class="line"><a name="l02061"></a><span class="lineno"> 2061</span>  inputTensorShapes.begin(),inputTensorShapes.end(), 2);</div><div class="line"><a name="l02062"></a><span class="lineno"> 2062</span>  data.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a> = desc;</div><div class="line"><a name="l02063"></a><span class="lineno"> 2063</span> </div><div class="line"><a name="l02064"></a><span class="lineno"> 2064</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="l02065"></a><span class="lineno"> 2065</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02066"></a><span class="lineno"> 2066</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02067"></a><span class="lineno"> 2067</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02068"></a><span class="lineno"> 2068</span> </div><div class="line"><a name="l02069"></a><span class="lineno"> 2069</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02070"></a><span class="lineno"> 2070</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02071"></a><span class="lineno"> 2071</span> </div><div class="line"><a name="l02072"></a><span class="lineno"> 2072</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02073"></a><span class="lineno"> 2073</span> </div><div class="line"><a name="l02074"></a><span class="lineno"> 2074</span>  inputHandle1->Allocate();</div><div class="line"><a name="l02075"></a><span class="lineno"> 2075</span>  inputHandle2->Allocate();</div><div class="line"><a name="l02076"></a><span class="lineno"> 2076</span>  outputHandle->Allocate();</div><div class="line"><a name="l02077"></a><span class="lineno"> 2077</span> </div><div class="line"><a name="l02078"></a><span class="lineno"> 2078</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0]);</div><div class="line"><a name="l02079"></a><span class="lineno"> 2079</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0]);</div><div class="line"><a name="l02080"></a><span class="lineno"> 2080</span> </div><div class="line"><a name="l02081"></a><span class="lineno"> 2081</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l02082"></a><span class="lineno"> 2082</span>  workload->Execute();</div><div class="line"><a name="l02083"></a><span class="lineno"> 2083</span> </div><div class="line"><a name="l02084"></a><span class="lineno"> 2084</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0], outputHandle.get());</div><div class="line"><a name="l02085"></a><span class="lineno"> 2085</span> </div><div class="line"><a name="l02086"></a><span class="lineno"> 2086</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02087"></a><span class="lineno"> 2087</span> }</div><div class="line"><a name="l02088"></a><span class="lineno"> 2088</span> </div><div class="line"><a name="l02089"></a><span class="lineno"> 2089</span> <span class="comment">//</span></div><div class="line"><a name="l02090"></a><span class="lineno"> 2090</span> <span class="comment">// Explicit template specializations</span></div><div class="line"><a name="l02091"></a><span class="lineno"> 2091</span> <span class="comment">//</span></div><div class="line"><a name="l02092"></a><span class="lineno"> 2092</span> </div><div class="line"><a name="l02093"></a><span class="lineno"> 2093</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<ResolveType<DataType::QAsymmU8></a>, 3></div><div class="line"><a name="l02094"></a><span class="lineno"> 2094</span> ConcatDifferentInputOutputQParamTest<DataType::QAsymmU8>(</div><div class="line"><a name="l02095"></a><span class="lineno"> 2095</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02096"></a><span class="lineno"> 2096</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02097"></a><span class="lineno"> 2097</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02098"></a><span class="lineno"> 2098</span>  <span class="keywordtype">bool</span> useSubtensor);</div><div class="line"><a name="l02099"></a><span class="lineno"> 2099</span> </div><div class="line"><a name="l02100"></a><span class="lineno"> 2100</span> <span class="keyword">template</span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<ResolveType<DataType::QSymmS16></a>, 3></div><div class="line"><a name="l02101"></a><span class="lineno"> 2101</span> ConcatDifferentInputOutputQParamTest<DataType::QSymmS16>(</div><div class="line"><a name="l02102"></a><span class="lineno"> 2102</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02103"></a><span class="lineno"> 2103</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02104"></a><span class="lineno"> 2104</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02105"></a><span class="lineno"> 2105</span>  <span class="keywordtype">bool</span> useSubtensor);</div><div class="line"><a name="l02106"></a><span class="lineno"> 2106</span> </div><div class="line"><a name="l02107"></a><span class="lineno"> 2107</span> <span class="comment">//</span></div><div class="line"><a name="l02108"></a><span class="lineno"> 2108</span> <span class="comment">// Implementation functions</span></div><div class="line"><a name="l02109"></a><span class="lineno"> 2109</span> <span class="comment">//</span></div><div class="line"><a name="l02110"></a><span class="lineno"> 2110</span> </div><div class="line"><a name="l02111"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#aa522d5b67ae3d8dbb1e51ad245578926"> 2111</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a2e952c053f3d7a035671a994352e2bc9">ConcatTest</a>(</div><div class="line"><a name="l02112"></a><span class="lineno"> 2112</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02113"></a><span class="lineno"> 2113</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02114"></a><span class="lineno"> 2114</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02115"></a><span class="lineno"> 2115</span> {</div><div class="line"><a name="l02116"></a><span class="lineno"> 2116</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02117"></a><span class="lineno"> 2117</span> </div><div class="line"><a name="l02118"></a><span class="lineno"> 2118</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02119"></a><span class="lineno"> 2119</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02120"></a><span class="lineno"> 2120</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02121"></a><span class="lineno"> 2121</span> </div><div class="line"><a name="l02122"></a><span class="lineno"> 2122</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02123"></a><span class="lineno"> 2123</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02124"></a><span class="lineno"> 2124</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02125"></a><span class="lineno"> 2125</span> </div><div class="line"><a name="l02126"></a><span class="lineno"> 2126</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02127"></a><span class="lineno"> 2127</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02128"></a><span class="lineno"> 2128</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02129"></a><span class="lineno"> 2129</span> </div><div class="line"><a name="l02130"></a><span class="lineno"> 2130</span>  <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l02131"></a><span class="lineno"> 2131</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l02132"></a><span class="lineno"> 2132</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l02133"></a><span class="lineno"> 2133</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">DataType::Float32</a>);</div><div class="line"><a name="l02134"></a><span class="lineno"> 2134</span> </div><div class="line"><a name="l02135"></a><span class="lineno"> 2135</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float,3></a> ret(outputTensorInfo);</div><div class="line"><a name="l02136"></a><span class="lineno"> 2136</span> </div><div class="line"><a name="l02137"></a><span class="lineno"> 2137</span>  ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>(</div><div class="line"><a name="l02138"></a><span class="lineno"> 2138</span>  {</div><div class="line"><a name="l02139"></a><span class="lineno"> 2139</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l02140"></a><span class="lineno"> 2140</span>  4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l02141"></a><span class="lineno"> 2141</span>  7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l02142"></a><span class="lineno"> 2142</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l02143"></a><span class="lineno"> 2143</span>  13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l02144"></a><span class="lineno"> 2144</span>  16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l02145"></a><span class="lineno"> 2145</span> </div><div class="line"><a name="l02146"></a><span class="lineno"> 2146</span>  19.0f, 20.0f, 21.0f,</div><div class="line"><a name="l02147"></a><span class="lineno"> 2147</span>  22.0f, 23.0f, 24.0f,</div><div class="line"><a name="l02148"></a><span class="lineno"> 2148</span>  25.0f, 26.0f, 27.0f,</div><div class="line"><a name="l02149"></a><span class="lineno"> 2149</span>  28.0f, 29.0f, 30.0f,</div><div class="line"><a name="l02150"></a><span class="lineno"> 2150</span>  31.0f, 32.0f, 33.0f,</div><div class="line"><a name="l02151"></a><span class="lineno"> 2151</span>  34.0f, 35.0f, 36.0f,</div><div class="line"><a name="l02152"></a><span class="lineno"> 2152</span> </div><div class="line"><a name="l02153"></a><span class="lineno"> 2153</span>  37.0f, 38.0f, 39.0f,</div><div class="line"><a name="l02154"></a><span class="lineno"> 2154</span>  40.0f, 41.0f, 42.0f,</div><div class="line"><a name="l02155"></a><span class="lineno"> 2155</span>  43.0f, 44.0f, 45.0f,</div><div class="line"><a name="l02156"></a><span class="lineno"> 2156</span>  46.0f, 47.0f, 48.0f,</div><div class="line"><a name="l02157"></a><span class="lineno"> 2157</span>  49.0f, 50.0f, 51.0f,</div><div class="line"><a name="l02158"></a><span class="lineno"> 2158</span>  52.0f, 53.0f, 54.0f,</div><div class="line"><a name="l02159"></a><span class="lineno"> 2159</span>  })</div><div class="line"><a name="l02160"></a><span class="lineno"> 2160</span>  );</div><div class="line"><a name="l02161"></a><span class="lineno"> 2161</span> </div><div class="line"><a name="l02162"></a><span class="lineno"> 2162</span>  <span class="keyword">auto</span> input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>(</div><div class="line"><a name="l02163"></a><span class="lineno"> 2163</span>  {</div><div class="line"><a name="l02164"></a><span class="lineno"> 2164</span>  1.0f, 2.0f, 3.0f,</div><div class="line"><a name="l02165"></a><span class="lineno"> 2165</span>  4.0f, 5.0f, 6.0f,</div><div class="line"><a name="l02166"></a><span class="lineno"> 2166</span>  7.0f, 8.0f, 9.0f,</div><div class="line"><a name="l02167"></a><span class="lineno"> 2167</span>  10.0f, 11.0f, 12.0f,</div><div class="line"><a name="l02168"></a><span class="lineno"> 2168</span>  13.0f, 14.0f, 15.0f,</div><div class="line"><a name="l02169"></a><span class="lineno"> 2169</span>  16.0f, 17.0f, 18.0f,</div><div class="line"><a name="l02170"></a><span class="lineno"> 2170</span> </div><div class="line"><a name="l02171"></a><span class="lineno"> 2171</span>  19.0f, 20.0f, 21.0f,</div><div class="line"><a name="l02172"></a><span class="lineno"> 2172</span>  22.0f, 23.0f, 24.0f,</div><div class="line"><a name="l02173"></a><span class="lineno"> 2173</span>  25.0f, 26.0f, 27.0f,</div><div class="line"><a name="l02174"></a><span class="lineno"> 2174</span>  28.0f, 29.0f, 30.0f,</div><div class="line"><a name="l02175"></a><span class="lineno"> 2175</span>  31.0f, 32.0f, 33.0f,</div><div class="line"><a name="l02176"></a><span class="lineno"> 2176</span>  34.0f, 35.0f, 36.0f,</div><div class="line"><a name="l02177"></a><span class="lineno"> 2177</span>  })</div><div class="line"><a name="l02178"></a><span class="lineno"> 2178</span>  );</div><div class="line"><a name="l02179"></a><span class="lineno"> 2179</span> </div><div class="line"><a name="l02180"></a><span class="lineno"> 2180</span>  <span class="keyword">auto</span> input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>(</div><div class="line"><a name="l02181"></a><span class="lineno"> 2181</span>  {</div><div class="line"><a name="l02182"></a><span class="lineno"> 2182</span>  37.0f, 38.0f, 39.0f,</div><div class="line"><a name="l02183"></a><span class="lineno"> 2183</span>  40.0f, 41.0f, 42.0f,</div><div class="line"><a name="l02184"></a><span class="lineno"> 2184</span>  43.0f, 44.0f, 45.0f,</div><div class="line"><a name="l02185"></a><span class="lineno"> 2185</span>  46.0f, 47.0f, 48.0f,</div><div class="line"><a name="l02186"></a><span class="lineno"> 2186</span>  49.0f, 50.0f, 51.0f,</div><div class="line"><a name="l02187"></a><span class="lineno"> 2187</span>  52.0f, 53.0f, 54.0f,</div><div class="line"><a name="l02188"></a><span class="lineno"> 2188</span>  })</div><div class="line"><a name="l02189"></a><span class="lineno"> 2189</span>  );</div><div class="line"><a name="l02190"></a><span class="lineno"> 2190</span> </div><div class="line"><a name="l02191"></a><span class="lineno"> 2191</span>  std::vector<unsigned int> wOrigin1 = {0, 0, 0}; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02192"></a><span class="lineno"> 2192</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02193"></a><span class="lineno"> 2193</span> </div><div class="line"><a name="l02194"></a><span class="lineno"> 2194</span>  std::vector<unsigned int> wOrigin2 = {2, 0, 0}; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02195"></a><span class="lineno"> 2195</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02196"></a><span class="lineno"> 2196</span> </div><div class="line"><a name="l02197"></a><span class="lineno"> 2197</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02198"></a><span class="lineno"> 2198</span> </div><div class="line"><a name="l02199"></a><span class="lineno"> 2199</span>  <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02200"></a><span class="lineno"> 2200</span> </div><div class="line"><a name="l02201"></a><span class="lineno"> 2201</span>  std::unique_ptr<ITensorHandle> inputHandle1 =</div><div class="line"><a name="l02202"></a><span class="lineno"> 2202</span>  subTensorsSupported ?</div><div class="line"><a name="l02203"></a><span class="lineno"> 2203</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02204"></a><span class="lineno"> 2204</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02205"></a><span class="lineno"> 2205</span> </div><div class="line"><a name="l02206"></a><span class="lineno"> 2206</span>  std::unique_ptr<ITensorHandle> inputHandle2 =</div><div class="line"><a name="l02207"></a><span class="lineno"> 2207</span>  subTensorsSupported ?</div><div class="line"><a name="l02208"></a><span class="lineno"> 2208</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02209"></a><span class="lineno"> 2209</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02210"></a><span class="lineno"> 2210</span> </div><div class="line"><a name="l02211"></a><span class="lineno"> 2211</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02212"></a><span class="lineno"> 2212</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="l02213"></a><span class="lineno"> 2213</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02214"></a><span class="lineno"> 2214</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02215"></a><span class="lineno"> 2215</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02216"></a><span class="lineno"> 2216</span> </div><div class="line"><a name="l02217"></a><span class="lineno"> 2217</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02218"></a><span class="lineno"> 2218</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02219"></a><span class="lineno"> 2219</span> </div><div class="line"><a name="l02220"></a><span class="lineno"> 2220</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02221"></a><span class="lineno"> 2221</span> </div><div class="line"><a name="l02222"></a><span class="lineno"> 2222</span>  inputHandle1->Allocate();</div><div class="line"><a name="l02223"></a><span class="lineno"> 2223</span>  inputHandle2->Allocate();</div><div class="line"><a name="l02224"></a><span class="lineno"> 2224</span>  outputHandle->Allocate();</div><div class="line"><a name="l02225"></a><span class="lineno"> 2225</span> </div><div class="line"><a name="l02226"></a><span class="lineno"> 2226</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0]);</div><div class="line"><a name="l02227"></a><span class="lineno"> 2227</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0]);</div><div class="line"><a name="l02228"></a><span class="lineno"> 2228</span> </div><div class="line"><a name="l02229"></a><span class="lineno"> 2229</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l02230"></a><span class="lineno"> 2230</span>  workload->Execute();</div><div class="line"><a name="l02231"></a><span class="lineno"> 2231</span> </div><div class="line"><a name="l02232"></a><span class="lineno"> 2232</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.output[0][0][0], outputHandle.get());</div><div class="line"><a name="l02233"></a><span class="lineno"> 2233</span> </div><div class="line"><a name="l02234"></a><span class="lineno"> 2234</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02235"></a><span class="lineno"> 2235</span> }</div><div class="line"><a name="l02236"></a><span class="lineno"> 2236</span> </div><div class="line"><a name="l02237"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ab8220058ed03ce1bfb0778c8cab3de00"> 2237</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 1></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a6b08fab8b8a00113a020b10b311f745e">Concat1dTest</a>(</div><div class="line"><a name="l02238"></a><span class="lineno"> 2238</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02239"></a><span class="lineno"> 2239</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02240"></a><span class="lineno"> 2240</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02241"></a><span class="lineno"> 2241</span> {</div><div class="line"><a name="l02242"></a><span class="lineno"> 2242</span>  <span class="keywordflow">return</span> Concat1dTestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02243"></a><span class="lineno"> 2243</span> }</div><div class="line"><a name="l02244"></a><span class="lineno"> 2244</span> </div><div class="line"><a name="l02245"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ae20568d2c28cd57ac16c48ced2a57790"> 2245</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ac56cadc167dab69f6fd407d2084ed170">Concat2dDim0Test</a>(</div><div class="line"><a name="l02246"></a><span class="lineno"> 2246</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02247"></a><span class="lineno"> 2247</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02248"></a><span class="lineno"> 2248</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02249"></a><span class="lineno"> 2249</span> {</div><div class="line"><a name="l02250"></a><span class="lineno"> 2250</span>  <span class="keywordflow">return</span> Concat2dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02251"></a><span class="lineno"> 2251</span> }</div><div class="line"><a name="l02252"></a><span class="lineno"> 2252</span> </div><div class="line"><a name="l02253"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#abb422df45da62171791c11fd78eeab65"> 2253</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a6dcfe04840950ab1bcf8f1790aa05502">Concat2dDim1Test</a>(</div><div class="line"><a name="l02254"></a><span class="lineno"> 2254</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02255"></a><span class="lineno"> 2255</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02256"></a><span class="lineno"> 2256</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02257"></a><span class="lineno"> 2257</span> {</div><div class="line"><a name="l02258"></a><span class="lineno"> 2258</span>  <span class="keywordflow">return</span> Concat2dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02259"></a><span class="lineno"> 2259</span> }</div><div class="line"><a name="l02260"></a><span class="lineno"> 2260</span> </div><div class="line"><a name="l02261"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a05f39b02c140b707b8fb11d0116b250e"> 2261</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a37fb7480d960917baf48e8d06f7b2b66">Concat2dDim0DiffInputDimsTest</a>(</div><div class="line"><a name="l02262"></a><span class="lineno"> 2262</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02263"></a><span class="lineno"> 2263</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02264"></a><span class="lineno"> 2264</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02265"></a><span class="lineno"> 2265</span> {</div><div class="line"><a name="l02266"></a><span class="lineno"> 2266</span>  <span class="keywordflow">return</span> Concat2dDim0DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager,</div><div class="line"><a name="l02267"></a><span class="lineno"> 2267</span>  tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02268"></a><span class="lineno"> 2268</span> }</div><div class="line"><a name="l02269"></a><span class="lineno"> 2269</span> </div><div class="line"><a name="l02270"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a5941eca58661b95a628ed44fef3342c6"> 2270</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a5e7803092126fd2862fdd8a2ed90e32a">Concat2dDim1DiffInputDimsTest</a>(</div><div class="line"><a name="l02271"></a><span class="lineno"> 2271</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02272"></a><span class="lineno"> 2272</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02273"></a><span class="lineno"> 2273</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02274"></a><span class="lineno"> 2274</span> {</div><div class="line"><a name="l02275"></a><span class="lineno"> 2275</span>  <span class="keywordflow">return</span> Concat2dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory,</div><div class="line"><a name="l02276"></a><span class="lineno"> 2276</span>  memoryManager,</div><div class="line"><a name="l02277"></a><span class="lineno"> 2277</span>  tensorHandleFactory,</div><div class="line"><a name="l02278"></a><span class="lineno"> 2278</span>  0.0f,</div><div class="line"><a name="l02279"></a><span class="lineno"> 2279</span>  0);</div><div class="line"><a name="l02280"></a><span class="lineno"> 2280</span> }</div><div class="line"><a name="l02281"></a><span class="lineno"> 2281</span> </div><div class="line"><a name="l02282"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a553f8e0b4c45a27211a0db518f0a6298"> 2282</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ae4c24060b33c16216f2faf4a7ef7c7ff">Concat3dDim0Test</a>(</div><div class="line"><a name="l02283"></a><span class="lineno"> 2283</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02284"></a><span class="lineno"> 2284</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02285"></a><span class="lineno"> 2285</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02286"></a><span class="lineno"> 2286</span> {</div><div class="line"><a name="l02287"></a><span class="lineno"> 2287</span>  <span class="keywordflow">return</span> Concat3dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02288"></a><span class="lineno"> 2288</span> }</div><div class="line"><a name="l02289"></a><span class="lineno"> 2289</span> </div><div class="line"><a name="l02290"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ab459b2dd37ee6e8b571833480c824408"> 2290</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a23c0f7c8c79ce30cccbd65f39d692ed6">Concat3dDim1Test</a>(</div><div class="line"><a name="l02291"></a><span class="lineno"> 2291</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02292"></a><span class="lineno"> 2292</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02293"></a><span class="lineno"> 2293</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02294"></a><span class="lineno"> 2294</span> {</div><div class="line"><a name="l02295"></a><span class="lineno"> 2295</span>  <span class="keywordflow">return</span> Concat3dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02296"></a><span class="lineno"> 2296</span> }</div><div class="line"><a name="l02297"></a><span class="lineno"> 2297</span> </div><div class="line"><a name="l02298"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a7d39bba5f0a9f984266f9d84a425d7c7"> 2298</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a47256d5dd1d5c65c719c40b2bfc7cc96">Concat3dDim2Test</a>(</div><div class="line"><a name="l02299"></a><span class="lineno"> 2299</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02300"></a><span class="lineno"> 2300</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02301"></a><span class="lineno"> 2301</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02302"></a><span class="lineno"> 2302</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02303"></a><span class="lineno"> 2303</span> {</div><div class="line"><a name="l02304"></a><span class="lineno"> 2304</span>  <span class="keywordflow">return</span> Concat3dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory,</div><div class="line"><a name="l02305"></a><span class="lineno"> 2305</span>  useSubtensor, 0.0f, 0);</div><div class="line"><a name="l02306"></a><span class="lineno"> 2306</span> }</div><div class="line"><a name="l02307"></a><span class="lineno"> 2307</span> </div><div class="line"><a name="l02308"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a4e079485ce4f5e002f96e6e4ce1e2662"> 2308</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a8260222560c2abfbea0f87869ae22062">Concat3dDim0DiffInputDimsTest</a>(</div><div class="line"><a name="l02309"></a><span class="lineno"> 2309</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02310"></a><span class="lineno"> 2310</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02311"></a><span class="lineno"> 2311</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02312"></a><span class="lineno"> 2312</span> {</div><div class="line"><a name="l02313"></a><span class="lineno"> 2313</span>  <span class="keywordflow">return</span> Concat3dDim0DiffInputDimsTestImpl<DataType::Float32>(</div><div class="line"><a name="l02314"></a><span class="lineno"> 2314</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02315"></a><span class="lineno"> 2315</span> }</div><div class="line"><a name="l02316"></a><span class="lineno"> 2316</span> </div><div class="line"><a name="l02317"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a03078758d0a241f00e2e6e68ed7379a9"> 2317</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aa6de98235dbd3060d428f6bb77b428ce">Concat3dDim1DiffInputDimsTest</a>(</div><div class="line"><a name="l02318"></a><span class="lineno"> 2318</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02319"></a><span class="lineno"> 2319</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02320"></a><span class="lineno"> 2320</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02321"></a><span class="lineno"> 2321</span> {</div><div class="line"><a name="l02322"></a><span class="lineno"> 2322</span>  <span class="keywordflow">return</span> Concat3dDim1DiffInputDimsTestImpl<DataType::Float32>(workloadFactory, memoryManager,</div><div class="line"><a name="l02323"></a><span class="lineno"> 2323</span>  tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02324"></a><span class="lineno"> 2324</span> }</div><div class="line"><a name="l02325"></a><span class="lineno"> 2325</span> </div><div class="line"><a name="l02326"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ae6abe39eda8d2acd37f46b42a5e8b16e"> 2326</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ab8a589490e332bc749972d21b7dff9a4">Concat3dDim2DiffInputDimsTest</a>(</div><div class="line"><a name="l02327"></a><span class="lineno"> 2327</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02328"></a><span class="lineno"> 2328</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02329"></a><span class="lineno"> 2329</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02330"></a><span class="lineno"> 2330</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02331"></a><span class="lineno"> 2331</span> {</div><div class="line"><a name="l02332"></a><span class="lineno"> 2332</span>  <span class="keywordflow">return</span> Concat3dDim2DiffInputDimsTestImpl<DataType::Float32>(</div><div class="line"><a name="l02333"></a><span class="lineno"> 2333</span>  workloadFactory, memoryManager, tensorHandleFactory, useSubtensor, 0.0f, 0);</div><div class="line"><a name="l02334"></a><span class="lineno"> 2334</span> }</div><div class="line"><a name="l02335"></a><span class="lineno"> 2335</span> </div><div class="line"><a name="l02336"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a11f8865a287d1048effbe34be448f4eb"> 2336</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a1662cadf61dca494a38d44fb32f116cc">Concat4dDim0Test</a>(</div><div class="line"><a name="l02337"></a><span class="lineno"> 2337</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02338"></a><span class="lineno"> 2338</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02339"></a><span class="lineno"> 2339</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02340"></a><span class="lineno"> 2340</span> {</div><div class="line"><a name="l02341"></a><span class="lineno"> 2341</span>  <span class="keywordflow">return</span> Concat4dDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02342"></a><span class="lineno"> 2342</span> }</div><div class="line"><a name="l02343"></a><span class="lineno"> 2343</span> </div><div class="line"><a name="l02344"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a9157ab7bc07208ca47303155d2cd12b8"> 2344</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a6e54f02f06e0a3eac39e726a091290df">Concat4dDim1Test</a>(</div><div class="line"><a name="l02345"></a><span class="lineno"> 2345</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02346"></a><span class="lineno"> 2346</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02347"></a><span class="lineno"> 2347</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02348"></a><span class="lineno"> 2348</span> {</div><div class="line"><a name="l02349"></a><span class="lineno"> 2349</span>  <span class="keywordflow">return</span> Concat4dDim1TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02350"></a><span class="lineno"> 2350</span> }</div><div class="line"><a name="l02351"></a><span class="lineno"> 2351</span> </div><div class="line"><a name="l02352"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a6e5e4396531527fadc52f3636d1bf73a"> 2352</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a27d4b7293d9b404c73850981d3496a50">Concat4dDim2Test</a>(</div><div class="line"><a name="l02353"></a><span class="lineno"> 2353</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02354"></a><span class="lineno"> 2354</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02355"></a><span class="lineno"> 2355</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02356"></a><span class="lineno"> 2356</span> {</div><div class="line"><a name="l02357"></a><span class="lineno"> 2357</span>  <span class="keywordflow">return</span> Concat4dDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02358"></a><span class="lineno"> 2358</span> }</div><div class="line"><a name="l02359"></a><span class="lineno"> 2359</span> </div><div class="line"><a name="l02360"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#aeb4067152ce120e31043b8477a787261"> 2360</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a410d43ad354ebb8995b7d9bc5b2c686c">Concat4dDim3Test</a>(</div><div class="line"><a name="l02361"></a><span class="lineno"> 2361</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02362"></a><span class="lineno"> 2362</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02363"></a><span class="lineno"> 2363</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02364"></a><span class="lineno"> 2364</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02365"></a><span class="lineno"> 2365</span> {</div><div class="line"><a name="l02366"></a><span class="lineno"> 2366</span>  <span class="keywordflow">return</span> Concat4dDim3TestImpl<DataType::Float32>(workloadFactory, memoryManager,</div><div class="line"><a name="l02367"></a><span class="lineno"> 2367</span>  tensorHandleFactory, 0.0f, 0, useSubtensor);</div><div class="line"><a name="l02368"></a><span class="lineno"> 2368</span> }</div><div class="line"><a name="l02369"></a><span class="lineno"> 2369</span> </div><div class="line"><a name="l02370"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a3f94cface3893686bb6776ed4926fcda"> 2370</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a972c1ddee6e494e695dc10e0982142b5">Concat4dDiffShapeDim0Test</a>(</div><div class="line"><a name="l02371"></a><span class="lineno"> 2371</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02372"></a><span class="lineno"> 2372</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02373"></a><span class="lineno"> 2373</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02374"></a><span class="lineno"> 2374</span> {</div><div class="line"><a name="l02375"></a><span class="lineno"> 2375</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim0TestImpl<DataType::Float32>(workloadFactory, memoryManager,</div><div class="line"><a name="l02376"></a><span class="lineno"> 2376</span>  tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02377"></a><span class="lineno"> 2377</span> }</div><div class="line"><a name="l02378"></a><span class="lineno"> 2378</span> </div><div class="line"><a name="l02379"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ad787ee57d74366f6a8aedb5bc94dc086"> 2379</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a79c356d8f1e3b0585635ee9b5a9e41a3">Concat4dDiffShapeDim1Test</a>(</div><div class="line"><a name="l02380"></a><span class="lineno"> 2380</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02381"></a><span class="lineno"> 2381</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02382"></a><span class="lineno"> 2382</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02383"></a><span class="lineno"> 2383</span> {</div><div class="line"><a name="l02384"></a><span class="lineno"> 2384</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim1TestImpl<DataType::Float32>(</div><div class="line"><a name="l02385"></a><span class="lineno"> 2385</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02386"></a><span class="lineno"> 2386</span> }</div><div class="line"><a name="l02387"></a><span class="lineno"> 2387</span> </div><div class="line"><a name="l02388"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a957f8b14b7676797dcde1fc860e86b37"> 2388</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a5d244b77eab878c84d0bdf67ef387d3c">Concat4dDiffShapeDim2Test</a>(</div><div class="line"><a name="l02389"></a><span class="lineno"> 2389</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02390"></a><span class="lineno"> 2390</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02391"></a><span class="lineno"> 2391</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02392"></a><span class="lineno"> 2392</span> {</div><div class="line"><a name="l02393"></a><span class="lineno"> 2393</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim2TestImpl<DataType::Float32>(workloadFactory, memoryManager,</div><div class="line"><a name="l02394"></a><span class="lineno"> 2394</span>  tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02395"></a><span class="lineno"> 2395</span> }</div><div class="line"><a name="l02396"></a><span class="lineno"> 2396</span> </div><div class="line"><a name="l02397"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#aa1c653d6aa6b4ccb38f5c8abd7e08aa0"> 2397</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<float, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a63a31f32393eda0ec427511ab8da5e55">Concat4dDiffShapeDim3Test</a>(</div><div class="line"><a name="l02398"></a><span class="lineno"> 2398</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02399"></a><span class="lineno"> 2399</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02400"></a><span class="lineno"> 2400</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02401"></a><span class="lineno"> 2401</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02402"></a><span class="lineno"> 2402</span> {</div><div class="line"><a name="l02403"></a><span class="lineno"> 2403</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim3TestImpl<DataType::Float32>(</div><div class="line"><a name="l02404"></a><span class="lineno"> 2404</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0, useSubtensor);</div><div class="line"><a name="l02405"></a><span class="lineno"> 2405</span> }</div><div class="line"><a name="l02406"></a><span class="lineno"> 2406</span> </div><div class="line"><a name="l02407"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a0fe26145da52f8562b32f068e20005ae"> 2407</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<Half, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#af8d15f0030605e3aa9d3679bbd207ae7">ConcatFloat16Test</a>(</div><div class="line"><a name="l02408"></a><span class="lineno"> 2408</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02409"></a><span class="lineno"> 2409</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02410"></a><span class="lineno"> 2410</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02411"></a><span class="lineno"> 2411</span> {</div><div class="line"><a name="l02412"></a><span class="lineno"> 2412</span>  <span class="keywordflow">return</span> Concat3dDim1TestImpl<DataType::Float16>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02413"></a><span class="lineno"> 2413</span> }</div><div class="line"><a name="l02414"></a><span class="lineno"> 2414</span> </div><div class="line"><a name="l02415"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a22bf322f7a53aa372791ac2e90411efb"> 2415</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<BFloat16, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ab37c1b501e6ffae4fb1d3e42860a7d1f">ConcatBFloat16Test</a>(</div><div class="line"><a name="l02416"></a><span class="lineno"> 2416</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02417"></a><span class="lineno"> 2417</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02418"></a><span class="lineno"> 2418</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02419"></a><span class="lineno"> 2419</span> {</div><div class="line"><a name="l02420"></a><span class="lineno"> 2420</span>  <span class="keywordflow">return</span> Concat3dDim1TestImpl<DataType::BFloat16>(workloadFactory, memoryManager, tensorHandleFactory, 0.0f, 0);</div><div class="line"><a name="l02421"></a><span class="lineno"> 2421</span> }</div><div class="line"><a name="l02422"></a><span class="lineno"> 2422</span> </div><div class="line"><a name="l02423"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a7e1c4089ee308ca4903fa8d2df351ebc"> 2423</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a2f6ac42e5f7a238f07a6bd5869ef767d">ConcatUint8DifferentQParamsTest</a>(</div><div class="line"><a name="l02424"></a><span class="lineno"> 2424</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02425"></a><span class="lineno"> 2425</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02426"></a><span class="lineno"> 2426</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02427"></a><span class="lineno"> 2427</span> {</div><div class="line"><a name="l02428"></a><span class="lineno"> 2428</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02429"></a><span class="lineno"> 2429</span> </div><div class="line"><a name="l02430"></a><span class="lineno"> 2430</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02431"></a><span class="lineno"> 2431</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02432"></a><span class="lineno"> 2432</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02433"></a><span class="lineno"> 2433</span> </div><div class="line"><a name="l02434"></a><span class="lineno"> 2434</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02435"></a><span class="lineno"> 2435</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02436"></a><span class="lineno"> 2436</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02437"></a><span class="lineno"> 2437</span> </div><div class="line"><a name="l02438"></a><span class="lineno"> 2438</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02439"></a><span class="lineno"> 2439</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02440"></a><span class="lineno"> 2440</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02441"></a><span class="lineno"> 2441</span> </div><div class="line"><a name="l02442"></a><span class="lineno"> 2442</span>  <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l02443"></a><span class="lineno"> 2443</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02444"></a><span class="lineno"> 2444</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02445"></a><span class="lineno"> 2445</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02446"></a><span class="lineno"> 2446</span> </div><div class="line"><a name="l02447"></a><span class="lineno"> 2447</span>  <span class="comment">// Quantized input1 tensor. Range [-3, 1]</span></div><div class="line"><a name="l02448"></a><span class="lineno"> 2448</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale1 = 0.015686f;</div><div class="line"><a name="l02449"></a><span class="lineno"> 2449</span>  <span class="keyword">const</span> int32_t inputOffset1 = 192;</div><div class="line"><a name="l02450"></a><span class="lineno"> 2450</span> </div><div class="line"><a name="l02451"></a><span class="lineno"> 2451</span>  <span class="keyword">auto</span> input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>(</div><div class="line"><a name="l02452"></a><span class="lineno"> 2452</span>  {</div><div class="line"><a name="l02453"></a><span class="lineno"> 2453</span>  1, 2, 3,</div><div class="line"><a name="l02454"></a><span class="lineno"> 2454</span>  4, 5, 6,</div><div class="line"><a name="l02455"></a><span class="lineno"> 2455</span>  7, 8, 9,</div><div class="line"><a name="l02456"></a><span class="lineno"> 2456</span>  10, 11, 12,</div><div class="line"><a name="l02457"></a><span class="lineno"> 2457</span>  13, 14, 15,</div><div class="line"><a name="l02458"></a><span class="lineno"> 2458</span>  16, 17, 18,</div><div class="line"><a name="l02459"></a><span class="lineno"> 2459</span> </div><div class="line"><a name="l02460"></a><span class="lineno"> 2460</span>  19, 20, 21,</div><div class="line"><a name="l02461"></a><span class="lineno"> 2461</span>  22, 23, 24,</div><div class="line"><a name="l02462"></a><span class="lineno"> 2462</span>  25, 26, 27,</div><div class="line"><a name="l02463"></a><span class="lineno"> 2463</span>  28, 29, 30,</div><div class="line"><a name="l02464"></a><span class="lineno"> 2464</span>  31, 32, 33,</div><div class="line"><a name="l02465"></a><span class="lineno"> 2465</span>  34, 35, 36,</div><div class="line"><a name="l02466"></a><span class="lineno"> 2466</span>  })</div><div class="line"><a name="l02467"></a><span class="lineno"> 2467</span>  );</div><div class="line"><a name="l02468"></a><span class="lineno"> 2468</span> </div><div class="line"><a name="l02469"></a><span class="lineno"> 2469</span>  <span class="comment">// Quatized input2 tensor. Range [-1, 4]</span></div><div class="line"><a name="l02470"></a><span class="lineno"> 2470</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> inputScale2 = 0.019608f;</div><div class="line"><a name="l02471"></a><span class="lineno"> 2471</span>  <span class="keyword">const</span> int32_t inputOffset2 = 50;</div><div class="line"><a name="l02472"></a><span class="lineno"> 2472</span> </div><div class="line"><a name="l02473"></a><span class="lineno"> 2473</span>  <span class="keyword">auto</span> input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>(</div><div class="line"><a name="l02474"></a><span class="lineno"> 2474</span>  {</div><div class="line"><a name="l02475"></a><span class="lineno"> 2475</span>  37, 38, 39,</div><div class="line"><a name="l02476"></a><span class="lineno"> 2476</span>  40, 41, 42,</div><div class="line"><a name="l02477"></a><span class="lineno"> 2477</span>  43, 44, 45,</div><div class="line"><a name="l02478"></a><span class="lineno"> 2478</span>  46, 47, 48,</div><div class="line"><a name="l02479"></a><span class="lineno"> 2479</span>  49, 50, 51,</div><div class="line"><a name="l02480"></a><span class="lineno"> 2480</span>  52, 53, 54,</div><div class="line"><a name="l02481"></a><span class="lineno"> 2481</span>  })</div><div class="line"><a name="l02482"></a><span class="lineno"> 2482</span>  );</div><div class="line"><a name="l02483"></a><span class="lineno"> 2483</span> </div><div class="line"><a name="l02484"></a><span class="lineno"> 2484</span>  <span class="comment">// Output has the same quantization parameters than input1,</span></div><div class="line"><a name="l02485"></a><span class="lineno"> 2485</span>  <span class="comment">// so that only the requantization of input2 is required</span></div><div class="line"><a name="l02486"></a><span class="lineno"> 2486</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> outputScale = 0.015686f;</div><div class="line"><a name="l02487"></a><span class="lineno"> 2487</span>  <span class="keyword">const</span> int32_t outputOffset = 192;</div><div class="line"><a name="l02488"></a><span class="lineno"> 2488</span> </div><div class="line"><a name="l02489"></a><span class="lineno"> 2489</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> ret(outputTensorInfo);</div><div class="line"><a name="l02490"></a><span class="lineno"> 2490</span> </div><div class="line"><a name="l02491"></a><span class="lineno"> 2491</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>(</div><div class="line"><a name="l02492"></a><span class="lineno"> 2492</span>  {</div><div class="line"><a name="l02493"></a><span class="lineno"> 2493</span>  1, 2, 3,</div><div class="line"><a name="l02494"></a><span class="lineno"> 2494</span>  4, 5, 6,</div><div class="line"><a name="l02495"></a><span class="lineno"> 2495</span>  7, 8, 9,</div><div class="line"><a name="l02496"></a><span class="lineno"> 2496</span>  10, 11, 12,</div><div class="line"><a name="l02497"></a><span class="lineno"> 2497</span>  13, 14, 15,</div><div class="line"><a name="l02498"></a><span class="lineno"> 2498</span>  16, 17, 18,</div><div class="line"><a name="l02499"></a><span class="lineno"> 2499</span> </div><div class="line"><a name="l02500"></a><span class="lineno"> 2500</span>  19, 20, 21,</div><div class="line"><a name="l02501"></a><span class="lineno"> 2501</span>  22, 23, 24,</div><div class="line"><a name="l02502"></a><span class="lineno"> 2502</span>  25, 26, 27,</div><div class="line"><a name="l02503"></a><span class="lineno"> 2503</span>  28, 29, 30,</div><div class="line"><a name="l02504"></a><span class="lineno"> 2504</span>  31, 32, 33,</div><div class="line"><a name="l02505"></a><span class="lineno"> 2505</span>  34, 35, 36,</div><div class="line"><a name="l02506"></a><span class="lineno"> 2506</span> </div><div class="line"><a name="l02507"></a><span class="lineno"> 2507</span>  176, 177, 178,</div><div class="line"><a name="l02508"></a><span class="lineno"> 2508</span>  179, 181, 182,</div><div class="line"><a name="l02509"></a><span class="lineno"> 2509</span>  183, 184, 186,</div><div class="line"><a name="l02510"></a><span class="lineno"> 2510</span>  187, 188, 189,</div><div class="line"><a name="l02511"></a><span class="lineno"> 2511</span>  191, 192, 193,</div><div class="line"><a name="l02512"></a><span class="lineno"> 2512</span>  195, 196, 197,</div><div class="line"><a name="l02513"></a><span class="lineno"> 2513</span>  })</div><div class="line"><a name="l02514"></a><span class="lineno"> 2514</span>  );</div><div class="line"><a name="l02515"></a><span class="lineno"> 2515</span> </div><div class="line"><a name="l02516"></a><span class="lineno"> 2516</span>  outputTensorInfo.SetQuantizationScale(outputScale);</div><div class="line"><a name="l02517"></a><span class="lineno"> 2517</span>  outputTensorInfo.SetQuantizationOffset(outputOffset);</div><div class="line"><a name="l02518"></a><span class="lineno"> 2518</span>  inputTensorInfo1.SetQuantizationScale(inputScale1);</div><div class="line"><a name="l02519"></a><span class="lineno"> 2519</span>  inputTensorInfo1.SetQuantizationOffset(inputOffset1);</div><div class="line"><a name="l02520"></a><span class="lineno"> 2520</span>  inputTensorInfo2.SetQuantizationScale(inputScale2);</div><div class="line"><a name="l02521"></a><span class="lineno"> 2521</span>  inputTensorInfo2.SetQuantizationOffset(inputOffset2);</div><div class="line"><a name="l02522"></a><span class="lineno"> 2522</span> </div><div class="line"><a name="l02523"></a><span class="lineno"> 2523</span>  std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02524"></a><span class="lineno"> 2524</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02525"></a><span class="lineno"> 2525</span> </div><div class="line"><a name="l02526"></a><span class="lineno"> 2526</span>  std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02527"></a><span class="lineno"> 2527</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02528"></a><span class="lineno"> 2528</span> </div><div class="line"><a name="l02529"></a><span class="lineno"> 2529</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02530"></a><span class="lineno"> 2530</span> </div><div class="line"><a name="l02531"></a><span class="lineno"> 2531</span>  <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02532"></a><span class="lineno"> 2532</span> </div><div class="line"><a name="l02533"></a><span class="lineno"> 2533</span>  std::unique_ptr<ITensorHandle> inputHandle1 =</div><div class="line"><a name="l02534"></a><span class="lineno"> 2534</span>  subTensorsSupported ?</div><div class="line"><a name="l02535"></a><span class="lineno"> 2535</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02536"></a><span class="lineno"> 2536</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02537"></a><span class="lineno"> 2537</span> </div><div class="line"><a name="l02538"></a><span class="lineno"> 2538</span>  std::unique_ptr<ITensorHandle> inputHandle2 =</div><div class="line"><a name="l02539"></a><span class="lineno"> 2539</span>  subTensorsSupported ?</div><div class="line"><a name="l02540"></a><span class="lineno"> 2540</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02541"></a><span class="lineno"> 2541</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02542"></a><span class="lineno"> 2542</span> </div><div class="line"><a name="l02543"></a><span class="lineno"> 2543</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02544"></a><span class="lineno"> 2544</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="l02545"></a><span class="lineno"> 2545</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02546"></a><span class="lineno"> 2546</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02547"></a><span class="lineno"> 2547</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02548"></a><span class="lineno"> 2548</span> </div><div class="line"><a name="l02549"></a><span class="lineno"> 2549</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02550"></a><span class="lineno"> 2550</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02551"></a><span class="lineno"> 2551</span> </div><div class="line"><a name="l02552"></a><span class="lineno"> 2552</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02553"></a><span class="lineno"> 2553</span> </div><div class="line"><a name="l02554"></a><span class="lineno"> 2554</span>  inputHandle1->Allocate();</div><div class="line"><a name="l02555"></a><span class="lineno"> 2555</span>  inputHandle2->Allocate();</div><div class="line"><a name="l02556"></a><span class="lineno"> 2556</span>  outputHandle->Allocate();</div><div class="line"><a name="l02557"></a><span class="lineno"> 2557</span> </div><div class="line"><a name="l02558"></a><span class="lineno"> 2558</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0]);</div><div class="line"><a name="l02559"></a><span class="lineno"> 2559</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0]);</div><div class="line"><a name="l02560"></a><span class="lineno"> 2560</span> </div><div class="line"><a name="l02561"></a><span class="lineno"> 2561</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l02562"></a><span class="lineno"> 2562</span>  workload->Execute();</div><div class="line"><a name="l02563"></a><span class="lineno"> 2563</span> </div><div class="line"><a name="l02564"></a><span class="lineno"> 2564</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0], outputHandle.get());</div><div class="line"><a name="l02565"></a><span class="lineno"> 2565</span> </div><div class="line"><a name="l02566"></a><span class="lineno"> 2566</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02567"></a><span class="lineno"> 2567</span> }</div><div class="line"><a name="l02568"></a><span class="lineno"> 2568</span> </div><div class="line"><a name="l02569"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a56611b98963e6b152447b8117dcb7451"> 2569</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a4332f7562287037bac9aed90d11a2db9">ConcatUint8Test</a>(</div><div class="line"><a name="l02570"></a><span class="lineno"> 2570</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02571"></a><span class="lineno"> 2571</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02572"></a><span class="lineno"> 2572</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02573"></a><span class="lineno"> 2573</span> {</div><div class="line"><a name="l02574"></a><span class="lineno"> 2574</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02575"></a><span class="lineno"> 2575</span> </div><div class="line"><a name="l02576"></a><span class="lineno"> 2576</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02577"></a><span class="lineno"> 2577</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02578"></a><span class="lineno"> 2578</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02579"></a><span class="lineno"> 2579</span> </div><div class="line"><a name="l02580"></a><span class="lineno"> 2580</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02581"></a><span class="lineno"> 2581</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02582"></a><span class="lineno"> 2582</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02583"></a><span class="lineno"> 2583</span> </div><div class="line"><a name="l02584"></a><span class="lineno"> 2584</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02585"></a><span class="lineno"> 2585</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02586"></a><span class="lineno"> 2586</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02587"></a><span class="lineno"> 2587</span> </div><div class="line"><a name="l02588"></a><span class="lineno"> 2588</span>  <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l02589"></a><span class="lineno"> 2589</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02590"></a><span class="lineno"> 2590</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02591"></a><span class="lineno"> 2591</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>);</div><div class="line"><a name="l02592"></a><span class="lineno"> 2592</span> </div><div class="line"><a name="l02593"></a><span class="lineno"> 2593</span>  <span class="comment">// Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them.</span></div><div class="line"><a name="l02594"></a><span class="lineno"> 2594</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> scale = 0.13497836f;</div><div class="line"><a name="l02595"></a><span class="lineno"> 2595</span>  <span class="keyword">const</span> int32_t offset = -7;</div><div class="line"><a name="l02596"></a><span class="lineno"> 2596</span> </div><div class="line"><a name="l02597"></a><span class="lineno"> 2597</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l02598"></a><span class="lineno"> 2598</span>  outputTensorInfo.SetQuantizationOffset(offset);</div><div class="line"><a name="l02599"></a><span class="lineno"> 2599</span>  inputTensorInfo1.SetQuantizationScale(scale);</div><div class="line"><a name="l02600"></a><span class="lineno"> 2600</span>  inputTensorInfo1.SetQuantizationOffset(offset);</div><div class="line"><a name="l02601"></a><span class="lineno"> 2601</span>  inputTensorInfo2.SetQuantizationScale(scale);</div><div class="line"><a name="l02602"></a><span class="lineno"> 2602</span>  inputTensorInfo2.SetQuantizationOffset(offset);</div><div class="line"><a name="l02603"></a><span class="lineno"> 2603</span> </div><div class="line"><a name="l02604"></a><span class="lineno"> 2604</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> ret(outputTensorInfo);</div><div class="line"><a name="l02605"></a><span class="lineno"> 2605</span> </div><div class="line"><a name="l02606"></a><span class="lineno"> 2606</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>(</div><div class="line"><a name="l02607"></a><span class="lineno"> 2607</span>  {</div><div class="line"><a name="l02608"></a><span class="lineno"> 2608</span>  1, 2, 3,</div><div class="line"><a name="l02609"></a><span class="lineno"> 2609</span>  4, 5, 6,</div><div class="line"><a name="l02610"></a><span class="lineno"> 2610</span>  7, 8, 9,</div><div class="line"><a name="l02611"></a><span class="lineno"> 2611</span>  10, 11, 12,</div><div class="line"><a name="l02612"></a><span class="lineno"> 2612</span>  13, 14, 15,</div><div class="line"><a name="l02613"></a><span class="lineno"> 2613</span>  16, 17, 18,</div><div class="line"><a name="l02614"></a><span class="lineno"> 2614</span> </div><div class="line"><a name="l02615"></a><span class="lineno"> 2615</span>  19, 20, 21,</div><div class="line"><a name="l02616"></a><span class="lineno"> 2616</span>  22, 23, 24,</div><div class="line"><a name="l02617"></a><span class="lineno"> 2617</span>  25, 26, 27,</div><div class="line"><a name="l02618"></a><span class="lineno"> 2618</span>  28, 29, 30,</div><div class="line"><a name="l02619"></a><span class="lineno"> 2619</span>  31, 32, 33,</div><div class="line"><a name="l02620"></a><span class="lineno"> 2620</span>  34, 35, 36,</div><div class="line"><a name="l02621"></a><span class="lineno"> 2621</span> </div><div class="line"><a name="l02622"></a><span class="lineno"> 2622</span>  37, 38, 39,</div><div class="line"><a name="l02623"></a><span class="lineno"> 2623</span>  40, 41, 42,</div><div class="line"><a name="l02624"></a><span class="lineno"> 2624</span>  43, 44, 45,</div><div class="line"><a name="l02625"></a><span class="lineno"> 2625</span>  46, 47, 48,</div><div class="line"><a name="l02626"></a><span class="lineno"> 2626</span>  49, 50, 51,</div><div class="line"><a name="l02627"></a><span class="lineno"> 2627</span>  52, 53, 54,</div><div class="line"><a name="l02628"></a><span class="lineno"> 2628</span>  })</div><div class="line"><a name="l02629"></a><span class="lineno"> 2629</span>  );</div><div class="line"><a name="l02630"></a><span class="lineno"> 2630</span> </div><div class="line"><a name="l02631"></a><span class="lineno"> 2631</span>  <span class="keyword">auto</span> input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>(</div><div class="line"><a name="l02632"></a><span class="lineno"> 2632</span>  {</div><div class="line"><a name="l02633"></a><span class="lineno"> 2633</span>  1, 2, 3,</div><div class="line"><a name="l02634"></a><span class="lineno"> 2634</span>  4, 5, 6,</div><div class="line"><a name="l02635"></a><span class="lineno"> 2635</span>  7, 8, 9,</div><div class="line"><a name="l02636"></a><span class="lineno"> 2636</span>  10, 11, 12,</div><div class="line"><a name="l02637"></a><span class="lineno"> 2637</span>  13, 14, 15,</div><div class="line"><a name="l02638"></a><span class="lineno"> 2638</span>  16, 17, 18,</div><div class="line"><a name="l02639"></a><span class="lineno"> 2639</span> </div><div class="line"><a name="l02640"></a><span class="lineno"> 2640</span>  19, 20, 21,</div><div class="line"><a name="l02641"></a><span class="lineno"> 2641</span>  22, 23, 24,</div><div class="line"><a name="l02642"></a><span class="lineno"> 2642</span>  25, 26, 27,</div><div class="line"><a name="l02643"></a><span class="lineno"> 2643</span>  28, 29, 30,</div><div class="line"><a name="l02644"></a><span class="lineno"> 2644</span>  31, 32, 33,</div><div class="line"><a name="l02645"></a><span class="lineno"> 2645</span>  34, 35, 36,</div><div class="line"><a name="l02646"></a><span class="lineno"> 2646</span>  })</div><div class="line"><a name="l02647"></a><span class="lineno"> 2647</span>  );</div><div class="line"><a name="l02648"></a><span class="lineno"> 2648</span> </div><div class="line"><a name="l02649"></a><span class="lineno"> 2649</span>  <span class="keyword">auto</span> input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>(</div><div class="line"><a name="l02650"></a><span class="lineno"> 2650</span>  {</div><div class="line"><a name="l02651"></a><span class="lineno"> 2651</span>  37, 38, 39,</div><div class="line"><a name="l02652"></a><span class="lineno"> 2652</span>  40, 41, 42,</div><div class="line"><a name="l02653"></a><span class="lineno"> 2653</span>  43, 44, 45,</div><div class="line"><a name="l02654"></a><span class="lineno"> 2654</span>  46, 47, 48,</div><div class="line"><a name="l02655"></a><span class="lineno"> 2655</span>  49, 50, 51,</div><div class="line"><a name="l02656"></a><span class="lineno"> 2656</span>  52, 53, 54,</div><div class="line"><a name="l02657"></a><span class="lineno"> 2657</span>  })</div><div class="line"><a name="l02658"></a><span class="lineno"> 2658</span>  );</div><div class="line"><a name="l02659"></a><span class="lineno"> 2659</span> </div><div class="line"><a name="l02660"></a><span class="lineno"> 2660</span>  std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02661"></a><span class="lineno"> 2661</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02662"></a><span class="lineno"> 2662</span> </div><div class="line"><a name="l02663"></a><span class="lineno"> 2663</span>  std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02664"></a><span class="lineno"> 2664</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02665"></a><span class="lineno"> 2665</span> </div><div class="line"><a name="l02666"></a><span class="lineno"> 2666</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02667"></a><span class="lineno"> 2667</span> </div><div class="line"><a name="l02668"></a><span class="lineno"> 2668</span>  <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02669"></a><span class="lineno"> 2669</span> </div><div class="line"><a name="l02670"></a><span class="lineno"> 2670</span>  std::unique_ptr<ITensorHandle> inputHandle1 =</div><div class="line"><a name="l02671"></a><span class="lineno"> 2671</span>  subTensorsSupported ?</div><div class="line"><a name="l02672"></a><span class="lineno"> 2672</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02673"></a><span class="lineno"> 2673</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02674"></a><span class="lineno"> 2674</span> </div><div class="line"><a name="l02675"></a><span class="lineno"> 2675</span>  std::unique_ptr<ITensorHandle> inputHandle2 =</div><div class="line"><a name="l02676"></a><span class="lineno"> 2676</span>  subTensorsSupported ?</div><div class="line"><a name="l02677"></a><span class="lineno"> 2677</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02678"></a><span class="lineno"> 2678</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02679"></a><span class="lineno"> 2679</span> </div><div class="line"><a name="l02680"></a><span class="lineno"> 2680</span> </div><div class="line"><a name="l02681"></a><span class="lineno"> 2681</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02682"></a><span class="lineno"> 2682</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="l02683"></a><span class="lineno"> 2683</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02684"></a><span class="lineno"> 2684</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02685"></a><span class="lineno"> 2685</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02686"></a><span class="lineno"> 2686</span> </div><div class="line"><a name="l02687"></a><span class="lineno"> 2687</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02688"></a><span class="lineno"> 2688</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02689"></a><span class="lineno"> 2689</span> </div><div class="line"><a name="l02690"></a><span class="lineno"> 2690</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02691"></a><span class="lineno"> 2691</span> </div><div class="line"><a name="l02692"></a><span class="lineno"> 2692</span>  inputHandle1->Allocate();</div><div class="line"><a name="l02693"></a><span class="lineno"> 2693</span>  inputHandle2->Allocate();</div><div class="line"><a name="l02694"></a><span class="lineno"> 2694</span>  outputHandle->Allocate();</div><div class="line"><a name="l02695"></a><span class="lineno"> 2695</span> </div><div class="line"><a name="l02696"></a><span class="lineno"> 2696</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0]);</div><div class="line"><a name="l02697"></a><span class="lineno"> 2697</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0]);</div><div class="line"><a name="l02698"></a><span class="lineno"> 2698</span> </div><div class="line"><a name="l02699"></a><span class="lineno"> 2699</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l02700"></a><span class="lineno"> 2700</span>  workload->Execute();</div><div class="line"><a name="l02701"></a><span class="lineno"> 2701</span> </div><div class="line"><a name="l02702"></a><span class="lineno"> 2702</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0], outputHandle.get());</div><div class="line"><a name="l02703"></a><span class="lineno"> 2703</span> </div><div class="line"><a name="l02704"></a><span class="lineno"> 2704</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02705"></a><span class="lineno"> 2705</span> }</div><div class="line"><a name="l02706"></a><span class="lineno"> 2706</span> </div><div class="line"><a name="l02707"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a9943b5dddb420292860d038bf59c0c7e"> 2707</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint16_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a80b1bff1d772096f5a10a26167425ded">ConcatUint16Test</a>(</div><div class="line"><a name="l02708"></a><span class="lineno"> 2708</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02709"></a><span class="lineno"> 2709</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02710"></a><span class="lineno"> 2710</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02711"></a><span class="lineno"> 2711</span> {</div><div class="line"><a name="l02712"></a><span class="lineno"> 2712</span>  <a class="code" href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">IgnoreUnused</a>(memoryManager);</div><div class="line"><a name="l02713"></a><span class="lineno"> 2713</span> </div><div class="line"><a name="l02714"></a><span class="lineno"> 2714</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 3;</div><div class="line"><a name="l02715"></a><span class="lineno"> 2715</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 6;</div><div class="line"><a name="l02716"></a><span class="lineno"> 2716</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l02717"></a><span class="lineno"> 2717</span> </div><div class="line"><a name="l02718"></a><span class="lineno"> 2718</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth1 = 3;</div><div class="line"><a name="l02719"></a><span class="lineno"> 2719</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight1 = 6;</div><div class="line"><a name="l02720"></a><span class="lineno"> 2720</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels1 = 2;</div><div class="line"><a name="l02721"></a><span class="lineno"> 2721</span> </div><div class="line"><a name="l02722"></a><span class="lineno"> 2722</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth2 = 3;</div><div class="line"><a name="l02723"></a><span class="lineno"> 2723</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight2 = 6;</div><div class="line"><a name="l02724"></a><span class="lineno"> 2724</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels2 = 1;</div><div class="line"><a name="l02725"></a><span class="lineno"> 2725</span> </div><div class="line"><a name="l02726"></a><span class="lineno"> 2726</span>  <span class="comment">// Defines the tensor descriptors.</span></div><div class="line"><a name="l02727"></a><span class="lineno"> 2727</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo({ outputChannels, outputHeight, outputWidth }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l02728"></a><span class="lineno"> 2728</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l02729"></a><span class="lineno"> 2729</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">DataType::QSymmS16</a>);</div><div class="line"><a name="l02730"></a><span class="lineno"> 2730</span> </div><div class="line"><a name="l02731"></a><span class="lineno"> 2731</span>  <span class="comment">// Arbitrary scale and offsets. They don't really matter as the Concat operator doesn't dequantize/quantize them.</span></div><div class="line"><a name="l02732"></a><span class="lineno"> 2732</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> scale = 0.13497836f;</div><div class="line"><a name="l02733"></a><span class="lineno"> 2733</span>  <span class="keyword">const</span> int32_t offset = -7;</div><div class="line"><a name="l02734"></a><span class="lineno"> 2734</span> </div><div class="line"><a name="l02735"></a><span class="lineno"> 2735</span>  outputTensorInfo.<a class="code" href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">SetQuantizationScale</a>(scale);</div><div class="line"><a name="l02736"></a><span class="lineno"> 2736</span>  outputTensorInfo.SetQuantizationOffset(offset);</div><div class="line"><a name="l02737"></a><span class="lineno"> 2737</span>  inputTensorInfo1.SetQuantizationScale(scale);</div><div class="line"><a name="l02738"></a><span class="lineno"> 2738</span>  inputTensorInfo1.SetQuantizationOffset(offset);</div><div class="line"><a name="l02739"></a><span class="lineno"> 2739</span>  inputTensorInfo2.SetQuantizationScale(scale);</div><div class="line"><a name="l02740"></a><span class="lineno"> 2740</span>  inputTensorInfo2.SetQuantizationOffset(offset);</div><div class="line"><a name="l02741"></a><span class="lineno"> 2741</span> </div><div class="line"><a name="l02742"></a><span class="lineno"> 2742</span>  <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint16_t, 3></a> ret(outputTensorInfo);</div><div class="line"><a name="l02743"></a><span class="lineno"> 2743</span> </div><div class="line"><a name="l02744"></a><span class="lineno"> 2744</span>  ret.<a class="code" href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">outputExpected</a> = MakeTensor<uint16_t, 3>(outputTensorInfo, std::vector<uint16_t>(</div><div class="line"><a name="l02745"></a><span class="lineno"> 2745</span>  {</div><div class="line"><a name="l02746"></a><span class="lineno"> 2746</span>  1, 2, 3,</div><div class="line"><a name="l02747"></a><span class="lineno"> 2747</span>  4, 5, 6,</div><div class="line"><a name="l02748"></a><span class="lineno"> 2748</span>  7, 8, 9,</div><div class="line"><a name="l02749"></a><span class="lineno"> 2749</span>  10, 11, 12,</div><div class="line"><a name="l02750"></a><span class="lineno"> 2750</span>  13, 14, 15,</div><div class="line"><a name="l02751"></a><span class="lineno"> 2751</span>  16, 17, 18,</div><div class="line"><a name="l02752"></a><span class="lineno"> 2752</span> </div><div class="line"><a name="l02753"></a><span class="lineno"> 2753</span>  19, 20, 21,</div><div class="line"><a name="l02754"></a><span class="lineno"> 2754</span>  22, 23, 24,</div><div class="line"><a name="l02755"></a><span class="lineno"> 2755</span>  25, 26, 27,</div><div class="line"><a name="l02756"></a><span class="lineno"> 2756</span>  28, 29, 30,</div><div class="line"><a name="l02757"></a><span class="lineno"> 2757</span>  31, 32, 33,</div><div class="line"><a name="l02758"></a><span class="lineno"> 2758</span>  34, 35, 36,</div><div class="line"><a name="l02759"></a><span class="lineno"> 2759</span> </div><div class="line"><a name="l02760"></a><span class="lineno"> 2760</span>  37, 38, 39,</div><div class="line"><a name="l02761"></a><span class="lineno"> 2761</span>  40, 41, 42,</div><div class="line"><a name="l02762"></a><span class="lineno"> 2762</span>  43, 44, 45,</div><div class="line"><a name="l02763"></a><span class="lineno"> 2763</span>  46, 47, 48,</div><div class="line"><a name="l02764"></a><span class="lineno"> 2764</span>  49, 50, 51,</div><div class="line"><a name="l02765"></a><span class="lineno"> 2765</span>  52, 53, 54,</div><div class="line"><a name="l02766"></a><span class="lineno"> 2766</span>  }));</div><div class="line"><a name="l02767"></a><span class="lineno"> 2767</span> </div><div class="line"><a name="l02768"></a><span class="lineno"> 2768</span>  <span class="keyword">auto</span> input1 = MakeTensor<uint16_t, 3>(inputTensorInfo1, std::vector<uint16_t>(</div><div class="line"><a name="l02769"></a><span class="lineno"> 2769</span>  {</div><div class="line"><a name="l02770"></a><span class="lineno"> 2770</span>  1, 2, 3,</div><div class="line"><a name="l02771"></a><span class="lineno"> 2771</span>  4, 5, 6,</div><div class="line"><a name="l02772"></a><span class="lineno"> 2772</span>  7, 8, 9,</div><div class="line"><a name="l02773"></a><span class="lineno"> 2773</span>  10, 11, 12,</div><div class="line"><a name="l02774"></a><span class="lineno"> 2774</span>  13, 14, 15,</div><div class="line"><a name="l02775"></a><span class="lineno"> 2775</span>  16, 17, 18,</div><div class="line"><a name="l02776"></a><span class="lineno"> 2776</span> </div><div class="line"><a name="l02777"></a><span class="lineno"> 2777</span>  19, 20, 21,</div><div class="line"><a name="l02778"></a><span class="lineno"> 2778</span>  22, 23, 24,</div><div class="line"><a name="l02779"></a><span class="lineno"> 2779</span>  25, 26, 27,</div><div class="line"><a name="l02780"></a><span class="lineno"> 2780</span>  28, 29, 30,</div><div class="line"><a name="l02781"></a><span class="lineno"> 2781</span>  31, 32, 33,</div><div class="line"><a name="l02782"></a><span class="lineno"> 2782</span>  34, 35, 36,</div><div class="line"><a name="l02783"></a><span class="lineno"> 2783</span>  }));</div><div class="line"><a name="l02784"></a><span class="lineno"> 2784</span> </div><div class="line"><a name="l02785"></a><span class="lineno"> 2785</span>  <span class="keyword">auto</span> input2 = MakeTensor<uint16_t, 3>(inputTensorInfo2, std::vector<uint16_t>(</div><div class="line"><a name="l02786"></a><span class="lineno"> 2786</span>  {</div><div class="line"><a name="l02787"></a><span class="lineno"> 2787</span>  37, 38, 39,</div><div class="line"><a name="l02788"></a><span class="lineno"> 2788</span>  40, 41, 42,</div><div class="line"><a name="l02789"></a><span class="lineno"> 2789</span>  43, 44, 45,</div><div class="line"><a name="l02790"></a><span class="lineno"> 2790</span>  46, 47, 48,</div><div class="line"><a name="l02791"></a><span class="lineno"> 2791</span>  49, 50, 51,</div><div class="line"><a name="l02792"></a><span class="lineno"> 2792</span>  52, 53, 54,</div><div class="line"><a name="l02793"></a><span class="lineno"> 2793</span>  }));</div><div class="line"><a name="l02794"></a><span class="lineno"> 2794</span> </div><div class="line"><a name="l02795"></a><span class="lineno"> 2795</span>  std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[0].</span></div><div class="line"><a name="l02796"></a><span class="lineno"> 2796</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window1(wOrigin1);</div><div class="line"><a name="l02797"></a><span class="lineno"> 2797</span> </div><div class="line"><a name="l02798"></a><span class="lineno"> 2798</span>  std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; <span class="comment">//Extent of the window is defined by size of input[1].</span></div><div class="line"><a name="l02799"></a><span class="lineno"> 2799</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">ConcatQueueDescriptor::ViewOrigin</a> window2(wOrigin2);</div><div class="line"><a name="l02800"></a><span class="lineno"> 2800</span> </div><div class="line"><a name="l02801"></a><span class="lineno"> 2801</span> </div><div class="line"><a name="l02802"></a><span class="lineno"> 2802</span>  std::unique_ptr<ITensorHandle> outputHandle = tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(outputTensorInfo);</div><div class="line"><a name="l02803"></a><span class="lineno"> 2803</span> </div><div class="line"><a name="l02804"></a><span class="lineno"> 2804</span>  <span class="keywordtype">bool</span> subTensorsSupported = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">SupportsSubTensors</a>();</div><div class="line"><a name="l02805"></a><span class="lineno"> 2805</span> </div><div class="line"><a name="l02806"></a><span class="lineno"> 2806</span>  std::unique_ptr<ITensorHandle> inputHandle1 =</div><div class="line"><a name="l02807"></a><span class="lineno"> 2807</span>  subTensorsSupported ?</div><div class="line"><a name="l02808"></a><span class="lineno"> 2808</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) :</div><div class="line"><a name="l02809"></a><span class="lineno"> 2809</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo1);</div><div class="line"><a name="l02810"></a><span class="lineno"> 2810</span> </div><div class="line"><a name="l02811"></a><span class="lineno"> 2811</span>  std::unique_ptr<ITensorHandle> inputHandle2 =</div><div class="line"><a name="l02812"></a><span class="lineno"> 2812</span>  subTensorsSupported ?</div><div class="line"><a name="l02813"></a><span class="lineno"> 2813</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">CreateSubTensorHandle</a>(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) :</div><div class="line"><a name="l02814"></a><span class="lineno"> 2814</span>  tensorHandleFactory.<a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">CreateTensorHandle</a>(inputTensorInfo2);</div><div class="line"><a name="l02815"></a><span class="lineno"> 2815</span>  </div><div class="line"><a name="l02816"></a><span class="lineno"> 2816</span> </div><div class="line"><a name="l02817"></a><span class="lineno"> 2817</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> data;</div><div class="line"><a name="l02818"></a><span class="lineno"> 2818</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="l02819"></a><span class="lineno"> 2819</span>  AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get());</div><div class="line"><a name="l02820"></a><span class="lineno"> 2820</span>  AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get());</div><div class="line"><a name="l02821"></a><span class="lineno"> 2821</span>  AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());</div><div class="line"><a name="l02822"></a><span class="lineno"> 2822</span> </div><div class="line"><a name="l02823"></a><span class="lineno"> 2823</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window1);</div><div class="line"><a name="l02824"></a><span class="lineno"> 2824</span>  data.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window2);</div><div class="line"><a name="l02825"></a><span class="lineno"> 2825</span> </div><div class="line"><a name="l02826"></a><span class="lineno"> 2826</span>  std::unique_ptr<IWorkload> workload = workloadFactory.<a class="code" href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">CreateConcat</a>(data, info);</div><div class="line"><a name="l02827"></a><span class="lineno"> 2827</span> </div><div class="line"><a name="l02828"></a><span class="lineno"> 2828</span>  inputHandle1->Allocate();</div><div class="line"><a name="l02829"></a><span class="lineno"> 2829</span>  inputHandle2->Allocate();</div><div class="line"><a name="l02830"></a><span class="lineno"> 2830</span>  outputHandle->Allocate();</div><div class="line"><a name="l02831"></a><span class="lineno"> 2831</span> </div><div class="line"><a name="l02832"></a><span class="lineno"> 2832</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle1.get(), &input1[0][0][0]);</div><div class="line"><a name="l02833"></a><span class="lineno"> 2833</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a>(inputHandle2.get(), &input2[0][0][0]);</div><div class="line"><a name="l02834"></a><span class="lineno"> 2834</span> </div><div class="line"><a name="l02835"></a><span class="lineno"> 2835</span>  workload->PostAllocationConfigure();</div><div class="line"><a name="l02836"></a><span class="lineno"> 2836</span>  workload->Execute();</div><div class="line"><a name="l02837"></a><span class="lineno"> 2837</span> </div><div class="line"><a name="l02838"></a><span class="lineno"> 2838</span>  <a class="code" href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a>(&ret.<a class="code" href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">output</a>[0][0][0], outputHandle.get());</div><div class="line"><a name="l02839"></a><span class="lineno"> 2839</span> </div><div class="line"><a name="l02840"></a><span class="lineno"> 2840</span>  <span class="keywordflow">return</span> ret;</div><div class="line"><a name="l02841"></a><span class="lineno"> 2841</span> }</div><div class="line"><a name="l02842"></a><span class="lineno"> 2842</span> </div><div class="line"><a name="l02843"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a587c146d14bc903593d666ec60b0b49e"> 2843</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 1></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ad3d1e6175529a26ba9698955b7cc0d8e">Concat1dUint8Test</a>(</div><div class="line"><a name="l02844"></a><span class="lineno"> 2844</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02845"></a><span class="lineno"> 2845</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02846"></a><span class="lineno"> 2846</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02847"></a><span class="lineno"> 2847</span> {</div><div class="line"><a name="l02848"></a><span class="lineno"> 2848</span>  <span class="keywordflow">return</span> Concat1dTestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02849"></a><span class="lineno"> 2849</span> }</div><div class="line"><a name="l02850"></a><span class="lineno"> 2850</span> </div><div class="line"><a name="l02851"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#aff1341e10235f1795762b1d1095ecf4b"> 2851</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aa101efe51ecfbd0e64eeba18ffa49f52">Concat2dDim0Uint8Test</a>(</div><div class="line"><a name="l02852"></a><span class="lineno"> 2852</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02853"></a><span class="lineno"> 2853</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02854"></a><span class="lineno"> 2854</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02855"></a><span class="lineno"> 2855</span> {</div><div class="line"><a name="l02856"></a><span class="lineno"> 2856</span>  <span class="keywordflow">return</span> Concat2dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02857"></a><span class="lineno"> 2857</span> }</div><div class="line"><a name="l02858"></a><span class="lineno"> 2858</span> </div><div class="line"><a name="l02859"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ad2234fe7fbfbfdfb5b21102e6b0058ac"> 2859</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aa8e37b566b2e5c533fb357eb560e1834">Concat2dDim1Uint8Test</a>(</div><div class="line"><a name="l02860"></a><span class="lineno"> 2860</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02861"></a><span class="lineno"> 2861</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02862"></a><span class="lineno"> 2862</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02863"></a><span class="lineno"> 2863</span> {</div><div class="line"><a name="l02864"></a><span class="lineno"> 2864</span>  <span class="keywordflow">return</span> Concat2dDim1TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02865"></a><span class="lineno"> 2865</span> }</div><div class="line"><a name="l02866"></a><span class="lineno"> 2866</span> </div><div class="line"><a name="l02867"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a8dffa45ae372dbede82bdb741bdfef3c"> 2867</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a2e6ce64cef93db0d06b69c4f249fac35">Concat2dDim0DiffInputDimsUint8Test</a>(</div><div class="line"><a name="l02868"></a><span class="lineno"> 2868</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02869"></a><span class="lineno"> 2869</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02870"></a><span class="lineno"> 2870</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02871"></a><span class="lineno"> 2871</span> {</div><div class="line"><a name="l02872"></a><span class="lineno"> 2872</span>  <span class="keywordflow">return</span> Concat2dDim0DiffInputDimsTestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02873"></a><span class="lineno"> 2873</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02874"></a><span class="lineno"> 2874</span> }</div><div class="line"><a name="l02875"></a><span class="lineno"> 2875</span> </div><div class="line"><a name="l02876"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#adfa4be1ded3f85405d00bbb29daf802d"> 2876</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 2></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a06290d393dfb595552fc39fd894d964a">Concat2dDim1DiffInputDimsUint8Test</a>(</div><div class="line"><a name="l02877"></a><span class="lineno"> 2877</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02878"></a><span class="lineno"> 2878</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02879"></a><span class="lineno"> 2879</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02880"></a><span class="lineno"> 2880</span> {</div><div class="line"><a name="l02881"></a><span class="lineno"> 2881</span>  <span class="keywordflow">return</span> Concat2dDim1DiffInputDimsTestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02882"></a><span class="lineno"> 2882</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02883"></a><span class="lineno"> 2883</span> }</div><div class="line"><a name="l02884"></a><span class="lineno"> 2884</span> </div><div class="line"><a name="l02885"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a53d309d9482909084c164413aa68467a"> 2885</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#adbfedf414aac270a5e285d057bfb58b6">Concat3dDim0Uint8Test</a>(</div><div class="line"><a name="l02886"></a><span class="lineno"> 2886</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02887"></a><span class="lineno"> 2887</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02888"></a><span class="lineno"> 2888</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02889"></a><span class="lineno"> 2889</span> {</div><div class="line"><a name="l02890"></a><span class="lineno"> 2890</span>  <span class="keywordflow">return</span> Concat3dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02891"></a><span class="lineno"> 2891</span> }</div><div class="line"><a name="l02892"></a><span class="lineno"> 2892</span> </div><div class="line"><a name="l02893"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a429f624579e1b6ed42b14197fc362310"> 2893</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ad9172e664679c6174261e811c017071d">Concat3dDim1Uint8Test</a>(</div><div class="line"><a name="l02894"></a><span class="lineno"> 2894</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02895"></a><span class="lineno"> 2895</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02896"></a><span class="lineno"> 2896</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02897"></a><span class="lineno"> 2897</span> {</div><div class="line"><a name="l02898"></a><span class="lineno"> 2898</span>  <span class="keywordflow">return</span> Concat3dDim1TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02899"></a><span class="lineno"> 2899</span> }</div><div class="line"><a name="l02900"></a><span class="lineno"> 2900</span> </div><div class="line"><a name="l02901"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a935316c06a7178dbc199694e7971b54c"> 2901</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ae8b0de60ee1ba3c2d5e4507195496399">Concat3dDim2Uint8Test</a>(</div><div class="line"><a name="l02902"></a><span class="lineno"> 2902</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02903"></a><span class="lineno"> 2903</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02904"></a><span class="lineno"> 2904</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02905"></a><span class="lineno"> 2905</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02906"></a><span class="lineno"> 2906</span> {</div><div class="line"><a name="l02907"></a><span class="lineno"> 2907</span>  <span class="keywordflow">return</span> Concat3dDim2TestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02908"></a><span class="lineno"> 2908</span>  workloadFactory, memoryManager, tensorHandleFactory, useSubtensor, 0.5f, -1);</div><div class="line"><a name="l02909"></a><span class="lineno"> 2909</span> }</div><div class="line"><a name="l02910"></a><span class="lineno"> 2910</span> </div><div class="line"><a name="l02911"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a0d9a73b3d2399435b5a440537aae0fb6"> 2911</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a0035d52bb64343a205f1e2702ed6f52a">Concat3dDim0DiffInputDimsUint8Test</a>(</div><div class="line"><a name="l02912"></a><span class="lineno"> 2912</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02913"></a><span class="lineno"> 2913</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02914"></a><span class="lineno"> 2914</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02915"></a><span class="lineno"> 2915</span> {</div><div class="line"><a name="l02916"></a><span class="lineno"> 2916</span>  <span class="keywordflow">return</span> Concat3dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02917"></a><span class="lineno"> 2917</span> }</div><div class="line"><a name="l02918"></a><span class="lineno"> 2918</span> </div><div class="line"><a name="l02919"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#aa71ef9dd8cce482564352b21fa017551"> 2919</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a23e070d290a76ea49b3dc802c315f852">Concat3dDim1DiffInputDimsUint8Test</a>(</div><div class="line"><a name="l02920"></a><span class="lineno"> 2920</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02921"></a><span class="lineno"> 2921</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02922"></a><span class="lineno"> 2922</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02923"></a><span class="lineno"> 2923</span> {</div><div class="line"><a name="l02924"></a><span class="lineno"> 2924</span>  <span class="keywordflow">return</span> Concat3dDim1DiffInputDimsTestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02925"></a><span class="lineno"> 2925</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02926"></a><span class="lineno"> 2926</span> }</div><div class="line"><a name="l02927"></a><span class="lineno"> 2927</span> </div><div class="line"><a name="l02928"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ad8be274610d7ec7bc0ed8f064c21f0ec"> 2928</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 3></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aa36dd3325483a70d872b4009321dcf40">Concat3dDim2DiffInputDimsUint8Test</a>(</div><div class="line"><a name="l02929"></a><span class="lineno"> 2929</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02930"></a><span class="lineno"> 2930</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02931"></a><span class="lineno"> 2931</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l02932"></a><span class="lineno"> 2932</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02933"></a><span class="lineno"> 2933</span> {</div><div class="line"><a name="l02934"></a><span class="lineno"> 2934</span>  <span class="keywordflow">return</span> Concat3dDim2DiffInputDimsTestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02935"></a><span class="lineno"> 2935</span>  workloadFactory, memoryManager, tensorHandleFactory, useSubtensor, 0.5f, -1);</div><div class="line"><a name="l02936"></a><span class="lineno"> 2936</span> }</div><div class="line"><a name="l02937"></a><span class="lineno"> 2937</span> </div><div class="line"><a name="l02938"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a3fed8fe700e7e517f65343aeb4df616c"> 2938</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#afc881fff20745a31065e74be62b33346">Concat4dDim0Uint8Test</a>(</div><div class="line"><a name="l02939"></a><span class="lineno"> 2939</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02940"></a><span class="lineno"> 2940</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02941"></a><span class="lineno"> 2941</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02942"></a><span class="lineno"> 2942</span> {</div><div class="line"><a name="l02943"></a><span class="lineno"> 2943</span>  <span class="keywordflow">return</span> Concat4dDim0TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02944"></a><span class="lineno"> 2944</span> }</div><div class="line"><a name="l02945"></a><span class="lineno"> 2945</span> </div><div class="line"><a name="l02946"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a9f38aae9785430bfe5e111b9372dacfc"> 2946</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a80c04e457e56741a208b6d84d77bae66">Concat4dDim1Uint8Test</a>(</div><div class="line"><a name="l02947"></a><span class="lineno"> 2947</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02948"></a><span class="lineno"> 2948</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02949"></a><span class="lineno"> 2949</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02950"></a><span class="lineno"> 2950</span> {</div><div class="line"><a name="l02951"></a><span class="lineno"> 2951</span>  <span class="keywordflow">return</span> Concat4dDim1TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02952"></a><span class="lineno"> 2952</span> }</div><div class="line"><a name="l02953"></a><span class="lineno"> 2953</span> </div><div class="line"><a name="l02954"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ab7ea1667cddecaedea53dbbefd2fea75"> 2954</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a8bcb164c6900742857a84e7c73fa7fe6">Concat4dDim2Uint8Test</a>(</div><div class="line"><a name="l02955"></a><span class="lineno"> 2955</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02956"></a><span class="lineno"> 2956</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02957"></a><span class="lineno"> 2957</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02958"></a><span class="lineno"> 2958</span> {</div><div class="line"><a name="l02959"></a><span class="lineno"> 2959</span>  <span class="keywordflow">return</span> Concat4dDim2TestImpl<DataType::QAsymmU8>(workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02960"></a><span class="lineno"> 2960</span> }</div><div class="line"><a name="l02961"></a><span class="lineno"> 2961</span> </div><div class="line"><a name="l02962"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#aa70d3cab4cbde75c62db08cc3b0d567e"> 2962</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ac222f8d0e05afd4cb187d2bafde9b46f">Concat4dDim3Uint8Test</a>(</div><div class="line"><a name="l02963"></a><span class="lineno"> 2963</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02964"></a><span class="lineno"> 2964</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02965"></a><span class="lineno"> 2965</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory, <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l02966"></a><span class="lineno"> 2966</span> {</div><div class="line"><a name="l02967"></a><span class="lineno"> 2967</span>  <span class="keywordflow">return</span> Concat4dDim3TestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02968"></a><span class="lineno"> 2968</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1, useSubtensor);</div><div class="line"><a name="l02969"></a><span class="lineno"> 2969</span> }</div><div class="line"><a name="l02970"></a><span class="lineno"> 2970</span> </div><div class="line"><a name="l02971"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ab9608e4ae205d0de8b1a6036583e8ca7"> 2971</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ae0683139ba76b335575fe37d96a26e2a">Concat4dDiffShapeDim0Uint8Test</a>(</div><div class="line"><a name="l02972"></a><span class="lineno"> 2972</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02973"></a><span class="lineno"> 2973</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02974"></a><span class="lineno"> 2974</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02975"></a><span class="lineno"> 2975</span> {</div><div class="line"><a name="l02976"></a><span class="lineno"> 2976</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim0TestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02977"></a><span class="lineno"> 2977</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02978"></a><span class="lineno"> 2978</span> }</div><div class="line"><a name="l02979"></a><span class="lineno"> 2979</span> </div><div class="line"><a name="l02980"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#ae5fc2fc93f2f994d76cd24b77e1c7f80"> 2980</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#a978768350bd24bb2d8b4a25459c265c7">Concat4dDiffShapeDim1Uint8Test</a>(</div><div class="line"><a name="l02981"></a><span class="lineno"> 2981</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02982"></a><span class="lineno"> 2982</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02983"></a><span class="lineno"> 2983</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02984"></a><span class="lineno"> 2984</span> {</div><div class="line"><a name="l02985"></a><span class="lineno"> 2985</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim1TestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02986"></a><span class="lineno"> 2986</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02987"></a><span class="lineno"> 2987</span> }</div><div class="line"><a name="l02988"></a><span class="lineno"> 2988</span> </div><div class="line"><a name="l02989"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a288ea85d287604fd5e470f5e16d3ad91"> 2989</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#ae0420c81890c5a5e7ecb20fb9d8c14d5">Concat4dDiffShapeDim2Uint8Test</a>(</div><div class="line"><a name="l02990"></a><span class="lineno"> 2990</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l02991"></a><span class="lineno"> 2991</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l02992"></a><span class="lineno"> 2992</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory)</div><div class="line"><a name="l02993"></a><span class="lineno"> 2993</span> {</div><div class="line"><a name="l02994"></a><span class="lineno"> 2994</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim2TestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l02995"></a><span class="lineno"> 2995</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1);</div><div class="line"><a name="l02996"></a><span class="lineno"> 2996</span> }</div><div class="line"><a name="l02997"></a><span class="lineno"> 2997</span> </div><div class="line"><a name="l02998"></a><span class="lineno"><a class="line" href="_concat_test_impl_8hpp.xhtml#a6621bf4fe6c56c6d1a5c7af4b529fa45"> 2998</a></span> <a class="code" href="struct_layer_test_result.xhtml">LayerTestResult<uint8_t, 4></a> <a class="code" href="_concat_test_impl_8cpp.xhtml#aff509d223f51a5d7ce024c7911f466f6">Concat4dDiffShapeDim3Uint8Test</a>(</div><div class="line"><a name="l02999"></a><span class="lineno"> 2999</span>  <a class="code" href="classarmnn_1_1_i_workload_factory.xhtml">IWorkloadFactory</a>& workloadFactory,</div><div class="line"><a name="l03000"></a><span class="lineno"> 3000</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">IBackendInternal::IMemoryManagerSharedPtr</a>& memoryManager,</div><div class="line"><a name="l03001"></a><span class="lineno"> 3001</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a>& tensorHandleFactory,</div><div class="line"><a name="l03002"></a><span class="lineno"> 3002</span>  <span class="keywordtype">bool</span> useSubtensor)</div><div class="line"><a name="l03003"></a><span class="lineno"> 3003</span> {</div><div class="line"><a name="l03004"></a><span class="lineno"> 3004</span>  <span class="keywordflow">return</span> Concat4dDiffShapeDim3TestImpl<DataType::QAsymmU8>(</div><div class="line"><a name="l03005"></a><span class="lineno"> 3005</span>  workloadFactory, memoryManager, tensorHandleFactory, 0.5f, -1, useSubtensor);</div><div class="line"><a name="l03006"></a><span class="lineno"> 3006</span> }</div><div class="ttc" id="structarmnn_1_1_permute_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_queue_descriptor.xhtml">armnn::PermuteQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00180">WorkloadData.hpp:180</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a80b1bff1d772096f5a10a26167425ded"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a80b1bff1d772096f5a10a26167425ded">ConcatUint16Test</a></div><div class="ttdeci">LayerTestResult< uint16_t, 3 > ConcatUint16Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02707">ConcatTestImpl.cpp:2707</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aff509d223f51a5d7ce024c7911f466f6"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aff509d223f51a5d7ce024c7911f466f6">Concat4dDiffShapeDim3Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDiffShapeDim3Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02998">ConcatTestImpl.cpp:2998</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8hpp_xhtml"><div class="ttname"><a href="_tensor_copy_utils_8hpp.xhtml">TensorCopyUtils.hpp</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ae4c24060b33c16216f2faf4a7ef7c7ff"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ae4c24060b33c16216f2faf4a7ef7c7ff">Concat3dDim0Test</a></div><div class="ttdeci">LayerTestResult< float, 3 > Concat3dDim0Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02282">ConcatTestImpl.cpp:2282</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ac56cadc167dab69f6fd407d2084ed170"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ac56cadc167dab69f6fd407d2084ed170">Concat2dDim0Test</a></div><div class="ttdeci">LayerTestResult< float, 2 > Concat2dDim0Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02245">ConcatTestImpl.cpp:2245</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ae36ae7043e1761dcfcac538fa29c47d6"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ae36ae7043e1761dcfcac538fa29c47d6">Concat2dDim1TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 2 > Concat2dDim1TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00556">ConcatTestImpl.cpp:556</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a1662cadf61dca494a38d44fb32f116cc"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a1662cadf61dca494a38d44fb32f116cc">Concat4dDim0Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDim0Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02336">ConcatTestImpl.cpp:2336</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a2e952c053f3d7a035671a994352e2bc9"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a2e952c053f3d7a035671a994352e2bc9">ConcatTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > ConcatTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02111">ConcatTestImpl.cpp:2111</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a0047c671718849c74d3bdbd38385f807"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a0047c671718849c74d3bdbd38385f807">Concat4dDim2TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDim2TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01568">ConcatTestImpl.cpp:1568</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a0035d52bb64343a205f1e2702ed6f52a"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a0035d52bb64343a205f1e2702ed6f52a">Concat3dDim0DiffInputDimsUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > Concat3dDim0DiffInputDimsUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02911">ConcatTestImpl.cpp:2911</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8b5d0f8a24e9d9238f412260a552acf8"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8b5d0f8a24e9d9238f412260a552acf8">armnn::TensorInfo::GetShape</a></div><div class="ttdeci">const TensorShape & GetShape() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00187">Tensor.hpp:187</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00122">WorkloadData.hpp:122</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a978768350bd24bb2d8b4a25459c265c7"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a978768350bd24bb2d8b4a25459c265c7">Concat4dDiffShapeDim1Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDiffShapeDim1Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02980">ConcatTestImpl.cpp:2980</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aa298272bc356453c2814543fd0e2cb5f"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aa298272bc356453c2814543fd0e2cb5f">Concat2dDim0DiffInputDimsTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 2 > Concat2dDim0DiffInputDimsTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00582">ConcatTestImpl.cpp:582</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a2e6ce64cef93db0d06b69c4f249fac35"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a2e6ce64cef93db0d06b69c4f249fac35">Concat2dDim0DiffInputDimsUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 2 > Concat2dDim0DiffInputDimsUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02867">ConcatTestImpl.cpp:2867</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a23e070d290a76ea49b3dc802c315f852"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a23e070d290a76ea49b3dc802c315f852">Concat3dDim1DiffInputDimsUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > Concat3dDim1DiffInputDimsUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02919">ConcatTestImpl.cpp:2919</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="_quantize_helper_8hpp_xhtml"><div class="ttname"><a href="_quantize_helper_8hpp.xhtml">QuantizeHelper.hpp</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aa36dd3325483a70d872b4009321dcf40"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aa36dd3325483a70d872b4009321dcf40">Concat3dDim2DiffInputDimsUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > Concat3dDim2DiffInputDimsUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02928">ConcatTestImpl.cpp:2928</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml">armnn::IWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8hpp_source.xhtml#l00022">WorkloadFactory.hpp:22</a></div></div>
<div class="ttc" id="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div>
<div class="ttc" id="struct_layer_test_result_xhtml_a73610ea6c776cc66e5a78dd842a39b8b"><div class="ttname"><a href="struct_layer_test_result.xhtml#a73610ea6c776cc66e5a78dd842a39b8b">LayerTestResult::outputExpected</a></div><div class="ttdeci">boost::multi_array< T, n > outputExpected</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00042">LayerTestResult.hpp:42</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_ac043991b839903b2ba9da884e4020848"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#ac043991b839903b2ba9da884e4020848">armnn::ITensorHandleFactory::CreateSubTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateSubTensorHandle(ITensorHandle &parent, TensorShape const &subTensorShape, unsigned int const *subTensorOrigin) const =0</div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a6dcfe04840950ab1bcf8f1790aa05502"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a6dcfe04840950ab1bcf8f1790aa05502">Concat2dDim1Test</a></div><div class="ttdeci">LayerTestResult< float, 2 > Concat2dDim1Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02253">ConcatTestImpl.cpp:2253</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a80c04e457e56741a208b6d84d77bae66"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a80c04e457e56741a208b6d84d77bae66">Concat4dDim1Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDim1Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02946">ConcatTestImpl.cpp:2946</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aa101efe51ecfbd0e64eeba18ffa49f52"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aa101efe51ecfbd0e64eeba18ffa49f52">Concat2dDim0Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 2 > Concat2dDim0Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02851">ConcatTestImpl.cpp:2851</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ab37c1b501e6ffae4fb1d3e42860a7d1f"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ab37c1b501e6ffae4fb1d3e42860a7d1f">ConcatBFloat16Test</a></div><div class="ttdeci">LayerTestResult< BFloat16, 3 > ConcatBFloat16Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02415">ConcatTestImpl.cpp:2415</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a27d4b7293d9b404c73850981d3496a50"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a27d4b7293d9b404c73850981d3496a50">Concat4dDim2Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDim2Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02352">ConcatTestImpl.cpp:2352</a></div></div>
<div class="ttc" id="_resolve_type_8hpp_xhtml"><div class="ttname"><a href="_resolve_type_8hpp.xhtml">ResolveType.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a053c769dcf82d66ef326c86980c02ba7">armnn::DataType::QSymmS16</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a06290d393dfb595552fc39fd894d964a"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a06290d393dfb595552fc39fd894d964a">Concat2dDim1DiffInputDimsUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 2 > Concat2dDim1DiffInputDimsUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02876">ConcatTestImpl.cpp:2876</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a79c356d8f1e3b0585635ee9b5a9e41a3"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a79c356d8f1e3b0585635ee9b5a9e41a3">Concat4dDiffShapeDim1Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDiffShapeDim1Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02379">ConcatTestImpl.cpp:2379</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aa6de98235dbd3060d428f6bb77b428ce"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aa6de98235dbd3060d428f6bb77b428ce">Concat3dDim1DiffInputDimsTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > Concat3dDim1DiffInputDimsTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02317">ConcatTestImpl.cpp:2317</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__software__tools_8dox_source.xhtml#l00006">01_00_software_tools.dox:6</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a44affeeb090c3c6a3062830562672e84"><div class="ttname"><a href="namespacearmnn.xhtml#a44affeeb090c3c6a3062830562672e84">armnn::IgnoreUnused</a></div><div class="ttdeci">void IgnoreUnused(Ts &&...)</div><div class="ttdef"><b>Definition:</b> <a href="_ignore_unused_8hpp_source.xhtml#l00014">IgnoreUnused.hpp:14</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ab8a589490e332bc749972d21b7dff9a4"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ab8a589490e332bc749972d21b7dff9a4">Concat3dDim2DiffInputDimsTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > Concat3dDim2DiffInputDimsTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02326">ConcatTestImpl.cpp:2326</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml">armnn::TensorShape</a></div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00020">Tensor.hpp:20</a></div></div>
<div class="ttc" id="structarmnn_1_1_queue_descriptor_with_parameters_xhtml_aad91b9bbf7aa365d304febe79a3d1333"><div class="ttname"><a href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">armnn::QueueDescriptorWithParameters::m_Parameters</a></div><div class="ttdeci">LayerDescriptor m_Parameters</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00057">WorkloadData.hpp:57</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_abd92409a35f1f4c41ee52c7471936fd8"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#abd92409a35f1f4c41ee52c7471936fd8">Generate3dPermuteVectorForConcat</a></div><div class="ttdeci">void Generate3dPermuteVectorForConcat(unsigned int numDimensions, unsigned int &concatDim, std::pair< PermutationVector, PermutationVector > &permutations)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00090">ConcatTestImpl.cpp:90</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a05eed8a9797d93f01639ca03025058f0"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a05eed8a9797d93f01639ca03025058f0">Concat4dDiffShapeDim2TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDiffShapeDim2TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01806">ConcatTestImpl.cpp:1806</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml">armnn::ConcatQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00120">WorkloadData.hpp:120</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ac222f8d0e05afd4cb187d2bafde9b46f"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ac222f8d0e05afd4cb187d2bafde9b46f">Concat4dDim3Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDim3Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02962">ConcatTestImpl.cpp:2962</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aa8e37b566b2e5c533fb357eb560e1834"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aa8e37b566b2e5c533fb357eb560e1834">Concat2dDim1Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 2 > Concat2dDim1Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02859">ConcatTestImpl.cpp:2859</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a32bb8d6cf5fc028bf501252767c08b21"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a32bb8d6cf5fc028bf501252767c08b21">armnn::IWorkloadFactory::CreateConcat</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreateConcat(const ConcatQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01332">WorkloadFactory.cpp:1332</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_abe8889e8150beef5fd204b2d87b49298"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#abe8889e8150beef5fd204b2d87b49298">armnn::TensorInfo::SetShape</a></div><div class="ttdeci">void SetShape(const TensorShape &newShape)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00189">Tensor.hpp:189</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a11311cacab78cfb69270c6fba87702e7"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a11311cacab78cfb69270c6fba87702e7">Concat2dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 2 > Concat2dTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const TensorInfo &outputTensorInfo, unsigned int dimension, const float qScale, const int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00460">ConcatTestImpl.cpp:460</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a6f4024f1c2bb14620c566ede118e0c4b"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a6f4024f1c2bb14620c566ede118e0c4b">Concat1dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 1 > Concat1dTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00422">ConcatTestImpl.cpp:422</a></div></div>
<div class="ttc" id="_tensor_helpers_8hpp_xhtml"><div class="ttname"><a href="_tensor_helpers_8hpp.xhtml">TensorHelpers.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_ab78e6fe963508c1ac5c00d04bb3361a3"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#ab78e6fe963508c1ac5c00d04bb3361a3">armnn::OriginsDescriptor::GetViewOrigin</a></div><div class="ttdeci">const uint32_t * GetViewOrigin(uint32_t idx) const</div><div class="ttdoc">Return the view origin at the int value idx. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00197">Descriptors.cpp:197</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a37fb7480d960917baf48e8d06f7b2b66"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a37fb7480d960917baf48e8d06f7b2b66">Concat2dDim0DiffInputDimsTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > Concat2dDim0DiffInputDimsTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02261">ConcatTestImpl.cpp:2261</a></div></div>
<div class="ttc" id="_permute_8hpp_xhtml"><div class="ttname"><a href="_permute_8hpp.xhtml">Permute.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_concat_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::ConcatQueueDescriptor::m_ViewOrigins</a></div><div class="ttdeci">std::vector< ViewOrigin > m_ViewOrigins</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00133">WorkloadData.hpp:133</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a91c4dfde57907d7698c7531785690a7f"><div class="ttname"><a href="_assert_8hpp.xhtml#a91c4dfde57907d7698c7531785690a7f">ARMNN_ASSERT_MSG</a></div><div class="ttdeci">#define ARMNN_ASSERT_MSG(COND, MSG)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00015">Assert.hpp:15</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a23c0f7c8c79ce30cccbd65f39d692ed6"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a23c0f7c8c79ce30cccbd65f39d692ed6">Concat3dDim1Test</a></div><div class="ttdeci">LayerTestResult< float, 3 > Concat3dDim1Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02290">ConcatTestImpl.cpp:2290</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_backend_internal_xhtml_a693b40e6b94e958836aeb0410ca186bd"><div class="ttname"><a href="classarmnn_1_1_i_backend_internal.xhtml#a693b40e6b94e958836aeb0410ca186bd">armnn::IBackendInternal::IMemoryManagerSharedPtr</a></div><div class="ttdeci">std::shared_ptr< IMemoryManager > IMemoryManagerSharedPtr</div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_backend_internal_8hpp_source.xhtml#l00092">IBackendInternal.hpp:92</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_aa97b8ca791f9380d2b678ff2ec923985"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#aa97b8ca791f9380d2b678ff2ec923985">Concat3dDim0DiffInputDimsTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dDim0DiffInputDimsTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01012">ConcatTestImpl.cpp:1012</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml">armnn::OriginsDescriptor</a></div><div class="ttdoc">An OriginsDescriptor for the ConcatLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00163">Descriptors.hpp:163</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a972c1ddee6e494e695dc10e0982142b5"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a972c1ddee6e494e695dc10e0982142b5">Concat4dDiffShapeDim0Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDiffShapeDim0Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02370">ConcatTestImpl.cpp:2370</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a8f747abd7bf17b57c3178c077a8e1949"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a8f747abd7bf17b57c3178c077a8e1949">Concat4dDiffShapeDim1TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDiffShapeDim1TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01738">ConcatTestImpl.cpp:1738</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a2f6ac42e5f7a238f07a6bd5869ef767d"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a2f6ac42e5f7a238f07a6bd5869ef767d">ConcatUint8DifferentQParamsTest</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > ConcatUint8DifferentQParamsTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02423">ConcatTestImpl.cpp:2423</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a685739c4eb65a580e075282cfe6787d6"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a685739c4eb65a580e075282cfe6787d6">armnn::TensorInfo::SetQuantizationScale</a></div><div class="ttdeci">void SetQuantizationScale(float scale)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00464">Tensor.cpp:464</a></div></div>
<div class="ttc" id="_assert_8hpp_xhtml_a5698be69cbd5dfe6c28fcd9867e8cbed"><div class="ttname"><a href="_assert_8hpp.xhtml#a5698be69cbd5dfe6c28fcd9867e8cbed">ARMNN_ASSERT</a></div><div class="ttdeci">#define ARMNN_ASSERT(COND)</div><div class="ttdef"><b>Definition:</b> <a href="_assert_8hpp_source.xhtml#l00014">Assert.hpp:14</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ae0683139ba76b335575fe37d96a26e2a"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ae0683139ba76b335575fe37d96a26e2a">Concat4dDiffShapeDim0Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDiffShapeDim0Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02971">ConcatTestImpl.cpp:2971</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a597f0b89f3fb6389802de4a51c979b17"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a597f0b89f3fb6389802de4a51c979b17">Concat4dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const TensorInfo &outputTensorInfo, unsigned int dimension, bool useSubtensor, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01421">ConcatTestImpl.cpp:1421</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_af8d15f0030605e3aa9d3679bbd207ae7"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#af8d15f0030605e3aa9d3679bbd207ae7">ConcatFloat16Test</a></div><div class="ttdeci">LayerTestResult< Half, 3 > ConcatFloat16Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02407">ConcatTestImpl.cpp:2407</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a908c80ff86d48fe1bc7cd4d4b1d00147"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a908c80ff86d48fe1bc7cd4d4b1d00147">CreateDescriptorForConcat</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcat(const std::vector< TensorInfo > &inputTensorInfos, unsigned int concatDim)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00026">ConcatTestImpl.cpp:26</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_a99b626c58a926dc7d6df78d22ec186c8"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#a99b626c58a926dc7d6df78d22ec186c8">CopyDataFromITensorHandle</a></div><div class="ttdeci">void CopyDataFromITensorHandle(void *memory, const armnn::ITensorHandle *tensorHandle)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00014">TensorCopyUtils.cpp:14</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ad9172e664679c6174261e811c017071d"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ad9172e664679c6174261e811c017071d">Concat3dDim1Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > Concat3dDim1Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02893">ConcatTestImpl.cpp:2893</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml">armnn::PermutationVector</a></div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00212">Types.hpp:212</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a2dcee0bc4bbae1f745324aed0f841a0a"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a2dcee0bc4bbae1f745324aed0f841a0a">armnn::IWorkloadFactory::CreatePermute</a></div><div class="ttdeci">virtual std::unique_ptr< IWorkload > CreatePermute(const PermuteQueueDescriptor &descriptor, const WorkloadInfo &info) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_factory_8cpp_source.xhtml#l01561">WorkloadFactory.cpp:1561</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a88cd4482bd3e898692dd42f3dcff60c0"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a88cd4482bd3e898692dd42f3dcff60c0">PermuteInputsForConcat</a></div><div class="ttdeci">void PermuteInputsForConcat(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, std::vector< TensorInfo > &inputTensorInfos, std::vector< T *> &inputData, std::vector< std::vector< T >> &inputDataStorage, PermutationVector &permuteVector, unsigned int &concatDim, TensorInfo &outputTensorInfo)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00171">ConcatTestImpl.cpp:171</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a6552cacc8f771f0e210949d56193c8d7"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a6552cacc8f771f0e210949d56193c8d7">Concat3dDim0TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dDim0TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00825">ConcatTestImpl.cpp:825</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a47256d5dd1d5c65c719c40b2bfc7cc96"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a47256d5dd1d5c65c719c40b2bfc7cc96">Concat3dDim2Test</a></div><div class="ttdeci">LayerTestResult< float, 3 > Concat3dDim2Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02298">ConcatTestImpl.cpp:2298</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a4e2a03f3c7e369896b804e963cfca10c"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a4e2a03f3c7e369896b804e963cfca10c">Concat3dDim1TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dDim1TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00899">ConcatTestImpl.cpp:899</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a5d244b77eab878c84d0bdf67ef387d3c"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a5d244b77eab878c84d0bdf67ef387d3c">Concat4dDiffShapeDim2Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDiffShapeDim2Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02388">ConcatTestImpl.cpp:2388</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a8fcf10f2804bcbbfef4fd86ef6a5ff2d"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a8fcf10f2804bcbbfef4fd86ef6a5ff2d">ExpandTensorShapeTo3dForPermute</a></div><div class="ttdeci">TensorShape ExpandTensorShapeTo3dForPermute(const TensorShape &inputShape)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00072">ConcatTestImpl.cpp:72</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_af1042834da8c73c7d8f46160b0b73b45"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#af1042834da8c73c7d8f46160b0b73b45">Concat2dDim1DiffInputDimsTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 2 > Concat2dDim1DiffInputDimsTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00662">ConcatTestImpl.cpp:662</a></div></div>
<div class="ttc" id="struct_layer_test_result_xhtml_ac9d44d346bb7c89f7a7aa31d2bee947f"><div class="ttname"><a href="struct_layer_test_result.xhtml#ac9d44d346bb7c89f7a7aa31d2bee947f">LayerTestResult::output</a></div><div class="ttdeci">boost::multi_array< T, n > output</div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00041">LayerTestResult.hpp:41</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a01c3919ddf83b3f3c527cc8433bbddff"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a01c3919ddf83b3f3c527cc8433bbddff">Concat2dDim0TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 2 > Concat2dDim0TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00518">ConcatTestImpl.cpp:518</a></div></div>
<div class="ttc" id="classarmnn_1_1_permutation_vector_xhtml_aae44e4154aa80fba7616747450ff69d5"><div class="ttname"><a href="classarmnn_1_1_permutation_vector.xhtml#aae44e4154aa80fba7616747450ff69d5">armnn::PermutationVector::IsEqual</a></div><div class="ttdeci">bool IsEqual(const PermutationVector &other) const</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00246">Types.hpp:246</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a6e54f02f06e0a3eac39e726a091290df"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a6e54f02f06e0a3eac39e726a091290df">Concat4dDim1Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDim1Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02344">ConcatTestImpl.cpp:2344</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ae0420c81890c5a5e7ecb20fb9d8c14d5"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ae0420c81890c5a5e7ecb20fb9d8c14d5">Concat4dDiffShapeDim2Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDiffShapeDim2Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02989">ConcatTestImpl.cpp:2989</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_af7bc28cdb723e0cea4c0b860f40fa47e"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#af7bc28cdb723e0cea4c0b860f40fa47e">Concat3dDim2TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dDim2TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00973">ConcatTestImpl.cpp:973</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a1deafe1b2777bcaadefe2371b3ebbb27"><div class="ttname"><a href="namespacearmnn.xhtml#a1deafe1b2777bcaadefe2371b3ebbb27">armnn::Concatenate</a></div><div class="ttdeci">void Concatenate(const ConcatQueueDescriptor &data)</div><div class="ttdef"><b>Definition:</b> <a href="_concatenate_8cpp_source.xhtml#l00014">Concatenate.cpp:14</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a8260222560c2abfbea0f87869ae22062"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a8260222560c2abfbea0f87869ae22062">Concat3dDim0DiffInputDimsTest</a></div><div class="ttdeci">LayerTestResult< float, 3 > Concat3dDim0DiffInputDimsTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02308">ConcatTestImpl.cpp:2308</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a65992de6287260bcb4be7a4852a22c9c"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a65992de6287260bcb4be7a4852a22c9c">Concat4dDim1TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDim1TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01527">ConcatTestImpl.cpp:1527</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a4a9959df06e443a3e38d234564ad7736"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a4a9959df06e443a3e38d234564ad7736">Concat4dDiffShapeDim3TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDiffShapeDim3TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01884">ConcatTestImpl.cpp:1884</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a410d43ad354ebb8995b7d9bc5b2c686c"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a410d43ad354ebb8995b7d9bc5b2c686c">Concat4dDim3Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDim3Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02360">ConcatTestImpl.cpp:2360</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="classarmnn_1_1_tensor_shape_xhtml_a157e27d41e9f6b21f0d3c025fa47dc24"><div class="ttname"><a href="classarmnn_1_1_tensor_shape.xhtml#a157e27d41e9f6b21f0d3c025fa47dc24">armnn::TensorShape::GetNumDimensions</a></div><div class="ttdeci">unsigned int GetNumDimensions() const</div><div class="ttdoc">Function that returns the tensor rank. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8cpp_source.xhtml#l00174">Tensor.cpp:174</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml">armnn::ITensorHandleFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="include_2armnn_2backends_2_i_tensor_handle_factory_8hpp_source.xhtml#l00041">ITensorHandleFactory.hpp:41</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_adbfedf414aac270a5e285d057bfb58b6"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#adbfedf414aac270a5e285d057bfb58b6">Concat3dDim0Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > Concat3dDim0Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02885">ConcatTestImpl.cpp:2885</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a78e8266be865fdd92cadd04d6e25ae1f"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a78e8266be865fdd92cadd04d6e25ae1f">armnn::OriginsDescriptor::GetNumDimensions</a></div><div class="ttdeci">uint32_t GetNumDimensions() const</div><div class="ttdoc">Get the number of dimensions. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00192">Descriptors.cpp:192</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a4332f7562287037bac9aed90d11a2db9"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a4332f7562287037bac9aed90d11a2db9">ConcatUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > ConcatUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02569">ConcatTestImpl.cpp:2569</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ac8fc5aa4f5e286f98708650f6a8682a6"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ac8fc5aa4f5e286f98708650f6a8682a6">Concat3dTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const TensorInfo &outputTensorInfo, unsigned int dimension, bool useSubtensor, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00730">ConcatTestImpl.cpp:730</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ad3d1e6175529a26ba9698955b7cc0d8e"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ad3d1e6175529a26ba9698955b7cc0d8e">Concat1dUint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 1 > Concat1dUint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02843">ConcatTestImpl.cpp:2843</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml"><div class="ttname"><a href="namespacearmnn_utils.xhtml">armnnUtils</a></div><div class="ttdef"><b>Definition:</b> <a href="_data_layout_indexed_8hpp_source.xhtml#l00013">DataLayoutIndexed.hpp:13</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a5e7803092126fd2862fdd8a2ed90e32a"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a5e7803092126fd2862fdd8a2ed90e32a">Concat2dDim1DiffInputDimsTest</a></div><div class="ttdeci">LayerTestResult< float, 2 > Concat2dDim1DiffInputDimsTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02270">ConcatTestImpl.cpp:2270</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a452d79381b887bbbcc645b44025c03ca"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a452d79381b887bbbcc645b44025c03ca">PermuteTensorData</a></div><div class="ttdeci">void PermuteTensorData(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const PermutationVector &mappings, TensorInfo &inputTensorInfo, const T *inputData, std::vector< T > &outputData)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00121">ConcatTestImpl.cpp:121</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</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 inputs and outputs to 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="struct_layer_test_result_xhtml"><div class="ttname"><a href="struct_layer_test_result.xhtml">LayerTestResult</a></div><div class="ttdef"><b>Definition:</b> <a href="_layer_test_result_8hpp_source.xhtml#l00030">LayerTestResult.hpp:30</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a08b924fe6933a0c221cc0194dffb2b7d"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a08b924fe6933a0c221cc0194dffb2b7d">Concat3dDim2DiffInputDimsTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dDim2DiffInputDimsTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01304">ConcatTestImpl.cpp:1304</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a733ae6b70d0bfa43433c3e7606992328"><div class="ttname"><a href="namespacearmnn.xhtml#a733ae6b70d0bfa43433c3e7606992328">armnn::CreateDescriptorForConcatenation</a></div><div class="ttdeci">OriginsDescriptor CreateDescriptorForConcatenation(TensorShapeIt first, TensorShapeIt last, unsigned int concatenationDimension)</div><div class="ttdoc">Convenience template to create an OriginsDescriptor to use when creating a ConcatLayer for performing...</div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00258">Descriptors.hpp:258</a></div></div>
<div class="ttc" id="structarmnn_1_1_origins_descriptor_xhtml_a35546e7b56e6e972a495b48748478ede"><div class="ttname"><a href="structarmnn_1_1_origins_descriptor.xhtml#a35546e7b56e6e972a495b48748478ede">armnn::OriginsDescriptor::GetNumViews</a></div><div class="ttdeci">uint32_t GetNumViews() const</div><div class="ttdoc">Get the number of views. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8cpp_source.xhtml#l00187">Descriptors.cpp:187</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a198a5812a5bf73d54928ec978f8cf7af"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a198a5812a5bf73d54928ec978f8cf7af">Concat4dDim0TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDim0TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01486">ConcatTestImpl.cpp:1486</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a8bcb164c6900742857a84e7c73fa7fe6"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a8bcb164c6900742857a84e7c73fa7fe6">Concat4dDim2Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDim2Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02954">ConcatTestImpl.cpp:2954</a></div></div>
<div class="ttc" id="_concat_test_impl_8hpp_xhtml"><div class="ttname"><a href="_concat_test_impl_8hpp.xhtml">ConcatTestImpl.hpp</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_ae8b0de60ee1ba3c2d5e4507195496399"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#ae8b0de60ee1ba3c2d5e4507195496399">Concat3dDim2Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 3 > Concat3dDim2Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02901">ConcatTestImpl.cpp:2901</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a460c8d79a829f1ed16f088c12acf41f3"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a460c8d79a829f1ed16f088c12acf41f3">ConcatDifferentInputOutputQParamTest</a></div><div class="ttdeci">LayerTestResult< T, 3 > ConcatDifferentInputOutputQParamTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01952">ConcatTestImpl.cpp:1952</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a9b4987fbd28822cfde9a5fcbf50793a5"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a9b4987fbd28822cfde9a5fcbf50793a5">Concat4dDim3TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDim3TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01609">ConcatTestImpl.cpp:1609</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a8ce943ec73def7b13345b918d787d7ca"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a8ce943ec73def7b13345b918d787d7ca">Concat3dDim1DiffInputDimsTestImpl</a></div><div class="ttdeci">LayerTestResult< T, 3 > Concat3dDim1DiffInputDimsTestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01164">ConcatTestImpl.cpp:1164</a></div></div>
<div class="ttc" id="namespacearmnn_utils_xhtml_abeaf4f6785039866fd075f4569ba8e84"><div class="ttname"><a href="namespacearmnn_utils.xhtml#abeaf4f6785039866fd075f4569ba8e84">armnnUtils::Permuted</a></div><div class="ttdeci">armnn::TensorShape Permuted(const armnn::TensorShape &srcShape, const armnn::PermutationVector &mappings)</div><div class="ttdef"><b>Definition:</b> <a href="_permute_8cpp_source.xhtml#l00098">Permute.cpp:98</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_tensor_handle_factory_xhtml_a375f11dd42ff042435e8771cf287b20c"><div class="ttname"><a href="classarmnn_1_1_i_tensor_handle_factory.xhtml#a375f11dd42ff042435e8771cf287b20c">armnn::ITensorHandleFactory::CreateTensorHandle</a></div><div class="ttdeci">virtual std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo) const =0</div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a63a31f32393eda0ec427511ab8da5e55"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a63a31f32393eda0ec427511ab8da5e55">Concat4dDiffShapeDim3Test</a></div><div class="ttdeci">LayerTestResult< float, 4 > Concat4dDiffShapeDim3Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, bool useSubtensor)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02397">ConcatTestImpl.cpp:2397</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_afc881fff20745a31065e74be62b33346"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#afc881fff20745a31065e74be62b33346">Concat4dDim0Uint8Test</a></div><div class="ttdeci">LayerTestResult< uint8_t, 4 > Concat4dDim0Uint8Test(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02938">ConcatTestImpl.cpp:2938</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a6b08fab8b8a00113a020b10b311f745e"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a6b08fab8b8a00113a020b10b311f745e">Concat1dTest</a></div><div class="ttdeci">LayerTestResult< float, 1 > Concat1dTest(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l02237">ConcatTestImpl.cpp:2237</a></div></div>
<div class="ttc" id="classarmnn_1_1_tensor_info_xhtml_a8846406ac37fbd2204f0be16ee05d5b7"><div class="ttname"><a href="classarmnn_1_1_tensor_info.xhtml#a8846406ac37fbd2204f0be16ee05d5b7">armnn::TensorInfo::GetNumElements</a></div><div class="ttdeci">unsigned int GetNumElements() const</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_8hpp_source.xhtml#l00192">Tensor.hpp:192</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a905e011ae8536bbd643dd09495524596"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a905e011ae8536bbd643dd09495524596">NeedPermuteForConcat</a></div><div class="ttdeci">bool NeedPermuteForConcat(const std::vector< TensorInfo > &inputTensorInfos, unsigned int concatDim)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00046">ConcatTestImpl.cpp:46</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_af61d63d5fc13eca28851f3fa65289c47"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#af61d63d5fc13eca28851f3fa65289c47">PermuteOutputForConcat</a></div><div class="ttdeci">void PermuteOutputForConcat(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, const TensorInfo &tensorInfo, const PermutationVector &permuteVector, std::unique_ptr< ITensorHandle > &&inputDataHandle, T *data)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l00241">ConcatTestImpl.cpp:241</a></div></div>
<div class="ttc" id="structarmnn_1_1_permute_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_permute_descriptor.xhtml">armnn::PermuteDescriptor</a></div><div class="ttdoc">A PermuteDescriptor for the PermuteLayer. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00118">Descriptors.hpp:118</a></div></div>
<div class="ttc" id="_tensor_copy_utils_8cpp_xhtml_ae15f1a3c55d2db87683577de9fa4437c"><div class="ttname"><a href="_tensor_copy_utils_8cpp.xhtml#ae15f1a3c55d2db87683577de9fa4437c">CopyDataToITensorHandle</a></div><div class="ttdeci">void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)</div><div class="ttdef"><b>Definition:</b> <a href="_tensor_copy_utils_8cpp_source.xhtml#l00009">TensorCopyUtils.cpp:9</a></div></div>
<div class="ttc" id="_concat_test_impl_8cpp_xhtml_a4b3d058400cdae1f9f42bca0bd4cdd78"><div class="ttname"><a href="_concat_test_impl_8cpp.xhtml#a4b3d058400cdae1f9f42bca0bd4cdd78">Concat4dDiffShapeDim0TestImpl</a></div><div class="ttdeci">LayerTestResult< T, 4 > Concat4dDiffShapeDim0TestImpl(IWorkloadFactory &workloadFactory, const IBackendInternal::IMemoryManagerSharedPtr &memoryManager, const armnn::ITensorHandleFactory &tensorHandleFactory, float qScale, int32_t qOffset)</div><div class="ttdef"><b>Definition:</b> <a href="_concat_test_impl_8cpp_source.xhtml#l01651">ConcatTestImpl.cpp:1651</a></div></div>
<div class="ttc" id="classarmnn_1_1_i_workload_factory_xhtml_a37f4eba7877deb34f4d8d64c9bcb9ab5"><div class="ttname"><a href="classarmnn_1_1_i_workload_factory.xhtml#a37f4eba7877deb34f4d8d64c9bcb9ab5">armnn::IWorkloadFactory::SupportsSubTensors</a></div><div class="ttdeci">virtual bool SupportsSubTensors() const =0</div></div>
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