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<div class="title">WorkloadDataValidation.cpp</div> </div>
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<a href="_workload_data_validation_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. 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="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.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="_exceptions_8hpp.xhtml">armnn/Exceptions.hpp</a>></span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> </div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include <<a class="code" href="_cpu_tensor_handle_8hpp.xhtml">backendsCommon/CpuTensorHandle.hpp</a>></span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include <<a class="code" href="_workload_8hpp.xhtml">backendsCommon/Workload.hpp</a>></span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> </div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="preprocessor">#include <<a class="code" href="_ref_workloads_8hpp.xhtml">reference/workloads/RefWorkloads.hpp</a>></span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#include <<a class="code" href="_ref_workload_factory_8hpp.xhtml">reference/RefWorkloadFactory.hpp</a>></span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="preprocessor">#include <boost/test/unit_test.hpp></span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> </div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="keyword">using namespace </span><a class="code" href="namespacearmnn.xhtml">armnn</a>;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <a class="code" href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a>(WorkloadInfoValidation)</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a5e734b459a947759e3ca5f76e2f278f2"> 22</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(BatchNormalizationQueueDescriptor_Validate_DifferentQuantizationData)</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> {</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape { 1, 3, 2, 2 };</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape { 1, 3, 2, 2 };</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> </div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputTensorInfo(inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, .1f, 125);</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputTensorInfo(outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>, .2f, 120);</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>  <a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">BatchNormalizationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> </div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> sameShape[] = { 10 };</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> sameInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(1, sameShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">armnn::DataType::QAsymmU8</a>);</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> sameTensor(sameInfo);</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>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</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>  invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">m_Mean</a> = &sameTensor;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">m_Variance</a> = &sameTensor;</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">m_Beta</a>= &sameTensor;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  invalidData.<a class="code" href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">m_Gamma</a> = &sameTensor;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  BOOST_CHECK_NO_THROW(<a class="code" href="classarmnn_1_1_ref_batch_normalization_workload.xhtml">RefBatchNormalizationWorkload</a>(invalidData, invalidInfo));</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> }</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> </div><div class="line"><a name="l00048"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a30f72f6909ab012842e16398984c77c0"> 48</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(QueueDescriptor_Validate_WrongNumOfInputsOutputs)</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>  <a class="code" href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">InputQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="comment">//Invalid argument exception is expected, because no inputs and no outputs were defined.</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_workload_factory.xhtml">RefWorkloadFactory</a>().CreateInput(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> }</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> </div><div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a12def4fb1722495397eaf309cb8ef712"> 56</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(RefPooling2dFloat32Workload_Validate_WrongDimTensor)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {2, 3, 4}; <span class="comment">// <- Invalid - input tensor has to be 4D.</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {2, 3, 4, 5};</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>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(3, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">Pooling2dQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> </div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> </div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  <span class="comment">// Invalid argument exception is expected, input tensor has to be 4D.</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_pooling2d_workload.xhtml">RefPooling2dWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> }</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#ae59870717f9ac828ab0bf3bc6804bfec"> 77</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(SoftmaxQueueDescriptor_Validate_WrongInputHeight)</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> {</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 4;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> </div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight + 1; <span class="comment">//Makes data invalid - Softmax expects height and width to be 1.</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> </div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };</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>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <a class="code" href="structarmnn_1_1_softmax_queue_descriptor.xhtml">SoftmaxQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> </div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> </div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  <span class="comment">//Invalid argument exception is expected, because height != 1.</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_softmax_workload.xhtml">RefSoftmaxWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> }</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a1a76fe496d4e2ee0b0893a36736ae288"> 108</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing)</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 1;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 1;</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 5;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 2;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 1;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 1;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 2;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// Define the tensor descriptors.</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> weightsDesc;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> biasesDesc;</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> weightsShape[] = { 1, 1, inputChannels, outputChannels };</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> biasShape[] = { 1, outputChannels, outputHeight, outputWidth };</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> </div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  weightsDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, weightsShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  biasesDesc = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, biasShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> </div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">FullyConnectedQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</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>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(weightsDesc);</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasTensor(biasesDesc);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  invalidData.<a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightTensor;</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  invalidData.<a class="code" href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasTensor;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">m_TransposeWeightMatrix</a> = <span class="keyword">false</span>;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="comment">//Invalid argument exception is expected, because not all required fields have been provided.</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  <span class="comment">//In particular inputsData[0], outputsData[0] and weightsData can not be null.</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_fully_connected_workload.xhtml">RefFullyConnectedWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> }</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a5133fd5b53fab2340f3655d9cb27e6c1"> 156</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(NormalizationQueueDescriptor_Validate_WrongInputHeight)</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> {</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 5;</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 32;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 24;</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = inputHeight + 1; <span class="comment">//Makes data invalid - normalization requires.</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="comment">//Input and output to have the same dimensions.</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> </div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> </div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a> normMethod = <a class="code" href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a>;</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a> normChannel = <a class="code" href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a>;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordtype">float</span> alpha = 1.f;</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordtype">float</span> beta = 1.f;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordtype">float</span> kappa = 1.f;</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  uint32_t normSize = 5;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <a class="code" href="structarmnn_1_1_normalization_queue_descriptor.xhtml">NormalizationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</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>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">m_NormChannelType</a> = normChannel;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">m_NormMethodType</a> = normMethod;</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">m_NormSize</a> = normSize;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">m_Alpha</a> = alpha;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">m_Beta</a> = beta;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  invalidData.<a class="code" href="structarmnn_1_1_queue_descriptor_with_parameters.xhtml#aad91b9bbf7aa365d304febe79a3d1333">m_Parameters</a>.<a class="code" href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">m_K</a> = kappa;</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="comment">//Invalid argument exception is expected, because input height != output height.</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_normalization_workload.xhtml">RefNormalizationWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> }</div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> </div><div class="line"><a name="l00203"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a454c029687438437ea707b94f88361e1"> 203</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(SplitterQueueDescriptor_Validate_WrongWindow)</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span> {</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 32;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 24;</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = inputNum;</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = inputChannels;</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 18;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = inputWidth;</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> </div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml">SplitterQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> </div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="comment">// Invalid, since it has only 3 dimensions while the input tensor is 4d.</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  std::vector<unsigned int> wOrigin = {0, 0, 0};</div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window(wOrigin);</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window);</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>  BOOST_TEST_INFO(<span class="stringliteral">"Invalid argument exception is expected, because split window dimensionality does not "</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="stringliteral">"match input."</span>);</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_splitter_workload.xhtml">RefSplitterWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span> </div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  <span class="comment">// Invalid, since window extends past the boundary of input tensor.</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window3(wOrigin3);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window3;</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  BOOST_TEST_INFO(<span class="stringliteral">"Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight"</span>);</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_splitter_workload.xhtml">RefSplitterWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> </div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window4(wOrigin4);</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window4;</div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> </div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};</div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <a class="code" href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a> window5(wOrigin4);</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  invalidData.<a class="code" href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window5);</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> </div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  BOOST_TEST_INFO(<span class="stringliteral">"Invalid exception due to number of split windows not matching number of outputs."</span>);</div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_splitter_workload.xhtml">RefSplitterWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> }</div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> </div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> </div><div class="line"><a name="l00261"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a198e5f41a13facee99cf14390282e186"> 261</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(ConcatQueueDescriptor_Validate_WrongWindow)</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>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputNum = 1;</div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputChannels = 3;</div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputHeight = 32;</div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputWidth = 24;</div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> </div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputNum = 1;</div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputChannels = 3;</div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputHeight = 32;</div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputWidth = 24;</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> </div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> </div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth};</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};</div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> </div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml">ConcatQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> </div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// Invalid, since it has only 3 dimensions while the input tensor is 4d.</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  std::vector<unsigned int> wOrigin = {0, 0, 0};</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window(wOrigin);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  BOOST_TEST_INFO(<span class="stringliteral">"Invalid argument exception is expected, because merge window dimensionality does not "</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="stringliteral">"match input."</span>);</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_concat_workload.xhtml">RefConcatWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> </div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="comment">// Invalid, since window extends past the boundary of output tensor.</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};</div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window3(wOrigin3);</div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span>  invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window3;</div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  BOOST_TEST_INFO(<span class="stringliteral">"Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight"</span>);</div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_concat_workload.xhtml">RefConcatWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> </div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> </div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};</div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window4(wOrigin4);</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>[0] = window4;</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>  std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  <a class="code" href="structarmnn_1_1_concat_queue_descriptor_1_1_view_origin.xhtml">armnn::ConcatQueueDescriptor::ViewOrigin</a> window5(wOrigin4);</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  invalidData.<a class="code" href="structarmnn_1_1_concat_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">m_ViewOrigins</a>.push_back(window5);</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> </div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span>  BOOST_TEST_INFO(<span class="stringliteral">"Invalid exception due to number of merge windows not matching number of inputs."</span>);</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_concat_workload.xhtml">RefConcatWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> }</div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> </div><div class="line"><a name="l00318"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a5f1870bd33bf34b70b985e24da85700f"> 318</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(AdditionQueueDescriptor_Validate_InputNumbers)</div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> {</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo;</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input2TensorInfo;</div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input3TensorInfo;</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> </div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape[] = {1, 1, 1, 1};</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>  input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span>  input2TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  input3TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</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>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span> </div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</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="comment">// Too few inputs.</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefAdditionWorkload<></a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> </div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, <span class="keyword">nullptr</span>);</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>  <span class="comment">// Correct.</span></div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  BOOST_CHECK_NO_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefAdditionWorkload<></a>(invalidData, invalidInfo));</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>  AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> </div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="comment">// Too many inputs.</span></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefAdditionWorkload<></a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> }</div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a77921e834125a4a2d63142cb8ac115f6"> 352</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(AdditionQueueDescriptor_Validate_InputShapes)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input2TensorInfo;</div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> </div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape1[] = {1, 1, 2, 1};</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> shape2[] = {1, 1, 3, 2};</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>  <span class="comment">// Incompatible shapes even with broadcasting.</span></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>  input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  input2TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape2, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span> </div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</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>  AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span>  AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</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>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefAdditionWorkload<></a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span>  }</div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span>  <span class="comment">// Output size not compatible with input sizes.</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>  input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span>  input2TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape1, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, shape2, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> </div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span>  <a class="code" href="structarmnn_1_1_addition_queue_descriptor.xhtml">AdditionQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</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>  AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> </div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="comment">// Output differs.</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefAdditionWorkload<></a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  }</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span> }</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#abf20db1f5c57d502fe310198a9264b7c"> 395</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch)</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>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input0TensorInfo;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> input1TensorInfo;</div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</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>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input0Shape[] = { 2, 2, 4, 4 };</div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  constexpr std::size_t dimensionCount = std::extent<decltype(input0Shape)>::value;</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> </div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="comment">// Checks dimension consistency for input tensors.</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)</div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  {</div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> input1Shape[dimensionCount];</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < dimensionCount; ++i)</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  {</div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  input1Shape[i] = input0Shape[i];</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  }</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span> </div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  ++input1Shape[dimIndex];</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>  input0TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input1Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> </div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">MultiplicationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> </div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span>  AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> </div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefMultiplicationWorkload<></a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  }</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> </div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="comment">// Checks dimension consistency for input and output tensors.</span></div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  {</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[dimensionCount];</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span>  <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0; i < dimensionCount; ++i)</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>  outputShape[i] = input0Shape[i];</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> </div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  ++outputShape[dimIndex];</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  input0TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  input1TensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, input0Shape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(dimensionCount, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> </div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  <a class="code" href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">MultiplicationQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span> </div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span> </div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_elementwise_workload.xhtml">RefMultiplicationWorkload<></a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  }</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> }</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#a1877eca1aa5adb4c0e1302d04c96d013"> 455</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(ReshapeQueueDescriptor_Validate_MismatchingNumElements)</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> {</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo;</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo;</div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  <span class="comment">// The input and output shapes should have the same number of elements, but these don't.</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputShape[] = { 1, 1, 2, 3 };</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputShape[] = { 1, 1, 1, 2 };</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> </div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span>  inputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, inputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  outputTensorInfo = <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a>(4, outputShape, <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>);</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> </div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  <a class="code" href="structarmnn_1_1_reshape_queue_descriptor.xhtml">ReshapeQueueDescriptor</a> invalidData;</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> invalidInfo;</div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> </div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span>  AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, <span class="keyword">nullptr</span>);</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">// InvalidArgumentException is expected, because the number of elements don't match.</span></div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span>  BOOST_CHECK_THROW(<a class="code" href="classarmnn_1_1_ref_reshape_workload.xhtml">RefReshapeWorkload</a>(invalidData, invalidInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> }</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> </div><div class="line"><a name="l00478"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#ad3a7db97268896c1bfa8df01f5834b3f"> 478</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(LstmQueueDescriptor_Validate)</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> {</div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a> dataType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a166495adc0d0f53bee6baecc577f5204">armnn::DataType::Float32</a>;</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span>  <span class="keywordtype">float</span> qScale = 0.0f;</div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  int32_t qOffset = 0;</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> </div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batchSize = 2;</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> outputSize = 3;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> inputSize = 5;</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  <span class="keywordtype">unsigned</span> numUnits = 4;</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> inputTensorInfo({batchSize , inputSize}, dataType, qScale, qOffset );</div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateInTensorInfo({batchSize , outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateInTensorInfo({batchSize , numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> </div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  <span class="comment">// Scratch buffer size with CIFG [batchSize, numUnits * 4]</span></div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo({batchSize, numUnits * 4}, dataType, qScale, qOffset);</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> cellStateOutTensorInfo({batchSize, numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputStateOutTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> outputTensorInfo({batchSize, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span> </div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo3({outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4({numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x5({numUnits, inputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo4x3({numUnits, outputSize}, dataType, qScale, qOffset);</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> tensorInfo3x4({outputSize, numUnits}, dataType, qScale, qOffset);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="structarmnn_1_1_lstm_queue_descriptor.xhtml">LstmQueueDescriptor</a> data;</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</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="l00508"></a><span class="lineno"> 508</span> </div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  AddInputToWorkload(data, info, inputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  AddInputToWorkload(data, info, outputStateInTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  AddInputToWorkload(data, info, cellStateInTensorInfo, <span class="keyword">nullptr</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>  AddOutputToWorkload(data, info, scratchBufferTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  AddOutputToWorkload(data, info, outputStateOutTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  AddOutputToWorkload(data, info, cellStateOutTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="comment">// AddOutputToWorkload(data, info, outputTensorInfo, nullptr); is left out</span></div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span> </div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToInputWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToForgetWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToCellWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputToOutputWeightsTensor(tensorInfo4x5);</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToForgetWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToInputWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToCellWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> recurrentToOutputWeightsTensor(tensorInfo4x3);</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToInputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellBiasTensor(tensorInfo4);</div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputGateBiasTensor(tensorInfo4);</div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToForgetWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellToOutputWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionWeightsTensor(tensorInfo3x4);</div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> projectionBiasTensor(tensorInfo3);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> inputLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> forgetLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> cellLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a> outputLayerNormWeightsTensor(tensorInfo4);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> </div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  data.m_InputToInputWeights = &inputToInputWeightsTensor;</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  data.m_InputToForgetWeights = &inputToForgetWeightsTensor;</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  data.m_InputToCellWeights = &inputToCellWeightsTensor;</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  data.m_InputToOutputWeights = &inputToOutputWeightsTensor;</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor;</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor;</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span>  data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor;</div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span>  data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor;</div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span>  data.m_CellToInputWeights = &cellToInputWeightsTensor;</div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  data.m_InputGateBias = &inputGateBiasTensor;</div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  data.m_ForgetGateBias = &forgetGateBiasTensor;</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  data.m_CellBias = &cellBiasTensor;</div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  data.m_OutputGateBias = &outputGateBiasTensor;</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  data.m_CellToForgetWeights = &cellToForgetWeightsTensor;</div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  data.m_CellToOutputWeights = &cellToOutputWeightsTensor;</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  data.m_ProjectionWeights = &projectionWeightsTensor;</div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  data.m_ProjectionBias = &projectionBiasTensor;</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span>  data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;</div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span>  data.m_ForgetLayerNormWeights = &forgetLayerNormWeightsTensor;</div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span>  data.m_CellLayerNormWeights = &cellLayerNormWeightsTensor;</div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  data.m_OutputLayerNormWeights = &outputLayerNormWeightsTensor;</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>  <span class="comment">// Flags to set test configuration</span></div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span>  data.m_Parameters.m_ActivationFunc = 4;</div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  data.m_Parameters.m_CifgEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  data.m_Parameters.m_PeepholeEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  data.m_Parameters.m_ProjectionEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  data.m_Parameters.m_LayerNormEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> </div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span>  <span class="comment">// check wrong number of outputs</span></div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  AddOutputToWorkload(data, info, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span> </div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  <span class="comment">// check wrong cifg parameter configuration</span></div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span>  data.m_Parameters.m_CifgEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> scratchBufferTensorInfo2({batchSize, numUnits * 3}, dataType, qScale, qOffset);</div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span>  SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo2, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  data.m_Parameters.m_CifgEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  SetWorkloadOutput(data, info, 0, scratchBufferTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> </div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="comment">// check wrong inputGateBias configuration</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  data.m_InputGateBias = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  data.m_InputGateBias = &inputGateBiasTensor;</div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> </div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">// check inconsistant projection parameters</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  data.m_Parameters.m_ProjectionEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span>  data.m_Parameters.m_ProjectionEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  data.m_ProjectionWeights = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  data.m_ProjectionWeights = &projectionWeightsTensor;</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">// check missing input layer normalisation weights</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  data.m_InputLayerNormWeights = <span class="keyword">nullptr</span>;</div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  data.m_InputLayerNormWeights = &inputLayerNormWeightsTensor;</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>  <span class="comment">// layer norm disabled but normalisation weights are present</span></div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  data.m_Parameters.m_LayerNormEnabled = <span class="keyword">false</span>;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  data.m_Parameters.m_LayerNormEnabled = <span class="keyword">true</span>;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> </div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  <span class="comment">// check invalid outputTensor shape</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span>  <a class="code" href="classarmnn_1_1_tensor_info.xhtml">armnn::TensorInfo</a> incorrectOutputTensorInfo({batchSize, outputSize + 1}, dataType, qScale, qOffset);</div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span>  SetWorkloadOutput(data, info, 3, incorrectOutputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span>  SetWorkloadOutput(data, info, 3, outputTensorInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> </div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span>  <span class="comment">// check invalid cell clipping parameters</span></div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  data.m_Parameters.m_ClippingThresCell = -1.0f;</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  data.m_Parameters.m_ClippingThresCell = 0.0f;</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span> </div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <span class="comment">// check invalid projection clipping parameters</span></div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  data.m_Parameters.m_ClippingThresProj = -1.0f;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  BOOST_CHECK_THROW(data.Validate(info), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a>);</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  data.m_Parameters.m_ClippingThresProj = 0.0f;</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  <span class="comment">// check correct configuration</span></div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span>  BOOST_CHECK_NO_THROW(data.Validate(info));</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> }</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span> </div><div class="line"><a name="l00625"></a><span class="lineno"><a class="line" href="_workload_data_validation_8cpp.xhtml#adbdb7da153a5596e84d312e8abbd152d"> 625</a></span> <a class="code" href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">BOOST_AUTO_TEST_CASE</a>(BiasPerAxisQuantization_Validate)</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> {</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nInput = 1u;</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cInput = 3u;</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hInput = 3u;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wInput = 3u;</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span> </div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> nOutput = nInput;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> cOutput = cInput;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> hOutput = 1u;</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  constexpr <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> wOutput = 1u;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span> </div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> inputShape { nInput, cInput, hInput, wInput };</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> outputShape{ nOutput, cOutput, hOutput, wOutput };</div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> weightShape{ cOutput, cInput, hInput, wInput };</div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_shape.xhtml">TensorShape</a> biasShape { cOutput };</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span> </div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> inputType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a0a3f57c876f5a230244c38e1453a8a6e">DataType::QAsymmU8</a>;</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> weightType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">DataType::QSymmS8</a>;</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  constexpr <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">DataType</a> biasType = <a class="code" href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">DataType::Signed32</a>;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span> </div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  constexpr <span class="keywordtype">float</span> perTensorScale = 1.5f;</div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> inputInfo (inputShape, inputType, perTensorScale);</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> outputInfo(outputShape, inputType, perTensorScale);</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="keyword">const</span> std::vector<float> weightPerAxisScales = { 2.50f, 3.50f };</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> weightInfo(weightShape, weightType, weightPerAxisScales, 0);</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>  <a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">Convolution2dQueueDescriptor</a> queueDescriptor;</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</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_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">m_BiasEnabled</a> = <span class="keyword">true</span>;</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>  <a class="code" href="structarmnn_1_1_workload_info.xhtml">WorkloadInfo</a> workloadInfo;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  AddInputToWorkload(queueDescriptor, workloadInfo, inputInfo, <span class="keyword">nullptr</span>);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  AddOutputToWorkload(queueDescriptor, workloadInfo, outputInfo, <span class="keyword">nullptr</span>);</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>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> weightTensor(weightInfo);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">m_Weight</a> = &weightTensor;</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span> </div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span>  <span class="comment">// Test 1: correct per-axis quantization values</span></div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  <span class="keyword">const</span> std::vector<float> biasPerAxisScales1 = { 3.75f, 5.25f };</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_tensor_info.xhtml">TensorInfo</a> biasInfo1(biasShape, biasType, biasPerAxisScales1, 0);</div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span> </div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasHandle1(biasInfo1);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasHandle1;</div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span> </div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  BOOST_CHECK_NO_THROW(queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Validate</a>(workloadInfo));</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">// Test 2: wrong per-axis quantization values</span></div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <span class="keyword">const</span> std::vector<float> biasPerAxisScales2 = { 4.00f, 5.00f };</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo2(biasShape, biasType, biasPerAxisScales2, 0);</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span> </div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasHandle2(biasInfo2);</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasHandle2;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  BOOST_CHECK_THROW(queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Validate</a>(workloadInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  <span class="comment">// Test 3: mismatched number of quantization scales</span></div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  <span class="keyword">const</span> std::vector<float> biasPerAxisScales3 = { 3.75f, 5.25f, 5.25f };</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  <span class="keyword">const</span> <a class="code" href="classarmnn_1_1_tensor_info.xhtml">TensorInfo</a> biasInfo3(biasShape, biasType, biasPerAxisScales3, 0);</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span>  <a class="code" href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">ScopedCpuTensorHandle</a> biasHandle3(biasInfo3);</div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span>  queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">m_Bias</a> = &biasHandle3;</div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> </div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  BOOST_CHECK_THROW(queueDescriptor.<a class="code" href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">Validate</a>(workloadInfo), <a class="code" href="classarmnn_1_1_invalid_argument_exception.xhtml">InvalidArgumentException</a>);</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span> }</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="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a>()</div><div class="ttc" id="structarmnn_1_1_multiplication_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_multiplication_queue_descriptor.xhtml">armnn::MultiplicationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00246">WorkloadData.hpp:246</a></div></div>
<div class="ttc" id="_output_shape_of_squeeze_8cpp_xhtml_ae3a6cb217a792718f2bd0e8f45e3ca9e"><div class="ttname"><a href="_output_shape_of_squeeze_8cpp.xhtml#ae3a6cb217a792718f2bd0e8f45e3ca9e">BOOST_AUTO_TEST_SUITE</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)</div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_convolution2d_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::Convolution2dDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00454">Descriptors.hpp:454</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::Convolution2dQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00201">WorkloadData.hpp:201</a></div></div>
<div class="ttc" id="_ref_workload_factory_8hpp_xhtml"><div class="ttname"><a href="_ref_workload_factory_8hpp.xhtml">RefWorkloadFactory.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_afbe59e02a5464703b865ea1ccfff49fd"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#afbe59e02a5464703b865ea1ccfff49fd">armnn::BatchNormalizationQueueDescriptor::m_Gamma</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Gamma</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00306">WorkloadData.hpp:306</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_ad5f8f205ba69eb186688ca1c2aec207c"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#ad5f8f205ba69eb186688ca1c2aec207c">armnn::BatchNormalizationQueueDescriptor::m_Beta</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Beta</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00305">WorkloadData.hpp:305</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8526ea7cf860d8e7f8340e9f9354f9f0"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8526ea7cf860d8e7f8340e9f9354f9f0">armnn::NormalizationDescriptor::m_K</a></div><div class="ttdeci">float m_K</div><div class="ttdoc">Kappa value used for the across channel normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00601">Descriptors.hpp:601</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_fully_connected_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_fully_connected_workload.xhtml">armnn::RefFullyConnectedWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_fully_connected_workload_8hpp_source.xhtml#l00018">RefFullyConnectedWorkload.hpp:18</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="structarmnn_1_1_splitter_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml">armnn::SplitterQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00101">WorkloadData.hpp:101</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::FullyConnectedQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00173">WorkloadData.hpp:173</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="_workload_test_utils_8hpp_xhtml"><div class="ttname"><a href="_workload_test_utils_8hpp.xhtml">WorkloadTestUtils.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_a281fcaec86e17c97f7b8402633f6b55a"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#a281fcaec86e17c97f7b8402633f6b55a">armnn::FullyConnectedDescriptor::m_TransposeWeightMatrix</a></div><div class="ttdeci">bool m_TransposeWeightMatrix</div><div class="ttdoc">Enable/disable transpose weight matrix. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00404">Descriptors.hpp:404</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6accedffbc6e5308e33d3843e8bdc0dad7">armnn::DataType::Signed32</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a174279be57d7596eeb04c6b7f7510f99"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a174279be57d7596eeb04c6b7f7510f99">armnn::NormalizationDescriptor::m_Alpha</a></div><div class="ttdeci">float m_Alpha</div><div class="ttdoc">Alpha value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00597">Descriptors.hpp:597</a></div></div>
<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin_xhtml"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor_1_1_view_origin.xhtml">armnn::SplitterQueueDescriptor::ViewOrigin</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00103">WorkloadData.hpp:103</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a40051a7aa82f25df43cc4244de04a7ec"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a40051a7aa82f25df43cc4244de04a7ec">armnn::BatchNormalizationQueueDescriptor::m_Mean</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Mean</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00303">WorkloadData.hpp:303</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_splitter_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_splitter_workload.xhtml">armnn::RefSplitterWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_splitter_workload_8hpp_source.xhtml#l00016">RefSplitterWorkload.hpp:16</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml_a8cd8696bb773a02714d3fc095794ec54"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml#a8cd8696bb773a02714d3fc095794ec54">armnn::BatchNormalizationQueueDescriptor::m_Variance</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Variance</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00304">WorkloadData.hpp:304</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437">armnn::NormalizationAlgorithmChannel</a></div><div class="ttdeci">NormalizationAlgorithmChannel</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00149">Types.hpp:149</a></div></div>
<div class="ttc" id="structarmnn_1_1_addition_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_addition_queue_descriptor.xhtml">armnn::AdditionQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00240">WorkloadData.hpp:240</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="structarmnn_1_1_fully_connected_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml">armnn::FullyConnectedQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00165">WorkloadData.hpp:165</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="classarmnn_1_1_ref_concat_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_concat_workload.xhtml">armnn::RefConcatWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_concat_workload_8hpp_source.xhtml#l00014">RefConcatWorkload.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_softmax_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_softmax_queue_descriptor.xhtml">armnn::SoftmaxQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00095">WorkloadData.hpp:95</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="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="structarmnn_1_1_normalization_descriptor_xhtml_a05945f080edf694b631960728b87aadb"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a05945f080edf694b631960728b87aadb">armnn::NormalizationDescriptor::m_NormMethodType</a></div><div class="ttdeci">NormalizationAlgorithmMethod m_NormMethodType</div><div class="ttdoc">Normalization method algorithm to use (LocalBrightness, LocalContrast). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00593">Descriptors.hpp:593</a></div></div>
<div class="ttc" id="_ref_workloads_8hpp_xhtml"><div class="ttname"><a href="_ref_workloads_8hpp.xhtml">RefWorkloads.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6">armnn::DataType</a></div><div class="ttdeci">DataType</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00032">Types.hpp:32</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="structarmnn_1_1_lstm_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_lstm_queue_descriptor.xhtml">armnn::LstmQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00387">WorkloadData.hpp:387</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="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a3369b66d9316a773a41711e3f590c041"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a3369b66d9316a773a41711e3f590c041">armnn::Convolution2dQueueDescriptor::m_Weight</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Weight</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00200">WorkloadData.hpp:200</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_descriptor_xhtml_aea202e14d8874cefd9a0f778022b7e25"><div class="ttname"><a href="structarmnn_1_1_fully_connected_descriptor.xhtml#aea202e14d8874cefd9a0f778022b7e25">armnn::FullyConnectedDescriptor::m_BiasEnabled</a></div><div class="ttdeci">bool m_BiasEnabled</div><div class="ttdoc">Enable/disable bias. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00402">Descriptors.hpp:402</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_normalization_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_normalization_workload.xhtml">armnn::RefNormalizationWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_normalization_workload_8hpp_source.xhtml#l00014">RefNormalizationWorkload.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_batch_normalization_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_batch_normalization_workload.xhtml">armnn::RefBatchNormalizationWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_batch_normalization_workload_8hpp_source.xhtml#l00014">RefBatchNormalizationWorkload.hpp:14</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_workload_factory_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_workload_factory.xhtml">armnn::RefWorkloadFactory</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_workload_factory_8hpp_source.xhtml#l00030">RefWorkloadFactory.hpp:30</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_elementwise_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_elementwise_workload.xhtml">armnn::RefElementwiseWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_elementwise_workload_8hpp_source.xhtml#l00021">RefElementwiseWorkload.hpp:21</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml_a041e495449e22774a34d92b0904c10bf"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml#a041e495449e22774a34d92b0904c10bf">armnn::Convolution2dQueueDescriptor::Validate</a></div><div class="ttdeci">void Validate(const WorkloadInfo &workloadInfo) const</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8cpp_source.xhtml#l01231">WorkloadData.cpp:1231</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a10d15f3df1ab52b3b915a4be1dbf386b"><div class="ttname"><a href="namespacearmnn.xhtml#a10d15f3df1ab52b3b915a4be1dbf386b">armnn::BOOST_AUTO_TEST_CASE</a></div><div class="ttdeci">BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)</div><div class="ttdef"><b>Definition:</b> <a href="_const_tensor_layer_visitor_8cpp_source.xhtml#l00268">ConstTensorLayerVisitor.cpp:268</a></div></div>
<div class="ttc" id="classarmnn_1_1_invalid_argument_exception_xhtml"><div class="ttname"><a href="classarmnn_1_1_invalid_argument_exception.xhtml">armnn::InvalidArgumentException</a></div><div class="ttdef"><b>Definition:</b> <a href="_exceptions_8hpp_source.xhtml#l00080">Exceptions.hpp:80</a></div></div>
<div class="ttc" id="structarmnn_1_1_splitter_queue_descriptor_xhtml_ab1794eb3e74c9700cd3d500fc06dc2e5"><div class="ttname"><a href="structarmnn_1_1_splitter_queue_descriptor.xhtml#ab1794eb3e74c9700cd3d500fc06dc2e5">armnn::SplitterQueueDescriptor::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#l00114">WorkloadData.hpp:114</a></div></div>
<div class="ttc" id="_cpu_tensor_handle_8hpp_xhtml"><div class="ttname"><a href="_cpu_tensor_handle_8hpp.xhtml">CpuTensorHandle.hpp</a></div></div>
<div class="ttc" id="structarmnn_1_1_convolution2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_convolution2d_queue_descriptor.xhtml">armnn::Convolution2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00192">WorkloadData.hpp:192</a></div></div>
<div class="ttc" id="structarmnn_1_1_mem_copy_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_mem_copy_queue_descriptor.xhtml">armnn::MemCopyQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00076">WorkloadData.hpp:76</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_afe1f0f09d49ad2befc01f8789187b7dd"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#afe1f0f09d49ad2befc01f8789187b7dd">armnn::NormalizationDescriptor::m_NormChannelType</a></div><div class="ttdeci">NormalizationAlgorithmChannel m_NormChannelType</div><div class="ttdoc">Normalization channel algorithm to use (Across, Within). </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00591">Descriptors.hpp:591</a></div></div>
<div class="ttc" id="_profiler_tests_8cpp_xhtml_af7f71af5c6c124222dd1c42c5df892f4"><div class="ttname"><a href="_profiler_tests_8cpp.xhtml#af7f71af5c6c124222dd1c42c5df892f4">BOOST_AUTO_TEST_SUITE_END</a></div><div class="ttdeci">BOOST_AUTO_TEST_SUITE_END()</div></div>
<div class="ttc" id="classarmnn_1_1_scoped_cpu_tensor_handle_xhtml"><div class="ttname"><a href="classarmnn_1_1_scoped_cpu_tensor_handle.xhtml">armnn::ScopedCpuTensorHandle</a></div><div class="ttdef"><b>Definition:</b> <a href="_cpu_tensor_handle_8hpp_source.xhtml#l00106">CpuTensorHandle.hpp:106</a></div></div>
<div class="ttc" id="_workload_8hpp_xhtml"><div class="ttname"><a href="_workload_8hpp.xhtml">Workload.hpp</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c"><div class="ttname"><a href="namespacearmnn.xhtml#a4dc0adc6737b5944e7671bee71788407acaf9b6b99962bf5c2264824231d7a40c">armnn::BoostLogSeverityMapping::info</a></div></div>
<div class="ttc" id="_exceptions_8hpp_xhtml"><div class="ttname"><a href="_exceptions_8hpp.xhtml">Exceptions.hpp</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_softmax_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_softmax_workload.xhtml">armnn::RefSoftmaxWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_softmax_workload_8hpp_source.xhtml#l00014">RefSoftmaxWorkload.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_batch_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_batch_normalization_queue_descriptor.xhtml">armnn::BatchNormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00293">WorkloadData.hpp:293</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="namespacearmnn_xhtml_ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db"><div class="ttname"><a href="namespacearmnn.xhtml#ad8ed01ff3ff33333d8e19db4d2818bb6a9945327825b115e93a3b89f4302e76db">armnn::DataType::QSymmS8</a></div></div>
<div class="ttc" id="structarmnn_1_1_fully_connected_queue_descriptor_xhtml_ab3437cee6b0687812104fc1b37cbe8b3"><div class="ttname"><a href="structarmnn_1_1_fully_connected_queue_descriptor.xhtml#ab3437cee6b0687812104fc1b37cbe8b3">armnn::FullyConnectedQueueDescriptor::m_Bias</a></div><div class="ttdeci">const ConstCpuTensorHandle * m_Bias</div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00174">WorkloadData.hpp:174</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9ac65d2e51c610dd3853a3c777aa8bfe9d">armnn::NormalizationAlgorithmMethod::LocalBrightness</a></div><div class="ttdoc">Krichevsky 2012: Local Brightness Normalization. </div></div>
<div class="ttc" id="structarmnn_1_1_pooling2d_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_pooling2d_queue_descriptor.xhtml">armnn::Pooling2dQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00186">WorkloadData.hpp:186</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc"><div class="ttname"><a href="namespacearmnn.xhtml#abe18a5033f2ab9c0de82c676b48f5437a810f43f3996922151c39b76143faeecc">armnn::NormalizationAlgorithmChannel::Across</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_pooling2d_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_pooling2d_workload.xhtml">armnn::RefPooling2dWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_pooling2d_workload_8hpp_source.xhtml#l00016">RefPooling2dWorkload.hpp:16</a></div></div>
<div class="ttc" id="namespacearmnn_xhtml_ad605d1661fa0d8c7fea651d82fbe11c9"><div class="ttname"><a href="namespacearmnn.xhtml#ad605d1661fa0d8c7fea651d82fbe11c9">armnn::NormalizationAlgorithmMethod</a></div><div class="ttdeci">NormalizationAlgorithmMethod</div><div class="ttdef"><b>Definition:</b> <a href="_types_8hpp_source.xhtml#l00155">Types.hpp:155</a></div></div>
<div class="ttc" id="classarmnn_1_1_ref_reshape_workload_xhtml"><div class="ttname"><a href="classarmnn_1_1_ref_reshape_workload.xhtml">armnn::RefReshapeWorkload</a></div><div class="ttdef"><b>Definition:</b> <a href="_ref_reshape_workload_8hpp_source.xhtml#l00014">RefReshapeWorkload.hpp:14</a></div></div>
<div class="ttc" id="structarmnn_1_1_reshape_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_reshape_queue_descriptor.xhtml">armnn::ReshapeQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00367">WorkloadData.hpp:367</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_a8275d51ef9a584feb95726ea0522f6e5"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#a8275d51ef9a584feb95726ea0522f6e5">armnn::NormalizationDescriptor::m_Beta</a></div><div class="ttdeci">float m_Beta</div><div class="ttdoc">Beta value for the normalization equation. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00599">Descriptors.hpp:599</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_descriptor_xhtml_aa70c05f1aad12fbd9d9ec43ea4557b03"><div class="ttname"><a href="structarmnn_1_1_normalization_descriptor.xhtml#aa70c05f1aad12fbd9d9ec43ea4557b03">armnn::NormalizationDescriptor::m_NormSize</a></div><div class="ttdeci">uint32_t m_NormSize</div><div class="ttdoc">Depth radius value. </div><div class="ttdef"><b>Definition:</b> <a href="_descriptors_8hpp_source.xhtml#l00595">Descriptors.hpp:595</a></div></div>
<div class="ttc" id="structarmnn_1_1_normalization_queue_descriptor_xhtml"><div class="ttname"><a href="structarmnn_1_1_normalization_queue_descriptor.xhtml">armnn::NormalizationQueueDescriptor</a></div><div class="ttdef"><b>Definition:</b> <a href="_workload_data_8hpp_source.xhtml#l00234">WorkloadData.hpp:234</a></div></div>
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